Optimism/Pessimism As a Mediator of Social Structural

Home , Distrust, Optimism

OPTIMISM/PESSIMISM AS A MEDIATOR OF SOCIAL STRUCTURAL

DISPARITIES

EFFECTS ON PHYSICAL HEALTH AND PSYCHOLOGICAL WELL-BEING:

A LONGITUDINAL STUDY OF HOSPITALIZED ELDERS

CHRISTOPHER J. BURANT

Submitted in partial fulfillment of the requirements

For the degree of Doctor of Philosophy

Dissertation Adviser: Dr. Kyle Kercher

Department of Sociology

CASE WESTERN RESERVE UNIVERSITY

August, 2006 CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

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candidate for the ______degree *.

(signed)______(chair of the committee)

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(date) ______

*We also certify that written approval has been obtained for any proprietary material contained therein. Table of Contents

List of Tables / 4

List of Figures / 5

Abstract / 9

Chapter 1: Introduction / 11

Does Hospitalization Contribute to Declining Health? / 14 Does Optimism/pessimism Play a Role in Recovery from Hospitalization? / 17 Is Optimism/pessimism a Trait or a State? / 18 Do Aspects of Social Structure Impact an Individual’s Level of Optimism and Pessimism? / 22 How is Optimism/pessimism Related to Social Structure and Human Agency? / 45 How does the Proposed Study Contribute to the Literature? / 47

Chapter 2: Literature Review of Optimism and Pessimism / 51

Optimism/Pessimism / 52 Definition of Optimism/Pessimism / 52 Measurement of Optimism/Pessimism / 54

Chapter 3: Literature Review Of Conceptual Relationships and Associated Hypotheses / 62

Consequences of Optimism and Pessimism / 62 Antecedents of Optimism and Pessimism / 69 Social Structural Disparities as an Antecedent to Physical Health and Psychological Well-Being / 74 Optimism/Pessimism as Mediator Of Social Structural Disparities Impact on Psychological Well-Being and Physical Health / 77 Causal Ordering of Physical Health and Psychological Well-Being / 81 Impact of Institutional Care Environment / 82 Summary / 89

Chapter 4: Research Design / 91

Background / 91 Proposed Study / 94 Determining the Window Eligible Data Set / 96 Measures / 99 Social Structural Disparities / 100

1 Dispositional Characteristics / 100 Psychological Well-Being / 108 Physical Health / 108

Chapter 5: Data Analytic Strategy / 111

Strategies for Longitudinal Analyses / 113 The Univariate Simplex Autoregressive Model / 115 The Bivariate Autoregressive Model / 117 The Latent Trajectory Model / 121 The Bivariate Latent Trajectory Model / 125 Combining Predictors of Longitudinal Analyses Depression and Physical Functioning / 127 Specification Search in the Development of Longitudinal Models with Predictors / 129 The Hybrid Autoregressive Latent Trajectory (ALT) Model / 134 The Bivariate Hybrid Autoregressive Latent Trajectory (ALT) Model / 135 Bivariate ALT Model with Predictors / 139

Chapter 6: Results I (Autoregressive Models) / 142

Autoregressive Model of Optimism and Pessimism / 142 Univariate Autoregressive Model of Depression / 145 Univariate Autoregressive Model of Physical Functioning / 152 Bivariate Autoregressive Models of Depression and Physical Functioning / 154 Cross Lagged Model / 154 Contemporaneous Model / 157 Summary / 161

Chapter 7: Results II ( Latent Trajectory Models and Autoregressive Latent Trajectory Models ) / 162

The Latent Trajectory Models / 162 The Univariate Latent Trajectory Model of Depression / 162 The Univariate Latent Trajectory Model of Physical Functioning / 170 The Bivariate Latent Trajectory Model of Depression and Physical Functioning / 177 The Bivariate Latent Trajectory Model of Depression and Physical Functioning with Optimism and Pessimism as Predictor / 183 The Development of the Bivariate Latent Trajectory Model with Health Disparities, Optimism and Pessimism, and Clinical Measures as Predictors / 189 Developing the Model between Disparities in Social Structures and Optimism and Pessimism / 190 Developing the Model between Disparities in Social Structures, Optimism and Pessimism, and Clinical Measures / 194

2 The Bivariate Latent Trajectory Model with Disparities in Social Structures, Optimism and Pessimism, and Clinical Measures as Predictors / 198

The Development of the Autoregressive Latent Trajectory Hybrid Model with Predictors / 211

Testing Optimism and Pessimism as Mediators of the Impact of Structural Disparities on Depression and Physical Functioning using the Autoregressive Latent Trajectory Hybrid Model / 223

Summary / 225

Chapter 8: Discussion and Conclusions / 230

How does the proposed study contribute to the literature? / 231

The First Contribution / 231 The Second Contribution / 237 The Third Contribution / 251 Is optimism or pessimism a trait or a state? / 253 The Fourth Contribution / 255

Implications of Findings / 259

Limitations / 261

Implications for Future Research / 262

Conclusions / 264

References/ 266

Appendix A: Sociodemographic Measures / 297

Appendix B: Dispositional Characteristic Measures / 299

Appendix C: Psychological Well-Being Measures / 301

Appendix D: Physical Health Measures / 303

List of Tables

Tables

4.1: Patient Instrument / 95

4.2 : Window eligible data set / 98

4.3: Descriptive Statistics on Measures in Figure 3.1 (Total N=910) / 101

5.1: Specification Search -Example of Summary Table of all Models / 131

5.2: Specification Search - Example of the Short List of the Best Model for Each Parameter / 132

6.1: Correlated errors in Figure 6.5 / 151

7.1: Six regression paths tested for mediation / 225

4 List of Figures

Figures

1.1: Proposed Conceptual Model of Optimism\Pessimism as Mediator of Social Inequality effects on Physical Health and Psychological Well-being ("Cross-sectional Perspective") / 13

1.2: Diagram of behavioral consequences of self-focused attention, including illustration of interrupt mechanism and assessment of out come expectancy (adapted from Carver & Scheier, 1982) / 23

3.1: Proposed Auto-regressive Longitudinal Model of Optimism\Pessimism as Mediator of Social Inequality effects on Physical Health and Psychological Well- being / 67

3.2: Proposed Mediation Model of Optimism and Pessimism with Social Status Characteristics as Time Invariant Predictors of the Latent Growth Curves of Physical Health and Psychological Well-being / 88

5.1: Univariate Simplex Autoregressive Model with Autocorrelated Measurement Errors and Disturbances / 116

5.2: Bivariate Autoregressive Crosslagged Model / 118

5.3: Bivariate Autoregressive Contemporaneous Model / 120

5.4: Univariate Latent Trajectory Model (Linear) / 122

5.5: Univariate Latent Trajectory Model (Quadratic) / 123

5.6: Univariate Latent Trajectory Model (Freely Estimated Slope) / 125

5.7: Bivariate Latent Trajectory Model / 127

5.8: Specification Search - Scree Plot Example 1 / 133

5.9: Univariate Hybrid Autoregressive Latent Trajectory (ALT) Model / 135

5.10: Bivariate Crosslagged ALT Model / 136

5.11: Bivariate Crosslagged ALT Base Model with Predictors / 141

5 6.1: Initial Bivariate Autoregressive Model of Optimism and Pessimism (Standardized Parameters) / 143

6.2: Standardized Results of Bivariate Autoregressive Model of Optimism and Pessimism (Structural Components shown) with Measurement Errors Correlated (not shown) / 144

6.3: Standardized Results of the Univariate Autoregressive Model of Depression (Structural Components and Factor Loadings shown) / 147

6.4: Standardized Results for the Univariate Autoregressive Model of Depression after adding Correlations between Measurement Errors and Disturbance Terms from Modification Indices (Structural Components, Factor Loadings, and Correlations with Disturbance Terms shown) / 148

6.5: Standardized Results for the Univariate Autoregressive Model of Depression after adding Correlations between Measurement Errors and Disturbance Terms from Modification Indices; Factor Loadings Constrianed to be Equal (Structural Components, Factor Loadings, and Correlations with Disturbance Terms shown) / 149

6.6: Standardized Results fot the Univariate Autoregressive Model of Physical Functioning (Structural Components and Factor Loadings shown) / 153

6.7: Standardized Results for the Bivariate Autoregressive Crosslagged Model of Depression and Physical Functioning / 156

6.8: Standardized Results of the Bivariate Autoregressive Contemporaneous Model of Depression and Physical Functioning / 160

7.1: The Univariate Latent Trajectory Model of Depression with Intercept and Linear Slope (Standardized Parameters) / 163

7.2: The Univariate Latent Trajectory Model of Depression with Intercept, Linear Slope, and Quadratic Slope (Standardized Parameters) / 164

7.3: The Univariate Latent Trajectory Model of Depression with Intercept and Freely Estimated Slope (Standardized Parameters) / 166

7.4: The Univariate Latent Trajectory Model of Depression with Intercept and Freely Estimated Slope (Unstandardized Parameters) / 167

7.5: Line Graph of the Trajectory of Depression / 169

7.6: The Univariate Latent Trajectory Model of Physical Functioning with Intercept

6 and Linear Slope (Standardized Parameters) / 171

7.7: The Univariate Latent Trajectory Model of Physical Functioning with Intercept, Linear Slope, and Quadratic Slope (Standardized Parameters) / 172

7.8: The Univariate Latent Trajectory Model of Physical Functioning with Intercept and Freely Estimated Slope (Standardized Parameters) / 173

7.9: The Univariate Latent Trajectory Model of Physical Functioning with Intercept and Freely Estimated Slope (Unstandardized Parameters) / 174

7.10: Line Graph of the Trajectory of Physical Functioning / 176

7.11: The Bivariate Latent Trajectory Model of Depression and Physical Functioning (Standardized Parameters) / 178

7.12: The Bivariate Latent Trajectory Model of Depression and Physical Functioning (Unstandardized Parameters) / 182

7.13: The Bivariate Latent Trajectory Model of Depression and Physical Functioning with Optimism and Pessimism as Predictors (Standardized Parameters) / 186

7.14: Standardized Results of the Model between Disparities in Social Structures and Optimism and Pessimism / 191

7.15: Scree Plot of Specification Search and List of Best Fitting Models per Number of Parameters for the Model between Disparities in Social Structures and Optimism and Pessimism / 192

7.16: Standardized Results of the Model between Disparities in Social Structures, Optimism and Pessimism, and Clinical Measures / 195

7.17: Scree Plot of Specification Search and List of Best Fitting Models per Number of Parameters for the Model between Disparities in Social Structures, Optimism and Pessimism, and Clinical Measures / 196

7.18: Standardized Results of the Bivariate Latent Trajectory Model with Disparities in Social Structures, Optimism and Pessimism, and Clinical Measures as Predictors / 201

7.19: Scree Plot of Specification Search and List of Best Fitting Models per Number of Parameters for the Intercept of Physical Functioning in Bivariate Latent Trajectory Model with Disparities ins Social Structures, Optimism and Pessimism, and Clinical Measures as Predictors / 204

7 7.20: Scree Plot of Specification Search and List of Best Fitting Models per Number of Parameters for the Freely Estimated Slope of Physical Functioning in Bivariate Latent Trajectory Model with Disparities ins Social Structures, Optimism and Pessimism, and Clinical Measures as Predictors / 205

7.21: Scree Plot of Specification Search and List of Best Fitting Models per Number of Parameters for the Intercept of Depression in Bivariate Latent Trajectory Model with Disparities ins Social Structures, Optimism and Pessimism, and Clinical Measures as Predictors / 206

7.22: Scree Plot of Specification Search and List of Best Fitting Models per Number of Parameters for the Freely Estimated Slope Intercept of Physical Functioning in Bivariate Latent Trajectory Model with Disparities ins Social Structures, Optimism and Pessimism, and Clinical Measures as Predictors / 207

7.23: The ALT Hybrid Bivariate Model of Physical Functioning and Depression (Predictors not Shown; Thick Lines Marked by the Word “opt” are Optional) / 214

7.24: Scree Plot of Specification Search and List of Best Fitting Models per Number of Parameters for the ALT Hybrid Bivariate Model of Physical Functioning and Depression with Health Disparities, Optimism and Pessimism, and Clinical Measures as Predictors / 215

7.25: Standardized Results of the ALT Hybrid Bivariate Model of Physical Functioning and Depression with Health Disparities, Optimism and Pessimism, and Clinical Measures as Predictors / 220

8 Optimism/Pessimism as a Mediator of Social Structural Disparities

Effects on Physical Health and Psychological Well-Being:

A Longitudinal Study of Hospitalized Elders

Abstract

CHRISTOPHER J. BURANT

Elders hospitalized for acute episodes of illness are often at risk for functional decline that can lead to “The Disablement Process” (Verbrugge and Jette, 1994). Studies show that one's level of optimism and pessimism impacts the recovery from chronic illnesses. The current study tests optimism and pessimism’s affect on recovery from acute conditions in 944 hospitalized elders at a large Midwest academic university affiliated hospital.

This study contributes to the literature in four primary ways.

First it expands the research on the relationship between optimism/pessimism and psychological well being and physical health in hospitalized elders. Second, the current study is the first to empirically test social structural disparities as antecedents to optimism and pessimism. Third, this study provides a more rigorous test concerning the dimensionality of the LOT. Finally, the present study uses a recently developed autoregressive latent trajectory (ALT) models to conduct a sophisticated analysis of the trajectory of recovery and the causal relationships between physical health and

9 psychological well-being.

Both factor analysis and a distinct pattern of relationships with antecedents and

sequella indicate that optimism and pessimism are two distinct constructs – i.e., they are

not opposite ends of a single variable. Optimism mediated the effect of ethnicity on the

initial levels of depression, Pessimism mediated the effect of education and income on

the initial levels of depression. Finally, ALT models found recovery from both

depression and physical dysfunction being most rapid initially and then tapering off in

later months.

In conclusion, an individual’s level of optimism and pessimism does impact their

initial level of physical functioning and depression, while also mediating some of the impact of social structural disparities on these outcomes. Further research needs to be developed to understand how elders might differentially recover from chronic illnesses

(e.g., cancers, heart diseases, diabetes) and surgeries (e.g., joint replacement) not specifically analyzed in the current study. With regards to the real world, clinical interventions should focus on those who are more susceptible (members of disadvantaged groups and those with low levels of optimism and high levels of pessimism to have lower levels of physical health and psychological well-being.

10 Chapter 1: Introduction

This report will focus on gaining a better understanding of the sociological and

psychological issues associated with the nature of the dispositional characteristic, optimism/pessimism. Typically optimism has been shown to have a beneficial effect on both physical health and psychological well being (Scheier & Carver, 1985, 1992).

Conversely, pessimism detrimentally affects these outcomes (Carver, Pozo-Kaderman,

Harris, Noriega, Scheier, Robinson, Ketcham, Moffat, & Clark, 1994). While optimism/pessimism studies have focused on chronic disease, such as cardiovascular disease and cancer across age groups, to date no studies have been conducted on

hospitalized elders treated for acute illnesses related to general medical often chronic

conditions. Typically acute illnesses are shorter in duration than chronic illnesses and

therefore optimism/pessimism may impact acute illnesses differently, but acute illnesses

may have some long-term consequences similar to chronic illnesses.

Research has shown that older adults hospitalized for acute episodes associated

with general medical outcomes are in danger for declining health (Hirsch, Sommers,

Olsen, Mullen, & Winograd, 1990; Inouye, Wagner, Acampora, Horwitz, Cooney, Hurst,

& Tirretti, 1993; Sager, Rudberg, Jalaluddin, Franke, Inouye, Landefeld, Siebens, &

Winograd, 1996). Declines in health, especially poorer functional status, for these older

adults have been related to deleterious outcomes, such as loss of ability to live

independently resulting in nursing home placement and mortality (Narain, Rubinstein,

Wieland, Rossbrook, Strom, Pietruszka, & Morley, 1988; Sager, Franke, Inouye,

Landefeld, Morgan, Rudberg, Siebens, & Winograd, 1996; Rudberg, Sager, & Zhang,

1996; Kane, Finch, Blewett, Chen, Burns, & Moskowitz, 1996; Covinsky, Justice,

11 Rosenthal, Palmer, and Landefeld, 1997). Overall, patients with acute illnesses, like

chronically ill patients, are vulnerable to declining health, nursing home placement, and

mortality, after hospital discharge.

Figure 1.1 represents a conceptual model of optimism/pessimism as mediator of

social structural disparities impact on physical health and psychological well being.

There are four components(A,B,C and D). Component D represents potential co-variate and is not a the key part of the model. These co-variates represent clinical measures of health that take into account the number of comorbordities (i..e., Charlson Comorbidity

Index) and illness severity (i.e., APACHE II Cinical Illness Severity Scale). These covariates can impact the outcomes and controlling for their influence may change the final model. Its importance will be developed later in the dissertation. Component A refers to social structural disparities.such as power differentials found within sociodemographic

group like gender and ethnicity. Component B represents the dispositional characteristics

optimism and pessimism . Component C is representative of the major health outcomes

of physical health and psychological well being. The model depicts how social structural disparities (Component A) impact dispositional characteristics (Component B) and health outcomes (Component C). The current study is the first to test the relationship between

Component A and B. The relationship between Components A and C has been well documented (for a review, see George, 1996). Additionally the model depicts the impact of dispositional characteristics (Components B) in health outcomes (Component C). An example of this relationship c an be found in Carver, Pozo-Kaderman, Harris, Noriega,

Scheier, Robinson, Ketcham, Moffat, & Clark (1994). Finally the conceptual model demonstrates how dispositional characteristics (Component A) mediate the relationship

CESD 10 Item ADL/IADL Short Version Short Outcomes Functional Limitations Well-Being Psychological C. Major Health

5 6

OPTIMISM PESSIMISM Dispositional Characteristics Characteristics B. Dispositional

3 Index Clinical Charlson APACHE II APACHE Comorbidity WAVE 1 Illness Severity Illness

Covariates 2

Illness Severity 1

4 STATUS INCOME EDUCATION SOCIOECONOMIC Inequalities INEQUALITIES A. Social Structural Social A. SOCIAL STRUCTURAL Physical Health and Psychological Well-being ("Cross-sectional Perspective") AGE GENDER ETHNICITY Figure 1.1: Proposed Conceptual Model of Optimism\Pessimism as Mediator of Social Inequality effects on DEMOGRAPHICS

13 between social structural disparities (Component A) and health outcomes (Component

C). This mediating relationship of dispositional characteristics represented by

optimism/pessimism has yet to be examined in the literature.

Does Hospitalization Contribute to Declining Health?

Hospitalization may have an adverse impact on elders. For example, the hospital

environment could play a role in patient decline and hinder recovery. Goffman’s Asylums

(1961) and Kahana’s (1974) and Lawton’s (1982, 1989) work on the ecological model models based on person-environment fit provide a theoretical foundation to understanding the hospital environment’s role in patient recovery.

While Goffman’s description of a total institution has some components that do not apply to an acute hospital setting, the following features of total institutions are found in hospitals. First, all aspects of life are conducted in the same place under a single authority. Second, All daily activities are tightly scheduled. Finally, the activities are brought together in a single rational plan designed to fulfill the official aims of the institution. These features contribute to what Goffman (1961) describes as a process of depersonalization that begins as soon as a patient is admitted and autonomy is restricted.

Patients are stripped of their personal belongings, receive an identification number and are often treated as “cases” and not persons. Furthermore, hospital routines often clash with personal routines.

How this depersonalization as well as the hospital environment impacts an individual can be found in Kahana’s (1974) and Lawton’s (1982,1989) work. The

14 ecological model, based on congruence in person-environment fit, used in long-term care

research (Kahana, Liang, & Felton, 1980) provides a theoretical lens to interpret the role of the hospital setting in contributing to a patient’s outcomes. To the extent that all patients are generally treated alike, there in a highly congregate environment which may

not fit the individual preferences of the patient. The ecological models view an older

person’s functional and well-being outcomes as a function of one’s personal background,

physical and social environmental features as well as the congruence between the older

person (needs and capacities) and the environment (supplies and demands).

Lawton (1982) refers to one’s personal background as competence in areas of

biological health, sensation-perception, motoric behaviors, and cognition and social

environment as environmental press. Press is the environmental force (physical,

interpersonal, or social) that activates an interpersonal need (Murray, 1938). In general,

press is typically linked to stress, but whereas stress focuses on the negative environmental demand, press can be positive, negative, or neutral (Lawton, 1982).

Optimism in the ecological model could contribute to one’s level of competence in

handling the environmental press associated with hospitalization. Conversely, pessimism

could contribute to the lack of competence needed to handle the environmental press

associated with hospitalization.

Kahana (1974) suggests that congruence in person-environment fit is a fruitful

theoretical model for interpreting how environments impact the well-being of older

persons. Furthermore for older individuals, who are experiencing a decline in their

adaptive capacities, person-environment fit is extremely important (Kahana, 1974).

Lawton (1982), in his environmental docility hypothesis, outlines how the role of the

15 environment is intensified for vulnerable individuals, such as acutely ill, hospitalized older patients. The environmental docility hypothesis (Lawton, 1982) posits that individuals with higher competence levels will have less vulnerability to environmental press, while individuals with lower competence levels would have greater vulnerability to environmental press. If a vulnerable individual is forced to stay in a setting with much environmental press, stress and discomfort could follow (Stern, 1970).

The acute care hospital setting may be perceived as a hostile environment for the patient in two ways. First, the patient has been placed into the hospital because he/she has declined in physical capacities and therefore is at lower competence level.

According to the environmental docility hypothesis, these individuals are at a greater risk for the effects of environmental press, as compared to those with higher competence levels. Second, the acute care hospital environment is not home. An individual is placed into an unfamiliar setting that may be uncomfortable and frightening. Different, often unknown persons are coming in out of the patient’s room to provide care. The patient is expected to stay in a bed that can be placed in different positions and is different from his/her bed. Unfamiliar decor, such as pictures, clocks, and furniture, may be disorienting. Noisy and cluttered halls may inhibit walking.

For patients with extreme health problems, limited access to visits from family and friends is often the norm. Bedrest has been found to lead to deconditioning and loss of muscle mass and vascular tone (Harper & Lyles, 1988; Hoenig & Rubinstein,; 1991;

Lazarus, Murphy, Coletta, McQuade, & Culpepper, 1991). Food deprivation may begin prior to admission and continue through the hospital stay because of change in diet or not being fed while waiting for diagnostic or therapeutic procedures (Sullivan, Patch, Walls,

16 & Lipschitz, 1991; Rudman & Fuller, 1989; Winograd & Brown, 1990). Side effects from medications may produce confusion or persistent sedation during the stay

(Montamat, Cusack, & Vestal, 1989; Lamy, 1990; Lesar, Briceland, Delcoure, Parmalee,

Masta-Gornic, & Pohl, 1990). Procedures such as urinary catheterization, physical restraints, enemas, and endoscopic procedures may not only lead to patients feeling uncomfortable, but also may prolong bedrest, prevent exercise, and even cause injury

(Lofgren, MacPherson, Granieri, Myllenbeck, Sprafka, 1989). It is apparent that hospitalization can contribute to the decline of individuals who are admitted to the hospital because their physical capacities are diminished. Conversely, as patients leave the hospital and return to the familiar environment of home it is expected that individual’s level of physical health and psychological well-being will improve. With regards to the person-environment fit model, optimism may contribute to one’s level of competence which in turn may help a person to be less susceptible to the environmental press associated with the hospital setting.

Does Optimism/pessimism Play a Role in Recovery from Hospitalization?

Optimism can serve as a personal resource to overcoming the stresses individuals face when dealing with an illness. In order to gain a better understanding of how optimism can serve as resource, a brief overview of optimism/pessimism will be provided. People view the world in different ways. Some people see the world through rose-colored glasses. They tend to have a favorable outlook on life. These optimistic individuals expect good things to happen to them (Scheier & Carver, 1985). On the other

17 hand, some people see the world through dark-colored glasses and have an unfavorable outlook on life. These pessimistic individuals expect bad outcomes (Scheier & Carver,

1985).

Scheier and Carver (1985) have applied a model of behavioral-self regulation to explain how optimism can impact behavior. Simply stated, as individuals become more and more successful at handling challenges, they become optimistic about their ability to deal with problems. When faced with an obstacle, an optimistic person will be more likely to renew efforts because he/she has had favorable experiences in the past when dealing with challenges. Finally, an optimistic person will gain confidence in his/her ability to handle difficult situations (such as illness), believing that future challenges will result in positive outcomes. Conversely, pessimism develops when individuals are unsuccessful in handling difficulties. When faced with a problem, a pessimistic person will be more likely to withdraw or disengage from the problem. Furthermore, a pessimistic person internalizes past failures and believes that future challenges (such as illness) will result in negative outcomes.

Is Optimism/pessimism a Trait or a State?

Scheier and Carver (1985, 1993) describe dispositional optimism as a global personality characteristic that reflects generalized expectancies of outcomes.

Expectancies are not limited to any specific one context or behavior (Scheier, Matthews,

Owens, Magovern, Lefebvre, Abbott, & Carver, 1989). Expectancy judgments regarding stressful situations, as described by Scheier & Carver (1985,1987) can range from very

18 specific (e.g., “Will I be able to get out of bed today?”) to the moderately general (e.g.,

“Will I recover from surgery?”) to the very general (e.g., “Do usually good things happen to me?”)

While specific expectancies may vary greatly, specific expectancies are often hard to identify, can change from day to day, and develop slowly over a long period of time

(Scheier et al.1989). Therefore measuring specific expectancies can be nearly impossible

(Scheier et al., 1989). On the other hand, Scheier et al. (1989) state that problems typically encountered by people during the course of daily living are general in scope.

General expectancies that are more general in scope and more stable as compared to specific expectancies, are more profitable to measure (Scheier et al., 1989) With this in mind Scheier and Carver developed the “Life Orientation Test” (LOT) to measure general expectancies based on dispositional optimism. Scheier et al.(1989) believe that the nature of dispositional optimism is traitlike and refers to the expectations that good outcomes will generally occur when handling challenges across many life domains.

Empirical proof typically favors treating dispositional optimism/pessimism as a personality trait (e.g., Plomin, Scheier, Bergeman, Pedersen, Nesselroade, & McClearn,

1992; Robinson-Whelen, Kim, MacCallum, & Kiecolt-Glaser, 1997; Scheier & Carver,

1985). Costa & McCrae (1986) have argued that a personality trait should be stable over time with mean levels of personality traits neither increasing or decreasing over periods as long as 40 years. In fact Siegler & Costa (1999) have demonstrated the stability of the

NEO-PI-R pesonality test for a ten year period with correlations of individual personality dimensions from Time 1 to Time 2 ranging from.64 to .80. Retest correlations over 6, 12, or 20 years decrease little when compared to short-term retest reliabilities (Costa &

19 McCrae, 1992; Finn, 1986). Scheier & Carver (1993) argue that if dispositional optimism is a personality trait then it should be stable overtime; Test-retest reliabilities for four week intervals of .78 (Billingsley, Waehler, & Hardin, 1993) and .79 (Scheier &

Carver, 1985) have been found. These test-retest relaiblities results fall within the correlation range of .64 to .80 that have been identified in Seigler and Costa’s previous study on personalities.

Costa and McCrae’s (1986) argument that mean levels of personality will change little over time is also supported by the literature on optimism/pessimism (Billingsley, et al., 1993; Schulz, Tompkins, & Rau, 1988). Billingsley, et al found that the LOT did not change significantly for individuals over a 4 week period. Schulz. et al. (1988) discovered that among support persons of stroke patients levels of LOT significantly declined over six months, while ordinarliy this would seem to be an indication of the instability of dispositional optimism, but LOT mean scores differed by less than 1 point

(Time 1, mean 21.95, sd=5.13; Time 2, mean=21.10, sd=5.33). Robinson-Whelen, et al.

(1997) discovered that Year 1 optimism explained 62% of the variance in Year 3 optimism for caregivers and 69% of the variance in Year 3 optimism for noncaregivers.

Furthermore, over this 2 year period Year 1 pessimism explained 70% of the variance of

Year 3 pessimism in caregivers and 79% of the variance of Year 3 pessimism in noncargivers (Robinson-Whelen, et al., 1997). Karen Matthews has found that a correlation of .69 in LOT scores over three years (Scheier & Carver, 1993). These studies support Scheier and Carver (1993) belief that the LOT is a “relatively enduring characteristic that changes little with the vagaries of life” (p. 27).

Further support for treating dispositional optimism as a personality trait can be

20 found in Plomin, et al’s (1992) behavioral genetic study of optimism and pessimism on fraternal and identical twins raised together and apart. Twin studies of this nature allow the investigator to test if an item (e.g, a potential personality trait) being examined is related to genetic or environmental influences. According to Plomin, and colleagues, correlations based on genetic influence on personality scores should range from .25 to

.50. and little evidence that being raised together makes twins similar. Furthermore, correlations of zero have never been found and replicated for any personality trait

(Plomin, et al.,1992). The correlations for hereditability was .23 for optimism and .27 for pessimism. The strength of these correlations were similar to those found in other personality traits measure in this data set (the Swedish Adoption/Twin Study of Aging)

(e.g., Pedersen, McClearn, Plomin, Nesselroade, Berg, & DeFaire, 1991; Plomin &

McClearn, 1990). While optimism was weakly associated with environmental influences

(r=.13), pessimism was not. Overall based on these findings it appears dispositional optimism and pessimism are dispositional, trait-like in nature.

Conversely, little research exists supporting optimism as state-like. Shifren and

Hooker (1995) studied variability of state optimism over 30 consecutive days. State- optimism refers to optimism measured everyday. Shifren and Hooker’s questions were based on the LOT, but the wording was changed from “In general” to “Right now”.(e.g,

Right now I am optimistic about the future). Overall, standard deviations across the 30 days for each of the 30 subjects ranged from (.71-7.10) indicating variablity in state optimism. Paired t-test between mean state optimism scores and trait optimism scores were significantly different for each subject. However mean state-optimism and trait optimism correlated fairly highly with each other (r=.54). While state-optimism does

21 vary.on a daily basis, trait optimism was not measured on a daily basis or even at mulitple times.

The current study will examine the stability of dispositional optimism. This is an important addition to the current literature for the following two reasons. First, to date there has been no research study that has measured the LOT at more than two different time periods. The current study offers a unique opportunity to test the stability of the

LOT over 5 time periods. Second, McCrae, Costa, Ostendorf, Angleitner, Hrebickova,

Avia, Sanz, Sanchez-Bernardos, Kusdil, Woodfield, Saunders, & Smith (2000) argue that while major life events have little impact on personality over time as long as 30 years, it is possible that some life events and experiences may affect some specific traits. The current study offers the unique opportunity to examine the stability of a trait, dispositional optimism over time as well as at the time of a major life event, hospitalization.

Do Aspects of Social Structure Impact an Individual’s Level of Optimism and

Pessimism?

Scheier and Carver (1985) uses a model of behavioral self-regulation to explain how optimism and pessimism affect an individual’s behavior. Figure 1.2 represents

Scheier and Carver’s model of how goal-directed behaviors are driven by a hierarchy of closed-loop negative feedback systems (1985). When a behavioral goal is identified the feedback system engages as a person focuses attention to the self. Focusing on the self leads one to change behaviors with the intention of reducing the discrepancy between current behavior and the goal. Therefore individuals faced with a challenge change their

22 behavior to reach their goals. Furthermore if a situational/contextual barrier or a

perception of personal model of how goal-directed behaviors are driven by a hierarchy

SELF-FOCUS

ATTEMPT DISCREPANCY REDUCTION

COMPLETE NO SUCCESSFUL DIFFICULTIES DISCREPANCY ? RESOLUTION

YES

INTERRUPT and ASSESS OUTCOME EXPECTANCY

YES CONFIDENT (HOPEFUL) ?

DISENGAGE FROM ATTEMPT

OVERT WITHDRAW YES NO WITHDRAW WITHDRAWAL BEHAVIORALLY MENTALLY POSSIBLE ?

Figure 1.2: Diagram of behavioral consequences of self-focused attention, including illustration of interrupt mechanism and assessment of out come expectancy (adapted from Carver & Scheier, 1982)

of closed-loop negative feedback systems (1985). When a behavioral goal is identified

the feedback system engages as a person focuses attention to the self. Focusing on the

self leads one to change behaviors with the intention of reducing the discrepancy between current behavior and the goal. Therefore individuals faced with a challenge change their

23 behavior to reach their goals. Furthermore if a situational/contextual barrier or a perception of personal inability hinder discrepancy reduction, an individual will stop the process to assess outcome expectancy and the likleihood of discrepancy reduction

(Scheier & Carver,1985). If an individual is faced with an obstacle, a person will evaluate the situation to determine if one can handle the challenge. If an individual believes in his/her abiliity to handle the challenge successfully, the result is renewed efforts. On the other hand if an individual perceives failure as the outcome, the result is reduced effort or diengagement. Renewed effort and disengagement are exacerbated by more self-focus (Scheier& Carver, 1985). The model of behavioral self-regulation focuses on the individual, but does not take into account the macro-level social processes associated with social structural disparities that can influence individuals.

While micro level social processes help drive the Carver and Scheier (1982) model of behavioral self-regulation (used to describe optimism/pessimism) (1985), the model does not take into account the larger societal structures behind these micro level processes—i.e., the impact of social structures (represented by sociodemographics) that can determine success or failure. Conflict theory, which is based on the works of Marx, offers a foundation for looking at the disparities that exist within classes or social structures and how these disparities can affect one’s level of optimism/pessimism. At the most basic level, these sociodemographic categories can be broken down into privileged and disadvantaged groups based on who controls the power and resources (Dahrendorf,

1959; Ritzer, 1988; Skaff, 1999). It stands to reason that those individuals who are in a position of power or who have more resources (privileged class) are also more likely to have successful results when dealing with difficult situations than individuals who are not

24 in a position of power or who have limited resources (disadvantaged class). In other words, the ability to control and mobilize the power and the resources to handle challenges places the individual in the privileged class at a distinct advantage.

Scheier and Carver (1985) have suggested that individuals who become more successful at handling challenges are also more likely to be optimistic about their abilities to do so. Conversely, pessimism develops within individuals when they are unsuccessful at handling difficulties. Disparities within social structures, with regard to power and resources, may give a person with a privileged status a distinct advantage in handling problematic situations successfully; continued successes, in turn, contribute to the development of optimism within that individual. Mechanisms such as education may contribute to how persons from higher social class can attain resources. If, on the other hand, a person with disadvantaged status lacks power and resources, he/she may already be at a disadvantage with respect to handling a difficult situation successfully. These failures may contribute to the development of pessimism in individuals. From this standpoint, social structures are antecedents to optimism/pessimism.

The current study will look at power disparities inherent in the following social structures: income, education, gender, ethnicity, and age. Pearlin’s (1989) work on the sociological study of stress offers a theoretical framework to establish the unities between social structures and inner functioning of individuals as previously seen in work by House

(1981). For a review of House’s theoretical framework utilizing a model developed by

Rosenberg and Pearlin’s (1978) to explain the relationship between social class and self- esteem please refer to the literature review. Rosenberg and Pearlin use the mechanisms of social comparison processes, reflected appraisals, self-perception theory, and

25 psychological centrality to explain the relationship between social class and self esteem.)

Pearlin (1989) makes the argument that the domains of the stress process arise from and

are influenced by the structural arrangements in which individuals exist. Kahana and

Kahana’s work (1996, 2003) on successful aging, also focuses on the stress process but integrates internal resources, such as optimism, into the stress model. Internal resources serve to ameliorate the negative consequences of stressful life events (Kahana & Kahana,

1996, 2003).

According to Pearlin, stress research offers a unique opportunity to look at how structured arrangements of people’s lives and the repeated experiences arising from these structures can deeply impact one’s well-being. In much the same way these structured arrangements can affect not only one’s well-being but one’s level of optimism, since optimism develops from successes in handling challenges which may be based on structured arrangements or repeated experiences arising from structures.

Often stressful situations can be traced to a persons location within the surrounding social structures (Pearlin, 1989). The most significant of these structures are based on the systems of stratifications found in societies such as, race, ethnicity, gender, age, social and economic class (Pearlin, 1989). Futhermore. Pearlin (1989) states that low status in any of these structures may be a source of stressful conditions.

Additionally, low status may be associated with hardship as compared to privilege, threat as compared to security, conflict as compared to harmony (Pearlin, 1989). These characteristics of low status may contribute to how pessimism develops in individuals because of the inability to handle the stressful conditions associated with existing in a low status.

26 In order to better understand how low status within a social structure can impact one’s level of optimism, it is important to look at the structural arrangements of individuals and the repeated experiences, events, social processes, and situations that arise from these structural arrangements. Furthermore, to gain a better understanding of how structural contexts contribute to lower status, one should examine the experiences that are tied to social structures. Since the current study looks at the social structures of gender, ethnicity, age, income, and education, illustrations of experiences, events, situations, or social processes tied to these structural arrangements will be offered to explain how low status develops in these structures.

Gender- The first social structure to be examined is gender. Low status in gender is associated with women, but why? Pearlin (1989) offers the following four theoretical explanations for how gender plays a role in the stress model and ultimately one’s well being as well as one’s level of optimism. First, gender is a characteristic that can influences the stressors to which people are exposed. In another words, men and women experience different stressful circumstances. Second, while men and women may experience the same stressor, the outcomes manifested by these stressors may be modified by gender. For example, outcomes (e.g., starting salaries) from similar occupational difficulties (e.g., budgetary constraints) may impact men and women differently. Third, the personal and social resources to handle stress may vary by gender.

Fourth, stress outcomes may be manifested differently for men and women. For example,

women are more prone to depression, on the other hand, men are more prone to behaviors

like drinking. While these explanations were used for gender they can also be applied to

27 studying low status in other social structures

Kahana and Kahana (2003) “preventive and corrective proactivity” (PCP) model of successful aging also describes how social structures (as reflected in demographic characteristics) impacts successful aging, an alternative of the stress model. Men and women as well as people from different ethnic backgrounds are differentially exposed to stressful events. For example, low status in a social structure may place a person at greater risk of being a victim of crime or experience role overload.

Kahana and Kahana (2003) offer a look at how different sociological theories can be used to understand gender differences. Structural functionlalist theories (e.g., Parsons

& Bales, 1955) support the argument that men and women have different social roles.

For example, expressive roles are typically associated with women while instrumental roles are expected in men (Kahana & Kahana, 2003). Furthermore, the division of labor causes women and men to develop different styles of adaptation that may follow individuals throughout their lives (Kahana & Kahana, 2003). From a conflict theory approach, Kahana and Kahana (2003) explain how low status is associated with being a woman. Women experience many social stressors and disadvantages over their life span because they have less valued social roles and often suffer economic and social discrimination. From the symbolic interactionist perspective, (e.g., Goffman,

1959)gender-related differences in roles impact varying levels and types of stress For example, the number of roles for women are increasing which in turn can lead to higher levels of stress often associated with role conflict. While the theoretical explanations offered by Pearlin as well as Kahana and Kahana were applied to gender, these examples can also be used universally to describe how low status can develop in different social

28 structures.

Stoller and Gibson (2000) also look at how social structural disparities influence

individuals. According to Stoller and Gibson (2000), labels such as race are social constructs. These labels structure opportunities and constraints for individuals as the go through their life course (Stoller & Gibson, 2000). Hess (1990) argues that the distinctions used to classify individuals into certain groups are social processes that superimpose social hierarchies on biological differences. Stoller and Gibson further argue that gender, race or class based hierarchies create systems of advantage and disadvantage. Often these advantages are unearned, because accessing these assets are based on an ascribed status not on an achieved status.

Issues of advantage and disadvantage have also been addressed in Dannefer’s

(2003) work on cummulative advavntage/disadvantage (CAD). Specifically, CAD theory explains how disparities in social structures are perpetuated and maintained over a lifetime. CAD basically refers to how “success breeds success” (Huber, 1998) and “the rich get richer, the poor get poorer” (Entwistle, Alexander, & Olson, 2001). Dannefer

(2003) argued that CAD systemically occurs and while rank plays a key role, it is not just tied to one’s position of origin in a structural group, but advantage occurs based on the interaction of a complex of forces within a specific structural group. Additionally, CAD occurs not at the individual level, but is viewed as component of any population or cohort that has a distinct set of individuals that can be ranked. With in gender this is exemplified by the glass ceiling, that many women face in moving “up the ladder” in an organization, yet men often have more of an advantage for promotion, based on an “old boy network” that is more likely to provide males with mentorship for advancement.

29 When trying to understand how low status is associated with being a women it becomes important to look at the personal histories and historic events that impact women over a life course. Stoller and Gibson (2000) point out that historic events such as World War II impacted women not only from the wartime separation of spouses, boyfriends, fathers, brothers and sons, but also from the occupational realm. They also noted that women often replaced male employees in the work force during the war. This was the starting point for a growing employment rate for women for the decades that followed. These issues become increasingly important in trying to gain a better understanding of the issues often associated with the “double jeopardy” hypothesis.

Double jeopardy refers to an individual who is a member of two low status positions within two social structures (for example, being female and old or being female and

African American). From the double jeopardy perspective. religion plays a major role in the life of elderly black woman, yet this institution reinforces these women’s disadvantaged position in society (Stoller and Gibson, 2000). Specifically, religion emphasizes a women’s role as a mother which in turn justifies their low status to men.

Ethnicity- While the previously discussed work offered different perspectives to understanding how being a female is often associated with low status, these explanations

can also be applied to ethnicity, age and education. Stoller and Gibson (2000) and Meyer

(2003) offers further insight into understanding how ethnic disparities can shape an

individual’s life.

Looking at how historical context can impact individuals, Stoller and Gibson

describe how the lack of opportunities for Blacks before the Civil Rights movement

30 produced negative events that resulted in racial discrimination. Elderly blacks experienced many of the discriminations that still affect their lives. They often developed adaptive resources to handle these discriminations. For example, elderly Blacks often believe in the perspective that they are “living constantly on tiptoe and in a react mode”

(Jones, 1991; p.190). To some extent, these societal conditions still exists for blacks whether the discrimination is intentional or not.

Blacks are still living in a climate of racism. McAdoo (1986). described this climate as a mundane extreme environment. White society imposes “extreme” difficulties for Blacks by rejecting their identities, values and economic opportunities

(McAdoo, 1986). These hindrances are not unique or extraordinary but “mundane” daily pressures and they demonstrate how pervasive, a daily reality racism is for Blacks

(McAdoo,1986).

Conversely, Stoller and Gibson (2000) also warns that the same heirarchies used to create disadvantage for those in low status also creates systems of privilege. Unearned advantages are often associated with being White, male, and middle or upper class For example, Whites can ignore issue of race because they spend most of their time in environments where being White is taken for granted. This advantage is denied Blacks because they are constantly reminded of their disadvantaged status. Hacker (1992) dramatically highlights this point by stating that “what every Black American knows, and whites should try to imagine, is how it feels to have an unfavorable – and unfair – identity imposed on you every waking day” (p.21).

Meyer’s work (2003) focuses on prejudice and racism as a stressor within the stress model. Structural disparities, tied to race and ethnicity, are often linked to stressful

31 events. They note for example, an African American is more likely to have encountered

job loss as compared to Whites. Meyer’s work offers explanations for prejudice that

emphasizes structural measures as compared to individual measures; subjective

assessments as compared to objective assessments; and daily discrimination events or

hassles as compared to major life events.

Meyer’s cites Adams (1990) approach at the structural level and looks at

institutional racism as a means to thwart prosperity, esteem and honor, and power and

influence. Link and Phelan (2001) describe how these institutional discrimination

barriers often go unrecognized at the individual level. For example, when an employer

disguises discriminatory acts such as denying promotions to African American

employees. Therefore, a Black employee may believe they have not been denied

promotion because of discrimination, even though the employer may purposely have a

hidden policy that excludes Blacks from promotions (Meyer, 2003).

When looking at subjective assessments Meyer’s describes how discrimination

may be hidden and go undetected by its victims. For instance, Contrada and colleagues

describe how members of minority groups often are motivated to detect discrimination to protect themselves, yet they are also motivated to ignore signs of discrimination as a means to avoid false alarms that can impact relationships as well as life satisfaction

(Contrada, Ashmore, Gary, Coups, Egeth, Sewell, Ewell, Goyal, and Chasse; 2000).

Meyer’s also suggests that in ambiguous situations individuals will try to maximize perceptions of personal control and reduce recognition of discrimination. Furthermore

Meyer states that healthier people often use strategies to underestimate prejudice and discrimination events. This phenomena may negatively bias results by influencing the

32 underestimation of how prejudice impacts health. The problem with interpreting only

subjective perceptions of racism is that the issues associated with prejudice and racism

are based on perceptions down playing the role of social influences (Meyer, 2003).

Another challenge to understanding prejudice focuses on how the magnitude of daily discrimination events or hassles impact African Americans (Meyer, 2003). These

daily hassles often influence African Americans as much as major life events. Meyer

states that these minor discrimination events affect many parts of one’s life. Williams,

Spencer, & Jackson (1999) describes the following examples of “everyday

discrimination”: 1) when African American males are followed in stores because they are under suspicion of shoplifting and 2) the ineffectiveness of African Americans to hail cabs. Meyer suggests that these events carry considerably more weight than traditional daily hassles not tied to prejudice. For African Americans the inability to hail a cab can bring back painful memories of linked to personal and communal histories of prejudice

(Meyer, 2003). For instance Cornell West in Race Matters(1993) gives a personal account of how being ignored by New York taxi cab drivers triggered “ugly racial memories of the past“ in his mind. Furthermore, West expounds on this experience by stating that “the memories cut like a knife at my soul as I waited on that godforsaken

corner (ppx-xi). Overall the explanations of the disparities found in race are quite

numerous and diverse.

Age- Structural disparities can also be found across age groups. Much of the recent work

on on age strata disparities is based on the life course theory, specifically on comparisons

of different age cohorts. A birth cohort is a group of people born at the same time who

33 face the same historical events and age together (Passuth & Bengston, 1988).

Competition for power and resources often occur between different age cohorts.

According to Henretta (1988) age in and of itself is not a source of conflict or cooperation, but may be related to these concepts because age is often the prerequisite for social-structural arrangements that influence the control of power and resources.

Furthermore, Henretta points out that the elderly are in weak social structural position because they lack some of the resources needed in any interaction. Overall, the elderly are in a lower position because of the social distribution of the resources and power

(Henretta, 1988).

From another perspective, Uhlenberg (1988) argues that the power of a cohort is often developmentally based. Major normative developmental stressors such as illness, losses, and person-environment (P-E) incongruence impact how well the elderly successfully age (Kahana & Kahana, 1996). These processes may place an elderly individual at a disadvantage as compared to individuals from younger cohorts. From a macro-level, the cohort representing the very old, increased illness and frailty, in addition to cohort attrition from death, reduces the cohort power and control in society (Henretta,

1988).

From an economic point of view changes in sizes of one cohort to another cohort can contribute to or reduce conflict over jobs (Easterlin, 1980). For example, the transition of younger cohorts into the work force maybe influenced by the size of the previous cohort in the work force. Older adults are often forced into early retirement because of economic stagnation or concern over youth unemployment (Henretta, 1988).

Another example of economic issues tied to disparities across age cohorts centers around

34 Social Security and Medicare. Henretta brings to attention the fact that Social Security

and Medicare are preprivilegedly financed by the taxes of the large “baby boom” cohort.

While money is currently available to support Social Securtiy there is strong evidence that

sufficient funds will not be available for the “baby boom” cohort” because the next cohort

is smaller. Baby boomers face a future of delayed retirement and/or cuts to Social

Security and Medicare for the current system to survive. The instability of these plans

demonstrates how disparities can develop across age cohorts.

Ageism is another example of inequality between age groups. Ageism reflects an

antogonism toward a specific age group (Neugarten,1996) and can be directed toward

young or old alike. Political and economic issues, specifically the struggle for age rights

are becoming challenging for not only the young but also for the old who are at risk of becoming victims in the fight for age rights (Neugarten, 1996). Neugarten points out that some believe age segregation is being practiced for the elderly in the form of age segregated residential areas, such as new retirement communities and deteriorated inner city neighborhoods where the old are left behind are liken to age ghettos fro the old.

Another example of ageism may be found in the anger that some have toward older people who are in power positions in the judiciary, legislative, business, and professional arenas, especially by those who perceive that the economic burden of the Social Security system is falling upon the middle aged taxpayer (Neugarten, 1996).

Another basis for inequality may exist in a new division in age cohorts redefining the old age cohort into two distinct cohorts, the young-old and the old-old. Neugarten provides a simplified definition of the cohorts state that the young-old group is made up of individuals 55 to 75, while the old-old group, consists of individuals older than 75.

35 The young old are actually closer in characteristics to the middle aged cohort as compared to the old-old. The great difference is that retirement is the major life event that serves as a marker for the young old cohort (Neugarten, 1996). Overall, this cohort is relatively healthy as compared to the old- old group. Neugarten argues that historical stereotypes of aging are largely based on the old-old. While the stereotypes of older persons being feeble, poor, sickly, isolated and desolated may be overused nonetheless these stereotypes are uncritically associated with those over 65 (Neugarten, 1996). While these cohorts both reflect old age, it seems that the old-old are at greater risk for prejudice based on

stereotypes than the young old. Therefore, social structural disparities seem to exist not only between the old old and younger cohorts starting with the middle aged but also with the young-old a group previously assumed to belong to the same old age cohort as the old-old.

Socioeconomic Status – Socioeconomic status (SES) is another source of social structural disparities. Disparities in SES has been studied extensively [e.g. , power (Williams &

Collins, 1995), crime (Treadwell & Ro, 2003), and politics (Smith, 2002)]. An overwhelming amount of research on SES focuses on the impact of SES on health (e.g.,

Bollen, Glanville,& Stecklov, 2001; Lynch, Kaplan, & Salonen, 1997; Marmot, Fuhrer,

Ettner, Marks, Bumpass, & Ryff, 1998; Schieman, 2002; Stoller & Gibson, 2000) The importance of the impact of SES on health will be examined. Special attention will be paid to the two dimensions of education and income used to assess socioeconomic status in the current study. Income and education are typically used to measure SES in the literature (Guadagnoli, Cleary, & McNeil, 1995; House, Lepkowski, Kinney, Mero,

36 Kessler, & Herzog, 1994).

The earliest work on the impact of SES on health can be traced to the work of

Engels ([1844] 1984). In 1840 the average life expectancy of of an individual from the

Liverpool upper class was 35 years, as compared to 22 years for business men and top

craftsmens and 15 years for day laborers (Engels, 1844). While these estimates may seem

exaggerated it was noted that there were extremely high rates of mortality among infants

and small children during this time. Why were there such discrepancies in the numbers?

Engels postulated that excess mortality may have been caused by working conditions

such as machine-paced employment, long hours, exposure to dust, fumes, poor atmospheric conditions and maintaining uncomfortable body positions. More recently

Williams and Collins (1995) state that inidividuals in low SES are more likely to be employed in occupational settings that have elevated exposures to toxic substances and bad working conditions. Lundberg (1991) tied working conditions as the major source of

SES differences in physical illness. These working conditions include heavy work and daily contact with poisons, dust, smoke, acid, explosives, and vibrations. While working conditions are not typically linked to SES, it is tied to occupation which is directly linked to education.

Stoller and Gibson (2000) provide a detailed review of how SES impacts health and mortality. For example, individuals with higher levels of education, income, wealth, and occupation are healthier, less functionally limited and less likely to die earlier as compared to individuals who are poor, poorly educated, and with lower job status

(Williams, 1990). Furthermore, Stoller and Gibson state that death in elderly individuals from heart disease, cancer, and stroke was more prevalent among people with less than 8

37 years of schooling, family incomes below $14,000, and careers in working class occupations. Additionally lower class individuals had poorer health across their life span as well as higher levels of disability and chronic disease at a younger age. An even more important finding is that SES level in early life impacts health status in old age (Joseph &

Kramer, 1996; Lynch, Kaplan, & Salonen, 1997).

Developing this further, parent’s level of SES is directly tied to adult children’s

SES level and risky health behavior, (Lynch, Kaplan,& Salonen, 1997). For example,

Lynch and colleagues state that men from wealthy families who had more than a high school education and a white collar job were more likely to own their home, have many material possessions, have lower rates of financial and job insecurity, unemployment, work related injuries and disability or early retirement. When looking at risky health behaviors, men born in high childhood SES were less likely to report drunken bouts and report higher levels of physical activity, while those born in low childhood SES were more likely to have the highest pack-years exposure to cigarettes and poorer diets ( e.g., less fruit and vegetables consumption, higher consumption of salt), From a psychological standpoint men whose parents were wealthy had significantly lower levels of hopelessness and cynical hostility (Lynch, Kaplan, & Salonen, 1997). It should be noted that hopelessness and pessimism are often used interchangeably (Beck, Weissman,

Lester, & Trexler, 1974). In other words, parents SES impacts men’s level of pessimism suggesting SES has a long term affect on an individual’s level of pessimism. Overall, these findings seem to suggest that parents SES may impact an individual health status even into old age. With this in mind, an argument can be made that SES is relatively stable across one’s lifetime.

38 When trying to gain a better understanding of the risk factors associated with lower SES, Link and Phelan (1995) focus on the importance of “contextualizing” risk factors. Specifically, what is it about people’s life circumstances that contribute to their exposure to risk factors such as poor diet, lack of exercise, or a stressful home life. They refer to these individual level risk factors “proximate causes” of disease. Often such proximate causes are exacerbated by the person’s social environment or limited access to resources. A person’s behavior is in response to the reduced life chances that are forced upon them from the social condition in which they exist (Stoller and Gibson, 2000).

Stoller and Gibson describe three types of risk factors (biomedical, social, and psychological). Biomedical risk factors refer to specific health conditions that contribute to serious disease that are more prevalent among those in lower SES as well as others in disadvantaged structural status. They point out that diabetes and hypertension are two conditions that contribute to serious illnesses and occur more often in individuals with lower SES. For example, diabetes impacts the vascular system, eyes, kidneys, the peripheral nervous system, and the heart. Hypertension contributes to coronary heart and cerebrovascular disease.

In addition to individual level risk factors, Stoller and Gibson (2000) also consider the importance of social risk factors. While many social risk factors are related to a lifetime of dangerous occupational conditions for people in lower SES (see earlier discussion), many social risk factors impact individuals living in inner-city neighborhoods including: a lifetime of greater exposure to crime, air and water pollution, accidents, hazardous wastes, pesticides, and industrial chemicals. Living in these stressful environments can compromise an individual’s health.

39 Psychological risk factors, according to Stoller and Gibson (2000), make up the

third set of risk factors for individuals of lower SES. Of particular interest are

psychological issues that focus on high stress, weak feelings of personal control and

mastery and perceptions of inadequate social ties. Higher stress levels impact the number

of disease outcomes (e.g., Williams, 1990). Individuals who are black or have lower SES

(McLeod & Kessler, 1990) are more likely to be exposed to more stressors than whites

and those of higher SES (Stoller & Gibson, 2000). These individuals were more likely to

experience a divorce, loss of a love one, and unemployment (McLeod & Kessler,1990).

Additionally they are more likely to be troubled by “daily hassles” which are everyday

annoyances such as struggles with substandard housing and unpaid bills (Stoller &

Gibson, 2000).

While stressors may play a role in one’s health outcomes, adaptive resources can

serve as a buffer to handling life stressors. Higher levels of personal control as compared

to feelings of powerlessness contribute to one’s health and serve to reduce the effects of

stressors (Stoller and Gibson, 2000). A sense of control over one’s life can be developed

through one’s work experience. For example, corporate executives and professionals have more opportunities to exercise authority, work at their own pace, problems solve as

compared to those in lower occupational status. These skills help to foster a sense of

control of one’s life. Education level can also be tied to the development of personal control because individuals with higher levels of education, such as graduate degrees are

in a better position to be placed in occupations that help to foster a sense of control. In

much the same way optimism can be associated with a higher level of personal control,

because optimistic individuals are better at taking control of challenges, because they

40 believe they will have success in their endeavors.

Another resource used to cope with the effects of stressors is social ties.

Berkman and Mullen (1997) suggest that strong social ties can lead to better health and mortality. While close friends and family can be a source of emotional support, these relationships can also be source of stress (Stoller and Gibson, 2000). Negative life events that can negatively impact one’s health are more prevalent among individuals with lower

SES (McLeod & Kessler, 1990) and in minorities. More importantly negative life events do not only occur to individuals, but these also have ramifications for family and friends.

For example, life events such as divorce, alcoholism problems, and drug addiction can be devastating to family and friends. Overall it is important to take into account that the social ties of the poor and minorities can not only serve as a source of support but also a source of stress (Williams, 1990).

In this section, special emphasis was placed on gaining a better understanding of the mechanisms that place individuals from lower structural positions at a disadvantage to individuals from higher structural positions. These mechanisms that contribute to the disadvantages in social structures may play a role in how pessimism develops in individuals. Theoretical underpinnings found in Pearlin’s work (1989) focuses on how structured arrangements, peoples lives, and the repetition of experiences from these structures can impacts one’s well-being. This fundamental approach can be applied to gender, ethnicity, age, and socioeconomic status (e.g., income and education).

Pearlin uses gender as a way to describe how differences in status are manifested.

First, gender is a characteristic that can affect the stressors that people are exposed to.

Second, how the same stressor may impact an outcome may be modified by gender.

41 Third, personal and social resources used in handling stressors may vary by gender.

Fourth, stress outcomes may be different for men and women. Kahana and Kahana

(2003) provide explanations gender disparities based on structural functionalist theories

(e.g., men and women have different social roles); on conflict theory (e.g., women are often held in less value social roles as a result must face economic and social disadvantage); and the symbolic interactionist approach (e.g., gender differences in roles impact stress). Stoller and Gibson (2000) approach to gender disparities focuses on looking at the personal histories and historic events that play a role in a woman’s life course.

In reviewing the mechanisms underlying differences in ethnicity, Stoller and

Gibson (2000) take an in depth look at how historical context plays a role in shaping individuals. McAdoo (1986) examines the mundane extreme environments imposed by white society forcing blacks to live under “mundane” daily pressures (such as, rejection

of identity, value and economic opportunities). Stoller and Gibson (2000) also point out

that the hierarchies used to create disadvantage in low status also supports sytems of

privilege. Finally, Meyer’s work (2003) describes how prejudice is manifested by

structural measures as compared to individual measures, subjective assessments as

compared to objective assessments, and daily discrimination events or hassles as

compared to major life events.

Inequality between age cohorts seems to focus around issues of power and

resources, developmental basis, economics, and ageism. Henretta (1988) states that age

is a prerequisite for many social structural arrangements that lead to competition for

power and resources. From a developmental perspective, Kahana and Kahana’s (1996)

42 work looks at how major developmental stressors including illness, losses, and P-E fit can place an elderly individual at a disadvantage to aging successfully. From an economic point as potential employment opportunities declines, Easterlin (1980) and Henretta

(1988) point out that older adults are forced into early retirement to make room for younger workers. Tension between age cohorts also occurs because current Social

Security and Medicare funds are being depleted and may not be available for younger cohorts. Additionally, Neugarten (1996) addresses the issue of ageism. Specifically,

Neugarten points out that the old are at risk to becoming victims in the fight for age rights. Ageism also appears in the form of age segregated residential areas. Finally,

Neugarten described how inequality can occur when between young-old cohorts and the old-old cohorts because the old-old are more likely to face prejudice based on stereotypes of elderly being feeble, frail, sickly, poor and desolate.

Structural disparities can also be found in SES. The earliest work on structural disparities was based on SES. Engels ([1844] 1984) argued that working conditions contributed to excess mortality rates in laborers. Similarly, a review by Stoller and

Gibson (2000) described how individuals with higher levels of education, income, wealth, and occupation are healthier and less functionally limited. Lynch, Kaplan, & Salonen’s work (1997) linked parents SES level with adult children’s SES level and risky health behavior (e.g., drunken biuts, highest pack-years exposure to cigarettes, and poorer diets.

Link and Phelan (1995) work focused on the importance of “contextualizing “ risk factor associated with lower SES. Specifically, why do people’s life circumstances expose individuals to risk factors. Finally, Stoller and Gibson (2000) elaborate on how risk factors fall into the following three categories: biomedical (e.g., health conditions such as

43 hypertension and diabetes), social (e.g., living and working in stressful environmental conditions), and psychological (e.g., low levels of personal control and mastery). Many mechanisms contribute to the disparities found in social structures. These mechanisms may contribute not only to disparities in social structures, but also to the development of pessimism for the disadvantaged and optimism for the advantaged.

How do social structures impact the development of optimism/pessimism?

How the linkage between social structures and optimism/pessimism develops may be explained in four theoretical mechanisms described in Rosenberg and Pearlin’s work

(1978) on social class and self esteem. The four mechanisms are social comparison processes, reflected appraisals, self-perception theory, and psychological centrality.

House (1981) suggests that the four mechanisms can be extended to any relationship between SES and individual personality and behavior, as well as the relation between other macro social phenomena and personality (1981, p.554).

Applying Rosenberg and Pearlin’s four theoretical mechanism to social structures and optimism/pessimism may contribute to a better understanding of this relationship.

Individuals use social comparison processes to evaluate themselves as compared to others on SES-related dimensions (Rosenberg & Pearlin, 1978). If they view their success and failures as being tied to comparisons based on SES-related dimensions then social structures may impact optimism/pessimism. Reflected appraisals refer to how individuals see themselves as they believe others see them (Rosenberg & Pearlin, 1978). In looking at the contextual underpinnings of reflected appraisals, Rosenberg and Pearlin’s work does not discuss the issue of social mobility. The following example is meant to be interpreted

44 contextually in the absence of social mobility. If individuals see their successes and failures tied to social structures because valued others’ opinions see them in those terms, then the successes and failures tied to social status will impact one’s level of optimism/pessimism.

Self-perceptions theory refers to how individuals draw conclusion about themselves based on efforts, action, and behaviors. SES is a products of efforts, actions, and behaviors such as success and failures (Rosenberg & Pearlin, 1978). Since optimism/pessimism are tied to success and failure, if individuals’ self perceptions of their successes or failures are tied to their socioeconomic status then these self perceptions should impact their level of optimism/pessimism. Psychological centrality refers to how the self-concept is made up of a complex set of elements (Rosenberg &

Pearlin, 1978). These elements are not weighted equally with some having more importance than others. In much the same way optimism/pessimism may be impacted by how much importance an individual places on successes or failure tied to social structures like income.

How is Optimism/pessimism Related to Social Structure and Human Agency?

When trying to understand how social structural components relate to optimism/pessimism and psychological well-being and physical health, one must be prepared to look at the issue of social structure and human agency. Settersten (1999) makes a compelling argument that researchers should try to gain a better understanding of

45 the bridge that exists between social structure and human agency, and how these issues

affect the development of an individual.

Sociologists often take the macro approach of looking at how social structures

shape human lives (Settersten, 1999), practically relegating the person to the role of a

passive participant being ruled by external structures. This sociological view, suggested

by Settersten (1999), overlooks the roles of personality traits and characteristics, desires,

aspirations, motivations, and expectations. Settersten refers to this model as structure

without agency (as italicized by Settersten).From this perspective social structures would

be the only contributor to psychological well-being and physical health.

On the other hand, the psychological approach, Settersten notes (1999), usually

ignores the powerful social and historical forces that can impact one’s life by enhancing

or constraining development. Settersten refers to this model as agency without structure

(as italicized by Settersten). From this perspective only one’s level of personal control

represented by optimism/pessimism impacts one’s psychological well-being and physical

health.

Settersten offers a third model of agency within structure (as italicized by

Settersten) as a means of bringing the psychological and sociological perspectives into a

46 influence human agency, Settersten states that one should be aware of both the

constraining and enabling effects that structures have on individual lives. This

perspective, not only explains how an individual can contribute to his/her level of

optimism/pessimism, but also how social structures can influence an individual’s level of

optimism/pessimism. This approach can also be used to explain how individuals who are

proactive and successful in creating their own lives can develop optimism even when

dealing with the adversity associated with existing in a lower class structure.

How does the Proposed Study Contribute to the Literature?

While conflict theory and Settersten’s work on structure and human agency

provide the sociological foundations for the study, the proposed study will contribute to

the literature in four primary ways (For references please see Figure 1.1). The first

contribution concerns two health related consequences of optimism/pessimism as

represented by causal 1 and 2. Arrow 1 represents psychological well-being as a

consequence of optimism/pessimism. This study will expand the dearth of research

conducted on the relationship between optimism/pessimism and psychological well

being. Causal path 2 refers to the relationship between optimism/pessimism and physical

health in hospitalized elders with acute illnesses or acute episodes of chronic illnesses,

this relationship has never been tested in the literature. The next element refers to the

antecedents of optimism/pessimism as represented by path 3, figure 1.1. The antecedents

of interest are based on social structures represented by sociodemographics (gender, ethnicity, age, income and education). This will expand the knowledge base of macro

47 level social influences on optimism/pessimism, because Scheier and Carver (1985) only refer to micro level social processes that impact one's expectations for success and failure which in turn contribute to the individuals level of optimism/pessimism. Additionally, because this investigation is testing the relationship of optimism/pessimism and its antecedents and consequences, it can also test whether or not optimism/pessimism mediates the relationship between sociodemographics and psychological well-being

(arrow 4) and the relationship between sociodemographics and physical health (arrow 5).

Previous literature has shown that sociodemographics can impact psychological well- being (e.g., George, 1996; La Gory & Fitzpatrick, 1992) and physical health (e.g., Clark and Maddox, 1992; George, 1996).

The proposed study's third contribution to the current literature focuses on identifying dimensional nature of optimism/pessimism as measured by the Life

Orientation Test (LOT) (Scheier & Carver, 1985). Initial findings in the sample show an optimism dimension and a pessimism dimension as seen in figure 1.1. Traditionally this measure has been treated as having a single dimension, recent literature has shown a bidimensional nature to the scale. This study will be the first to extensively test the dimensionality of the LOT by exploratory factor analysis, confirmatory factor analysis, the strength of the correlations between the two dimensions, and a test for a distinct pattern of correlations between the two dimensions and a set of external variates.

The fourth contribution takes advantage of the longitudinal nature of the proposed study, because with a multi-wave study one can test the proper causal ordering between measures. Figure 1.1 does not contain the longitudinal part of the study because inclusion would make the figure too complicated. However the basic purpose of the longitudinal

48 part of the study will be described below. Specifically of interest is finding a solution

to the often debated question concerning the proper causal ordering between

psychological well-being and physical health. Traditionally, physical health has predicted psychological well-being (Bradburn, 1969; Lawton, 1983; Lawton, Moss, Kleban,

Glicksman, & Rovine, 1991), but this relationship has only been tested in cross-sectional studies. Therefore, to empirically test the predictive direction of the true relationship between these variables it is imperative to study their relationship across time. This study can provide the opportunity to accomplish this.

The relationship between physical health and psychological well-being (as measured by the 10 item short form CESD and Life Satisfaction Index [Neugarten,

Havighurst, & Tobin, 1961]) will be tested longitudinally to identify the true predictive relationship between the 2 concepts. Seven time intervals (admission to the hospital, hospital discharge, 1 month post hospital discharge, 3 month post hospital discharge, 6- month post hospital discharge, and 12 month post hospital discharge) will be used to test the relationship between physical health and psychological well-being (see figure3). This can be tested by looking at the contemporaneous effects of psychological well-being as a consequence of physical health while simultaneously testing physical health as a consequence of psychological well-being. The only information about the relationship between physical health and psychological well-being taken at admit is the correlation because at admit these variables are taken at the same time point and therefore causal inferences based on statistics cannot be tested. Additionally while testing the data longitudinally the autoregressions can be assessed across all time periods for each variable. Autoregressions are a natural extension of the longitudinal analysis. A benefit

49 of studying the autoregressions is that one can assess the strength of the correlations between time periods. This can be useful in interpreting if institutionalization impacts one's level of optimism/pessimism, psychological well-being and physical health by checking to see if the correlation or covariance for these variables between admit and discharge is weaker than at other time periods.

Understanding how optimism/pessimism impacts the progression of recovery of elders post-hospitalization over time can provide clinicians with useful information on recovery expectations for patients. Since optimism/pessimism has been associated with health outcomes, a better understanding of how optimism/pessimism functions in elders during hospitalization and as well as post hospital discharge will help researchers to identify if optimistic individuals are more likely to recover or recover in a timely shion from the stress associated with hospitalization. Conversely, individuals displaying high levels of pessimism may be at risk to longer recovery times or worse no recovery at all from the stress associated with hospitalization. Moreover, if pessimism hinders recovery, then practitioners may be able to recommend additional services (e.g. home healthcare or other forms of formal support) to assist patients in their recovery. On the other hand, individuals displaying higher levels of optimism may take a more active role in shortening their recovery time by participating in services recommended by practitioners such as physical therapy or occupational therapy.

50 Chapter 2: Literature Review Of Optimism and Pessimism

The previous sections have provided an explanation of the importance of the

proposed study, this chapter will provide a detailed review of optimism and pessimism,

and the LOT’s factor structure, followed by a literature review of the conceptualizations

of the hypotheseses in chapter 3 . In review of Chapter 1, patients recovering from acute

illness or acute episodes of chronic illness are at similar risk to functional decline as those

recovering from chronic illnesses. Optimism/pessimism has been known to play a role

in recovery from chronic diseases (e.g., Carver, Pozo-Kaderman, Harris, Noriega,

Scheier, Robinson, Ketcham, Moffat, & Clark, 1994; Scheier & Carver, 1985, 1992), yet

no study exists on optimism/pessimism’s impact on recovery from acute illnesses.

Another related question to recovery is could post hospital or hospital experiences also

impact a patient’s optimism/pessimism? In general,optimism/pessimism as previously

mentioned is relatively stable and should not change, but the hospitalization may be such

a traumatic life event that optimism/pessimism may be impacted. However, in the

current study comparisons of optimism/pessimism at admit and post discharge can detect

potential differences in levels of optimism/pessimism.

Sociodemographics may play a role in how optimism/pessimism develops in

individuals. In addition, studies have shown how sociodemographics can affect one’s

psychological well-being (e.g., George, 1996; La Gory & Fitzpatrick, 1992) and physical

health (e.g., Clark and Maddox, 1992; George, 1996). The proposed study will look at

optimism/pessimism as a mediatior of social demographics effect on psychological well-

being and physical health.

In order to develop an understanding of how social demographics may impact

51 optimism/pessimism, psychological well-being and physical health, conflict theory and

Settersten’s work on social structure and human agency will provide a theoretical lens to look at these phenomena.

One of the unique aspects of this research is that it studies optimism/pessimism in hospitalized elders treated for acute illnesses. This is an area that has never been studied.

The proposed study will contribute to the literature in four primary ways. First, this will expand the knowledge base on the impact of optimism/pessimism on the psychological well-being and physical health elder patients treated for acute illnesses. Second, this will be the first study to look at the role that social structures (represented by the following sociodemographics: gender, ethnicity, age, income, and education) have on optimism/pessimism. Third, this study will be the first to give an extensive evaluation of the dimensional nature of optimism/pessimism as measured by the Life Orientation Test.

Finally this research will be able to test the proper causal ordering of the relationship between physical health and psychological well-being based on longitudinal analyses of multiple waves of data. With this in mind, a more detailed literature review of the key concepts will be forthcoming. Discussions on the relationship of these key concepts with one another will include hypotheses.

Optimism/Pessimism

Definition of Optimism/Pessimism

Definitions of optimism/pessimism in prior literature have been consistently associated with generalized expectancies of positive and negative future outcomes (Beck,

Weissman, Lester, & Trexler,1974; Carver & Scheier,1982; Dember, Marti, Hummer,

52 Howe, & Melton, 1989; Melges & Bowlby, 1969; Minkoff, Bergman, Beck, and Beck,

1973; Reker and Wong, 1985; Scheier & Carver, 1985; Wenglert & Rosen, 2000)..

Simply stated, people view the world in different ways. Some people see the world

through rose colored glasses, they tend to have a favorable outlook on life. These

optimistic individuals expect good things rather than bad things to happen to them

(Scheier & Carver, 1985). On the other hand, some people see the world through dark

colored glasses and have an unfavorable outlook on life. These pessimistic individuals

expect bad outcomes (Scheier & Carver, 1985). In general, optimism/pessimism is considered a personality characteristic.

Early research (see Beck et al., 1974, Minkopf et al., 1973 ) referred to pessimism

as hopelessness and optimism as hope (Beck et al., 1974) often times interchanging these terms. Beck et al. (1974), Melges & Bowlby (1969), and Minkopf et al., (1973) define hopelessness and hope, like optimism/pessimism, in terms of unfavorable or favorable expectations of future events.

Melges and Bowlby (1969) and Sheier and Carver (1985) expand the definition of optimism/pessimism beyond just generalized expectations of future outcomes to include explanations of how past successes and failures contribute to how these expectations develop in individuals. Based on previous experiences individuals will try to estimate the likelihood of having a success or failure in future endeavors. Scheier and Carver (1985) further develop the concept of optimism/pessimism by stating that successes contribute to renewed efforts in challenges even with setbacks, while consistent failures result in withdrawing effort from challenges.

Five scales have been identified as measuring the personality trait

53 optimism/pessimism. The literature seems to be split on how optimism/pessimism

should be assessed. Three scales directly measure the global personality measure of dispositional optimism/pessimism (Beck et al., 1974; Dember et al., 1989; Scheier &

Carver, 1985), while the two other scales attempt to measure optimism based on the

expectancy of life events occurring (probabilities for occurrence were assessed) (Reker &

Wong, 1984; Wernglet & Rosen, 2000). Overall, the Life Orientation Test developed by

Scheier and Carver (1985) appears to be the most widely used scale of all of the measures

of optimism/pessimism in physical health and psychological well-being research.

Measurement of Optimism/Pessimism

There have been many debates on the dimensionality of optimism/pessimism.

Traditionally optimism has been treated as a unidimensional measure with high scores

representing high levels of optimism and low scores representing pessimism (e.g., Scheier

& Carver, 1985). The bidimensional model of optimism/pessimism has also been

advocated (e.g. Chang, Dzurilla, Maydeu-Olivares, 1994). Understanding the factor

structure of the Optimism scale allows one to better understand its impact on health

issues. If the LOT is interpreted as a two-factor scale then it allows researchers to take

into account both optimism and pessimism as issues in health. Limiting ourselves to just

a single optimism scale may confound the contribution of the two separate components

impact on health. A review of the literature seems to indicate that many attempts have

been made to understand the dimensionality of optimism/pessimism.

Beck et al. (1974) identified two separate dimensions of optimism/pessimism and

a third factor of future expectations based on a principal components analysis of the

54 Hopelessness Scale. However they should have submitted the scale to more rigorous tests including: principal axis factor analyses, confirmatory factor analysis, testing the correlations among the dimensions, and testing for patterns of correlations among the dimensions with a set of external variates.

Dember and Brooks (1989) using a optimism/pessimism scale developed by

Dember et al. (1989) argued for the use of two separate dimensions of optimism/pessimism, but offered only weak evidence based on correlations in previous studies (r=.54 and .57) of the separate dimensions. There was no evidence of exploratory or confirmatory factor analysis having been used. Additionally, patterns of correlation for optimism/pessimism with happiness measures were similar, but different patterns were observed with questions about religious commitment.

Scheier and Carver (1985) in a test of their LOT treated the scale as unidimensional even though exploratory principal factor analysis of 624 undergraduate students suggested clean primary factor loadings and low secondary loading between the two factors. Confirmatory factor analysis supported treating the scale as either unidimensional or bidimensional with the two factor model having better goodness of fit overall. Additionally, Scheier and Carver argued that the correlation corrected for measurement error between the two factors was .64 suggesting that the scale should be treated as unidimensional. Scheier and Carver (1985) did not test for correlations between the two LOT factors and a set of external variables. This information would have been useful in interpreting the dimensionality of the LOT.

Lai (1997) also argues for a unidimensional LOT scale for the reason of parsimony. In the study, Lai tested two populations (230 undergraduates and 173

55 working adults). Exploratory principal axis factor analyses demonstrated a two-factor model was appropriate, but Lai (1997) argued that the large percentage of variance explained by the first factor in both samples (41% for undergraduates and 42% for adults) gives partial support for unidimensionality. Lai's final argument for unidimensionality is that pessimism and optimism did not correlate differently with a measure of health in both samples. Lai's argument is flawed because he only tested 3 external correlates

(health, negative affect, and positive affect). Additionally, the adult sample did show different patterns of correlation for health with pessimism (.14) and optimism (-.22) as well as positive affect with pessimism (-.29) and optimism (.41). Correlations between optimism and pessimism were -.38 for adults and -.47 for undergraduates, which seems to support bidimensionality. Another criticism is that Lai did not run a confirmatory factor analysis and the LOT was based on a Chinese version of the LOT which may have confounded the results.

Hjelle, Belongia, and Nesser (1996) studied 436 college students to identify the psychometric properties of the LOT. This is a relatively weak study only identifying the correlation between optimism and pessimism (-.53) as proof of unidimensionality.

Neither exploratory nor confirmatory factor analysis was conducted. Hjelle et al. suggested that because attributional style correlated with a similar magnitude to optimism

(.37) and optimism (-.36), that this was additional support for the unidimensional nature

of the LOT. Realistically, a larger set of external correlates should have been tested in order to identify the true factor structure of the LOT.

Mehrabian and Ljunggren (1997) using a six item Revised Life Orientation Test

(LOTR) identified a single factor among 101 undergraduates. A principal components

56 analysis identified a single factor. While a confirmatory factor analysis of a two factor model of the LOTR (3-optimism items and 3-pessimism items) fit the data better than a single factor LOTR model, the adjusted for measurement error correlation between optimism and pessimism was -.77 suggesting a unidimensional scale. When looking at these results one should consider the following confounding issues. First, the sample size was rather small. Second, a principal axis factor analyses should have been run instead of a principal components analysis. Third, the LOTR may react differently than the LOT.

Fourth, the authors should have tested for different patterns in correlations between the

LOTR two factors and a set of external variables.

Marshall, Wortman, Kusalas, Hervig, & Vickers (1992) have given the most extensive test of dimensionality of the eight item LOT and the Beck’s et al. (1974)

Hopelessness Scale. The factor structure of the Hopelessness Scale, another measure of optimism/pessimism was not as clear-cut as the factor structure found in the LOT.

Therefore further review of the findings will be limited to the LOT. They found a two- factor model of the LOT in two samples (sample 1, N=346; sample 2, N=543) of male navy recruits undergoing basic military training. Marshall et al. tested the dimensionality of the LOT with principal-axis factor analysis, confirmatory factor analysis and tested for differential patterns in correlations with a limited set of covariates (neuroticism, extraversion, positive affect and negative affect). Overall a two-factor LOT model was supported. Although the adjusted for measurement error correlation between optimism and pessimism was relatively high for sample 1 (r=-.54) and for sample 2 (r=-47). While testing for distinct patterns of correlation with external covariates, not surprising were the findings that positive affect and extraversion were more strongly related to optimism,

57 while negative affect and neuroticism were more strongly related to pessimism. Ideally, it

would be more useful to see a larger set of external correlates to further test the

dimensionality of the LOT.

Another concern is that the study was conducted only on a male sample of similar

aged naval recruits this may limit the generalizability of the findings. Arguably, females

may have responded differently to the LOT than males, since other psychological

measures such as psychological well-being indicate that women as compared to men have

higher levels of depressive symptoms (Dean, Kolody, Wood, & Matt, 1992; Keith 1993;

Krause and Goldenhar, 1992; Krause & Liang, 1993) and psychological distress (Krause,

Herzog, & Baker, 1992).

Mroczek, Spiro, Aldwin, Ozer, and Bosse (1993) have supported a two-factor

model in 1192 men aged 41 to 86. Mroczek et al. found that the correlation corrected for

unreliability between optimism and pessimism -.36 . Exploratory and confirmatory factor

analyses were never conducted. While testing for different patterns of correlation

between the two-factors and a set of external correlates was never discussed, a correlation

table did indicate differences in patterns of correlations.

Chang, D'Zurilla, and Maydeu-Olivares (1994) have found the LOT to be

bidimensional, the Hopelessness Scale (Beck et al., 1974) to be unidimensional, and

Dember et al. (1989) Scale of optimism and pessimism to be multidimensional in a

sample of 389 undergraduates. Using weighted least-squares confirmatory analysis to test the factor structure, the two factor solution for the LOT had the best fit, for the

Hopelessness Scale the single factor model had similar fit to the two factor solution. The adjusted for measurement error correlation between the two LOT factors was -.54 and -

58 .93 for the optimism and pessimism dimensions of the Hopelessness Scale. A review of the correlations between optimism and pessimism and a psychological stress measure indicated a different pattern of correlations. Additionally convergent and discriminant validity were tested, as indices of optimism were more highly correlated to each other than to the pessimism indices, and vice versa (Chang et al., 1994). The biggest drawback to this study is that a large set of external correlates would have helped confirm the bidimensional nature of the LOT. As a side note Dembers et al. (1989) optimism/pessimism scale was submitted to exploratory factor analysis the results were found to be complex and multidimensional as well as difficult to interpret theoretically.

Additionally Chang et al. (1994) suggest that Dember et al's optimism/pessimism scale taps other related and overlapping psychological constructs.

Sharpe, Hickey, and Wolf (1994) used a modified version of the LOT on 90 frail older ladies. The response to the scale items was changed from a 5 point scale to a dichotomous (agree/disagree) scale. Principal axis factor analysis identified a two-factor solution. However reliability scores were low for optimism .56 and .64 for pessimism.

Using the dichotomous scale over all weakened the study (Sharpe et al., 1994), unfortunately the 5 point scale confused the subjects. Distinct patterns of correlations with external variates might have helped in the interpretation of the modified scale, but over all the two-factor solution seems to be consistent across many studies even when unreliable measures are used.

Chang and McBride-Chang (1996) identified a two factor structure to the LOT in

108 students. Principal components analysis extracted a two factor solution.

Confirmatory factor analysis also indicted that a two factor model fit the data better than a

59 single factor model. Chang and McBride-Chang (1996) did not test for distinct patterns

of correlates between the two-factors and a set of external variables.

Robinson-Whelen, Kim, MacCallum,and Kiecolt-Glaser (1997) tested a sample of

224 middle-aged and older adults consisting of 113 caregivers of individuals with

dementia and 111 noncaregivers. The LOT was administered at Years 1 and 3 of a multi-

wave study. Although exploratory factor analysis was not conducted on the LOT,

confirmatory factor analyses of years 1 and 3 LOT have the two-factor model of the LOT

fitting the data significantly better than a single factor model for both waves of data.

Optimism and pessimism were fairly independent among noncaregivers correlating at -

.23 for year 1 and -.26 for year 2 (corrected for measurement error). Caregivers on the

other had strong correlations (corrected for measurement error) between pessimism and

optimism at year 1 (-.61) and at year 2 (-.63). Robinson-Whelen et al. (1997) suggest

that in stressed individuals optimism and pessimism may only be a partially independent

construct. Distinct patterns of correlation between the two-factors and a set of external

correlates were difficult to interpret because there was no correlation table provided,

instead path diagrams were supplied, but the explanations of the parameters were unclear.

A goal of this investigation is to test the dimensionality of optimism/pessimism

using the LOT. Preliminary results used for a poster presented at the 1999 annual meetings of the Gerontological Society Of America (Burant, Kercher, Kahana, &

Fortinsky, 1999), supported a 2 factor solution for the LOT. The findings are the first to support a bidimensional factor structure to the LOT scale based on a thorough regimen of testing for factor structures. These techniques include: exploratory principal-axis factor

analysis, confirmatory factor analysis, testing correlations between the factors, and

60 identifying distinct patterns of correlations with external variables. Each of these procedures provided additional evidence to support the use of multiple factors to represent LOT.

61 Chapter 3: Literature Review Of Conceptual Relationships and

Associated Hypotheses

The literature review will continue with theoretical explanations that are tied to

the relationships among the key components of the model. Hypotheses will follow each

theoretical explanation. The following subsections cover the theoretical foundations and hypotheses for each relationship in the study: a) Consequences of optimism/pessimism; b)

Antecedents of optimism/pessimism; c) Sociodemographics as an antecedent to physical health and psychological well-being; d) Optimism/pessimism as mediator of sociodemographics impact of on psychological well-being; e) Causal ordering of physical health and psychological well-being; and f) Impact of the institutional care environment.

Consequences of Optimism and Pessimism

In order to better understand how optimism/pessimism works within individuals

Scheier and Carver (1985) have proposed applying a model of behavioral-self regulation

to explain how optimism/pessimism can impact behavior. An overview (see Figure 1.2)

of Scheier and Carver's model shows that goal-directed behaviors are guided by a

hierarchy of closed-loop negative feedback systems (1985, p.220). The behavior guiding

feedback system becomes engaged when a person focuses attention to the self when a

behavioral goal becomes noticeable (Scheier & Carver, 1985). Scheier and Carver (1985)

suggest in general, that focusing on the self causes one to change behaviors in order to

reduce the discrepancy between present behavior and the goal. In another words, when

62 faced with a challenge individuals focus on changing their behavior to handle or attain their goals.

If a difficulty in discrepancy reduction occurs, because of a situational/contextual barrier or perception of personal inability to execute a behavior, one may stop the process to assess outcome expectancy and the likelihood of discrepancy reduction (Scheier &

Carver, 1985). In another words, when faced with a difficult challenge, an individual will stop and check to see if they can handle the challenge. If they believe that they can successfully handle the challenge, the result is renewed effort. If the individual believes that they will be unable to successfully handle the challenge, the result is reduced effort or disengagement. According to Scheier and Carver (1985) renewed effort and disengagement both are magnified by more self-focus.

From an aging perspective, pessimism may contribute to an elderly individual’s vulnerability to the processes associated with disengagement. According to the disengagement theory of aging, as described by Cumming and Henry (1961), disengagement refers to the inevitable process of the altering of the quality (at the least) or severing (at the worst) of relationships between an aging person and members of society. This withdrawal or disengagement is a mutual process resulting in decreased interaction between the aging person and others in his/her social system (Cumming &

Henry, 1961). Disengagement or withdrawal ultimately is the social adjustment for the aging individual and the social system to prepare for his/her’s death. Cumming and

Henry argue that disengagement is not only reflected in sociological but also psychological changes, manifested by loss of morale. Pessimism may exacerbate this loss of morale. As mentioned earlier, pessimism may develop when individuals have

63 difficulties or failures in being able to handle the challenges of life. An aging person

must deal with extreme challenges such as widowhood, loss of friends, retirement, and

declining health. Failure to successfully deal with these situations may reinforce or

contribute to one’s level of pessimism. Individuals exhibiting high levels of pessimism

may be at greater risk for disengagement.

Applying the model to an individual can demonstrate how optimism/pessimism

develops separately in an individual. If optimism and pessimism really are independent

than different mechanisms may be driving them. This implies that if positive experiences

drive optimism that they may not drive pessimism and conversely negative experiences

may drive pessimism but have no affect on optimism. With this in mind let's look at how

optimism/pessimism develops independently. As individuals become more and more

successful at handling challenges, they become optimistic that they can deal with

problems in their life. When faced with an obstacle, an optimistic person will be more

likely to renew efforts because they have had favorable experiences in the past dealing

with challenges. Finally an optimistic person will gain confidence in their ability to

handle difficult situations therefore believing that future challenges will result in positive

outcomes. Conversely, pessimism develops within individuals, when they are

unsuccessful in handling difficulties. When faced with a problem, a pessimistic person

will be more likely to withdraw or disengage himself or herself from the problem.

Furthermore, a pessimistic person builds self-doubt in themselves based on past failures therefore believing that future challenges will result in negative outcomes.

With this in mind let's look at health and psychological well-being as a consequence of optimism/pessimism. Scheier and Carver (1985) were the first to discuss

64 the impact of optimism on one's level of health. Furthermore, they reasoned that more

optimistic individuals would report being bothered less by physical symptoms. The

findings supported this hypothesis. Scheier and Carver's work spawned many others to

look at the impact of optimism/pessimism on disease (e.g., Desharnais, Godin, Jobin,

Valois, and Ross, 1990; Lauver and Tak, 1995; O'Brien, VanEgeren, and Mumbry; 1995;

Scheier and Carver, 1985, 1992; Schulz, Bookwala, Knapp, Scheier, and Williamson,

1996; Shepperd, Morato, & Pbert, 1996 ). While the use of the LOT to measure

optimism/pessimism has been used in health studies focusing on its beneficial impact on

health outcomes, disparity exists on the different ways to use it. Specifically, the LOT

scale has frequently been used as a single scale usually containing 8 items (e.g., Carver,

Pozo, Harris, Noriega, Scheier, Robinson, Ketcham, Moffat, & Clark, 1993b) and on

some occasions 4 items (e.g., Scheier, Matthews, Owens, Magovern, Lefebvre, Abbott, &

Carver, 1989) while on some studies 2 separate 4 item scales representing separate

dimensions of pessimism and optimism (e.g., Schulz, et al., 1996) are created.

In the following studies on heart disease optimism has had a beneficial impact on

cardiac patients recovery. Scheier, et al. (1989) using a 4 item abbreviated single LOT

scale, measured post heart attack, found that optimists (high scale score) fared better than pessimists (low scale score) in recovering from Coronary Artery Bypass Surgery.

Optimists actually had better physiological reactions for example, fewer Q waves, a sign

of heart attack (Myocardial Infarction [MI]), lower levels of AST, an enzyme released

when heart muscle damage occurs form a MI, quicker recovery times, and returned to

vigorous physical exercise in shorter times than pessimists (Scheier, et al., 1989).

Shepperd, et al. (1996) using a single 8 item LOT scale found that optimistic patients

65 with coronary disease entering into cardiac rehabilitation program had better therapeutic results than their pessimistic counterparts. Specifically, optimists had lower levels of saturated fat, body fat, and global coronary risk, while at the same time increasing their aerobic capacity as compared to pessimistic program participants (Shepperd, et al., 1996).

Desharnais, et al. (1990) using a single 8 item LOT Scale found that optimistic patients who previously had a heart attack had lower scores on perceived susceptibility to MI, severity of another MI, and fear of having another MI.

A review of the literature on the impact of optimism/pessimism on cancer has similar results. Schulz, et al. (1996) using the 2 separate scale of pessimism and optimism found high levels of pessimism in younger patients to be associated with nonsurvivorship, while optimism in younger patients had no effect on survivorship.

Levels of pessimism and optimism in older patients were not associated with survivorship

Lauver and Tak (1995) using a single 8-item optimism scale found optimistic women with breast cancer symptoms to have less delay and anxiety in care as well as expected more favorable outcomes of care seeking. Carver, Pozo-Kaderman, Harris, Noriega,

Scheier, Robinson, Ketcham, Moffat, & Clark, (1994) using a single 8- item LOT scale found optimism in breast cancer patients was associated with higher levels of psychological well-being, better quality of sex life, and lower incidence of intrusive thoughts about the surgery or the disease. Carver, et al. (1993b) used a single 8-item scale of optimism on women with early stage breast cancer. Findings suggest that optimism is related with lower levels of distress, more active coping and planning early in the crisis, and lower levels of behavioral disengagement. While the benefits of optimism on one's health has been demonstrated, studies on optimism applying to health issues in

66 8 CESD 10 Item 10 Health ADL/IADL OPTIMISM/ Physical PESSIMISM Short Version 7 Well-Being Dispositional Psychological Characteristics 4 5 6 6 8 6 CESD 10 Item Health ADL/IADL Physical Short Version 7 Well-Being Psychological 6 6 8 CESD 10 Item 10 Health ADL/IADL OPTIMISM/ Physical PESSIMISM Short Version Well-Being 7 Dispositional Psychological Characteristics 4 5 6 6 6 8 CESD 10 Item 10 Health ADL/IADL OPTIMISM/ Physical PESSIMISM Short Version Well-Being 7 Dispositional Psychological Characteristics 4 5 6 6 6 8 CESD 10 Item 10 Health ADL/IADL OPTIMISM/ Physical PESSIMISM Short VersionShort 7 Well-Being Dispositional Psychological Characteristics 4 5 6 6 Figure 3.1:Figure Proposed Auto-regressive LongitudinalModelof Optimism\Pessimismas Mediator of Social Inequality effects on Physical Healthand Psychological Well-being 6 Index CESD Clinical 10 Item 10 Charlson Health ADL/IADL ADMIT 1 WEEK 1 MONTH 3 MONTHS 6 MONTHS 12 MONTHS APACHE II OPTIMISM/ Comorbidity Physical PESSIMISM WAVE 1 Short Version Illness Severity Illness Covariates Well-Being Dispositional Psychological Characteristics Physical Health

1 3

AGE INCOME STATUS GENDER ETHNICITY EDUCATION DEMOGRAPHICS SOCIOECONOMIC INEQUALITIES

STRUCTURAL SOCIAL

67 an elderly population have been limited to inclusion in studies of the general population

(e.g., Schulz et al., 1996).

Optimism/pessimism has also been shown to impact self-rated health and

psychological well-being. Robinson-Whelen, Cheongtag, MaCallum, & Kiecolt-Glaser

(1997) treated the LOT as consisting of 2 factors (optimism and pessimism). The purpose of this paper was to determine which of these two factors were the stronger predictors of anxiety, perceived stress, depression, and subjective health. Overall higher levels of pessimism were shown to predict higher levels of anxiety and perceived stress as well as lower self-rated health scores. However, pessimism did not predict depression.Optimism, on the other hand, did not predict any of the 4 outcomes.

This current investigation proposes to test the consequences of optimism/pessimism please refer to Figure 3.1, arrows 4 and 5. (Figure 3.1 refers to a more complex model of Figure1 including the longitudinal multiple waves of data.)

These arrows represent how optimism/pessimism impacts psychological well being and physical health at each time period. Arrow 5 contributes to the state of the literature, because there is a dearth of literature on psychological well-being as a consequence of optimism/pessimism, especially based in a hospitalized elder sample. Likewise arrow 4 contributes to research, because it represents the only study to take into account the health consequences of optimism/pessimism in elders hospitalized with acute illnesses. Based on prior empirical evidence reviewed earlier the following hypotheses are posited.

H1: Higher levels of optimism will produce higher levels of physical functioning and lower levels of depression.

68 H2: Higher levels of pessimism will produce lower levels of physical functioning and higher levels of depression..

Antecedents of Optimism and Pessimism

While micro-level social processes help drive Scheier and Carver's (1982) model of behavioral self-regulation used to describe optimism/pessimism (1985), the model does not take into account the larger macro-level societal structures that can drive the micro-level processes that can shape one's optimism/pessimism. Specifically, this refers to the impact of social structures (represented by sociodemographics) on optimism/pessimism, because structural components like SES can determine success or failure. Conflict theory based in the works of Marx offers a foundation for looking at the disparities that exist within classes or social structures and how these disparities can affect ones level of optimism/pessimism. At the most basic level these sociodemographic classes can be broken-down into privileged and disadvantaged groups based on who controls the power and resources (Dahrendorf, 1959; Ritzer, 1988; Skaff, 1999). It stands to reason that those individuals that are in a position of power or who have more resources (privileged class) are also more likely to be have successful results when dealing with difficult situations than individuals who are not in a position of power or who have limited resources (disadvantaged class). In another words, the ability to control and mobilize the power and the resources to handle challenges places the individual in the privileged class at a distinct advantage over the person from the disadvantaged class who may be constrained in their efforts to handle problems because of lack of power or resources. With this in mind how do disparities within

69 sociodemographic groups relate to optimism/pessimism.

Please recall that Scheier and Carver (1985) have suggested that individuals who

become more successful at handling challenges are also more likely to be optimistic about

their ability to handle problems. Conversely, pessimism develops within individuals

when they are unsuccessful in handling difficulties. Disparities, in regard to power and

resources, within social structures may give a person with a privileged status a distinct advantage in handling problematic situations successfully therefore with continued successes this will contribute to the development of optimism within that individual. On the other hand, if a person in a disadvantaged status is lacking the power and resources they may already be at a disadvantage to handle a difficult situation successfully. These failures in the ability to handle challenges may contribute to the development of pessimism in individuals.

To date there have been no studies that have explored the relationship between social structures and optimism/pessimism. However, several studies have investigated the impact of social structures on trait like characteristics including: personality (House,

1981), self-esteem (Rosenberg & Pearlin, 1978), development of the self (Ryff, Marshall,

& Clarke,1999; George, 1999; Atchley, 1999; Hendricks, 1999, Skaff, 1999), sense of control (Downey & Moen,1987; Mirowsky & Ross, 1984; Pearlin & Radabaugh,1976,

Ross & Mirowsky; 1992), and powerlessness (Ross & Mirowsky, 1989). These studies shed light on how social structure may impact the formation of optimism/pessimism in individuals.

Rosenberg and Pearlin’s work on self-esteem (1978) offers the most comprehensive explanation of how social structures play a role in the development of a

70 trait like characteristic (self-esteem). In fact, Rosenberg and Pearlin’s explanation has been adapted to explain how social structures impact the formation of personality (House,

1990) and the development of the self -concept (Hendricks, 1999). House’s support for

Rosenberg and Pearlin’s work is so strong that he states “The logic of their analysis could be extended to any relationship between SES and individual personality and behavior, and to the relation between other macro social phenomena and personality” (1981, p.554).

With this in mind, it is feasible to apply Rosenberg and Pearlin’s work to the relationship between social structures (macro social phenomena) and optimism/pessimism.

Rosenberg and Pearlin provide four explanations for the relationship between social class and self-esteem. These include social comparison processes, reflected appraisals, self-perception theory, and psychological centrality. Social comparison processes, as described by Rosenberg and Pearlin (1978), focus on how individuals compare their own group or position with that of others. The whole concept of social class is based on disparities, such as, superior vs. inferior, higher vs. lower, better vs. worse (Rosenberg & Pearlin (1978). Kaplan (1971) states the influence of social structures on self esteem occurs when higher classes pride themselves on their superior status, while lower classes are painfully aware of their relative inferiority. When applying this approach to social structures and optimism/pessimism, individuals can perceive how members of higher social classes have more successes, which in turn contribute, to one’s level of optimism/pessimism. Conversely, members of lower classes may be aware of their failures relative to the successes of higher classes.

Reflected appraisals refer to Mead’s work (1934) on how people see themselves as how they believe others see them. In interpreting how the significant individuals in

71 one’s life with whom that individual interact with judge him/her by his/her social class, one gains insight on how others see him/her (Rosenberg and Pearlin, 1978). Rosenberg and Pearlin state that the extent to which individuals see themselves from others’ status based viewpoints is how social status will impact one’s self-esteem. For example, if an individual sees himself/herself as belonging to a lower class because other’s see him/her that way and being a member of the lower class has negative connotations, then this will impact his/ her self esteem. When applying this approach to social structures and optimism/pessimism, if individuals see their successes and failures tied to social structures because valued others’ opinions see them in those terms, then the successes and failures tied to social status will impact one’s level of optimism/pessimism.

Self-perception theory, according to Rosenberg and Pearlin (1978), refers to how an individual’s conception of themselves is formed by observing themselves as other do.

Essentially, individuals learn about themselves by observing their own behavior and its outcomes. Rosenberg and Pearlin state that “we draw conclusion about ourselves in part by observing our actions or their outcomes (e.g., successes or failures)”(1978, p.65). As socioeconomic status is achieved in adults and not ascribed, people may draw inferences about their self worth from their SES (House, 1981). Since optimism/pessimism are tied to success and failure, if individuals’ self perceptions of their successes or failures are tied to their socioeconomic status then these self perceptions should impact their level of optimism/pessimism.

Psychological centrality refers to how individuals place different levels of importance on the elements that make up their self-concept. Typically these elements are made up of dispositions (e.g. intelligence, kindness, morality , optimism) and social

72 identity components (e.g., race, sex, religion, age, social class) (Rosenberg & Pearlin,

1978). According to Rosenberg and Pearlin, the impact of any of the self-concept

elements on global self-esteem will depend on its importance or unimportance, centrality

or peripheriality. Social status may have a powerful influence one’s self esteem if he or

she value status highly. For example, individuals with higher income are likely to have

higher self esteem than those with lower income (Rosenberg and Pearlin,1978). This

relationship depends on if income is considered very important to the person (Rosenberg

& Pearlin, 1978). In much the same way optimism/pessimism may be impacted by how

much importance an individual places on successes or failure tied to social structures like

income. Within the proposed study four sociodemographic structural components have been identified for investigation. These include income, education, ethnicity, and gender.

Within each of the four sociodemographic groups the following are the privileged classes:

Individuals with higher levels of income and education, males, and whites. Preliminary correlation results (used for a poster presented at the 1999 annual meeting of the

Gerontological Society of America [Burant, Kercher, Kahana, & Fortinsky, 1999]) testing the relationship between these sociodemographics and the 2-factor version of the LOT support the use of these variables in the model. In a sample of 797, higher levels of

Pessimism are associated with lower levels of income (r=. 31), education (r=. 32), being

nonwhite (r=. 17), and being female (r=. 13). Higher levels of optimism were mildly

associated with lower levels of education (r=. 13) and being nonwhite (r=. 19).

Considering the rather large sample size these correlations should be stable.

The current study proposes to test the antecedents of optimism/pessimism please

refer to Figure 3.1, arrow 1. This arrow represents how social structures (as represented

73 by sociodemographics) impact optimism/pessimism. Arrow 1 represents a contribution

to the field because, to date there have been no studies that have tested how

sociodemographics drive optimism/pessimism. This is extremely important because

since sociodemographics such as SES can contribute to one's success and failures, which can in turn, affect one's level of optimism/pessimism. With the previous background on antecedents and preliminary analyses testing the bivariate relationship between sociodemographics and optimism and pessimism the following hypotheses are posited.

H3: Individuals in the privileged social structural groups (e.g., the younger old , males, whites, the better educated, and with higher incomes) will display higher levels of optimism. H4: Individuals in the disadvantaged social structural groups (e.g., older elderly, females, African Americans, the less educated, and with lower income levels) will display higher levels of pessimism.

Social Structural Disparities as an Antecedent to Physical Health and Psychological

Well-Being

Based on the proposed model (see Figure 3.1), social structural disparities will predict Physical Health and Psychological Well-being. George (1996) gives examples how the disparities within the sociodemographic groups do impact one's physical health and well-being (George, 1996). Further support of the disparities found in society are offered by Engels ([1844] 1984) and Gerhardt (1989). Engels describes that when comparing laborers to the professionals, laborers were more likely to have a shortened life span because of the terrible living and working conditions faced by them. Gerhardt states

74 that differences in exposure to socioeconomic resources define one's susceptibility to

disease. A review of the literature concerning ethnicity, gender, income, and education as

related to physical health and psychological well-being, also shows that privileged class in most cases fairs better than the disadvantaged class. The one exception pertains to ethnicity and psychological well-being where there are no differences between African

Americans and Whites (Weissman, Bruce, Leaf, Florio, & Holzer, 1991).

As far as physical health issues are concerned older African Americans rate their health as significantly lower than older Whites ( Ferraro, 1993; Ford, Haug, Jones, Roy,

& Folmar, 1990; Krause, 1987; Mutran & Ferraro, 1988). Chronic health conditions

(Ford et al., 1990) and functional decline are more prevalent among African Americans than Whites (Angel, Angel, & Himes, 1992; Clark & Maddox, 1992; Crimmins & Saito,

1993). Sociological reasons for such disparity between African Americans and Whites may include lack of resources for proper health maintenance and access to medical care.

Additionally, African Americans may have a distrust of the medical system stemming from past reports of medical neglect against African Americans. For example, the

Tuskegee Syphilis Study, in which African Americans males were prevented from receiving treatment in order to track the natural progression of the disease.

When looking at differences in gender George (1996) points out that with the exception of mortality older women have poorer health than males. Older females report more chronic illnesses (NCHS, 1992) and higher functional impairment than older men

(Ferraro, 1993; Mutran & Ferraro, 1988; Penning & Strain, 1994). As far as psychological well-being is concerned women report higher levels of depressive symptoms (Dean, Kolody, Wood, & Matt, 1992; Keith 1993; Krause and Goldenhar,

75 1992; Krause & Liang, 1993) as well as psychological distress than men (Krause, Herzog,

& Baker, 1992). While one must consider the biological influences on gender health one can't help but wonder if women are prone to illness because of their disadvantaged position in society. For example, heart attacks in middle aged women, until recently, have been ignored because this cohort was not supposed to have heart problems.

Income also has been associated with differences in one's level of physical health.

For example, in longitudinal studies individuals with higher income were less like to decline functionally (Boult, Kane, Louis, Boult, & McCaffrey, 1994: Camacho,

Strawbridge, Cohen, & Kaplan, 1993; Clark & Maddox, 1992; Crimmins & Saito, 1993;

Rogers, Rogers, & Belanger, 1992). When looking at psychological well-being higher levels of income were associated with lower amounts of depressive symptoms (La Gory

& Fitzpatrick, 1992; Norris & Murrell, 1987; Ulbrich, Warheit, & Zimmerman, 1989).

Sociologically, the disparities in equality across income levels may be associated with better access to health care (e.g., what's better the Cleveland Clinic or the Free Clinic?), better diets, and being able to pay for treatments.

Disparities in education levels are also associated with physical health. Education is associated with lower prevalence of chronic illness (House, Kessler, & Herzog, 1990).

A higher level of education is also negatively related to functional impairments

(Auslander, & Litwin, 1991; Ferraro, 1993; Mutran & Ferraro, 1988) and better self-rated health (Hansell & Mechanic, 1991; Idler, 1993; Johnson & Wolinsky, 1993; Levkoff,

Cleary, & Wettle, 1987). When taking into account psychological well-being, education is inversely related to depressive symptoms (La Gory & Fitzpatrick, 1992; Norris &

Murrell, 1987; Ulbrich, Warheit, & Zimmerman, 1989). Sociologically, education is a

76 resource and a source of power enabling individuals to better choose medical care, keep better informed on health promoting behaviors, and make decisions concerning treatments.

As proposed here in order to test social structural disparities as an antecedent to psychological well-being and physical health please refer to Figure 3.1, arrows 4 and 5.

These arrows represent how social structural disparities impact psychological well being and physical health, one of the most widely studied areas of research. The following hypotheses based on previous empirical evidence were posited.

Optimism/Pessimism as Mediator Of Social Structural Disparities Impact on

Psychological Well-Being and Physical Health

When trying to understand how optimism/pessimism can serve as a mediator between the impact of structural components on an individual’s level of psychological well-being and physical health it is important to look at the issue of social structure and human agency. Settersten’s (1999) work suggested that in order to know how an individual develops researchers need to gain a better understanding of the bridge that exists between social structure and human agency and the role these macro and micro issues have on development. These issues are often seen as the difference between sociology and psychology in the role of human development.

Sociologists often follow the macro approach by looking at how social structures shape human lives (Settersten, 1999). All but relegating the person as a passive participant being ruled by the structures in which they exist. The sociological

77 perspective, as suggested by Settersten (1999), deemphasizes the roles of personality traits and characteristics, desires, aspirations, motivations, and expectations. Settersten refers to this model as structure without agency (as italicized by Settersten). An example of this can be found in Meyer’s work (2003) on prejudice, in which institutional racism is described. Specifically, Meyer’s describes how institutional barriers often go undetected at the individual level. This can occur when an employer disguises discriminatory acts by denying promotions to African American employees. The individual may believe that the reason for not receiving a promotion was not tied to discrimination. This is a covert example of the structure without agency model, because the individual is unaware of how one’s location in a structure shapes one’s life. On the other hand, it is important to be aware that the structure without agency model can be dangerous because when taken to the extreme negative attributes in one’s life are interpreted in a way that will

“externalize“ blame and overlooking or ignoring of one’s personal responsibility. The limitations of this model with in the context of the current study include only looking at the impact of social structures on ones physical health and psychological well-being.

From another perspective, the psychological approach, as suggested by Settersten

(1999), usually does not take into account the powerful social and historical forces that plays a role in one’s life by contributing to or constraining one’s development. Settersten refers to this model as agency without structure (as italicized by Settersten). According to Fernández-Ballesteros, Díez-Nicolás, Caprara, Barbaranelli, & Bandura (2002), perceived self-efficacy is the foundation of human agency. Perceived self-efficacy, as described by Fernández-Ballesteros et al.(2002),as the ability to manage one’s own life circumstances. Specifically, if individuals do not believe they can forestall undesired

78 outcomes and produce desired ones because of their actions they have little incentives to act or to persevere in the face of adversity (Fernández-Ballesteros et al.,2002). Using this

perspective does not take into the societal structures that influence one’s personal

efficacy. Fernández-Ballesteros et al.(2002) make the point that research on perceived efficacy has been limited to study of individual agency. The agency without structure

model can also lead to dangerous interpretation because individuals are seen to be

responsible for their own negative outcomes. This view can impact social policy to the point of denying assistance to those that may need it because after all they are to blame for their own problems and circumstances. The limitations of this approach in the current study include only looking at the impact of human agency through one’s personal control as represented by optimism/pessimism on ones physical health and psychological well- being.

The third model of agency within structure (as italicized by Settersten) brings together both psychological and sociological perspectives into a single model. This model is in response to Settersten’s original argument for the bridging of social structures

and human agency. Settersten (1999) argues that to undertstand the lives of individuals

one must take into account aspects of both the agency without structure model and

structure without agency model. In other words., the agency within structure approach,

views people as proactive in creating or constructing their own life yet remaining

interactive with their environments. Additionally, Settersten makes the point that when

trying to understand how structures may influence human agency, one should be aware of

not only the constraining effects that structures have on individual lives, but also the

enabling effects.

79 Fernández-Ballesteros et al. (2002) offer an explanation of the mechanisms that occur in the relationship between sociostructural influences and personal agency. This work seems to parallel Settersten’s work on the agency within structure model.

Fernández-Ballesteros et al. (2002) argue that sociostructural influences work through psychological mechanisms to create behavioral effects. Bandura (1999) and Elder (1995) argue that the aspirations as well as beliefs of efficacy to take some control over one’s life circumstances serves as mediator between the relationship of socioeconomic status’ impact on psychosocial functioning. Advantages based on SES are among the foundations for resources and access to opportunity structures that contribute to the development and exercise of personal efficacy (Fernández-Ballesteros et al.,2002).

Overall. SES fosters a sense of personal efficacy and aspirations (Bandura, Barbaranelli,

Caprara, & Pastorelli, 2001). In a similar vein, an argument can be made that optimism/pessimism acts much like personal efficacy because both deal with taking control in one’s life to handle challenges. In the current study, optimism/pessimism would serve as a mediator between sociostructural influences on health and psychological well-being.

One advantage of this proposed model is that an agency within structure model can be tested. While, this model not only looks at how individuals take control of their own well-being through their own sense of optimism/pessimism, it also addresses how structure impacts the human agent’s level of optimism/pessimism. Furthermore, one can ascertain how both structure and agency impact one’s level of physical health and psychological well-being. In another words, what role does the agency within structure model play in physical health and psychological well-being.

80 Logically if social structural disparities are antecedent to optimism/pessimism and psychological well-being and physical health are consequences of optimism/pessimism and social structural disparities impacts psychological well-being and physical health, then optimism/pessimism can serve as a mediator between social structural disparities and physical health and psychological well-being. As proposed to test optimism/pessimism as mediator of social structural disparities impact on antecedent to psychological well-being and physical health please refer to Figure 3.1, the two general hypotheses for the mediating arrows are as follows:

H5: Individuals in privileged groups (younger elderly, males, whites, the better educated, and with higher incomes) will display more optimism which, in turn, will lead to better psychological well-being and physical health. Accordingly, controlling for the mediating effects of optimism will reduce the direct effect of social inequalities on psychological well-being and physical health .

H6: Individuals in privileged groups (younger elderly, males, whites, the better educated, and with higher incomes) will experience less pessimism which, in turn, will lead to better psychological well-being and physical health. Accordingly, controlling for the mediating effects of pessimism will reduce the direct effect of social inequalities on psychological well-being and physical health .

Causal Ordering of Physical Health and Psychological Well-Being

The proposed model (see Figure 3.1) tests the relationship between physical health and psychological well-being longitudinally across 6 time periods (admission to the hospital, hospital discharge, 1 month post hospital discharge, 3 month post hospital

81 discharge, 6 month post hospital discharge, and 12 month post hospital discharge).

Traditionally, physical health has predicted psychological well-being (Bradburn, 1969;

Lawton, 1983; Lawton, Moss, Kleban, Glicksman, & Rovine, 1991), but this relationship has only been tested in cross-sectional studies. Therefore, to empirically test the predictive direction of the true relationship between these variables it is imperative to study their relationship across time. This study provides the opportunity to accomplish this.

The relationship between physical health and psychological well-being will be tested longitudinally to identify the true predictive relationship between the 2 concepts.

Five time intervals (hospital discharge, 1 month post hospital discharge, 3 month post hospital discharge, 6 month post hospital discharge, and 12 month post hospital discharge) will be used to test the relationship between physical health and psychological well-being. As proposed here to test the causal ordering of physical health and psychological well-being please refer to Figure 3.1, arrows 7 and 8. These arrows represent the contemporaneous effects between physical health and psychological well- being. The analysis represented by arrows 7 and 8 will contribute to the fields understanding of the causal ordering between physical health and psychological well- being area in which limited research has been conducted.

Impact of Institutional Care Environment

When trying to gain an understanding of how the hospital setting can impact a patient, one can look to Goffman’s Asylums (1961) and the ecological models based on

82 person-environment fit for frail eldery in institutional settings (Kahana, 1974; Lawton,

1982, 1989). Goffman’s work on total institutions offers a beginning framework to

interpret how patients interact with the institutional environment of a hospital.

Goffman discusses five types of what he viewed “total institutions." The hospital

seems to be most closely related to the first type described as institutions designed to care

for people who were incapable of caring for themselves, but who posed no threat in society. (For a more detailed examination of the other four types, see Asylums, by Erving

Goffman, 1961, page, 4) Goffman (1961) said, “A total institution may be defined as a

place of residence and work where a large number of like situated individuals, cut off

from the wider society for an appreciable period of time, together lead an enclosed,

formally administered round of life.” (page 4) He continues with, “The total institution is

a social hybrid, part residential community, part formal organization” (page 12). Central

to the theme of total institutions is that many human needs are handled by bureaucratic

organizations of whole blocks of people (Goffman, 1961, pg 6). While these statements

sum up many aspects of the hospital experience, the only difference seems to be

associated with length of stay, because patients are not usually cut off from society for an

appreciable amount of time during hospitalization. Nevertheless hospital patient are still

cut off from society for some time.

In trying to further understand how hospitals have characteristics similar to “total

institutions”, these are the four central features of total institutions:

1) All aspects of life are conducted in the same place and under the same single authority. 2) Each phase of the member’s daily activity is carried on in the immediate company of a large batch of others all of whom are treated alike and are required

83 to do the same thing together. 3) All facets of the days activities are tightly scheduled. 4) The various enforced activities are brought together into a single rational plan supposedly designed to fulfill the official aims of the institution.

Based on these central features hospitals clearly have features 1,3, and 4. Fortunately for patients feature 2 really does not apply, they are usually given the privacy to carry out their daily activities away from other patients. Overall when looking at the hospital as a total institution one must be aware of how the qualities that hospitals share with total institutions impact its patients. The ecological model, based on congruence in person- environment fit, used in long-term care research (Kahana, Liang, & Felton, 1980) provides a theoretical lens to interpret how the hospital setting may contribute to a patient’s outcomes.

The ecological models recognize that an older person’s functional and well-being outcomes are a function of one’s personal background, physical and social environmental features, as well as the congruence between the older person (needs and capacities) and the environment (supplies and demands). Lawton (1982) refers to one’s personal background as competence and the physical and social environment as environmental press.

Competence is best described by five processes: Biological health, sensory and perceptual capacity, motor skills, cognitive capacity, and ego strength (Lawton, 1982).

Personal competence can be described as the upper limit of an individual's capacity to function in areas of biological health, sensation-perception, motoric behaviors, and cognition (Lawton, 1982).

84 Press is the environmental force (physical, interpersonal, or social) that activates

an interpersonal need (Murray, 1938). In general, press is related to stress, but whereas

stress focuses on the negative environmental demand, press can be positive, negative, or

neutral (Lawton, 1982). Lawton (1989) describes five categories of environments that

exhibit press, these include: personal, small group, suprapersonal, social and physical.

Personal is made up of individuals who interact with the person. Small group

environments are the group of which that person is a member. Suprapersonal environments reflect the general characteristics of others physically proximate to the person. Social environment is based on the characteristics of the social structure at the aggregate level beyond any personal characteristic such as culture, values, norms, and laws. The physical environment is anything that is inanimate.

Kahana (1974) suggests that congruence in person-environment fit is a fruitful theoretical scheme for interpreting the impact of environments on the well-being of older persons. Furthermore for older individuals, who may be experiencing a decline in their adaptive capacities, person-environment fit is extremely important (Kahana, 1974).

Lawton (1982), in his environmental docility hypothesis, outlines how the role of the environment is magnified for vulnerable individuals, such as acutely ill, hospitalized older patients. The environment docility hypothesis (Lawton, 1982) posits that individuals with higher competence levels will remain relatively independent of the behavioral effects of environmental press, while lower competent individuals suggest greater vulnerability to environmental press. A slight change in press will have a greater impact on adequacy of behavioral outcomes for less competent individuals than for higher competent persons (Lawton, 1982). Conversely, an improvement to the

85 environment (e.g., raising the temperature in the room of a cold patient) will have a greater impact on the disabled as compared to the higher competent individuals (Lawton,

1989). If a vulnerable individual is forced to stay in a dissonant milieu, as in the case of acute care hospitals, stress and discomfort follow (Stern, 1970).

The acute care hospital setting may be perceived as a hostile environment for the patient in two ways. First, the patient has been placed into the hospital because they have had at the least a temporary decline in physical capacities. According to the environmental docility hypothesis, these individuals are at a distinct disadvantage to environmental press than those with higher competence levels. Second, when looking at the acute care hospital environment, clearly the hospital setting is not home. An individual is placed into an unfamiliar surrounding that may be uncomfortable and frightening. Different, often unknown persons are coming in out of the patient’s room to check on the person. He/she is expected to stay in a bed that is raised and quite different from his/her own bed. Unfamiliar decor, such as pictures, clocks, and furniture, may be disorienting. Noisy and cluttered halls may inhibit walking. A process of depersonalization, as described by Goffman (1961), may begin as soon as a patient enters the hospital and autonomy is restricted. Patients are stripped of their personal belongings, receive an identification number and often treated more like ‘cases’ than persons. Hospital routines often clash with personal routines. For patients with extreme health problems, limited access to family and friends visits is often the norm. Bedrest has been found to lead to deconditioning and loss of muscle mass and vascular tone (Harper

& Lyles, 1988; Hoenig & Rubinstein,; 1991; Lazarus, Murphy, Coletta, McQuade, &

Culpepper, 1991). Partial starvation may begin prior to admission and continued through

86 the hospital stay because of change in diet or not being fed while waiting for diagnostic or therapeutic procedures (Sullivan, Patch, Walls, & Lipschitz, 1991; Rudman & Fuller,

1989; Winograd & Brown, 1990). Side effects from medications may produce confusion or persistent sedation during the stay (Montamat, Cusack, & Vestal, 1989; Lamy, 1990;

Lesar, Briceland, Delcoure, Parmalee, Masta-Gornic, & Pohl, 1990). Procedures such as urinary catheterization, physical restraints, enemas, and endoscopic procedures may not only lead to patients feeling uncomfortable, but also may prolong bedrest, prevent exercise, and even cause injury (Lofgren, MacPherson, Granieri, Myllenbeck, Sprafka,

1989). It is easy to see how hospitalization can contribute to the decline of individuals who are admitted to the hospital because their physical capacities are diminished.

Conversely, as patients leave the hospital and return to the familiar environment of home it is expected that individual’s level of physical health and psychological well-being will improve, while one’s level of optimism/pessimism (a traitlike characteristic) will remain relatively stable.

The longitudinal design of this study provides an opportunity to test the impact of institutionalization on optimism/pessimism, physical health, and psychological well- being. It is predicted that patients will have the greatest decline at discharge (1 week), and slowly signs of improvement will be seen as time passes on, however, since optimism/pessimism is a personality trait and therefore relatively stable, it is not expected to change at discharge. When testing the impact of institutionalization on optimism/pessimism, physical health and psychological well-being please refer to Figure

3.1, arrows 6. Arrows 6 represent the autoregression between time intervals that is a natural artifact of longitudinal analysis. (The proposed research did not distinguish

T6 T6 5 5 4 4 T5 T5 Slope Slope 3 3 2 2 0 0 1 1 T4 T4 1 1 1 1 1 1 T3 T3 (CES-D) 1 1 (ADL/IADL) 1 1 T2 T2 of Physical Health of Physical Intercept Intercept 1 1 Latent Growth Curve Latent Growth Latent Growth Curve of Curve Latent Growth T1 T1 Psychological Well-Being Index Clinical Charlson APACHE II APACHE Comorbidity WAVE 1 Illness Severity Illness Covariates Physical Health OPTIMISM PESSIMISM Dispositional Characteristics Figure 3.2: Proposed Mediation Model of Optimism and Pessimism with Social Status Characteristics as Time as Characteristics Status Social with and Pessimism of Optimism Model Mediation 3.2: Proposed Figure Well-being and Psychological Health Physical of Curves of the Latent Growth Predictors Invariant AGE STATUS INCOME GENDER ETHNICITY EDUCATION DEMOGRAPHICS SOCIOECONOMIC INEQUALITIES

SOCIAL STRUCTURAL

between being discharged to home as compared to a nursing home, because when

looking at patients with complete data on the Life Orientation Test, the number of

patients who had been admitted to a nursing home (n=68) was relatively small.

Therefore the sample size of the subgroup did not have enough power to run a longitudinal analysis.)

Additionally, latent trajectory analyses (see figure 3.2) can be used to test the longitudinal nature of the relationships among optimism/pessimism, physical health, and psychological well-being. The advantage of using latent trajectory models is that recovery can be tracked over the length of the study. Lines of trajectory of physical health and psychological well-being for each individual can assessed. The models provide an unstandardized factor loading that can be used to produce an overall line of trajectory. This line of trajectory tracks if a person improves, declines, stays the same, or fluctuates in terms of recovery from hospitalization. The line of trajectory can be linear or curvilinear in nature. A separate graph of the line of trajectory will be calculated for optimism/pessimism, physical health, and psychological well-being provided that the latent trajectory models fit the data well. Once the models are tested and found stable, a new model can be developed that includes social structural disparities and clinical measures as predictors of the individual latent trajectory models

(see Figure 3.2).

Summary

The proposed study will contribute to the literature in four primary ways.

First this study will expand the thus far limited research conducted on the relationship

between optimism/pessimism and psychological well being and physical health in

hospitalized elders with acute illnesses or acute exacerbations of chronic illnesses. This relationship has never been tested in the literature. Second, prior research has offered only limited theoretical foundations for looking at how social structural disparities manifested through power differentials may influence optimism/pessimism. For example, Scheier and Carver (1985) only refer to micro level social processes that

impact one's expectations for success and failure, which in turn contribute to the

individual’s level of optimism/pessimism. The proposed study will expand the

knowledge base by being the first to empirically test social structural disparities as

antecedents to optimism/pessimism. Third, much scholarly debate has occurred over the

dimensionality of the LOT, this study will attempt to clarify this issue. Specifically, this

study will be the first to extensively test the dimensionality of the LOT by exploratory

factor analysis, confirmatory factor analysis, the strength of the correlations between the

two dimensions, and a test for a distinct pattern of correlations. Finally, most prior

research has used a cross-sectional analyses to test the relationship optimism/pessimism

,physical health and psychological well-being—generally assuming

optimism/pessimism affects physical health which, in turn, affects psychological well-

being. The current longitudinal study will allow empirical tests of potential reciprocal

affects among optimism/pessimism, physical health and psychological well-being by

testing crosslagged and contemporaneous autoregressive models. Additionally, latent

trajectory models will assess the trajectory of recovery in physical health and

psychological well-being for hospitalized elders.

Chapter 4: Research Design

Background

In order to test this model and hypotheses, this study proposes a secondary analysis of the data collected from the Claude D. Pepper Older American Older

Americans Independence Center at Case Western Reserve University based on a study to determine the efficacy of the University Hospital's Acute Care for the Elderly (ACE)

Unit's Prehab Program in acutely ill, hospitalized elder aged 70 years or older (NIA grant # AG 10418-05). This study will be referred as the Pepper Study.

The ACE Unit was designed with the intention of preventing functional decline in elders during hospitalization which may lead to disability, loss of quality of life, institutionalization, and death (NIA grant # AG 10418-05 proposal) by providing a better person-environment fit for the patient. On the Ace unit a comprehensive interdisciplinary intervention was implemented and designed around the following six components: the specially designed environment of the ACE unit providing a more homelike setting (e.g., with carpeting, pictures and clocks on the wall, special dining and exercise areas); interdisciplinary collaborative care (e.g., team oriented approach to care); multidimensional assessment and pharmacological prescriptions to maintain or restore functional decline (e.g., reduced use of sedatives); review of medications and procedures in order to keep staff up to date; post discharge planning for informal caregiver's network; and transitional care at home provided by a visiting nurse emphasizing the rehabilitative and preventive protocols established in the ACE unit

(NIA grant # AG 10418-05 proposal).

The original study was conducted using a longitudinal, experimental design.

Multiple waves of data were collected starting at baseline (subject recall of data two

weeks prior to admission), admission, discharge, 1 month post-discharge, 3 month post-

discharge, 6 month post-discharge, and 12 month post-discharge. Elderly patients

entering the hospital for acute care were randomly assigned to either the ACE Unit or to

the comparison group who received usual care on conventional medical wards. Patients

had to be 70 years or older and admitted for non-elective medical service to be

considered for the study. Patients were excluded if they were admitted from a nursing

home or were admitted to a surgical unit, oncology unit, intensive care unit, or special

unit such as orthopedics.

While patients were not being treated for chronic illnesses, many patients were

treated for acute flare-ups associated with a chronic condition. An examination of the

major type of illnesses or medical reasons for the hospitalized patients in the current

study revealed that 21.6% (n=344) of the patients had pulmonary related illnesses; 20%

(n=319) had gastrointestinal related illnesses; 14.5% (n=231) had cardiovascular related

illnesses; 13.3% (n=212) had infectious related illnesses; 10.7% (n=170) had neurologic

related illnesses; and 10.2% (n=162) had metabolic related illnesses. A further review

of patient comorbidities revealed that 27.4% (n=438) of the patients had congestive

heart failure; 18.5% (n=295) had demetia; 17.2% (n=275) had chronic lung disease;

16.7% (n=267) had cerebral vascular disease; 14.3% (n=229) had myocardial infarction; and 14.3% (n=229) had diabetes.

The sample at admission was 1632 subjects. Patient data were collected form three sources: the patient, a family member proxy, or a nurse proxy. A proxy was used

only when an individual was unable to answer the questions because of they did not

speak English, some physical condition such as being deaf, or when a patient was

determined to have cognitive impairment based on 5 or more errors on the Short

Portable Mental Status Questionnaire (SPMSQ) (Pfeiffer,1975). Additionally data were

collected from medical charts to ascertain reasons for admission, comorbid illnesses, severity of illness, and physicians’ orders (e.g., for medications, treatments, bedrest, and restraints). Face to Face patient interviews were conducted in the hospital, while post- discharge interviews were conducted by telephone. Proxies were interviewed face to face whenever possible at the hospital or by telephone.

Many measures of data quality control and management were put into place to ensure data quality. Research assistants were trained to use the structured interviews as well as specific protocols necessary to minimize potential bias while collecting data

(NIA grant # AG 10418-05 proposal). Well designed forms and procedures were developed in previous studies conducted by the investigators and similar formats were used to ensure compatibility with the clinical setting. Training and question by question manuals written in the preliminary studies and modified during the current study were used to train data collectors. Additionally, the project manager and interviewers met weekly to handle any data collection problems. Inter-rater reliability was tested among interviewers on a subsample of interviews and found to be quite high. Discrepancies were assessed and discussed among interviewers to find a systematic solution to handling differences in coding. In a similar vein, medical chart abstractors were also tested for inter-rater reliability on a subsample of medical charts and found to be highly reliable.

Data entry was handled using the Mainframe version of SAS 6.0. Forms that

replicated the actual interviews were created in SAS and used for data entry. Logic and

range checks were built into the forms whenever necessary to ensure accuracy of the

data. The mainframe system has two advantages: first, files and backups are maintained

by the hospital and secondly, mainframe systems are not susceptible to computer

viruses, therefore preventing potential loss of data. Additionally, personnel highly

experienced in data entry were used to enter the data. Unfortunately, data were not double data entered, however previous studies in which data were double entered using

this system yielded extremely low error rates.

A subsample of surveys were pulled and answers were verified with the entered

data and errors were found in less than 0.5% of the data sampled. In addition, every

variable was inspected for outliers and proper responses using frequencies and

univariate statistics and corrections were made accordingly. The error rate of less than

0.5% did not change. SPSS data sets were created by converting the SAS data using

DBMS Copy, a data conversion program. Frequencies and univariate statistics were run on both the SAS and the SPSS datasets to test for potential errors in the conversion, no errors were found.

Proposed Study

The proposed study will be a secondary data analysis based on a prospective design. The data will be from the Pepper Study and will track hospitalized elders admitted for an acute illness or an acute episode of a chronic illness over a year post

Table 4.1: Patient Instrument Item/Variable Baseline Admit DC Day 30 Day 90 Month 6 Month 12

Demographics X Income X RACE X

Life Orientation X X X X ------X D 4/95

Charlson X Comorbidities APACHE II X

ADLS X X X X X X X IADLs X X X X X X X Depression X X X X X X (CES-D)

D= Instrument sections deleted

discharge. Seven waves of data were collected (admission to the hospital, hospital

discharge, 1 month post hospital discharge, 3 month post hospital discharge, 6 month post

hospital discharge, and 12 month post hospital discharge). Data were collected originally on 1632 patients at admit based on both patient and proxy interviews. The patient only data will be used because data on patient's level of optimism/pessimism and psychological well-being were not collected from surrogates. The actual patient sample size at admit was N=910, at discharge was N=805, at 1 month post-hospital discharge was N=621, at 3 month post-hospital discharge was N=547, at 6 month post-hospital discharge was N=554, and at 12 month post-hospital discharge was N=470. The specific measures of data collected at each wave can be found in Table 4.1. While the data set shows signs of drop outs over time, Amos will be used to impute full estimation values for missing data in the analysis.

Determining the Window Eligible Data Set

Missing data is a problematic issue when dealing with longitudinal data sets. For

example, individuals may quit the study or not participate at a specific wave. In these

cases individuals will have incomplete data. AMOS allows analysis of incomplete data

using Full Information Maximum Likelihood (FIML) estimation. The current data set

offered many challenges. Six waves of data were collected: admit, discharge, 1, 3, 6, and

12 month post discharge. Since the time frames for the post discharge follow ups used

discharge as the baseline for interviews, the current study will also use the discharge as

the baseline for all longitudinal analyses. While the intent of the study was to collect data

at the exact time interval (e.g., 1 month)for each of the follow up time periods,

logistically this was impossible because often subjects could not be reached on that

specific date. Therefore windows of 30 days or 1 month were used as one of the

eligibility criteria for inclusion in the study. Data were analyzed in 1-2, 3-4, 6-7, and 12-

13 months increments.

Table 4.2 shows the steps needed to identify the analytic data set. The final

dataset had 944 subjects with at least one wave of data (the requirement needed to run the

FIML procedure). Base numbers for each wave of data were created through a several

step process. All data were collected from patients only, no proxy data was used. The

first step was to identify if any data had been collected from a patient (please refer to line

2a). The second step was identifying if a patient was window eligible, in other words was data collected from a specific patient within the 30 day window. In order to ascertain this information the date of the interview for a specific wave had to be present (please refer to line 2b); both the date of the interview for a specific wave and the discharge date had to be present in the patient’s data (please refer to line 2c); finally window eligibility was determined by subtracting discharge date from the date of interview for a specific wave, dates had to fall within 1-2, 3-4,6-7, and 12-13 month post discharge waves (please refer

to line 2d). The final step was to determine which subjects had a least 1 window eligible

wave of data (please refer to line 1). Using the FIML procedure allowed for 944 subjects

to remain in the study (please refer to line 1) as compared to using the 263 subjects

remaining when using listwise deletion to handle missing data (please refer to line 2e).

The FIML estimation procedure in AMOS offers an advantage over listwise

Table 4.2 : Window eligible data set WAVE Admit Discharge 1-2 3-4 6-7 12-13 Months Months Months Months 1. Any information available with at least one wave having 944 944 944 944 944 944 patient data. 2. Patient only data, proxy data deleted a. Any 910 805 621 547 554 470 information available b. Date of 839 791 620 547 554 470 interview present (NA) (NA) c. Date of 753 791 596 515 505 439 interview and (NA) (NA) discharge interview date d. Window - - 541 475 462 389 eligible (NA) NA e. Listwise 263 263 263 263 263 263 Deletion

deletion in handling missing data. Using FIML retains an effective sample size for

accurately calculating standard errors that will be between the 263 sample found using

listwise deletion and the complete non missing data set of 944. Based on how well the

words, if missing values are predicted by variables in the analysis, FIML will provide

unbiased parameter estimates. Issues of nonrandom missing data are of particular interest

in longitudinal data because drop out rates are not a random process and to the extent this

missingness is predicted by variables included in the data analysis FIML will provide

unbiased parametric estimates. Even when data is missing due in part to variables not in

missing values are estimated from other variables in the analysis, use of listwise deletion

will bias parametric estimations from datasets with nonrandomly missing data. In other

words, the analysis, FIML should provide less biased parameter estimates than those found using listwise deletion (Muthen,Kaplan, & Hollis1987;Graham, Hofer, &

MacKinnon, 1996). In summation, in many real world settings data approximates the

conditions under which FIML provides unbiased parameter estimates.

Measures

Please refer to tables 3 for descriptive statistics of all continuous and ordinal

variables mentioned in Figure 3.1. Ordinal variables will also be treated as continuous

variables for future analyses, since treating ordinal level data as continuous does not bias

the overall results.

Social Structural Disparities

Social Structural disparities are represented by the following sociodemographics five measures: gender, ethnicity, education, income and age. All responses were based on self reports during interviews. Please refer to Appendix A for the specific interview questions and responses to sociodemographic measures. Ethnicity consisted of five responses (African American, Asian, Hispanic, White, and other). None of the subjects declared Native American as their ethnicity. Education was measured in five categories of years in school ranging from 0-8 years to 16+ years or college graduate. Income based on an ordinal scale ranging from <$, 5000 to $50,000 or higher. Age was based on self report and confirmed by hospital records. Frequencies for all sociodemographic statistics can be found in Table 4.3.

Dispositional Characteristics

Dispositional characteristics were measured using the Life Orientation Test developed by Scheier and Carver (1985). The LOT is an eight item scale of optimism/pessimism. Four items are positively worded creating an optimism component and four items are negatively worded creating a pessimism component. (A review of the

LOT items can be found in Appendix B). While much debate has been given to the dimensionality of the scale (i.e., 1 vs. 2 dimensions), preliminary analyses of the psychometric properties in the Pepper admit sample indicate a 2 factor solution of optimism and pessimism. The LOT was measured at Admit, 1 week, 1 month, 3 months, and 12 months. Descriptive statistics of the total eight item scale, the four item optimism

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Table 4.3: Descriptive Statistics on Measures in Figure 3.1 (Total N=910)

Cronbach’s Item/Variable N Mean SD Range Skewness Kurtosis Alpha Age 910 78.90 6.47 70-100 .64 -.20 Female 910 .66 .47 0-1 -.66 -1.56 White 910 .64 .48 0-1 -.57 -1.68 Years of Schooling * 869 2.09 1.38 0-4 -.10 -1.19 Income* 607 4.11 2.28 1-8 .41 -1.09 Married 909 .37 .48 0-1 .52 -1.73 Life Orientation Test Optimism Admit 834 10.55 2.40 3-16 -.93 .83 .69 In uncertain times, I expect 828 2.57 .83 0-4 -1.08 .02 the best I look on the 841 2.73 .80 0-4 -1.27 .95 bright side Optimistic about my 835 2.52 .89 0-4 -.90 -.35 future Every cloud has a silver 830 2.72 .83 0-4 -1.16 .82 lining Discharge 330 10.65 2.38 3-16 -.77 .80 .67 In uncertain times, I expect 328 2.59 .85 0-4 -.97 -.07 the best I look on the 330 2.75 .78 1-4 -1.32 1.08 bright side Optimistic about my 330 2.57 .89 1-4 -.77 -.51 future Every cloud has a silver 330 2.75 .82 0-4 -1.26 1.35 lining 1 month 575 10.34 2.35 3-16 -1.07 .63 .71 In uncertain times, I expect 563 2.57 .81 1-4 -1.14 -.11 the best I look on the 579 2.64 .75 1-4 -1.37 .64 bright side Optimistic about my 573 2.47 .87 1-4 -.81 -.79 future Every cloud has a silver 571 2.66 .78 0-4 -1.26 .90 lining

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3 months 492 10.39 2.32 3-15 -1.31 .93 .73 In uncertain times, I expect 487 2.56 .80 1-4 -1.19 -.12 the best I look on the 492 2.71 .70 1-4 -1.81 1.85 bright side Optimistic about my 491 2.45 .87 1-4 -.92 -.89 future Every cloud has a silver 486 2.68 .74 0-4 -1.45 1.21 lining 12 months 421 10.55 2.28 3-16 -1.35 1.49 .74 In uncertain times, I expect 418 2.56 .77 1-4 -1.30 -.06 the best I look on the 422 2.71 .72 1-4 -1.66 1.61 bright side Optimistic about my 420 2.56 .81 1-4 -1.15 -.13 future Every cloud has a silver 416 2.72 .71 0-4 -1.79 2.35 lining

Pessimism Admit 831 9.56 2.61 2-16 -.54 -.32 .72 If something can go wrong 831 2.00 1.01 0-4 .08 -1.63 for me, it will I hardly expect things to go my 825 2.45 .89 0-4 -.84 -.65 way Things never work out the 837 2.66 .79 0-4 -1.21 .68 way I want them to Rarely count on good things 826 2.46 .87 0-4 -.84 -.75 happening Discharge 329 9.29 2.71 3-16 -.31 -.53 .72 If something can go wrong 328 1.84 1.01 0-4 .33 -1.48 for me, it will I hardly expect things to go my 326 2.44 .91 0-4 -.69 -.87 way Things never

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work out the 328 2.65 .79 1-4 -1.10 .34 way I want them to Rarely count on good things 329 2.37 .95 0-4 -.54 -1.17 happening 1 month 572 9.82 2.47 3-16 -.74 -.08 .70 If something can go wrong 567 2.15 .97 0-4 -.22 -1.60 for me, it will I hardly expect things to go my 572 2.53 .83 1-4 -1.04 -.38 way Things never work out the 573 2.66 .75 0-4 -1.39 .96 way I want them to Rarely count on good things 573 2.46 .88 0-4 -.93 -.80 happening 3 months 489 9.93 2.27 4-14 -.90 .10 .66 If something can go wrong 489 2.16 .95 1-4 -.30 -1.77 for me, it will I hardly expect things to go my 488 2.55 .78 1-4 -1.20 -.09 way Things never work out the 487 2.68 .71 1-4 -1.51 1.23 way I want them to Rarely count on good things 487 2.53 .81 0-4 -1.22 -.18 happening 12 months 420 9.94 2.48 0-16 -1.03 .53 .73 If something can go wrong 414 2.15 .98 0-4 -.33 -1.71 for me, it will I hardly expect things to go my 420 2.61 .77 0-4 -1.43 -.58 way Things never work out the 421 2.67 .72 0-4 -1.64 1.47 way I want them to Rarely count on good things 416 2.49 .85 0-4 -1.10 -.55 happening

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CESD 10-item Short Version Negative Affect Admit 813 .90 1.10 0-3 .81 -.83 .73 Felt depressed 813 .34 .47 0-1 .69 -1.53 Felt Lonely 811 .26 .44 0-1 1.12 -.74 Felt Sad 811 .30 .46 0-1 .86 -1.26 Discharge 616 .86 1.11 0-3 .88 -.75 .76 Felt depressed 615 .34 .47 0-1 .68 -1.55 Felt Lonely 616 .25 .44 0-1 1.13 -.73 Felt Sad 612 .27 .44 0-1 1.06 -.88 1 month 588 .57 .97 0-3 1.53 .96 .76 Felt depressed 589 .21 .40 0-1 1.46 .14 Felt Lonely 586 .17 .38 0-1 1.74 1.03 Felt Sad 584 .19 .39 0-1 1.58 .51 3 months 503 .52 .93 0-3 1.61 1.27 .74 Felt depressed 503 .17 .38 0-1 1.73 1.01 Felt Lonely 501 .18 .38 0-1 1.71 .93 Felt Sad 502 .17 .38 0-1 1.75 1.07 6 months 501 .43 .97 0-3 1.53 .96 .78 Felt depressed 503 .14 .35 0-1 2.07 2.28 Felt Lonely 502 .14 .34 0-1 2.14 2.58 Felt Sad 496 .14 .35 0-1 2.07 2.29 12 months 434 .46 .90 0-3 1.87 2.19 .77 Felt depressed 434 .15 .36 0-1 1.97 1.89 Felt Lonely 432 .16 .37 0-1 1.86 1.48 Felt Sad 432 .15 .35 0-1 2.01 2.07 Positive Affect Admit 817 .61 .82 0-2 .81 -1.01 .72 Was happy 801 .35 .48 0-1 .65 -1.58 Enjoyed life 809 .27 .44 0-1 1.05 -.90 Discharge 617 .77 .85 0-2 .46 -1.46 .70 Was happy 610 .43 .50 0-1 .29 -1.92 Enjoyed life 612 .34 .47 0-1 .68 -1.55 1 month 589 .47 .77 0-2 1.23 -.19 .78 Was happy 588 .24 .43 0-1 1.24 -.45 Enjoyed life 582 .23 .42 0-1 1.26 -.41 3 months 500 .37 .77 0-2 1.23 -.19 .78 Was happy 495 .20 .40 0-1 1.50 .26 Enjoyed life 496 .17 .37 0-1 1.81 1.27 6 months 502 .35 .69 0-2 1.68 1.21 .77 Was happy 495 .18 .38 0-1 1.67 .80 Enjoyed life 500 .17 .37 0-1 1.78 1.18 12 months 433 .34 .70 0-2 1.74 1.31 .84 Was happy 432 .19 .39 0-1 1.61 .58 Enjoyed life 427 .15 .36 0-1 1.97 1.88

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Somatic Issues Admit 814 1.57 1.13 0-3 -.10 -1.38 .63 Felt everything 813 .55 .50 0-1 -.21 -1.96 was effort Restless sleep 812 .54 .50 0-1 -.16 -1.98 Could not going 810 .48 .50 0-1 .10 -2.00 Discharge 618 1.58 1.13 0-3 -.12 -1.37 .62 Felt everything 614 .55 .50 0-1 -.19 -1.97 was effort Restless sleep 615 .54 .50 0-1 -.16 -1.98 Could not going 605 .49 .50 0-1 .04 -2.00 1 month 591 1.05 1.11 0-3 .56 -1.10 .67 Felt everything 591 .37 .48 0-1 -.54 -1.72 was effort Restless sleep 591 .36 .48 0-1 .58 -1.67 Could not going 585 .32 .47 0-1 .78 -1.39 3 months 503 .98 1.06 0-3 .64 -.93 .62 Felt everything 502 .35 .48 0-1 .63 -1.61 was effort Restless sleep 501 .33 .47 0-1 .73 -1.48 Could not going 501 .29 .45 0-1 .92 -1.16 6 months 501 .78 1.05 0-3 1.00 -.42 .71 Felt everything 500 .30 .46 0-1 .89 -1.22 was effort Restless sleep 501 .27 .44 0-1 1.04 -.92 Could not going 497 .21 .41 0-1 1.43 .06 12 months 435 .79 1.05 0-3 1.00 -.38 .71 Felt everything 435 .27 .44 0-1 1.05 -.91 was effort Restless sleep 434 .30 .46 0-1 .88 -1.23 Could not going 429 .22 .41 0-1 1.38 -.10

ADL Admit 900 3.63 1.73 0-5 -.94 -.56 .85 Bathing 897 .63 .48 0-1 -.56 -1.69 Dressing 897 .67 .47 0-1 -.75 -1.45 Eating 892 .82 .38 0-1 -1.67 .80 Transferring 897 .68 .47 0-1 -.77 -1.41 Toileting 899 .82 .39 0-1 -1.81 .72 Discharge 892 3.84 1.67 0-5 -1.22 .06 .86 Bathing 890 .66 .47 0-1 -.70 -1.52 Dressing 887 .74 .44 0-1 -1.11 -.78 Eating 891 .87 .34 0-1 -2.16 2.66 Transferring 890 .74 .44 0-1 -1.10 -.79 Toileting 888 .83 .37 0-1 -1.78 1.17 1 month 797 3.96 1.56 0-5 -1.35 .54 .85 Bathing 796 .66 .47 0-1 -.67 -1.55

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Dressing 796 .74 .44 0-1 -1.13 -.73 Eating 796 .87 .33 0-1 -2.36 3.13 Transferring 796 .82 .39 0-1 -1.66 .75 Toileting 793 .87 .34 0-1 -2.17 2.73 3 months 729 4.08 1.54 0-5 -1.55 1.06 .87 Bathing 728 .69 .46 0-1 -.84 -1.29 Dressing 728 .78 .42 0-1 -1.34 -.21 Eating 729 .89 .32 0-1 -2.44 3.95 Transferring 728 .84 .37 0-1 -1.85 1.43 Toileting 725 .89 .34 0-1 -2.52 4.34 6 months 689 4.18 1.49 0-5 -1.73 1.67 .88 Bathing 690 .73 .45 0-1 -1.02 -.95 Dressing 689 .81 .39 0-1 -1.62 -.62 Eating 689 .90 .30 0-1 -2.62 4.86 Transferring 688 .86 .35 0-1 -2.03 2.14 Toileting 689 .89 .32 0-1 -2.45 4.00 12 months 644 4.16 1.49 0-5 -1.70 1.60 .87 Bathing 644 .72 .45 0-1 -1.00 -1.00 Dressing 644 .79 .41 0-1 -1.45 .11 Eating 644 .89 .31 0-1 -2.49 4.24 Transferring 644 .87 .34 0-1 -2.18 2.76 Toileting 643 .89 .31 0-1 -2.52 4.35

IADL Admit 898 7.92 4.55 0-14 -.06 -1.24 .87 Use Phone 893 1.67 .68 0-2 -1.81 1.60 Driving 895 .72 .91 0-2 .57 -1.56 Shopping 896 .63 .89 0-2 .79 -1.27 Make meal 898 .91 .96 0-2 .18 -1.90 Housework 894 .87 .97 0-2 .27 -1.88 Take own 895 1.61 .72 0-2 -1.50 .60 medication Handle money 896 1.51 .82 0-2 -1.17 -.48 Discharge 829 9.08 4.35 0-14 -.38 -1.11 .88 Use Phone 829 1.78 .56 0-2 -2.40 4.42 Driving 825 .83 .91 0-2 .35 -1.71 Shopping 824 .83 .90 0-2 .34 -1.68 Make meal 822 1.18 .92 0-2 -.37 -1.73 Housework 821 1.12 .95 0-2 -.24 -1.86 Take own 829 1.74 .59 0-2 -2.10 3.08 medication Handle money 828 1.59 .77 0-2 -1.45 .28 1 month 691 9.50 4.75 0-14 -.66 -1.01 .91 Use Phone 689 1.77 .57 0-2 -2.33 4.07 Driving 690 .98 .93 0-2 .03 -1.85 Shopping 691 1.02 .93 0-2 -.03 -1.85 Make meal 690 1.34 .89 0-2 -.73 -1.34

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Housework 690 1.29 .92 0-2 -.60 -1.56 Take own 690 1.61 .71 0-2 -1.50 .63 medication Handle money 689 1.51 .81 0-2 -1.17 -.45 3 months 609 10.12 4.53 0-14 -.87 -.66 .91 Use Phone 609 1.78 .53 0-2 -2.43 4.79 Driving 607 1.10 .93 0-2 -.20 -1.81 Shopping 609 1.16 .90 0-2 -.33 -1.70 Make meal 608 1.47 .84 0-2 -1.05 -.74 Housework 607 1.43 .86 0-2 -.95 -.99 Take own 609 1.62 .71 0-2 -1.53 .74 medication Handle money 608 1.57 .78 0-2 -1.37 .08 6 months 591 10.05 4.73 0-14 -.86 -.72 .92 Use Phone 589 1.77 .56 0-2 -2.34 4.18 Driving 573 1.12 .94 0-2 -.25 -1.82 Shopping 591 1.17 .91 0-2 -.34 -1.71 Make meal 591 1.41 .87 0-2 -.91 -1.08 Housework 591 1.40 .88 0-2 -.88 -1.12 Take own 589 1.61 .70 0-2 -1.51 .73 medication Handle money 589 1.55 .78 0-2 -1.30 -.10 12 months 509 10.15 4.65 0-14 -.88 -.64 .92 Use Phone 509 1.75 .57 0-2 -2.19 3.59 Driving 509 1.10 .95 0-2 -.20 -1.86 Shopping 509 1.18 .92 0-2 -.36 -1.72 Make meal 508 1.48 .83 0-2 -1.11 -.64 Housework 507 1.47 .86 0-2 -1.05 -.81 Take own 509 1.63 .70 0-2 -1.58 .88 medication Handle money 507 1.55 .78 0-2 -1.31 -.10

Apache II Clinical 905 10.79 3.41 5-24 .86 .92 Illness Severity Charlson Comorbidity 890 1.70 1.72 0-10 1.44 2.54 Index

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scale and the four item pessimism scale across all the waves of data can be found in Table

4.3.

Psychological Well-Being

Psychological well-being was assessed by looking at depressive symptomatology.

Depressive symptomology was measured by a 10-item short form CES-D scale designed by Kohout, Berkman, Evans, & Cornoni-Huntley (1993) to be used in elderly populations. The yes-no format of each item was found to be easier to administer and less confusing and stressful for elderly respondents (Kohout et al., 1993). The scale and responses can be found in Appendix C. Factor analyses of the short version CES-D yielded a 4 factor solution similar to Radloff’s standard CES-D scale. The short version

CES-D did tap the dimensions of depressed affect, positive affect, somatic complaints, and interpersonal problems (Kohout et al., 1993). The short version CES-D was given at admit, 1 week, 1 month, 3 months, 6months, and 12 months. Descriptive statistics of the total scale across all waves of data can be found in Table 4.3.

Physical Health

Physical health was measured with a subjective health question, the ADL, the

IADL, the Charlson Comorbidity Index, and the Apache II Clinical Illness Severity scale.

The subjective health question was “Compared to other people your age, would you say your health is excellent, very good, good, fair, or poor?” It was measured at admit, 1 week, 1 month, 3 months, and 12 months. Descriptive statistics on subjective health across the five data waves can be found in Table 4.3.

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The Activities of Daily Living (Katz, Ford, Moskowitz, Jackson, & Jaffee; 1963) was used to determine functional health and disability in five basic activities of daily living. The scale measured one’s ability to maintain their independence in bathing, dressing, transferring, toileting, and eating. Items were recoded such that high scores were indicative of independence. Items and responses can be found in Appendix D.

ADL was given at admit, 1 week, 1 month, 3months, 6 months, and 12 months.

Descriptive statistics across all waves of data are supplied in Table 4.3.

The Instrumental Activities of Daily (Lawton & Brody, 1969) was designed to capture the level of functioning above the basic activities of daily living. Specific questions and responses can be found in Appendix D. This measure assesses one’s level of instrumental competence to handle more complex tasks of self-maintenance, such as shopping, cooking, and housekeeping. The following seven activities were measured: using the telephone, getting to places out of walking distance, shopping, preparation of meals, doing light housework, properly taking medication, and handling money. Item scores were recoded to create dichotomous scores with scores of 1 indicating complete independence without any help in a specific task. Therefore a complete IADL score of 7 indicates independence in all 7 tasks. The IADL scale was given at admit, 1 week, 1 month, 3months, 6 months, and 12 months. Descriptive statistics across all waves of data are reported in Table 4.3.

The Charlson Comorbidity Index is a weighted checklist of chronic illnesses that was created by Charlson, Pompei, Ales, & MacKenzie (1987). Scores for more severe diseases receive higher point values. The following are examples of how scores are given: Diabetes receives a score of 1; Diabetes with end organ disease or any tumor

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receives a score of 2; Moderate or severe liver disease receives a score of 3; Metastatic solid tumors or AIDS receive a score of 6. Illness scores are then summed. Higher scores reflect higher levels of comorbidity. All data for the Charlson Comorbidity Index were collected from a chart review of medical records at admission. Descriptive statistics of the Charlson Comorbidity Index can be found in Table 4.3.

Illness severity was measured with the APACHE II (acute physiology and chronic health evaluation) developed by Knaus, Draper, Wagner, and Zimmerman (1985). This measure was created based on a chronic health screen score, Glascow coma scale score, a score assigned for vital signs, an arterial blood gas score, a blood chemistry score, urine output score, and a hematology score. Higher scores are indicative of patients who are at greater risk of death. Data for Apache II scores were collected from chart reviews of medical records at admission. Descriptive statistics of the APACHE II can be found in

Table 4.3.

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Chapter 5: Data Analytic Strategy

To properly use the LOT for the proposed project, the factor structure of the LOT must be identified before other analyses can be conducted. As mentioned earlier, to date there has not been an extensive study of the factor structure of the LOT, therefore the current study will be the first to identify whether or not the LOT is unidimensional or multidimensional. These findings are the first to utilize a thorough regimen of testing for the multidimensional factor structure of the LOT scale in elderly populations. Not only will these analyses provide information on how to interpret optimism/pessimism, but it will also determine how to use the LOT in additional analyses (e.g., Multipe linear regressions, structural equation modeling).

The study will use exploratory factor analysis (EFA) based on principal axis factoring with oblimin rotation to initially identify the factor structure of the LOT. Two criteria helped provide guidelines in selecting the number of factors to extract: (1)

Eigenvalues that were 1.0 or higher; (2) examination of distinct elbows in scree plots of eigenvalues. Items were included in a given factor if their loading was at least .40. In addition, items were checked for possible (secondary) cross loadings (>.3) on other factors. Cross loadings indicate items that potentially measure more than one factor.

More generally, we tried to extract the number of factors that provided the “cleanest “ and most “interpretable” factor loadings – i.e., items that had high primary loadings (>.4) and low secondary loadings (<.3), and that appeared to measure the same content as other items loading on the same factor. Additionally, correlations between factors were examined to determine whether factors were highly correlated with one another. If

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factors are not highly correlated with each other, the argument can be made for treating the factor structure as multidimensional. Based on the EFA results, a confirmatory factor analysis using AMOS (Arbuckle, 1996) will be tested to determine how well the factor model fit the data. The final step in assessing whether the LOT is made up of more than a single (unidimensional) factor was to test multiple factors for distinct patterns of external correlates. The set of external correlates used were measures of sociodemographics, psychological well being, and functional and physical health.

Results of preliminary analyses, presented at the 1999 annual meeting of the

Gerontological Society of America in San Francisco (Burant, Kercher, Kahana, &

Fortinsky, 1999), supported a 2 factor solution to the LOT with the proposed data set. An optimism factor and a pessimism factor were identified. For clarity purposes further analyses will treat the LOT factors as two separate composite variables. Since AMOS can handle missing data, it will also be used to run the regression analyses. Additionally structural equation modeling can adjust for measurement error, test multiple regression analyses and confirmatory factor analyses simultaneously and provide goodness of fit indices to assess how well the data fits complex models.

The current study offers some unique challenges in analyzing the data.

Specifically, the hypotheses require testing the causal relationships across a set of predictors of change of outcomes that are measured repeatedly over time. In addition the model must test if optimism and pessimism mediate the relationships between social structural disparities and depression and physical functioning over time. Structural equation modeling testing a model, which includes causal relationships among a set of predictors of change in longitudinally measured outcomes, requires a multi-step process.

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In order for AMOS to test models of this complexity, smaller parts of the model must be

tested, developed and stabilized, before combining these sub models in to the final model.

When testing structural equation models that have longitudinal outcomes with a set of predictors, the longitudinal part of the models must be stabilized first because of their complexity before introducing the predictors in to the model.

The forthcoming section will describe the different types of longitudinal analyses as well as the analyses used for testing the hypotheses (ie., causal relations among the predictors of change as well as testing for optimism and pessimism as a mediator of the impact of social structural disparities on depression and physical functioning). An overview of the Specification Search option in AMOS to perform exploratory structural equation modeling will also be provided. The Specification Search procedure is an alternative to modification indices that allows the testing of many alternative models simultaneously. This procedure provides a set of alternative models for the researcher to use in evaluating which model is most interpretable based on diagnostic output that helps determine which model has the best combination of overall fit and parsimony.

Strategies for Longitudinal Analyses

Several strategies have been used to analyze longitudinal data (e.g. Joreskog,

1979; Willet & Sayer, 1994; Ferrer & McArdle, 2003 Duncan & Duncan, 2004) . Among the strategies that have been gaining attention are the autoregressive model (e.g.,

Burkholder & Harlow, 2003; Kosloski, Stull, Kercher, & Van Dussen, 2005) and the latent trajectory model (e.g., MacCallum, Kim, Malarkey, & Kiecolt-Glaser, 1997; Byrne

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& Crombie, 2003),. Curran and Bollen’s work (1998, 1999, 2001; Bollen & Curran,

2004) have highlighted these approaches as well as the synthesizing of both approaches

into a hybrid autoregressive latent trajectory (ALT) model. The current study will focus

on all three approaches to gain a better understanding of the relationship between physical

functioning and depressive symptomatology. Additionally, these models will be tested

with a set of predictors to determine if the impact of social structural disparities on

depression and physical functioning are mediated by optimism and pessimism.

Curran and Bollen (2001) provide an extensive review of the different models that

can be used for longitudinal analyses. In order to understand the ALT model it is

imperative to understand its component parts: the autoregressive model and the latent

trajectory. Having been portrayed as competing methods for analyzing longitudinal data,

much debate has been given to which approach is “superior”. Curran and Bollen (2001) argue that each method addresses different issues and are equally viable options.

The autoregressive model is best suited for handling time-specific relationships of two constructs. More specific, the relationships between two constructs over time can be examined to understand the order of causality between two constructs. On the other hand, the latent trajectory approach is more appropriate for comparing individual differences in continuous development trajectories over time. In other words, the latent

trajectory model measures how an individual grows over time for a specific construct and

compares this line of change with other individuals’ lines of change. These lines of

development can be linear or curvilinear and the latent trajectory assesses the variance of

these lines. The hybrid ALT model allows for the melding of both autoregressive and

latent trajectory models to address issues of causality between two constructs as well as

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comparisons of individual growth trajectories over time (Curran and Bollen, 2001; Bollen and Curran 2004; Hamaker, 2005). A detailed description of each of the steps used in longitudinal analyses will be provided as it pertains to the current study.

The Univariate Simplex Autoregressive Model

The univariate autoregressive model was originally designed to study correlations across a set of ordered tests. It is referred to as univariate because the focus is on a single variable measured over time. The key characteristic of the univariate autoregressive model is that variables measured at a later time period have progressively lower correlations as a function of increasing time (Humphreys, 1960; Joreskog, 1970,1979;

Burkholder & Harlow, 2003; Ferrer & McArdle, 2003). Additionally, any change in the construct over time is the result of the function of adding the direct impact of the immediately preceding measure of the construct plus any random disturbance (McArdle

& Epstein, 1987; Burkholder & Harlow, 2003). Therefore, each measure is a result of the same construct measured at the previous time period and any random disturbance (Ferrer

& McArdle, 2003).

The term autoregressive refers to the process of regressing the measure at one time point on its previous time point value. Variables measured at earlier time points than the immediate previous time point have no direct impact on the current measure

(Curran & Bollen, 2001). For example, a variable assessed at time 4 can only be directly impacted by the same variable measured at time 3, but not at time 2 or earlier. Time 2 and time 4 have a correlation of zero, when controlling for time 3. It is assumed that time

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3 is completely mediating the relationship between time 2 and time 4. Figure 5.1 is a path diagram for the univariate autoregressive model with autocorrelated measurement errors and disturbance for depression. This model is the simplest of all autoregressive models and is sometimes referred to as a first order simplex autoregressive model. Additionally, the model depicted in Figure 5.1 has latent constructs with multiple indicators which allow for one of the major benefits of using SEM, the adjustment of measurement error in the model.

Figure 5.1: Univariate Simplex Autoregressive Model with Autocorrelated Measurement

Errors and Disturbances

EPA1 ENA1 EPA2 ENA2 EPA3 ENA3

Positive Negative Positive Negative Positive Negative Affect 1 Affect 2 Affect 2 Affect 2 Affect 3 Affect 3

Depression 1 Depression 2 Depression 3

D1 D2 D3

With regard to the current study, optimism, pessimism, depression, and physical functioning were all submitted to univariate autoregressive models separately. This was a three step process. The first step was to test the univariate auto regressive model as portrayed in Figure 5.1. The next two steps were added to improve overall model fit and

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to test the stability of the model. Step two was to correlate the errors terms associated

with each measure across time lags. Finally, disturbance terms associated with each

endogenous measure were correlated across time lags.

Of special interest is modeling the stability of optimism and pessimism for time

invariance, since these are traits by definition these are relatively stable and not expected

to change over time. From a theoretical perspective if optimism and pessimism are

proven to be stable, there is no need to use the autoregressive models associated with

these variables as parts of more complex models. Essentially these measures will not

contribute any information to future models that cannot be obtained from these measures

at admission. Additionally, if optimism and pessimism are proven to be time invariant measures, models using only two constructs measured at one time interval are more parsimonious than models including the univariate autoregressive models of optimism and pessimism.

The Bivariate Autoregressive Model

The bivariate autoregressive model combines two univariate autoregressive model into one model. Bivariate autoregressive models allow for not only the autoregressive coefficients but also crosslagged coefficients. The advantage of analyzing crosslagged effects is to test for causality between 2 variables controlling for each variable’s previous time score as well as the autoregressive component of the model. Causality is identified if the crosslags of one variable (VAR1) on the other variable (VAR2) is consistently

larger than the crosslags of VAR2 on VAR1. This model is referred to as a bivariate

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autoregressive crosslagged model, because it focuses on 2 variables across time.

Multivariate autoregressive crosslagged models which focus on more than 2 variables

across time can also be tested. Development of the bivariate models is a multistage

process. Bivariate autoregressive crosslagged models are among the most complicated of

SEMs. New models are built from previously tested models. AMOS uses the

information from these previous models as start values for more complex models.Figure

5.2 represents a bivariate autoregressive crosslagged model testing the relationship between depression and physical functioning.

Figure 5.2: Bivariate Autoregressive Crosslagged Model

Depression 1 Depression 2 Depression 3 Depression 4 Depression 5

Da1 Da2 Da3 Da4 Da5

Db1 Db2 Db3 Db4 Db5

Functionality 1 Functionality 2 Functionality 3 Functionality 4 Functionality 5

With regards to the current study, understanding the causal relationship between depression and physical functioning has been an important issue in gerontological and medical research. With this in mind this study will attempt to clarify this issue, the first step is to place both of the previously tested univariate models of depression and physical functioning into a bivariate autoregressive model correlating the disturbances between

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variables within the same wave. The second step is to add the crosslags from the immediately previous time period of depression to the immediately next time period of physical functioning as well as from the immediately previous time period of physical functioning to the immediately next time period of depression. For example when looking at waves 2 and 3, depression at time 2 is crosslagged on to physical functioning at time 3, while physical functioning at time 2 is crosslagged on to depression at time 3.

Additionally, the autoregressive paths for depression at time 2 going to depression at time

3 and from physical functioning at time 2 to physical functioning at time 3 must be present to test for causal ordering. The autoregressive paths must be present to identify if the crosslags from one variable (e.g., depression at time 2) to the next wave variable (e.g., physical functioning at time 3) predict any thing above and beyond that which is predicted by the autoregressive path of time 2 physical functioning to time 3 physical functioning

A special form of the bivariate autoregressive model is known as the bivariate autoregressive contemporaneous model. While the autoregressive crosslagged model relies on controlling two variables at the immediate prior time period, the contemporaneous model controls for the two variables within the same wave as well as the autoregressive component of the model. The advantage of this model is that causality can be tested within in the same time wave as the phenomena are happening, as compared to crosslagged models that test causality across two time waves. Figure 5.3 shows the path diagram of the bivariate autoregressive contemporaneous model for the relationship between depression and physical functioning.

In testing the bivariate autoregressive contemporaneous model the same first step

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is used as used in testing the crosslagged model. The first step is to place both of the

previously tested univariate models of depression and physical functioning into a

bivariate autoregressive model correlating the disturbances between variables within the

same wave. The second step is to add the contemporaneous paths from depression to

physical functioning with in the same wave as well as from physical functioning to

depression within the same wave. For example when looking at wave 3, depression at

time 3 predicts physical functioning at time 3, while physical functioning at time 3

predicts depression at time 3. Additionally, the autoregressive paths for depression at

Figure 5.3: Bivariate Autoregressive Contemporaneous Model

Depression 1 Depression 2 Depression 3 Depression 4 Depression 5

Da1 Da2 Da3 Da4 Da5

Db1 Db2 Db3 Db4 Db5

Functionality 1 Functionality 2 Functionality 3 Functionality 4 Functionality 5

time 2 going to depression at time 3 and from physical functioning at time 2 to physical functioning at time 3 must be present to test for causal ordering. The autoregressive paths must be present to identify if the contemporaneous path from one variable (e.g., depression at time 2) to the next variable (e.g., physical functioning at time 2) within the same wave predict any thing above and beyond that which is predicted by the autoregressive path of time 2 physical functioning to time 3 physical functioning.

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Conversely the contemporaneous path from physical functioning at time 2 to depression at time 2 can also be tested..

The Latent Trajectory Model

The auto regressive models previously described do not take into account earlier periods of the measure beyond what is captured in a study. These models also treat the autoregressive and cross-lagged effects as the same for all individuals (Rogosa, 1995).

While autoregessive models rely on time-adjacent relations of a measure, a more appropriate approach to studying individual differences in continuous trajectories over time is the Latent Trajectory Model (Stoolmiller, 1994, 1995; Stoolmiller & Bank, 1995;

Ferrer & McArdle, 2003). In other words, everyone has a different line of development over time for a specific measure (eg., depression, physical functioning), latent trajectories can be used to analyze the variance in these individual lines. The line of trajectory for depression and physical function measured during the current study is representative of the line of best fit over all time periods for depression and physical functioning. As each individual’s trajectory line is fit, fixed effects represented by the average intercept and average slope are calculated. Random effects represented by the variability around these averages can also be calculated (Muthen & Curran, 1997; Rovine & Molenaar, 2000).

A clearer understanding of how a latent trajectory model works can be found by examining how a latent trajectory is constructed (see Figure 5.4). Five time periods of depression are represented in this model, since only one measure is being represented in this model is referred to as a univariate latent trajectory model. The model is broken down into two latent constructs. The first construct is the intercept factor. This represents

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the mean starting point of depression across the five time periods. In order to assess the intercept all factor loadings must be set to 1, essentially forcing all loadings to be equal to the first time period. The second construct is the slope factor. This represents the line of trajectory over time. Initially, models test the slope as a linear function. In Figure 5.4, this is accomplished by setting the factor loadings 0 for baseline, 1 for data collection at 1 month post hospital discharge, 3 for data collection at 3 months post discharge, 6 for data collection at 6 months post discharge, and 12 for data collection at 12 month post discharge. In order to produce the means (fixed effects) and variances (random effects)

Figure 5.4: Univariate Latent Trajectory Model (Linear)

Ec1 Ec2 Ec3 Ec4 Ec5

0 0 0 0 0

CESD1 CESD2 CESD3 CESD4 CESD5 1

3 2 1 1 1 1 1 1 0

Depression Depression Linear Intercept Slope

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of the two constructs, the means of the intercept and the slope factors are allowed to be freely estimated. In addition the intercepts to each item indicator must be set to 0, this will allow the means of the indicators to be explained by the intercept and slope (for example, see McCardle and Aber, 1990). The covariance between the intercept and the slope in a latent trajectory model is set to be freely estimated. An advantage of using the latent trajectory model, as compared to the autoregressive approach, is that the observed means and covariance structure of the data can be modeled (McArdle & Epstein, 1987;

Meredith & Tisak,1990; Muthen, 1991). Often the trajectory line of development is not linear and may be curvilinear over time. Non-linearity in individual lines of trajectory can also be tested by using the latent trajectory approach. The purpose of testing for

Figure 5.5: Univariate Latent Trajectory Model (Quadratic)

Ec1 Ec2 Ec3 Ec4 Ec5

0 0 0 0 0

CESD1 CESD2 CESD3 CESD4 CESD5 1

3 4

9 4

1 1 1 1

1 3 1 0

1 0 6

1 2

Depression Depression Depression Linear Quadratic Intercept Slope Slope

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nonlinearity is to examine if data fits the nonlinear model better than it fits the linear

model. In order to test for nonlinearity an additional quadratic slope latent construct must

be added to the previous latent trajectory model (see Figure 5.5). Factor paths going to

each of the indicators from the quadratic slope factor will be added to the model. Factor

loadings associated with the paths will be the squared factor loadings of the previous

slope factor. In Figure 5.5, the quadratic slope factor loadings are as follows: 1) the

baseline remains at 0; 2) the 1 month data remains at 1; 3) 3 month data is now set to 9

(i.e., 32); 6 month is now set at 36 (i.e., 62); 12 month is now set at 144 (i.e., 122).

Additionally, the quadratic slope factor’s mean and variance are freely estimated. The

quadratic slope factor is also correlated with the intercept and slope factors. An

alternative latent trajectory model to assess the linear and nonlinear trajectory of a given

measure over time has been suggested by Stoolmiller (1994, 1995) and Duncan, Duncan,

Strycker, Li, & Alpert (1999) and Duncan & Duncan (2004). In this model (see Figure

5.6) there are only two constructs, the intercept factor and the slope factor. This model

allows for different types of trajectories (i.e, linear and nonlinear) to be analyzed

simultaneously. Figure 5.6 five waves of data using depression as the measure of interest.

This alternative latent trajectory is similar to the first growth curve model (see Figure

5.4a). The intercept factor is identical and all factor loadings are set to 1. The difference

is how the factor loadings in the slope factor model are setup. In Figure 5.6, the factor

loadings are as follows: 1) The baseline is set to 0; the 1 month wave is set as 1; 3) the 3,

6, and 12 month waves are allowed to be estimated freely. Any shape of trajectory can be

analyzed without forcing the trajectory to fit a specific shape based on preset factor

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Figure 5.6: Univariate Latent Trajectory Model (Freely Estimated Slope)

Ec1 Ec2 Ec3 Ec4 Ec5

0 0 0 0 0

CESD1 CESD2 CESD3 CESD4 CESD5 1 1 1 1 1 1 0

Depression Depression Freely Intercept Estimated Slope

loadings as when testing the models using linear and quadratic slope factors. This alternative latent trajectory model may be especially useful when data are not collected at equal time intervals, as is the case in the current study. As with the previous two latent trajectory models, the means and variances of the intercept and slope factors are set to be freely estimated as well as the covariance between the factors. All three models will be tested to determine which model best fits the data.

The Bivariate Latent Trajectory Model

In the simplest form bivariate latent trajectory models combine two univariate

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latent trajectory models into a single model (see Figure 5.7). This model takes into account change in two measures over time (e.g., MacCallum, Kim, Malarkey, & Kiecolt-

Glaser, 1997: Ferrer & McArdle, 2003, Duncan & Duncan, 2004). In other words, the bivariate latent trajectory model can simultaneously test the trajectory or line of best fit for 2 measures across the time periods. In the current study the latent trajectory models of depression and physical functioning will be tested simultaneously in one model as in figure 5.7.

The following steps are used to create a bivariate latent trajectory model. First each variable that is measured over time will be fitted to a univariate latent trajectory model. The best fitting model based on testing the three types of univariate latent trajectory model (i.e., testing for linear, quadratic, or freely estimated slope) will be used in the bivariate latent curve model. In the current study the two best fitting univariate models of depression and physical functioning will be combined into a bivariate model.

Typically growth factors are estimated for each construct, the relation between changes over time in the construct is made at the growth factor level (Curran & Hussong, 2003).

In other words, covariances are added to the model among the factors across constructs to examine unique relationships across constructs. In the current study (see figure 5.7), the physical functioning intercept, physical functioning slope, depression intercept, and depression slope represent the constructs at the growth factor level. In this model, six covariances have been added to test the unique relationships across these constructs. In a latent trajectory model the relationships (i.e., covariances or correlations) among the constructs are evaluated at the growth trajectory level, not at the level of the repeated

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Figure 5.7: Bivariate Latent Trajectory Model

Ea1 Ea2 Ea3 Ea4 ea5 0 0 0 0 0

ADL IADL1 ADL IADL2 ADL IADL3 ADL IADL4 ADL IADL5

3 1 1 2 1 1 1

1 1 0 Functionality Functionality Linear Slope Intercept

Depression Depression Intercept 1 0 Linear Slope 1 1

3 2 1 1 6 1

CESD1 CESD2 CESD3 CESD4 CESD5

0 0 0 0 0 Ec1 Ec2 Ec3 Ec4 Ec5

measurements over time, because the latent trajectory model models the relations among the repeated measures (observed items) as a continuous trajectory (Curran & Willoughby,

2003).

Combining Predictors of Longitudinal Analyses Depression and Physical Functioning

The first predictors of the various longitudinal analyses were the mediators,

optimism and pessimism. These variables were introduced to the best fitting bivariate

autoregressive crosslagged models and the best fitting latent trajectory model. With

regard to the bivariate autoregressive crosslagged model, optimism and pessimism are

added as time invariant latent constructs predicting depression and physical functioning at

discharge (first wave of data). Since depression and physical functioning are repeatedly

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measured over time and used in an autoregressive model, optimism and pessimism’s

(measured at wave 1) impact on depression and physical functioning must come at wave

1. Thus by definition measures in autoregressive models can only be predicted by the immediate preceding wave of a specific measure not any waves measured earlier in time.

Therefore, optimism and pessimism’s direct impact on depression and physical functioning over time can only occur at wave 1, since wave 1 depression and physical functioning will mediate the subsequent relationship between optimism and pessimism at wave 1 and depression and physical functioning measured beyond wave 1. If the data does not support the bivariate autoregressive crosslagged models, then further autoregressive models testing for predictors will not be developed.

Treating optimism and pessimism as predictors of depression and physical functioning in the bivariate latent trajectory model was also the first step to testing the mediation in the bivariate latent trajectory model. These variables were introduced into the best fitting bivariate latent trajectory model. When adding exogenous predictors to the bivariate latent trajectory model, the time invariant constructs of optimism and pessimism impact on depression and physical functioning will be assessed at the latent trajectory construct level, not at the level of the repeated measures over time as suggested by Curran and Bollen (2001). In other words regression paths will be added from optimism and pessimism to the constructs of the intercept and slope of depression and the intercept and slope of physical functioning. The purpose of this model is to determine if optimism and pessimism impacts the initial scores of depression and physical functioning as well as the change in trajectory over the five waves of depression and physical functioning. If it is determined that the data does not fit this model, then the bivariate

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latent trajectory model with predictors of change will not be developed. These models

were the first step to building more complex models using predictors of longitudinal

measures of depression and physical functioning.

Specification Search in the Development of Longitudinal Models with Predictors

The next step is to add the predictors of social structural disparities (age, ethnicity,

gender, education, and income) and clinical measures of health (Charlson Comorbidity

and APACHEII) to the optimism and pessimism models previously tested. The

interrelations among all predictors must be included in addition to the relationships of the

predictors to the outcomes of depression and physical functioning must be added to the

longitudinal models. These models are extremely complicated and fortunately AMOS

offers an alternative through the specification search module for exploratory structural

equation modeling to help the researcher find the most interpretable model through

diagnostic tools that identify the most parsimonious and best fitting model from a large

set of models. Typically, models are developed based on using modification indices to

add paths and removal of nonsignificant paths. This process is time consuming, but the specification search option simplifies the process.

The specification search allows the investigator to choose which paths (both

regressions and covariances) in a model to set to optional. When optional paths are

present in a model, AMOS fits all combinations of paths including models with and without all optional paths. For example, a model with 1 optional path will be fitted with

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and with out the optional path. For a model with four optional paths, the specification

search will fit 16 (24) models, using every possible combination of paths (Arbuckle,

2003). Each additional optional path increases the number of paths to be tested exponentially, for example, moving to eight optional paths provides 28 (i.e., 256) models

to test.

Evaluation of the various models for interpretability based on a combination of fit

and parsimony is a multi-step process. Three goodness of fit indices, the Akaike

information criterion (AIC), the Browne-Cudeck criterion (BCC), and the Bayes

information criterion (BIC) are used in assessing the models. These goodness of fit

indices address the issue of parsimony and badness of fit in model fit. More complex

models and poorly fitting models are penalized with higher scores. Therefore the best

fitting models always receive the lowest scores. These indices are used for the purpose of

model comparisons and not for the evaluation of an isolated model (Arbuckle and

Wothke, 1999). An added advantage to using these indices is that any two models, even

those that are not nested can be compared for fit and parsimony.

AMOS’s specification search procedure provides several tools for assessing the

best overall models. These include: 1) a summary table of all models (see table 5.1); 2) a short list of the best model for each number of parameters (see table 5.2); 3) Scree plots

(see Figure 5.8). It should be noted the short list of the best model for each number of

parameters might need further clarification. In this list each model represents the best model for each specific number of parameters (for example in Table 5.2, Model 6 is the best fitting model with 89 parameters as compared to all other models with 89 parameters). With regard to the scree plots, these are not in the traditional sense based on

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eigenvalues, but are based on comparative changes in goodness of fit among the best

fitting models for each specific number of parameters.

The AIC was the goodness of fit index used by the Specification Search in the

current research as recommended by the author of the AMOS program (Arbuckle,

personal communication, Apr. 2005). It should be noted that several of the best fitting models are assessed to determine the best logical model, and all three indices will yield

Table 5.1: Specification Search -Example of Summary Table of all Models

similar results in identifying several parsimonious models to choose from for interpretability. Furthermore, the scree plots for the AIC, BIC, and BCC will always yield identical results.

In deciding on the best model to choose using the summary table or the short list of the best model for each number of parameters (see Tables 5.1 and 5.2) the lowest scores for AIC, BIC, and BCC represent the best fitting models. Models with scores

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close to the lowest score are also plausible models for consideration. In table 5.1, the best

fitting model based on the AIC is model 16 with 91 parameters, while model 12 with 90

parameters and an AIC of 3.123 may also be considered. Usually all models with AICs

of 2 or less should be considered as suggested by Burnham and Anderson (1998).

Table 5.2: Specification Search - Example of the Short List of the Best Model for Each

Parameter

Using the short list of the best model for each parameter (Table 5.2), the best fitting model again based on the AIC is model 16 with 91 parameters. Overall the best fitting model of all the best fitting models (based on number of parameters) will be found on the short list.

In reviewing the scree plots, the models represented in these graphs are the same ones found in the short list of best fitting models based on numbers of parameters. In

Figure 5.8, the best fitting model based on the scree plot is identified as the model before the elbow. For the current example the best fitting model is the model is the model with

89 parameters (Model 6 on the short list). Based on the information found in the tables and the scree plot, three models (model 6 with 89 parameters, model 12 with 90 parameters, and model 16 with 91 parameters) were chosen to identify the most

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interpretable model based on a combination of parsimony and goodness of fit. In other words, while these three models may all be plausible solutions, it is the responsibility

of the researcher to determine which model has the most substantively significant paths or combination of paths that contribute the most to the model fit. A detailed explanation of the goodness of fit indices, tables, and plots used in the Specification Search can be

Figure 5.8: Specification Search - Scree Plot Example 1

65 60 55 50 45 40 35 30 25 20 15 10 5 0

88 89 90 91 Number of Parameters

found in the AMOS 5.0 Update to the AMOS User’s Guide (Arbuckle 2003). The

Specification Search was used to determine the best model for testing the relationship among predictors and the mediators of optimism and pessimism.

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The Hybrid Autoregressive Latent Trajectory (ALT) Model

While the autoregressive simplex models focuses on the time-specific

relationships among repeated measures of one or more constructs, the latent trajectory

model focuses on the of trajectory of each individual as represented by the score of the

repeated measure for an individual. The problem that exists is that the strength of one model is the weakness of the other model. While it is true that one model may fit the data better than the other model, it may also be true that the data fits both models equally well.

Another option is that the data may be best represented by a combination of these two models. The combined model is referred to as an Autoregressive Latent Trajectory (ALT) model.

In order to gain a better understanding of the ALT model it is easier to look at how the simplest or univariate form of this model is set up. Figure 5.9 represents a univariate ALT model of depression. In this model, the latent trajectory model for depression remains, but additional autoregressive paths have been added between each repeated measure item of depression. This model combines the random intercept and slope factor that captures the underlying continuous growth trajectories found in the latent trajectory model with the time-specific influences between the repeated measures found in the univariate simplex autoregressive models (Curran & Hussong, 2003; Curran

& Willoughby, 2003; Bollen & Curran,.2001). It should be noted that the mean and intercepts only enter into the ALT model though the latent trajectory factors (i.e., the depression intercept and depression slope construct) and not as part of the repeated

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Figure 5.9: Univariate Hybrid Autoregressive Latent Trajectory (ALT) Model

Ec1 Ec2 Ec3 Ec4 Ec5

0 0 0 0 0

CESD1 CESD2 CESD3 CESD4 CESD5 1

3 2 1 1 1 1 1 1 0

Depression Depression Linear Intercept Slope

measures as in the autoregressive simplex model (Curran & Hussong, 2003; Curran &

Willoughby, 2003). The advantage of running the ALT model is that the impact of the latent trajectory model is controlled for the unique contributions of the autoregressive simplex model and conversely the autoregressive simplex model is controlled for any effects from the latent trajectory model.

The Bivariate Hybrid Autoregressive Latent Trajectory (ALT) Model

The bivariate hybrid autoregressive latent trajectory model combines two univariate ALT models into a single model, as seen in figure 5.10. In this figure, the ALT

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model testing physical functioning is combined with the ALT model of depression.

These models are extremely complicated and must be developed over a series of steps.

AMOS uses the start values from solutions of previously tested models, therefore previously clean (i.e., free of errors) solutions form the foundation of new models to be developed and tested.

In the current research, the first step was to establish the bivariate latent trajectory model previously mentioned for physical functioning and depression (please, refer to figure 5.10). It should be mentioned as a reminder that six covariances have been added among the factors to test the relationships that exist cross the intercepts and slopes of

Figure 5.10: Bivariate Crosslagged ALT Model

Functionality Functionality Intercept 1 0 Linear Slope 1 1

1 1

1 3

6 2 1

ADL IADL1 ADL IADL2 ADL IADL3 ADL IADL4 ADL IADL5

0 0 0 0 0 Ea1 Ea2 Ea3 Ea4 ea5

Ec1 Ec2 Ec3 Ec4 Ec5 0 0 0 0 0

CESD1 CESD2 CESD3 CESD4 CESD5

1 3

1 1 1 1 2

1 1 0 Depression Depression Intercept Linear Slope

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physical functioning and depression. This model served as the foundation to develop the

bivariate ALT model. The first step was to covary or correlate all intrawave repeated

measure error terms (i.e., measurement error of time 1 depression is correlated with the

measurement error of time 1 physical functioning). The second step, after this model has

been tested and confirmed to be error free, is to add the autoregressive paths across the repeated measures of depression and physical functioning. Autoregressive paths should have a small effect size. ALT models with auto regressive paths with large effect sizes need to be adjusted at time 1 to allow for the large effect sizes (Curran and Bollen, 2001:

Bollen & Curran, 2004). If the model has weak autoregressive effects then the ALT model does not need to be manipulated to fit. This step must be carried out to determine the appropriate model to use before any crosslagged effects can be added. The third step is to test the crosslagged effects. This is done in a similar manner as described earlier for

Figure 5.2. Crosslags are added from the immediately previous time period of depression to the immediately next time period of physical functioning as well as from the immediately previous time period of physical functioning to the immediately next time period of depression. For example when looking at waves 2 and 3, depression at time 2 is crosslagged on to physical functioning at time 3, while physical functioning at time 2 is crosslagged on to depression at time 3. This model will test if earlier levels of one measure (e.g., depression at time 2) predict later levels of another measure (e.g., physical functioning at time 3), controlling for any latent trajectory effects.

This model will test how strongly related the four constructs are related to each other partialing out the autoregressive and crosslagged effects of the repeated measures of depression and physical functioning. It will also provide the latent trajectories for

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depression and physical functioning partialing out for the autoregressive and crosslagged

effects of the repeated measures of depression and physical functioning. This model will

also test the lagged prediction between depression and physical functioning (found in the

autoregressive and crosslagged components of the model) after the latent trajectory

processes have been partialed out. The bivariate ALT model with lagged effects, as

suggested by Curran and Bollen (2001) as well as, Bollen and Curran (2004), estimates

both the stable component of development overtime as represented by the latent factors of

depression and physical functioning as well as the time specific differences in depression

and physical functioning at any specific time point.

It should be noted that this model will also be analyzed testing contemporaneous

(within wave) effects instead of crosslagged effects between depression and physical functioning. Results from the ALT model using contemporaneous effects would be interpreted in a similar manner as the ALT model using crosslagged effect model. The benefit of the contemporaneous ALT model is that prediction can be tested within the same time wave and not across time waves as with a crosslagged ALT model. The negative to using the contemporaneous ALT model is that they are more complex and harder to stabilize than the crosslagged ALT model.

The Bivariate ALT models were developed using the Specification Search option of AMOS. Specifically, the autoregressive, crosslagged, and contemporaneous paths ts of the ALT model were set as optional paths to help determine the model with the best combination of fit and parsimony. The first step in assessing the bivariate hybrid ALT model using Specification Search is to have a stable and error free bivariate ALT model with all appropriate autoregressive and crosslagged paths present. In the case of the

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contemporaneous model crosslagged paths are replaced with contemporaneous paths.

These models will serve as the base model for all bivariate ALT models tested with the

Specification Search. All these paths need to be viable and present initially, so these paths could be set to optional, as a means to “weeding out” the poorer fitting and less parsimonious models. The second step was to set all of the autoregressive paths as optional. The best fitting and most parsimonious models at each step were identified using the model summary table, the short list of the best model for each parameter and the scree plot for the AIC. After the best fitting and most parsimonious autoregressive model is chosen, this model will be used as the base model to test the ALT crosslagged model.

All crosslagged paths were set to optional with the best fitting and most parsimonious model being chosen as the final model. These steps were also applied to the ALT contemporaneous model except the contemporaneous paths that replaced the crosslagged paths were set to optional. If either the crosslagged or contemporaneous ALT models are not stable then the model will be not be used for the current research.

Bivariate ALT Model with Predictors

Figure 5.11 represents how a set of predictors is added to a bivariate crosslagged

ALT model. The paths among the predictors identified in Figure 5.11 represent the best fitting and most parsimonious model from a previous specification search. This model tests how a set of sociodemographics representing social structural disparities (age, ethnicity, gender, education, education) are mediated by optimism and pessimism controlling for clinical measures (Charlson Comorbidity and APACHEII). The next step

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was to add the best fitting bivariate latent trajectory model of depression and physical functioning. After this model was stabilized autoregressive paths and crosslagged paths were added. This model was submitted to a specification search testing setting the autoregressive and crosslagged paths to optional. The final model tested combined the best model from the specification search for predictors with the best model from the specification search.

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Figure 5.11: Bivariate Crosslagged ALT Base Model with Predictors 0 0 Ea5 ADL IADL5 CESD5 0 0 Ea4 ADL IADL4 CESD4 0 0 Ea3 ADL IADL3 CESD3 0

2 0

1 6

1 Ea2 6 ADL IADL2 3 CESD2

0 0 1 3 Ec1 Ec2 Ec3 Ec4 Ec5 Ea1 ADL

IADL1

0 CESD1

1 1 1 1

1 1

0 1 1 Depression Linear Slope Functionality Linear Slope

1 Intercept Depression Intercept APACHEII Comorbidity Functionality lifecg lifech lifecf lifecc Optimism Pessimism lifece Lifecc lifecb Lifeca Income Education Age Gender Ethnicity

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Chapter 6: Results I (Autoregressive Models)

Autoregressive Model of Optimism and Pessimism

Theoretically, optimism and pessimism are personality traits that should be stable

over time. Autoregressive models of optimism and pessimism tested for this dissertation

supported the stability of these measures over time and the use of these variables as

measured at one time point (admission). The initial bivariate autoregressive model was

created with optimism and pessimism as latent constructs each with four indicators. This

model was set up in a similar manner as the univariate autoregressive model with latent

constructs depicted in Figure 5.1. Figure 6.1 represents the structural components of the

model because the measurement component visually would have been “cluttered” and

difficult to interpret. The initial bivariate autoregressive model fit the data poorly (Chi

Square= 2325.10; df=727; p<.001; TLI=.74; CFI=.77; RMSEA=.05; RMSEA 90% CI=

.046-.05). Additionally, this model (see figure 6.1) was characterized by extremely high

standardized betas in excess of .90 for the first four time waves (admit, discharge, 30 day post discharge, and 90 day post discharge). Intrawave correlations between optimism and pessimism were extremely unstable ranging from -.08 (at wave 3) to -1.61 (at discharge).

Both of these together indicate that high levels of multicollinearity exist for each measure (e.g., optimism) across waves, because these measures are so stable and do not change over time. In other words, the extreme stability in the model induces multicollinearity. Furthermore the large betas lead to instability and empirical underidentification, because high collinearity does not provide enough unique

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information to properly fit the model. Additionally, using latent constructs corrects for measurement error, not only contributes to the high betas, but also to the high out of

range and unreliable estimates found in the intrawave correlations among the

disturbances.

Figure 6.1: Initial Bivariate Autoregressive Model of Optimism and Pessimism

(Standardized Parameters)

.95 .91 .93 .82 Optimism Optimism Optimism Optimism Optimism Admit Discharge 30 Day 90 Day 360 Day

Da1 Da2 Da3 Da4 Da5

-.23 -1.61 -.08 -.57 -.29

Db1 Db2 Db3 Db4 Db5

.96 .95 .95 .75 Pessimism Pessimism Pessimism Pessimism Pessimism Admit Discharge 30 Day 90 Day 360 Day

In an attempt to produce a better fitting model as well as resolve the unstable intrawave correlations, lagged 1st order autocorrelations of measurement error associated

with the indicator items were added across waves. An autocorrelation refers to

correlations between one wave of data and the immediate next wave of data for a specific

measure (e.g., optimism). For example adding a correlation between the disturbance term

of optimism at discharge and the disturbance term of optimism at day 30 is an

autocorrelation. This model was also poor fitting (Chi square =1685.66; df=695; p<.001;

TLI=.83; CFI=.86; RMSEA=.04; RMSEA 90% CI= .037-.041). Figure 6.2 represents the

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structural components of the model, measurement components (i.e., items, error terms and correlation of error terms) were not shown to simplify the model. Again, standardized regression paths were quite high and stable for the first four waves of data ranging from .88 to .99. The intrawave correlations between optimism and pessimism were also unstable ranging from -.22 at admit to -.82 at wave 3. The wave 3 intrawave correlation went from -.08 in the initial model to -.82 in the second model. Additionally, the intrawave correlation at discharge went form to -1.61 to .72 in the second model. All of these changes are indicative of instability caused by multicollinearity in the measures across waves. In other words the optimism and pessimism across time are so highly correlated with one another that it causes the model to become unstable as represented by the high correlations and beta weights needed to fit the model to the data.

Figure 6.2: Standardized Results of Bivariate Autoregressive Model of Optimism and

Pessimism (Structural Components shown) with Measurement Errors Correlated (not shown)

.88 .93 .89 .79 Optimism Optimism Optimism Optimism Optimism Admit Discharge 30 Day 90 Day 360 Day

Da1 Da2 Da3 Da4 Da5

-.22 -.72 -.82 -.47 -.33

Db1 Db2 Db3 Db4 Db5

.88 .99 .88 .73 Pessimism Pessimism Pessimism Pessimism Pessimism Admit Discharge 30 Day 90 Day 360 Day

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Since optimism and pessimism are latent constructs, structural equation modeling

can be used to constrain the factor loadings of each wave to be equal. These factor

loadings were constrained to be equal in a third model as an attempt to produce a better

fitting model and resolve the instability in the model. This model did not come to

solution. The decision was made to treat optimism and pessimism as single time invariant constructs in future models because these measures were so highly correlated

and did not change over time. From a theoretical standpoint, these findings support the

use of optimism and pessimism as personality traits that are by nature stable across time

and unlikely to change.

Univariate Autoregressive Model of Depression

This model tested the time specific relationships of depression over time. In order

to test for the causal relationship between physical functioning and depression, this model

had to be stabilized. This is accomplished by using modification indices provided in the

AMOS output. Modification indices are suggested paths to be added to the model to

improve overall fit. AMOS needs complete data to run modification indices to develop

the best fitting model, with this in mind an imputed missing values data set was created

using the Expectation Maximization (EM) algorithm in SPSS. The problem with using

the data set with EM imputed missing values is that standard errors around parametric

estimates are underestimated because the sample size has been inflated. In other words,

the standard errors are not adjusted for missing values. Additionally, the Chi Square

goodness of fit index is inflated because the sample size is artificially inflated, which in

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turn impacts all goodness of fit indices that use the Chi Square in estimation.

AMOS remedies these issues by using Full Information Maximum Likelihood

(FIML) estimation to handle missing data. The original data file with missing values is

used when running the FIML procedure. A data set is not imputed, but parametric

estimates and goodness of fit indices are provided for the full sample size, while properly

adjusting standard errors and goodness of fit indices for missing data. Whereas FIML is

the preferred method for handling missing data, it does not allow for the use of

modification indices. Therefore, the model was tested and developed with the EM

imputed missing data set to allow for the use of modification indices. The final model

was tested using the original data set with missing values and the FIML procedure in

AMOS. Parametric estimates were consistent across when running models with both

methods, but the FIML method made proper adjustments to standard errors and goodness

of fit indices.

The first univariate autoregressive with 1st order autocorrelated measurement

errors model (see Figure 6.3) yielded a rather poor fitting model (Chi Square=285.46;

df=74; p<.001; TLI=.88; CFI=.93; RMSEA=.06; RMSEA 90% CI .05.-06). The autocorrelated measurement errors were only across lags (i.e., the errors of time 1 correlated on the errors of time 2, errors of time 2 on the error of time 3, etc.). Figure 6.3 shows the structural components as well as the factor loadings of the subscales measuring depression. The autocorrelated errors were not included in the figure to make the model more interpretable. The decision was made to develop the model using modification indices to build a better fitting model. The strategy for adding paths was based on logic and focusing on correlations among like measures (e.g., somatic complaints at discharge

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with somatic complaints at 180 days) or disturbance terms. Ten additional paths were added (eight among measurement error terms and 2 among disturbance terms.

Figure 6.3: Standardized Results of the Univariate Autoregressive Model of Depression

(Structural Components and Factor Loadings shown)

Negative .7 Affect DC 3 .67 Positive DEPRESSION DDC Affect DC DISCHARGE .65 Somatic Complaints DC .59 Negative .7 Affect 30 5 Positive .74 DEPRESSION D30 Affect 30 DAY 30 .74 Somatic Complaints 30 .73 Negative .7 Affect 90 6

Positive .76 DEPRESSION D90 Affect 90 DAY 90 .68 Somatic Complaints 90 .77

Negative . Affect 180 80

Positive .75 DEPRESSION D180 Affect 180 DAY 180 .73 Somatic Complaints 180 .69

Negative .80 Affect 360

Positive .81 DEPRESSION D360 Affect 360 DAY 360 .70 Somatic Complaints 360

The final model (see Figure 6.4) yielded a good fitting model (Chi

Square=121.08; df= 64; p<.001; TLI=.96; CFI=.98; RMSEA=.03; RMSEA 90% CI =

.02-.04). Figure 6.4 shows the structural components as well as the factor loadings of

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Figure 6.4: Standardized Results for the Univariate Autoregressive Model of Depression

after adding Correlations between Measurement Errors and Disturbance Terms from

Modification Indices (Structural Components, Factor Loadings, and Correlations with

Disturbance Terms shown)

Negative .7 Affect DC 3 Positive .67 DEPRESSION DDC Affect DC DISCHARGE .64 Somatic Complaints DC .58 Negative .7 Affect 30 6 Positive .74 DEPRESSION D30 Affect 30 DAY 30 .72 Somatic Complaints 30 .72 Negative .7 Affect 90 6

Positive .77 DEPRESSION .40 D90 Affect 90 DAY 90 .66 Somatic Complaints 90 .73 .36 Negative . Affect 180 79

Positive .76 DEPRESSION D180 Affect 180 DAY 180 .73 Somatic Complaints 180 .36 Negative .78 Affect 360

Positive .82 DEPRESSION D360 Affect 360 DAY 360 .69 Somatic Complaints 360

the subscales measuring depression. One final step was to test if factor loadings across the

waves were equal. This test was conducted to determine if the subscales did not change over time. If a given subscale had changed then the given subscale would be

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Figure 6.5: Standardized Results for the Univariate Autoregressive Model of Depression

after adding Correlations between Measurement Errors and Disturbance Terms from

Modification Indices; Factor Loadings Constrianed to be Equal (Structural Components,

Factor Loadings, and Correlations with Disturbance Terms shown)

Negative . Affect DC 69

Positive .69 DEPRESSION DDC Affect DC DISCHARGE .68 Somatic Complaints DC .57 Negative .7 Affect 30 7 Positive .75 DEPRESSION D30 Affect 30 DAY 30 .71 Somatic Complaints 30 .72 Negative .7 Affect 90 6

Positive .77 DEPRESSION .40 D90 Affect 90 DAY 90 .67 Somatic Complaints 90 .73 .37 Negative . Affect 180 79

Positive .78 DEPRESSION D180 Affect 180 DAY 180 .70 Somatic Complaints 180 .36 Negative .79 Affect 360 Positive .80 DEPRESSION D360 Affect 360 DAY 360 .69 Somatic Complaints 360

measuring something different over time and not be time invariant. The final model with equal factor loadings (see Figure 6.5) fit the data well (Chi Square=127.92; df=72; p<.001; TLI=.97; CFI=.98; RMSEA= .03; RMSEA 90% CI = .02-.04). This model was nested in the previous model (see Figure 6.4). These models were not significantly

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different from one another, therefore the factor loadings did not vary over time. Figure

6.5 shows the structural components as well as the factor loadings of the subscales

measuring depression. The model in Figure 6.5 will be used as the base model

representing depression for all subsequent analyses. Standardized factor loadings ranged from (.69 to .79) for negative affect across time; (.69 to .80) for positive affect across time; (.67 to .71) for somatic complaints across time. Regression path weights predicting the next wave of data for the depression latent constructs were: 1)from discharge to 30 day (standardized beta =.57, unstandardized beta=.58, S.E.=.06, p<.001); from 30 day to

90 day (standardized beta =.72, unstandardized beta=.69, S.E.=.05, p<.001); from 90 day to 180 day (standardized beta =.74, unstandardized beta=.72, S.E.=.05, p<.001); from 180 day to 360 day (standardized beta =.36, unstandardized beta=.37, S.E.=.07, p<.001). The over all high standardized beta weights suggest that depression is highly stable over time.

The weaker depression score found from 180 to 360 days is probably associated with the larger time differential of 180 days as compared to the shorter time differentials associated with the other time waves.

The first order autocorrelations of the measurements, and 10 additional correlations based on the modification indices of the model represented by Figure 6.5 can be found in Table 6.1. 11of the 12 correlations among the first order autocorrelations were significant at the .05 level and ranged in correlation from .14 to .35. The only nonsignificant path was between the measurement errors of positive affect at 180 days post discharges and positive affect at 360 day post discharge. The order of the additional correlations suggested by the modification indices was represented the steps in Table 6.1.

Overall 8 additional correlations between measurement errors were added and 2

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correlations among disturbance terms were added. Additional correlations among the measurement errors were mostly associated with somatic complaints (5 additional paths), followed by negative affect (2 additional paths) and positive affect (1 additional path).

These additional correlations ranged from .14 to .40

Table 6.1: Correlated errors in Figure 6.5

1st order autocorrelated errors R p value

Error Positive Affect (discharge) to Error Positive Affect (30 .25 <.001 day) Error Negative Affect (discharge) to Error Negative Affect (30 .14 .03 day) Error Somatic Complaints (discharge) to Error Somatic .18 .002 Complaints (30 day) Error Positive Affect (30 day) to Error Positive Affect (90 day) .26 <.001

Error Negative Affect (30 day) to Error Negative Affect (90 .26 <.001 day) Error Somatic Complaints (30 day) to Error Somatic .35 <.001 Complaints (90 day) Error Positive Affect (90 day) to Error Positive Affect (180 .15 .04 day) Error Negative Affect (90 day) to Error Negative Affect (180 .30 <.001 day) Error Somatic Complaints (90 day) to Error Somatic .32 <.001 Complaints (180 day) Error Positive Affect (180 day) to Error Positive Affect (360 -.02 .81 day) Error Negative Affect (180 day) to Error Negative Affect (360 .21 .007 day) Error Somatic Complaints (180 day) to Error Somatic .35 <.001 Complaints (360 day)

Correlated Errors suggested by Modification Indices

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Step1: Error Positive Affect (discharge) to Error Positive .27 <.001 Affect (360 day) Step2: Error Somatic Complaints (30 day) to Error Somatic .44 <.001 Complaints (360 day) Step3: Error Somatic Complaints (90 day) to Error Somatic .32 <.001 Complaints (360 day) Step 4: Error Negative Affect (90 day) to Error Negative .34 <.001 Affect (360 day) Step 5: Error Negative Affect (30 day) to Error Negative .27 <.001 Affect (360 day) Step 6: Error Somatic Complaints (30 day) to Error Somatic .26 <.001 Complaints (180 day) Step 7: Error Somatic Complaints (discharge) to Error Somatic .20 .002 Complaints (180 day) Step 8: Error Somatic Complaints (discharge) to Error Somatic .14 .04 Complaints (360 day) Step 9: Disturbance (discharge) to Disturbance (360 day) .40 <.001

Step 10: Disturbance (30 day) to Disturbance (360 day) .37 <.001

Univariate Autoregressive Model of Physical Functioning

This model tests the time specific relations of physical functioning over time.

Physical functioning was treated as a construct with a single item indicator that combined

ADL and IADL into a single measure. AMOS allows for the adjustment of measurement error in single item indicators by using the following formula (1-relaibility) x the variance. Cronbach’s Alphas for each time point were attained and the measurement error for each wave of data calculated. The proper error variances were assigned to each measurement error. Three models were tested. The first model tested only the autoregressive component of the model. The second model tested the first order auto correlations across the measurement errors. The final model had all disturbance terms

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autocorrelated across waves.

Figure 6.6 represented an acceptable fitting model (Chi Square=61.24; df = 6, p<.001; TLI=.92; CFI=.97; RMSEA=.10: RMSEA 90% CI = .08-.12). Attempts were

Figure 6.6: Standardized Results fot the Univariate Autoregressive Model of Physical

Functioning (Structural Components and Factor Loadings shown)

.95 PHYSICAL ADLIADDC FUNCTIONING DDC DISCHARGE

.82

.96 PHYSICAL ADLIAD30 FUNCTIONING D30 DAY 30

.89

.95 PHYSICAL ADLIAD90 FUNCTIONING D90 DAY 90

.95

.96 PHYSICAL ADLIAD6M FUNCTIONING D180 DAY 180

.87

.96 PHYSICAL ADLIAD12 FUNCTIONING D360 DAY 360

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made to build better fitting models were unsuccessful. The second model testing for first

order autocorrelated measurement errors did not come to solution. This model was

extremely unstable with a standardized regression weight in excess of 1 and an

autocorrelation in excess of 3. The third model testing for autocorrelated disturbance

terms also did not come to solution. It was decided that the most stable model was the

first simplex autoregressive model tested with no autocorrelations. This model will be

used as the base model representing physical functioning for all subsequent analyses.

This model was characterized by the following strong autoregressive coefficients: from

discharge to 30 day (standardized beta =.82, unstandardized beta=.81, S.E.=.03, p<.001);

from 30 day to 90 day (standardized beta =.89, unstandardized beta=.85, S.E.=.03,

p<.001); from 90 day to 180 day (standardized beta =.95, unstandardized beta=1.00,

S.E.=.03, p<.001); from 180 day to 360 day (standardized beta =.87, unstandardized

beta=.87, S.E.=.03, p<.001). The over all high standardized beta weights suggest that

physical functioning is highly stable over time.

Bivariate Autoregressive Models of Depression and Physical Functioning

Cross Lagged Model

Testing of the bivariate autoregressive cross lagged models occurred in several

steps in order to test the final cross lagged model. As mentioned earlier AMOS uses the

results of earlier tested models as start values for more complex models. This bivariate

autoregressive cross lagged model was created by combining the best univariate

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autoregressive and autocorrelated model of depression (see Figure 6.5) with the best fitting autoregressive model of physical functioning (see Figure 6.6). The first step was to correlate intrawave disturbance terms between physical function and depression. The next steps were adding the cross lagged paths from physical functioning to depression one path at a time to provide start values for subsequent models in AMOS. A detailed explanation of this procedure is provided in the data analytic strategy section of this dissertation.

The final step was to include the cross lagged paths from depression to physical functioning. This step by step process resulted in the final bivariate autoregressive cross lagged model (see Figure 6.7). This model fitted the data extremely well (Chi Square =

327.55; df= 140; p<.001; TLI = .94; CFI = .96; RMSEA = .04, RMSEA 90% CI= .03-

.04). While the model fit the data well the cross lagged regression paths were fairly weak including the two strongest paths. These paths included higher levels of physical functioning at discharge predicting lower levels of depression at 30 days post discharge

(standardized beta =-.14, unstandardized beta=-.014, S.E. = .005, p=.004) and higher levels of physical functioning at 180 days predicting lower levels of depression at 360 days (standardized beta =-.16, unstandardized beta=-.014, S.E. = .005, p=.004). All other cross lagged path’s standardized betas ranged from -.02 to .01. Of special interest is that none of the cross lagged paths from depression to physical functioning were significant, but 2 cross lagged paths from physical functioning to depression were significant. This analysis offers weak support for the argument that physical functioning causally predicts depression, because the strongest paths were fairly weak and 2 of the cross lagged paths from physical functioning at 30 day to depression at 90 day and physical functioning at 90

155

day to depression at 180 day had standardized betas of .00 and -.01 respectively.

The lack of consistency in the cross lagged paths from physical functioning to

depression raises the question: does lack of physical functioning cause depression? The current model (see FigureXXg) at best partially supports this finding. While the model

Figure 6.7: Standardized Results for the Bivariate Autoregressive Crosslagged Model of

Depression and Physical Functioning

.52 .72 .73 .35 Depression Depression Depression Depression Depression Dis c harge Day 30 Day 90 Day 180 Day 360

. dist2 .39 dist3 31 dist4 dist5 dist6

-.26 -.11 -.37 -.55 6 - -.01 - 2 . 4 . 1 . 0 0 . 0 0 0 . 1 . 0 - 1 . 2 - 1 0 - . 0 dista2 dista3 dista4 dista5 dista6

Functioning Functioning Functioning Functioning Functioning Dis c harge .82 Day 30 .88 Day 90 .94 Day 180 .86 Day 360

fits the data well, the weak standardized betas found in the cross lagged paths do not

support further development of this model with a set of predictors. However, the weak

regression paths may be affected by growth process. Therefore, analysis of the cross lags

using a bivariate hybrid ALT model in which the trajectories of physical functioning and

depression are controlled may yield a better model to understanding the causal nature of

these measures. The hybrid ALT model testing for cross lagged effects will be examined

after the Latent Trajectory models have been developed and stabilized.

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It should be noted that the autoregressive paths for the cross lagged model (see

Figure 6.7) acted in a similar manner as the univariate autoregressive simplex models for

depression (see Figure 6.5) and physical functioning (see Figure 6.6). The

autoregressive path weights predicting the next wave of data for the depression latent

constructs in the crosslagged model were: from discharge to 30 day (standardized beta

=.52, unstandardized beta=.53, S.E.=.06, p<.001); from 30 day to 90 day (standardized

beta =.72, unstandardized beta=.69, S.E.=.05, p<.001); from 90 day to 180 day

(standardized beta =.73, unstandardized beta=.72, S.E.=.06, p<.001); from 180 day to 360

day (standardized beta =.35, unstandardized beta=.37, S.E.=.07, p<.001). There were

strong autoregressive coefficients for physical functioning in the crosslagged model: from

discharge to 30 day (standardized beta =.82, unstandardized beta=.82, S.E.=.04, p<.001);

from 30 day to 90 day (standardized beta =.88, unstandardized beta=.84, S.E.=.03,

p<.001); from 90 day to 180 day (standardized beta =.94, unstandardized beta=1.00,

S.E.=.03, p<.001); from 180 day to 360 day (standardized beta =.87, unstandardized

beta=.87, S.E.=.04, p<.001). The over all high autoregressive standardized beta weights

suggest that depression and physical functioning are highly stable over time.

Contemporaneous Model

Another way of using the autoregressive model to determine causal ordering is the

bivariate autoregressive contemporaneous model. This model is similar to the cross

lagged model except instead of adding cross lagged paths, contemporaneous regression

paths are added within waves (e.g., depression at day 30 predicts physical functioning at

day 30 and physical functioning at day 30 predicts depression at day 30). The

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contemporaneous model by nature is nonrecursive (two variables predict each other).

The benefit of the contemporaneous model is that causality is determined within the same

time wave, so the relationship is immediate and not lagged over time.

As with the cross lagged model, the complexity of the contemporaneous model dictates a step by step process to develop the final model. The models had to be built in steps so that AMOS could use the results of simpler models as start values for more

complex models. The first step was to correlate intrawave disturbance terms between

physical function and depression. The next steps were adding the contemporaneous

(intrawave) regression paths from physical functioning to depression one path at a time to

provide start values for subsequent models in AMOS. The final step added the

contemporaneous regression paths from depression to physical functioning.

The final model (see Figure 6.8) fit the data well (Chi Square = 327.17; df = 140;

p<.001; TLI = .94; CFI = .96; RMSEA = .04; RMSEA 90% CI =.03-.04). The 4

contemporaneous reciprocal effects all had low stability indices (<.01). Stability indices

that fall between -1 and +1 are considered stable (Arbuckle and Wothke, 1999). The

contemporaneous model results were comparable to the cross lagged model. While the

data fits the data well the contemporaneous paths were also weak like the cross lagged

regression paths in the cross lagged model. Both models suggest that physical

functioning predicts depression with the strongest paths occurring at 30 day and at 360

days. The two strongest significant contemporaneous paths were relatively small. The two paths represent higher levels of physical functioning at day 30 predicting lower levels of depression at day 30 (standardized beta =-.18, unstandardized beta=-.017, S.E. =

.006, p=.003) and higher levels of physical functioning at 360 days predicting lower

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levels of depression at 360 days (standardized beta =-.19, unstandardized beta=-.018, S.E.

= .006, p=.004). All other contemporaneous standardized paths ranged from -.03 to .05.

None of the contemporaneous from depression to physical functioning were significant,

but two contemporaneous paths from physical functioning to depression were significant.

Like the cross lagged model, the contemporaneous model only partially supports

the argument that physical functioning predicts depression. However, the significant standardized contemporaneous paths are fairly weak, and the standardized contemporaneous betas from physical functioning to depression at day 90 and day 180

were only .00 and -.02 respectively. The inconsistency in the strength of the regression paths of physical functioning predicting depression is a concern in establishing the causal ordering between these two measures. Figure 6.8 only partially supports the causal ordering that physical functioning predicts depression. Although the current model fits

the data well, these inconsistencies do not support further development of the model with a set of predictors.

As in the case of the cross lagged model (see Figure 6.7) running a separate autoregressive contemporaneous model does not take into account the underlying growth processes that may be distorting the contemporaneous parameter estimates used for establishing causal order. Therefore the contemporaneous model will be tested within the

Hybrid ALT model which controls for any growth processes that may be impacting causal ordering. The hybrid ALT model testing for contemporaneous effects will be examined after the Latent Trajectory models have been developed and stabilized.

It should be noted that the autoregressive paths for the contemporaneous model

(see Figure 6.8) acted in a similar manner as the univariate autoregressive simplex models

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for depression (see Figure 6.5) and physical functioning (see Figure 6.6).

The autoregressive path weights predicting the next wave of data for the depression latent

constructs in the contemporaneous model were: from discharge to 30 day (standardized

Figure 6.8: Standardized Results of the Bivariate Autoregressive Contemporaneous

Model of Depression and Physical Functioning

.52 .72 .73 .35 Depression Depression Depression Depression Depression Dis c harge Day 30 Day 90 Day 180 Day 360

dist2 .39 dist3 .32 dist4 dist5 dist6

. -

9 0 .

1 0 .

. . 0

1 0 0 .00

-.37 - 5 - . . 3 .02 -.51 1

-.20 -

dista2 dista3 dista4 dista5 dista6

.83 .88 .94 .87 Functioning Functioning Functioning Functioning Functioning Dis c harge Day 30 Day 90 Day 180 Day 360

beta =.52, unstandardized beta=.53, S.E.=.06, p<.001); from 30 day to 90 day

(standardized beta =.72, unstandardized beta=.69, S.E.=.05, p<.001); from 90 day to 180 day (standardized beta =.73, unstandardized beta=.72, S.E.=.06, p<.001); from 180 day to

360 day (standardized beta =.35, unstandardized beta=.36, S.E.=.07, p<.001). The over all high standardized beta weights suggest that depression is highly stable over time.

There were strong autoregressive coefficients for physical functioning in the contemporaneous model: from discharge to 30 day (standardized beta =.83,

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unstandardized beta=.83, S.E.=.04, p<.001); from 30 day to 90 day (standardized beta

=.88, unstandardized beta=.84, S.E.=.03, p<.001); from 90 day to 180 day (standardized beta =.95, unstandardized beta=1.00, S.E.=.03, p<.001); from 180 day to 360 day

(standardized beta =.87, unstandardized beta=.87, S.E.=.04, p<.001). The over all high standardized beta weights suggest that physical functioning is highly stable over time.

Summary

In summary, the autoregressive models were run as a bivariate model with crosslagged or contemporaneous effects to determine the causal relationship between physical functioning and depression. The optimism and pessimism autoregressive models would not converge to an admissible solution. Stability coefficient were in excess of 1, indicating that optimism and pessimism were so highly correlated across waves that that these measures did not change over time. Results were largely inconclusive with some weak and inconsistent evidence supporting physical functioning predicting depression. Nevertheless, this model was not developed with a set of predictors because the autoregressive models were inconclusive.

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Chapter 7: Results II ( Latent Trajectory Models and

Autoregressive Latent Trajectory Models )

The Latent Trajectory Models

The univariate latent trajectory model tests the line of trajectory for a specific

measure over time. Three univariate latent trajectory models will be tested to assess

which model best fits depression as well as which model best fits physical functioning

over time. The first model examines the intercept (individual initial score) and the linear

slope (linear line of trajectory). The second model added a quadratic slope (curvilinear

line of trajectory) to the first model. The third model included the intercept construct and

a freely estimated slope, which allowed for any shape of the line of trajectory. (For a

more detailed explanation of the latent trajectory model please refer to the Data Analytic

section of the dissertation.) The two best fitting latent trajectory models of depression

and physical functioning were combined into a bivariate latent trajectory model. The

FIML procedure for handling missing data was used for all latent trajectory models.

The Univariate Latent Trajectory Model of Depression

Each time period of depression in the latent trajectory model was measured as a single

scale combining negative affect, positive affect, and somatic complaints. The first model

assessed the intercept and linear slope of depression (see Figure 7.1 for standardized

results). This model fit the data extremely poorly (Chi Square = 148.44; df = 10; p<.001;

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TLI = .70; CFI = .80; RMSEA= .12; RMSEA 90% CI = .10-.14). The next model (see

Figure 7.2 for standardized results) examined the intercept, linear slope and the quadratic slope. This model also fit the data poorly (Chi Square = 64.04; df = 6; p<.001; TLI = .79;

CFI = .92; RMSEA= .10; RMSEA 90% CI = .08-.12). Additionally 3 standardized factor loadings were in excess of upper limit of 1.00, therefore the model is

Figure 7.1: The Univariate Latent Trajectory Model of Depression with Intercept and

Linear Slope (Standardized Parameters)

Ec1 Ec2 Ec3 Ec4 Ec5

.39 .55 .57 .54 .52

Depression Depression Depression Depression Depression

Discharge Day 30 Day 90 Day 180 Day 360

. . . 0 1

7 5 3 7 4

4 . 6 7

7 0 . . 6 . 5 7 7 1 . . .0 0

LINEAR INTERCEPT SLOPE

-.34

163

Figure 7.2: The Univariate Latent Trajectory Model of Depression with Intercept, Linear

Slope, and Quadratic Slope (Standardized Parameters)

Ec1 Ec2 Ec3 Ec4 Ec5

.46 .53 .61 .70 .84

Depression Depression Depression Depression Depression

Discharge Day 30 Day 90 Day 180 Day 360

6 5 9 6

. 7 2 2

8 0

. .

0 0 .

7 .

. 9 2 9

1 8 0

7 .

. 0 . . 7 1 . 78 1 6 . .3 2 LINEAR QUADRATIC INTERCEPT SLOPE SLOPE

-.29 -.96

.25

still unacceptable. The final model (see Figure 7.3 for standardized results) consisted of

164

an intercept and a freely estimated slope. All measurement errors were constrained to be equal because theoretically the measurement of depression should be consistent and not change over time. Constraining the measurement errors resulted in a better fitting model of depression (Chi Square = 25.13; df = 11; p=.009; TLI = .97; CFI = .98; RMSEA= .04;

RMSEA 90% CI = .02-.06), than the final model (Chi Square = 19.52; df = 7; p=.007;

TLI = .96; CFI = .98; RMSEA= .04; RMSEA 90% CI = .02-.07) not constraining measurement errors to be equal.

The parameters of the best fitting model will be described in detail. When examining the parametric results of a latent trajectory model, useful information is provided from both the standardized and unstandardized results. Figure 7.3 represents the best fitting latent trajectory model with standardized parametric estimates. The correlation between the intercept (individual’s initial score for depression) and the freely estimated slope is -.54. Higher initial levels of depression are associated with a line of trajectory that represents a weaker slope of depression over time. In other words, those individuals with higher initial levels of depression have more recovery from depression over a year as compared to those with lower initial levels of depression.

The explained variance in depression over time is 64% for depression at discharge, 56% at day 30, 57% at day 90, 59% at day 180, and 58% at day 360. The intercept and freely estimated slope account for the explained variance of depression in this latent trajectory model. The standardized factor loadings represent how strong the intercept or freely estimated slope predicts each wave of depression. For example, the factor loading of the intercept on depression at discharge is .80, while the factor loading of the freely estimated slope is .00. Therefore the strongest predictor of depression at

165

Figure 7.3: The Univariate Latent Trajectory Model of Depression with Intercept and

Freely Estimated Slope (Standardized Parameters)

Ec1 Ec2 Ec3 Ec4 Ec5

.64 .56 .57 .59 .58

Depression Depression Depression Depression Depression

Discharge Day 30 Day 90 Day 180 Day 360

. . . 7 6

3 7 0 8 0

8 . 8 8

8 5 . . 5 . 7 8 8 3 . . .0 0

FREELY INTERCEPT ESTIMATED SLOPE

-.54

166

Figure 7.4: The Univariate Latent Trajectory Model of Depression with Intercept and

Freely Estimated Slope (Unstandardized Parameters)

0, 2.25 0, 2.25 0, 2.25 0, 2.25 0, 2.25

Ec1 Ec2 Ec3 Ec4 Ec5

1 1 1 1 1 0 0 0 0 0

Depression Depression Depression Depression Depression Discharge Day 30 Day 90 Day 180 Day 360

1 1

. .

. 4 2

0 1 0 0

1 8

0 0 1 3 0 . .

. 0 0 .0 1 1 0 1 . .0 0 1 1 .0 0

FREELY 3.16, 3.99 -1.08, 1.46 INTERCEPT ESTIMATED SLOPE

-1.29

167

discharge is the intercept. Overall, the intercept as compared to the freely estimated slope

is the strongest predictor based on factor loadings at day 30 (.88 vs. .53), at day 90 (.88 vs. .60), at day 180 (.85 vs. .73), and at day 360 (.87 vs. .67).

A look at the unstandardized parametric results (see Figure 7.4) represent the trajectory as well as the mean and variance of the intercept and the freely estimated slope of depression. The unstandardized factor loadings represent the weightings of depression used to calculate the mean score at each wave starting at 0 for discharge, 1 at day 30, 1.13 at day 90, 1.41 at day 180, 1.28 at day 360. Two numbers are associated with each of the constructs (intercept and the freely estimated slope). The first number represents the mean and the second represents the variance of the construct. It should be noted that variances need to be significantly different from zero, because if the variance is not significant then the construct has no variance and is essentially a constant and should not be included in the model. The mean for the intercept or initial score of depression was

3.16. The variance was 3.99 (p<.001). In other words, the initial score of depression varied across the population. The mean for the freely estimated slope of depression was –

1.08. The variance was 1.46 (p<.001). In other words, the variance in slopes means that among subjects there were different lines of trajectory. Both variances were significant and therefore should be included in the model. The unstandardized information can be used to calculate the change in level of depression over the year. In order to calculate the depression level (see Equation 1) at any given time point the following formula was used:

This formula yielded the following depression means at each wave starting at 3.16 for discharge, 2.08 at day 30, 1.94 at day 90, 1.64 at day 180, 1.78 at day 360. Figure 7.5

168

Equation 1:

Depression at (x) wave = Mean of Intercept +

(Mean of Slope * Unstandardized Factor

Loading at (x) wave)

is the line graph of the line of trajectory represented by the freely estimated slope in

Figure 7.4.

Figure 7.5: Line Graph of the Trajectory of Depression

L 3.16

3.00

n o

i 2.50

pr e

D 2.08 L 2.00 1.94 L 1.78 L 1.64 L

0.00 1.00 3.00 6.00 12.00 Months

169

The Univariate Latent Trajectory Model of Physical Functioning

The first model assessed the intercept and linear slope of physical functioning (see

Figure 7.6). This model fit the data fairly well (Chi Square = 122.52; df = 10; p<.001; TLI

= .90; CFI = .93; RMSEA= .11; RMSEA 90% CI = .09-.13). The next model (see

Figure 7.7) examined the intercept, linear slope and the quadratic slope. This model did

not converge to an admissible solution. The final model (see Figure 7.8) consisted of an

intercept and a freely estimated slope. It should be noted that start values of 3, 6, and 12

(representing 3, 6, and 12 month data waves) were added as unstandardized loadings for the latent trajectory model to converge to a solution. Measurement errors for physical functioning were not constrained to equal, because constraining the measurement errors to be equal resulted in a poorer fitting model (Chi Square = 80.85; df = 11; p<.001; TLI =

.94; CFI = .96; RMSEA= .08; RMSEA 90% CI = .07-.10). The final model without measurement errors constrained to be equal was the best fitting model (Chi Square =

14.42; df = 7, p. = 004; TLI = .99; CFI = .996; RMSEA= .03; RMSEA 90% CI = .01-.06).

Figure 7.8 represents the best fitting latent trajectory model with standardized parametric estimates. The correlation between the intercept (individual’s initial score for physical functioning) and the freely estimated slope is -.37. In other words, higher initial levels of physical functioning are associated with a line of trajectory that represents a weaker slope of physical functioning over time. In other words, those individuals with higher initial levels of functioning have less recovery in physical functioning. While this statement may seem counterintuitive, keep in mind that high functioning individuals will

170

Figure 7.6: The Univariate Latent Trajectory Model of Physical Functioning with

Intercept and Linear Slope (Standardized Parameters)

EA1 EA2 EA3 EA4 EA5

.65 .77 .86 .77 .82

Functioning Functioning Functioning Functioning Functioning

Discharge Day 30 Day 90 Day 180 Day 360

. . . 2 3

0 9 1 3 1

8 . 8 9

8 0 . . 6 . 4 8 8 3 . . .0 0

LINEAR INTERCEPT SLOPE

-.06

171

Figure 7.7: The Univariate Latent Trajectory Model of Physical Functioning with

Intercept, Linear Slope, and Quadratic Slope (Standardized Parameters)

Ec1 Ec2 Ec3 Ec4 Ec5

.73 .77 .89 .87 .63

Functioning Functioning Functioning Functioning Functioning

Discharge Day 30 Day 90 Day 180 Day 360

. 6

. 3

1 .

4 5 3 0 1

8 9 9 . . . 6

.0 8 8

. 1 1

0 .

8 . 5 0

8 0 1 0 . . 5

. 0 . 1 7 .0 2 LINEAR QUADRATIC INTERCEPT SLOPE SLOPE

-.21 -1.02

.19

172

Figure 7.8: The Univariate Latent Trajectory Model of Physical Functioning with

Intercept and Freely Estimated Slope (Standardized Parameters)

Ea1 Ea2 Ea3 Ea4 Ea5

.85 .75 .92 .81 .73

Functioning Functioning Functioning Functioning Functioning

Discharge Day 30 Day 90 Day 180 Day 360

. . . 6 4

5 9 2 9 3

3 . 9 9

9 3 . . 1 . 0 9 9 7 . . .0 0

FREELY INTERCEPT ESTIMATED SLOPE

-.37

173

Figure 7.9: The Univariate Latent Trajectory Model of Physical Functioning with

Intercept and Freely Estimated Slope (Unstandardized Parameters)

0, 10.53 0, 16.60 0, 4.97 0, 13.18 0, 19.52

Ea1 Ea2 Ea3 Ea4 Ea5

1 1 1 1 1 0 0 0 0 0

Functioning Functioning Functioning Functioning Functioning Discharge Day 30 Day 90 Day 180 Day 360

1 1

. .

. 7 3

0 6 0 0

7 6

0 0 1 0 0 . .

. 0 0 .0 1 1 0 1 . .0 0 1 1 .0 0

FREELY .00, 57.94 1.04, 9.37 INTERCEPT ESTIMATED SLOPE

-8.60

174

not have as much room for physical recovery as compared to individuals who are low in functioning. This is especially true for individuals who can perform all the activities of daily living, these individuals cannot recover to a higher level of functioning.

The explained variance in physical functioning over time is .85% at discharge, 75% at day 30, 92% at day 90, 81% at day 180, and 73% at day 360. As previously mentioned, the standardized factor loadings represent how strong the intercept or freely estimated slope predicts each wave of depression. Overall, the intercept as compared to the freely estimated slope is the strongest predictor based on factor loadings at discharge (.92 vs.

.00), at day 30 (.93 vs. .37), at day 90 (.99 vs. .63), at day 180 (.91 vs. .65), and at day 360

(.90 vs. .49).

A look at the unstandardized parametric results (see Figure 7.8) shows the line of trajectory as well as the mean and variance of the intercept and the freely estimated slope.

The unstandardized factor loadings represent the following the weightings of depression used to calculate the mean score at each wave starting at 0 for discharge, 1 at day 30, 1.60 at day 90, 1.77 at day 180, 1.36 at day 360. The mean for the intercept or initial score of physical functioning was .00. The variance was 57.94 (p<.001). In other words, the initial score of physical functioning varied across the sample. The mean for the freely estimated slope of depression was 1.04. The variance was 9.37 (p=.03). In other words, the variance in slopes means that among subjects there were different lines of trajectory.

Both variances were significant and valid for testing differences across slopes. Therefore, both should be included in the model. The unstandardized information can be used to calculate the change in level of physical functioning over the year. In order to calculate the physical functioning level (see, Equation 2) at any given time point the following

175

calculation was used:

Equation 2:

Physical Functioning at (x) wave = Mean of Intercept +

(Mean of Slope * Unstandardized Factor

Loading at (x) wave)

This formula yielded the following physical functioning means at each wave starting at 0 for discharge, 1.04 at day 30, 1.66 at day 90, 1.84 at day 180, 1.41 at day 360. Figure

Figure 7.10: Line Graph of the Trajectory of Physical Functioning

L 1.84 1.66 1.50

g n

i 1.41

i t

c L 1.04

n 1.00

si y

h 0.50 P

0.00 0.00 L

0. 00 1. 00 3. 00 6. 00 12. 00 Months

176

7.10 is the line graph that represents of the line of trajectory or change in physical functioning over time represented by the freely estimated slope in Figure 7.9.

The Bivariate Latent Trajectory Model of Depression and Physical Functioning

The two best fitting latent trajectory models of depression and physical functioning were combined into a single model. Measurement errors of physical functioning component of the bivariate latent trajectory model were not constrained to be equal. Conversely, the measurement errors of depression were constrained to be equal.

Correlations among the physical functioning intercept, the physical functioning freely estimated slope, the depression intercept, and the depression freely estimated slope were added to the model. Start values had to be used for the freely estimated slopes of both physical functioning and depression. These start values were set at 3, 6, 12 (representing

3, 6, and 12 month data waves). The bivariate latent trajectory model (see figure 7.11) fit the data well (Chi Square = 99.33; df = 39; p<.001; TLI = .97; CFI = .98; RMSEA= .04;

RMSEA 90% CI = .03-.05).

Figure 7.11 represents the bivariate latent trajectory model of depression and physical functioning with standardized parametric estimates. The correlation between the intercept (individual’s initial score for depression) and the freely estimated slope of depression is -.52 (p<.001). In other words, higher initial levels of depression are associated with a line of trajectory that represents a weaker slope of depression over time.

In other words, those individuals with higher initial levels of depression have more recovery from depression over a year as compared to those with lower initial levels of depression.

177

The correlation between the intercept (individual’s initial score for physical functioning) and the freely estimated slope of physical functioning is -.33 (p=.04).

Higher initial levels of physical functioning are associated with a line of trajectory that represents a weaker slope of physical functioning over time. In other words, those individuals with higher initial levels of functioning have less recovery in physical functioning. As previously mentioned, high functioning individuals will not have as

Figure 7.11: The Bivariate Latent Trajectory Model of Depression and Physical

Functioning (Standardized Parameters)

Ea1 Ea2 Ea3 Ea4 Ea5

Functioning Functioning Functioning Functioning Functioning Discharge Day 30 Day 90 Day 180 Day 360

.6 . 0 6

2 7 . . 9 3 4

9 . 9 4 0 8

. . 7

9 . FREELY .88 .00 ESTIMATED INTERCEPT -.33 SLOPE FUNCTIONING FUNCTIONING

-.44 .19 .15 -.40

FREELY INTERCEPT -.52 ESTIMATED SLOPE DEPRESSION DEPRESSION

.86 .00

2 6 4 . .8 .8 .84 .5 .6 3 8 7 7 .

Depression Depression Depression Depression Depression Discharge Day 30 Day 90 Day 180 Day 360

Ec1 Ec2 Ec3 Ec4 Ec5

178

much room for physical recovery as compared to individuals who are low in functioning

The correlation between the intercept of depression and the intercept of physical functioning was -.44 (p<.001). In other words, higher initial levels of physical functioning are associated with lower initial levels of depression. The correlation between the freely estimated slope of depression and the freely estimated slope of physical functioning is -.40 (p<.001). The line of trajectory that represents a stronger slope of depression over time is associated with a line of trajectory that represents a weaker slope of physical functioning over time. In other words, individuals who have less recovery from depression have less recovery in physical functioning.

The correlation between the intercept of depression and the freely estimated slope of physical functioning is .19 (p=.01). Higher initial levels of depression are associated

with a line of trajectory that represents a stronger slope of physical functioning over time.

In other words, individuals with higher initial levels of depression have more recovery in

physical functioning over a year. While this may seem counterintuitive, keep in mind that

individuals at baseline who have higher initial levels of depression have lower initial

levels of physical functioning, therefore there is more room to recover in physical functioning. Conversely, individuals with lower initial levels of depression have less recovery in physical functioning over the year. One should take into account, that individuals with lower initial levels of depression have higher initial levels of functioning, therefore these individuals have less room to recover functionally. After all,

if an individual has total control of their physical functioning initially, they cannot

improve their level of physical functioning. There is no room to recover.

The correlation between the intercept of physical functioning and the freely

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estimated slope of depression is .15 (p=.04). Higher initial levels of physical functioning

are associated with a line of trajectory that represents a stronger slope of depression over time. In other words, individuals with higher initial levels of physical functioning have less recovery from depression over the year. While this may seem counterintuitive, keep in mind that individuals at baseline who have higher initial levels of physical functioning have lower initial levels of depression; therefore there is less room to recover from depression. Simply stated, individuals who are not depressed initially do not need to recover from depression over the year. Conversely, individuals with lower initial levels

of physical functioning have more recovery in depression over time. One should take

into account, that individuals with lower initial levels of physical functioning have higher

initial levels of depression functioning, therefore these individuals have more room to

recover from depression.

Figure 7.11 also shows the explained variance and the strongest loading factor for

each wave of depression and physical functioning over time. The explained variance in depression over time is 64% for depression at discharge, 57% at day 30, 58% at day 90,

60% at day 180, and 58% at day 360. The intercept and freely estimated slope of depression account for the individual explained variance of depression at each wave of

data in the bivariate latent trajectory model. The standardized factor loadings represent

how strong the intercept or freely estimated slope predicts each wave of depression. As

mentioned in previous example, the factor loading of the intercept on depression at

discharge is .80, while the factor loading of the freely estimated slope is .00. Therefore

the strongest predictor of depression at discharge is the intercept. Overall, the intercept of

depression as compared to the freely estimated slope is the strongest predictor based on

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factor loadings at day 30 (.88 vs. .54), at day 90 (.87 vs. .62), at day 180 (.84 vs. .73), and

at day 360 (.86 vs. .66).

The explained variance in physical functioning over time is 81% at discharge,

75% at day 30, 92% at day 90, 82% at day 180, and 73% at day 360. As previously

mentioned, the standardized factor loadings represent how strong the intercept or freely

estimated slope predicts each wave of physical functioning. Overall, the intercept as

compared to the freely estimated slope is the strongest predictor based on factor loadings

at discharge (.90 vs. .00), at day 30 (.92 vs. .34), at day 90 (.97 vs. .60), at day 180 (.89 vs. .62), and at day 360 (.88 vs. .47).

Figure 7.12 represents the bivariate latent trajectory model with unstandardized

parametric estimates. The unstandardized parametric results show the factor loadings as

well as the mean and variance of the intercept and the freely estimated slope for

depression and physical functioning. The unstandardized factor loadings for depression

represent the weightings of depression used to calculate the mean score at each wave

starting at 0 for discharge, 1 at day 30, 1.16 at day 90, 1.42 at day 180, 1.26 at day 360.

Two numbers are associated with each of the constructs (intercept and the freely

estimated slope). The mean for the intercept or initial score of depression was 3.14. The

variance was 3.98 (p<.001). In other words, the initial score of depression varied across

the population. The mean for the freely estimated slope of depression was –1.00. The

variance was 1.49 (p<.001). In other words, the variance in slopes means that among

subjects there were different lines of trajectory. The formula from the previous section

(see, Equation 1) was used to determine the mean scores at each wave starting at 3.14 for

discharge, 2.14 at day 30, 1.98 at day 90, 1.72 at day 180, and 1.88 at day 360. The

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trajectory of the mean scores was nearly identical to the trajectory found in Figure 7.5.

The unstandardized factor loadings for physical functioning represent the weightings of physical functioning used to calculate the mean score at each wave starting at 0 for discharge, 1 at day 30, 1.69 at day 90, 1.91 at day 180, 1.43 at day 360. The mean

Figure 7.12: The Bivariate Latent Trajectory Model of Depression and Physical

Functioning (Unstandardized Parameters)

0, 12.88 0, 16.19 0, 4.98 0, 13.01 0, 19.45 Ea1 Ea2 Ea3 Ea4 Ea5

1 0 1 0 1 0 1 0 1 0 Functioning Functioning Functioning Functioning Functioning Discharge Day 30 Day 90 Day 180 Day 360

1. 1 6 .

9 9

0 0 1 1

0 1

. 0 . . 0 0 0

. 1 1.0 0 4

1 3 .

1 FREEL Y 0 .02, 55.74 1.0 -6.75 .00 ESTIMATED .96, 7.59 INTERCEPT SLOPE FUNCTIONING FUNCTIONING 1 -6.53 1.07 .36 -1.34

FREEL Y INTERCEPT -1.27 ESTIMATED -1.00, 1.49 3.14, 3.98 DEPRESSION SLOPE

1.00 00 DEPRESSION 1 .

2 0 6 . 2

0 1 0 1 1 1 . . . 1 . . 00 1 4 0 1 . 00 0 1 0 0 0 0 0 Depression Depression Depression Depression Depression Discharge Day 30 Day 90 Day 180 Day 360 1 1 1 1 1

Ec1 Ec2 Ec3 Ec4 Ec5 0, 2.24 0, 2.24 0, 2.24 0, 2.24 0, 2.24

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for the intercept or initial score of physical functioning was .02. The variance was 55.74

(p<.001). In other words, the initial score of physical functioning varied across the population. The mean for the freely estimated slope of physical functioning was .96. The

variance was 7.59 (p=.02). In other words, the variance in slopes means that among

subjects there were different lines of trajectory. The formula from the previous section

(see Equation 2) was used to determine the mean scores at each wave starting at .02 for

discharge, .98 at day 30, 1.64 at day 90, 1.85 at day 180, and 1.39 at day 360. The

trajectory based on the mean scores was similar to the trajectory identified in Figure 7.10

The Bivariate Latent Trajectory Model of Depression and Physical Functioning with

Optimism and Pessimism as Predictors

Optimism and pessimism are the mediators of interest for the dissertation. In

order to test for mediation the relationship between the mediatiors (optimism and

pessimism) and the outcomes of interest (intercept of depression, freely estimated slope

of depression, intercept of physical functioning, and freely estimated of slope of physical

functioning) need to be established. Optimism and pessimism are treated as latent

constructs each consisting of 4 items. The initial model will have optimism and

pessimism predicting all 4 outcomes. Nonsignificant regression paths will be removed

resulting in the final model. The initial model fit the data well (Chi Square = 220.19; df =

130; p<.001; TLI = .97; CFI = .98; RMSEA= .03; RMSEA 90% CI = .02-.03). Four

nonsignificant paths were removed in the following order: 1) pessimism to the freely

estimated slope of physical functioning; 2) optimism to the freely slope of physical

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functioning; 3) optimism to the intercept of physical functioning; 4) pessimism to the

freely estimated slope of depression. The final model’s (see Figure 7.13) goodness of fit

indices were identical to the initial model (Chi Square = 224.70; df = 134; p<.001; TLI =

.97; CFI = .98; RMSEA= .03; RMSEA 90% CI = .02-.03). Removing the four regression

paths did not impact the model fit. In other words, these paths contributed nothing to the

overall model fit.

Figure 7.13 represents the final model with standardized parametric estimates.

Optimism predicted lower levels of the intercept of depression (standardized beta=-.37,

unstandardized beta= -2.25, S.E. = .38, p<.001) and a line of trajectory (freely estimated

slope of depression) that represents higher levels of depression over 4 waves of time

(standardized beta=.24, unstandardized beta= .86, S.E. = .29, p=.003). This suggests that

optimists initially had lower depression scores therefore; they had a greater range of

possible depression scores to increase their level of depression. On the other hand,

individuals with low levels of optimism initially had higher depression scores, but had a

smaller range of possible depression scores to increase their level of depression over 4

waves of time. Interpreting this statement in terms of recovery, optimists have lower

initial depression score, therefore do not recover from depression as quickly as

individuals with low optimism and higher initial levels of depression. Simply stated, optimists are likely not to be depressed so there is no need for them to recover from depression as compared to individuals with low levels of optimism.

Pessimism predicted higher initial values of the intercept of depression

(standardized beta=.36, unstandardized beta= -1.29, S.E. = .16, p<.001) and lower initial levels of the intercept of physical functioning (standardized beta= -.21, unstandardized

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beta= -2.72, S.E. = .59, p<.001). Overall, optimism predicted well being (depression) initially as well as it’s line of trajectory, but had no impact on physical functioning.

Simply stated, optimism impacted recovery from depression, bur had no impact on initials levels of physcial functioning or recovery in physical functioning. Pessimism impacted well being (depression) and physical health (physical functioning) only at the initial wave (discharge), but did not the impact the slopes of well being and physical health over time. In other words, levels of pessimism had no impact on recovery in physical functioning or depression.

When the constructs of intercepts and freely estimated slopes of depression and physical functioning become endogenous by adding optimism and pessimism as exogenous (predictors) variables, any correlations among these constructs must occur through their disturbance terms. Findings were similar to the results found in the bivariate latent trajectory model of depression and physical functioning (see Figure XXS).

The correlation between the disturbance terms of the intercept (individual’s initial score for depression) and the freely estimated slope of depression is -.55 (p<.001). In other words, higher initial levels of depression are associated with a line of trajectory that represents a weaker slope of depression over time. As mentioned previously, individuals with higher levels of depression have more recovery from depression over a year as compared to those with lower initial levels of depression.

The correlation between the disturbance of the intercept (individual’s initial score for physical functioning) and the freely estimated slope of physical functioning is -.34

(p=.04). In other words, higher initial levels of physical functioning are associated with a line of trajectory that represents a weaker slope of physical functioning over time. Simply

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stated, those individuals with higher initial levels of functioning have less recovery in

physical functioning.

The correlation between the disturbance terms of the intercept of depression and

intercept of physical functioning was -.43 (p<.001). In other words, higher initial levels

of physical functioning are associated with lower initial levels of depression. The

Figure 7.13: The Bivariate Latent Trajectory Model of Depression and Physical

Functioning with Optimism and Pessimism as Predictors (Standardized Parameters)

Ea1 Ea2 Ea3 Ea4 ea5 .83 .76 .89 .81 .72 Functioning Functioning Functioning Functioning Functioning Discharge Day 30 Day 90 Day 180 Day 360

.5 . 7 6

0 6 .3 .

7 .8 .79 4 4 8 9 1 . 8 .0

eoa eoc eod eog 9 .7 0 . FREELY lifeca lifecc lifecd lifecg EST IM A T ED INTERCEPT SLOPE FUNCTIONING

1 3 FUNCTIONING

0 7 5 -

. . .18 7

4 .00 .

. .05 dai .1 das 9

OPTIMISM -

.24 4 9

ADMIT 2 . 1 - .

1 -. 2 2 3 -.55 . 2 8 dci dcs - -. .06 FREELY PESSIMISM INTERCEPT EST IM A T ED SLOPE ADMIT DEPRESSION .36 .88 00 DEPRESSION .8 .

.33 .8 6 .54 7 4

3 9 6 4 2

5 8 6 . 6 6 . . 8 . 4

6 . . 9 8 7 . .

0 lifecb lifece lifecf lifech

epb epe epf eph Depression Depression Depression Depression Depression Discharge Day 30 Day 90 Day 180 Day 360 .64 .56 .57 .59 .58 Ec1 Ec2 Ec3 Ec4 Ec5

correlation between the disturbance terms of the freely estimated slope of depression and the freely estimated slope of physical functioning is -.40 (p<.001). In other words, the line of trajectory that represents a stronger slope of depression over time are associated

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with a line of trajectory that represents a weaker slope of physical functioning over time.

In simple terms, individuals who have less recovery from depression have less recovery in physical functioning.

The correlation between the disturbance terms of the intercept of depression and the freely estimated slope of physical functioning is .21 (p=.02). In other words, higher initial levels of depression are associated with a line of trajectory that represents a stronger slope of physical functioning over time. Simply stated, individuals with higher initial levels of depression have more recovery in physical functioning over a year. While this may seem counterintuitive, keep in mind that individuals at baseline who have higher initial levels of depression have lower initial levels of physical functioning, therefore there is more room to recover in physical functioning. Conversely, individuals with lower initial levels of depression have less recovery in physical functioning over the year.

Remember that individuals with lower initial levels of depression have higher initial levels of functioning, therefore these individuals have less room to recover functionally.

After all, if an individual has total control of their physical functioning initially, they cannot improve their level of physical functioning. There is no room to recover.

The correlation between the disturbance terms of the intercept of physical functioning and the freely estimated slope of depression is .18 (p=.02). Higher initial levels of physical functioning are associated with a line of trajectory that represents a stronger slope of depression over time. In other words, individuals with higher initial levels of physical functioning have less recovery from depression over the year. While this may seem counterintuitive, keep in mind that individuals at baseline who have higher initial levels of physical functioning have lower initial levels of depression; therefore there is less

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room to recover from depression. Simply stated, individuals who are not depressed

initially do not need to recover from depression over the year.

Figure 7.13 also shows the explained variance and the strongest loading factor for

each wave of depression and physical functioning over time. The explained variance in depression over time is 64% for depression at discharge, 56% at day 30, 57% at day 90,

59% at day 180, and 58% at day 360. The standardized factor loadings represent how strong the intercept or freely estimated slope predicts each wave of depression. Overall,

the intercept as compared to the freely estimated slope is the strongest predictor based on

factor loadings at discharge (.80 vs. .00), at day 30 (.89 vs. .54), at day 90 (.88 vs. .62), at

day 180 (.86 vs. .74), and at day 360 (.87 vs. .68).

The explained variance in physical functioning over time is 82% at discharge,

75% at day 30, 92% at day 90, 82% at day 180, and 73% at day 360. Overall, the

intercept as compared to the freely estimated slope is the strongest predictor based on

factor loadings at discharge (.90 vs. .00), at day 30 (.92 vs. .35), at day 90 (.97 vs. .61), at

day 180 (.89 vs. .63), and at day 360 (.88 vs. .47).

A look at the unstandardized parametric estimates indicated similar trends in

unstandardized factor loadings as found in the previous univariate and bivariate latent

trajectories models. Basically, the trajectory of depression and physical function shows

that the most recovery occurs from discharge to 30 days with the most recovery occurring

at day 180 with a slight decline in recovery at day 360. The unstandardized factor

loadings for depression are 0 at discharge, going to 1 at day 30, 1.15 at day 90, 1.42 at day

180, 1.27 at day 360. The unstandardized factor loadings for physical functioning are 0 at

discharge, 1 at day 30, 1.67 at day 90, 1.88 at day 180, 1.42 at day 360. It should be noted

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that the means and variances are not provided for endogenous variables therefore the means and variances of the intercepts and slopes of depression and physical functioning are not provided. Trajectories were not calculated because means were unavailable.

The Development of the Bivariate Latent Trajectory Model with Health Disparities,

Optimism and Pessimism, and Clinical Measures as Predictors

This model needs to be developed in several stages because of its complexity.

The first step is to develop the model between health disparities (age, gender (female), ethnicity (white), education, and income) and the dispositional characteristics of optimism and pessimism. This saturated model was first tested with all possible regression paths predicting each of the endogenous variables. All regression paths with a significance level of greater than .01 will be resubmitted to a specification search using the exploratory structural equation modeling module of AMOS (for a detailed review of the specification search procedure please refer to the data analytic strategy section of the dissertation). A cut off of .01 for significance was chosen as a means of “penalizing” the analysis for testing all combination of predictors and preventing the use of the analysis with all regression paths as a “fishing expedition”. The best fitting model based on the specification search with significant tested regressions paths (p<.01) will serve as the base model for the next step.

The second model to be tested will add two endogenous clinical variables

(APACHEII and the Charlson Comorbidity Index) to the best fitting model from the

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previous step. All of the variables from the previous model will be added as predictors of the APACHEII and the Charlson Comorbidity Index. These regression paths will be submitted as optional paths to the specification search. The best fitting model, as identified by the specification search, with all the significant tested regression paths

(p<.01) will be used as the starting model for the third step.

The third and final model will add the best fitting bivariate latent trajectory model of depression and physical functioning to the second model that tested the relationships among predictors (health disparities, dispositional characteristics, and clinical variables).

The four significant regression paths from optimism and pessimism to the intercepts and slopes of depression and physical functioning (as found in model 7.13) will be treated as required paths in the specification search. Optional regression paths used for the specification search were added from the remaining predictors (health disparities and clinical measures) to the intercepts and slopes of depression and physical functioning.

Developing the Model between Disparities in Social Structures and Optimism and

Pessimism

Figure 7.14 represents the final model. The predictors were sequentially ordered in the following manner: 1) gender (female), age, and ethnicity (white) were the most antecedent; 2) the next temporally was education; 3) followed by income; 4) finally optimism and pessimism. (For a detailed explanation of the causal ordering of health disparities and optimism and pessimism please refer to the introduction.) The specification search tests all possible combinations of optional paths. It should be noted,

190

that the number of models to be tested will grow exponentially with each optional path

added to the model. For example, if 9 paths are optional 512 models will be tested,

adding one optional path will increase the number of models tested from 512 to 1024.

With this in mind, the more required paths set in the specification paths the less optional

paths and models needed to be tested. With regards to Figure 7.14, zero order

correlations were used as guidelines to identify 9 required paths. The following 9

required paths were: 1) ethnicity (white) to optimism; 2) education to pessimism; 3)

Figure 7.14: Standardized Results of the Model between Disparities in Social Structures

and Optimism and Pessimism

eoa eoc eod eog lifeca lifecc lifecd lifecg

7 . 7

. .

0 4 5 1 4

de OPTIMISM ADMIT EDUCATION 21 -. do 9 AGE .4

2 .13

1 - .

WHITE .11 - - . . . 4 2 3 9

4 3 0

FEMALE .2 6

- .1 6 dp

INCOME -.21 PESSISM di ADMIT

5 7 5 . 6

6 6 . . lifecb lifece lifecf lifech epb epe epf eph

income to pessimism; 4) ethnicity (white) to income; 5) gender (female) to income; 6)

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Figure 7.15: Scree Plot of Specification Search and List of Best Fitting Models per

Number of Parameters for the Model between Disparities in Social Structures and

Optimism and Pessimism

1 Model 21 – 49 Parameters

-1

-2

50 60 70 80 90 100 Number of Parameters

education to income; 7) ethnicity (white) to education; 8) gender (female) to education; 9) optimism and pessimism as a covariance. Nine additional optional paths (not shown)

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were tested with the specification search.. The 9 optional paths included: 1) age to optimism; 2) gender (female) to optimism; 3) education to optimism; 4) income to optimism; 5) age to pessimism; 6) gender (female) to pessimism; 7) ethnicity (white) to pessimism; 8) age to income; 9) age to education. These optional paths in combination with the required paths created a saturated model in which all endogenous variables were analyzed with all possible predictors. The scree plot (see Figure 7.15, the model before the elbow) as well as the list of best fitting models per number of parameters provided by the specification search suggests that a model with 49 parameters and 55 degrees of freedom fit the data best. This model fit the data well (Chi Square = 108.04; df = 55; p<.001; TLI = .95; CFI = .97; RMSEA= .03; RMSEA 90% CI = .02-.04). While this model fit the data the best, three regression paths were not significant at the .01 level with weak significant betas ranging from -.05 to -.09. Therefore the decision was made to use the best fitting model with 46 parameters and 58 degrees of freedom (see Figure

7.14). This model had all regression paths that not only met the significant level criteria of .01 level, but were significant at the .001 level. This model fit the data well with nearly identical goodness of fit indices as found in the model with 49 parameters and 55 degrees of freedom (Chi Square = 118.47; df = 58; p<.001; TLI = .95; CFI = .97;

RMSEA= .03; RMSEA 90% CI = .03-.04).

The final model as represented by Figure 7.14 had eight regression paths significant at the .01 level. First, whites had lower levels of optimism (standardized beta= -.21, unstandardized beta= -.15, S.E. = .03, p<.001). Second, individuals with higher levels of education had lower levels of pessimism (standardized beta= -.23, unstandardized beta= -.09, S.E. = .02, p<.001). Third, individuals with higher levels of

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income had lower levels of pessimism (standardized beta= -.21, unstandardized beta= -

.05, S.E. = .01, p<.001). Fourth, whites had higher levels of income (standardized beta=

.26, unstandardized beta= 1.20, S.E. = .17, p<.001). Fifth, female had lower levels of

income (standardized beta= -.16, unstandardized beta= -.75, S.E. = .15, p<.001). Sixth,

individuals with higher levels of education had higher levels of income (standardized

beta= .44, unstandardized beta= .72, S.E. = .06, p<.001). Seventh, whites had higher

levels of education (standardized beta= .49, unstandardized beta= 1.39, S.E. = .08,

p<.001). Eighth, females had lower levels of education (standardized beta= -.13, unstandardized beta= -.38, S.E. = .08, p<.001). Additionally optimism was negatively

correlated with pessimism was -.30 (p<.001).

Developing the Model between Disparities in Social Structures, Optimism and

Pessimism, and Clinical Measures

Two additional clinical measures (the APACHEII, a measure of illness severity,

and the Charlson Comorbidity Index) were added to the final model in Figure 7.14.

Figure 7.16 represents the final model with the clinical measures added. With regards to

temporal ordering, the APACHEII and the Charlson Comorbidity Index occurred

simultaneously and were sequentially ordered after optimism and pessimism in the

model. Exploratory structural equation modeling using the specification search was also

applied to the current analysis.

The following four paths were set as required based on zero order correlations: 1)

age to APACHEII; 2) ethnicity (white) to APACHEII; 3) gender (female) to the Charlson

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Comorbidity Index; 4) pessimism to the Charlson Comorbidity Index. The following ten regression paths were set to optional (not shown in Figure 7.16) for the specification search: 1) gender (female) to APACHEII; 2) education to APACHEII; 3) income to

APACHEII; 4) optimism to APACHEII; 5) pessimism to APACHEII; 6) age to the

Charlson Comorbidity Index; 7) ethnicity (white) to the Charlson Comorbidity Index; 8) education to the Charlson Comorbidity Index; 9) income to the Charlson Comorbidity

Index; 10) optimism to the Charlson Comorbidity Index. These 10 optional paths

Figure 7.16: Standardized Results of the Model between Disparities in Social Structures,

Optimism and Pessimism, and Clinical Measures

eoa eoc eod eog lifeca lifecc lifecd lifecg

. 7 . 8 0 4 7 1 . 4 .5 de OPTIMISM DISCHARGE EDUCATION 1 -.2

3 do 9 1 4 . . - .13 AGE APACHE II

-.13

1 .

- dap

. - 2 1 WHITE . 3 . 4

4 9

. - -.13 FEMALE CHARLSON . COMORBIDITY - 2 .1 6 6 dp .17 -.21 dco INCOME PESSIMISM DISCHARGE .6 7 . di 5 6 2 . 7 5 6 . lifecb lifece lifecf lifech

epb epe epf eph

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Figure 7.17: Scree Plot of Specification Search and List of Best Fitting Models per

Number of Parameters for the Model between Disparities in Social Structures, Optimism and Pessimism, and Clinical Measures

2 Model 2 – 55 Parameters

-1

-2

50 60 70 80 90 100 110 120 130 140 Number of Parameters

resulted in 1024 models being tested by exploratory structural equation modeling using

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the specification search procedure.

The scree plot (see Figure 7.17) as well as the list of best fitting models per

number of parameters provided by the specification search suggested that the best fitting

model with 55 parameters and 80 degrees of freedom. This model fit the data well (Chi

Square = 134.56; df = 80; p<.001; TLI = .96; CFI = .97; RMSEA= .03; RMSEA 90% CI

= .02-.04). While this model fit the data best, the only optional regression path remaining

in the model was from age to the Charlson Comorbidity Index (standardized beta= -.06,

unstandardized beta= -.016, S.E. = .009, p=.06). This path was not significant at the .01

level. Therefore, the decision was made to use the best fitting model with 54 parameters

and 81 degrees of freedom (see Figure 7.16). None of the optional paths reached a

significant level of .01. All regression paths in this model were set as optional paths and met the .01 significant level criteria, and were all significant at the .001 level. This model’s overall fit (Chi Square = 138.56; df = 81; p<.001; TLI = .96; CFI = .97;

RMSEA= .03; RMSEA 90% CI = .02-.04) did not did not degrade from the fit of the model with 55 parameters and 80 degrees. Since the model represented in Figure 7.16 had nearly identical goodness of fit to the more complex model with 55 parameters and

80 degrees of freedom, it was more parsimonious and therefore chosen as the base model

for future analyses.

The final model as represented by Figure 7.16 had twelve regression paths

significant at the .01 level. First, whites had lower APACHEII scores (standardized beta=

-.13, unstandardized beta= -.92, S.E. = .23, p<.001). Second, older individuals had

higher APACHEII scores (standardized beta= .13, unstandardized beta= .07, S.E. = .02,

p<.001). Third, individuals with higher levels of pessimism had more comorbidities

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(based on the Charlson Comorbidity Index) (standardized beta= .17, unstandardized beta=

.53, S.E. = .13, p<.001). Fourth, females had less comorbidities (standardized beta= -.13,

unstandardized beta= -.47, S.E. = .12, p<.001). Whites had lower levels of optimism

(standardized beta= -.21, unstandardized beta= -.15, S.E. = .03, p<.001). Sixth,

individuals with higher levels of education had lower levels of pessimism (standardized

beta= -.24, unstandardized beta= -.09, S.E. =.02, p<.001). Seventh, individuals with

higher levels of income had lower levels of pessimism (standardized beta= -.21, unstandardized beta= -.05, S.E. = .01, p<.001). Eighth, whites had higher levels of

income (standardized beta= .26, unstandardized beta= 1.21, S.E. = .17, p<.001). Ninth, females had lower levels of income (standardized beta= -.16, unstandardized beta= -.75,

S.E. = .15, p<.001). Tenth, individuals with higher levels of education to had higher levels of income (standardized beta= .44, unstandardized beta= .72, S.E. = .06, p<.001).

Eleventh, whites had higher levels of education (standardized beta= .49, unstandardized beta= 1.39, S.E. = .08, p<.001). Twelfth, females had lower levels of education

(standardized beta= -.13, unstandardized beta= -.38, S.E. = .08, p<.001). Additionally

optimism was negatively correlated with pessimism was -.30 (p<.001).

The Bivariate Latent Trajectory Model with Disparities in Social Structures, Optimism and Pessimism, and Clinical Measures as Predictors

The final step of testing the model was to add the intercepts and freely estimated slopes of depression and physical functioning to the model represented in Figure 7.16.

These constructs were added temporally after the APACHEII and the Charlson

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Comorbidity. This final model (see Figure 7.18) was also established using the specification search of the exploratory structural equation modeling module of AMOS.

The first step was to add the paths from optimism and pessimism to the intercepts and slopes of depression and physical functioning as identified in Figure 7.13. The next step was to add all possible paths from the disparities in social structures, optimism and pessimism, APACHEII and the Charlson Comorbidity to the intercepts and freely estimated slopes of depression and physical functioning. Using the criteria of keeping only the paths with significant levels of .01, any regression path not reaching this significant level was set as optional. All regression paths that were significant at < .01 were included in the specification search as required paths. Eight paths were set as required and twenty-four paths were set as optional.

Testing a model with 24 optional paths is problematic, because testing all possible combination of paths is equivalent to testing 224 or 16,777,216 models. The decision was made to run the specification search on each of the constructs (intercepts and slopes of depression and physical functioning) of the latent growth curve individually. The final model will be built in steps based on the results of each previous specification search.

Four specification results will be run with each new model building off the results of the previous search. Using this method tested 5 optional paths for the intercept of physical functioning resulting in 32 models to be tested; 6 optional paths for the slope of physical functioning resulting in 64 models to be tested; 7 optional paths for the intercept of depression resulting in 128 models to be tested; 6 optional paths for the slope of depression to be tested. Logistically, testing 288 models across 4 specification search was more time efficient than running one specification search with 16,777,216 models,

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therefore it was chosen as the desired approach for this specification search.

In total there were 8 required regression paths and 24 optional paths tested using using 4

specification searches. The 8 required regression paths were broken down the following ways. Age, gender (female), and pessimism predicted the intercept of physical functioning. The Charlson Comorbidity predicted the freely estimated slope of physical functioning. Optimism and pessimism predicted the intercept of depression. Gender

(female) and optimism predicted the freely estimated slope of depression. The 24

optional regression paths were broken down the following ways. Ethnicity (white),

education, income, the APACHEII, and the Charlson Comorbidity were predictors of the

intercept of physical functioning. Age, ethnicity (white), gender (female), education,

income, and the APACHEII were the predictors of the freely estimated slope of physical

functioning. Age, ethnicity (white), gender (female), education, income, the APACHEII,

and the Charlson Comorbidity were the predictors of the intercept of the depression. Age,

ethnicity (white), education, income, the APACHEII, and the Charlson Comorbidity were

the predictors of the freely estimated slope of depression.

Scree plots and the list of best fitting models were used to identify the best fitting

and most parsimonious model for each of the 4 specification searches. Additionally the

criterion of keeping only the regression paths significant at the .01 level was also applied.

The first specification search was run to identify the predictors of the intercept of physical

functioning. Figure 7.19 represents the scree plot and the list of best fitting models for

the first specification search. The models with 88 parameters (df=262), 89 parameters

(df=261), 90 parameters (df=260), and 91 parameters (df=259) were identified as models

to examine. The model with 90 parameters was chosen because it offered the most stable

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Figure 7.18: Standardized Results of the Bivariate Latent Trajectory Model with Disparities in Social Structures, Optimism and Pessimism, and Clinical Measures as Predictors .58 .72 Ea5 Functn Day 360 Day Depress 360 Day .82 .59 Ea4 Functn Day 180 Day Depress Day 180 Day .91 .57 Ea3 Functn Day 90 Day 90 Depress

8 .56 5 .75

.4 6

. 8

7 Ea2 . Day 30 Functn Day 30 1 Depress .6

.64 .80

4 3 6 .3 . Ea1 Ec1 Ec2 Ec3 Ec4 Ec5 Functn Depress

Discharge

0 Discharge

.0 4

5 8

0 7

9 8 9 8

8 .

9 .

8 . .

9 8 . 0 . Slope Slope . 0 2 0 . 9 9 . . Depression Functionality Freely EstimatedFreely

Freely EstimatedFreely .13

. 9

1 . - 5 .2

Intercept

9 . 0 - 9 Depression Intercept

APACHEII 3 Charlson -. Comorbidity Functionality

3 0

3 2

1 3

7 . .

1 1

. .

- -

1 .

9 -

1 .

. -

3 9

0 . . -

3 lifech

lifecg .6

4 5

. .65

0 7 . lifecf lifecd

.78

Optimism

2 Pessimism 7 . -.2 6 lifece

lifecc 0

.4 2 2

. 2 5 - . . - lifecb lifeca

.43

3 1 6 . .2 - Education

16 Income 9 -. 4 .

Age

White

2 1 . - . Female 9 0

1 1 .

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and reasonable solution. This solution included adding the optional regression paths from total income and comorbidities to the intercept of physical functioning. While the comorbidities path was significant at .018 and did not meet the .01 criteria, removing the path resulted in a major degradation of the model’s goodness of fit.

The model with 91 parameters had an optional path that was not significant. The

89 parameter model did not fit the data as well as the 90 parameter model. The one optional path added was from education to the intercept of physical functioning, but income was not the optional path . This was inconsistent with models that had additional predictors, in which education was not significant, but income was significant. The model with 88 parameters also did not fit the data as well as the model with 90 parameters. The model with 90 parameters fit the data well (Chi square =403.28, df =

260, p<.001; TLI = .96; CFI = .97; RMSEA= .02; RMSEA 90% CI = .02-.03). This model had 5 predictors of the intercept of physical functioning; three required paths (age, being female, and pessimism) and 2 optional paths (income and comorbidities). It should be noted that parameter results (i.e., Betas) will be provided in the final model after the fourth specification search. The 90 parameter model will serve as the base model for the next specification search of the predictors of the freely estimated slope of physical functioning.

Figure 7.20 represents the scree plot and the list of best fitting models for the second specification search for the predictors of the freely estimated slope of physical functioning. The following three models were identified as potential best fitting models: the models with 90 parameters (df=260), 91 parameters (df=261), 92 parameters. The 92 parameter model added income and ethnicity as predictors of the freely estimated slope of

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physical functioning, however these paths were not significant at the .01 level. The 91 parameter model added gender as a predictor, but this was not significant at the .01 level.

The 90 parameter model had no additional paths. The 90 parameter model was chosen as the best fitting model because there was no difference in the goodness of fit between the

90, 91, and 92 parameters. In other words adding paths to the 90 parameter model did not improve its goodness of fit, there the most parsimonious model was chosen. The only predictor of the freely estimated slope of physical functioning was comorbidities, which was a required path. Since there were no paths added to the model from the previous specification search the goodness of fits were identical. This model was the base for the third specification search for the predictors of the intercept of depression.

Figure 7.21 represents the scree plot and the list of best fitting models for the third specification search for the predictors of the intercept of depression. The specification search results suggested that the models with 90 parameters (df=260), 91 parameters

(df=259), and 92 parameters (df=258) be further examined. The optional paths of the

APACHEII and ethnicity to the intercept of depression in the model with 92 parameters were not significant at the >01 level. The optional path of ethnicity to the intercept of depression was also not significant at the .01 level in the model with 91 parameters.

Since there was little difference between the goodness of fit between the models (i.e., goodness of fit changed only .001 for TLI; .002 for CFI and 0 for RMSEA across models), it was decided to go with the model with 90 parameters because it was the most parsimonious. Optimism and pessimism were the only predictors of the intercept of depression and were required paths for the specification search. Since there were no paths added to the model from the previous specification search the goodness of fits were

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Figure 7.22 represents the scree plot and the list of best fitting models for the Fourth specification search for the predictors of the freely estimated slope of depression functioning. The specification search for predictors of the freely estimated slope of

Figure 7.19: Scree Plot of Specification Search and List of Best Fitting Models per

Number of Parameters for the Intercept of Physical Functioning in Bivariate Latent

Trajectory Model with Disparities ins Social Structures, Optimism and Pessimism, and

Clinical Measures as Predictors

18 16 14

12 10

8 6 4

2 0

-2

90 91 92 93 Number of Parameters

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Figure 7.20: Scree Plot of Specification Search and List of Best Fitting Models per

Number of Parameters for the Freely Estimated Slope of Physical Functioning in

Bivariate Latent Trajectory Model with Disparities ins Social Structures, Optimism and

Pessimism, and Clinical Measures as Predictors

-1

91 92 93 94 95 96 Number of Parameters

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Figure 7.21: Scree Plot of Specification Search and List of Best Fitting Models per

Number of Parameters for the Intercept of Depression in Bivariate Latent Trajectory

Model with Disparities ins Social Structures, Optimism and Pessimism, and Clinical

Measures as Predictors

-1

-2

91 92 93 94 95 96 97 Number of Parameters

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Figure 7.22: Scree Plot of Specification Search and List of Best Fitting Models per

Number of Parameters for the Freely Estimated Slope Intercept of Physical Functioning

in Bivariate Latent Trajectory Model with Disparities ins Social Structures, Optimism and

Pessimism, and Clinical Measures as Predictors

-1

-2

91 92 93 94 95 96 Number of Parameters

identical. This model was the base for the fourth specification search for the predictors of the freely estimated slope of depression. suggested that models with 90 parameters

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(df=260), 91 parameters (df=259), and 92 parameters (df=258). The 90 parameter model

was the baseline model and no optional paths were added. The 91 parameter added one

optional path from comorbidities to the freely estimated slope of depression. This path was significant at he .01 level. The 92 parameter model added the optional predictors of age along with the comorbidities to the model. Age was not significant at the .01 level.

The decision was made to use the model with 91 parameters. This model had 3 predictors of the freely estimated of depression; two required paths (being female and optimism) and 1 optional path (comorbidities). This model was the final model chosen based on the 4 specification searches. This model fit the data well (Chi square =398.07, df = 259, p<.001; TLI = .96;

CFI = .97; RMSEA= .02; RMSEA 90% CI = .02-.03).

The final model had 23 regression paths (8 required and 3 optional for testing the slopes and intercepts of physical functioning and depression) as well as 12 regression paths form the previous model (see Figure 7.16). There were eight significant required regression paths. First, Older patients had lower initial physical functioning scores based on the intercept of physical functioning (standardized beta= -.29, unstandardized beta= -

.34, S.E. = .04, p<.001). Second, females had lower initial physical functioning scores based on the intercept of physical functioning (standardized beta= -.09, unstandardized beta= -1.36, S.E. = .51, p=.007). Third, individuals with higher levels of pessimism had lower initial physical functioning scores based on the intercept of physical functioning

(standardized beta= -.11, unstandardized beta= -1.43, S.E. = .58, p=.01). Fourth, individuals with more comorbidities had less of a change in physical functioning over the year, based on the freely estimated slope of physical functioning (standardized beta= -.19,

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unstandardized beta= -.31, S.E. = .10, p=.001). In other words, individuals with more

comorbidities had less recovery in physical functioning. Fifth, individuals with higher

levels of optimism have lower initial levels of depression, based on the intercept of

depression (standardized beta= -.39, unstandardized beta= -2.31, S.E. = .38, p<.001).

Sixth, individuals with higher levels of pessimism have higher initial levels of

depression, based on the intercept of depression (standardized beta= .33, unstandardized

beta= 1.19, S.E. = .16, p<.001). Seventh, females had more increase in depression over the year, based on the freely estimated slope of depression (standardized beta= .20, unstandardized beta= .52, S.E. = .12, p<.001). In other words, females were less likely to recover from depression as compared to males. Eighth, individuals with higher levels of optimism had more increase in depression over the year, based on the freely estimated slope of depression (standardized beta= .25, unstandardized beta= .90, S.E. = .29, p=.002). In other words, optimists are less likely to recover from depression as compared to individuals with low levels of optimism. Remember while this may seem counterintuitive, optimists have such low initial levels of depression that they do not have high enough levels of depression from which to recover.

Three optional regression paths were also significant. First, individuals with more comorbidities had lower initial physical functioning scores, based on the intercept of physical functioning (standardized beta= -.09, unstandardized beta= -.40, S.E. = .16, p=.01). Second, individuals with higher incomes had higher initial physical functioning scores, based on the intercept of physical functioning (standardized beta= .19, unstandardized beta= .63, S.E. = .12, p<.001). Third, individuals with more comorbidities had more increase in depression over a year, based on the freely estimated

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slope of depression (standardized beta= .13, unstandardized beta= .09, S.E. = .03, p=.007). In other words, individuals with more comorbidities have less recovery from depression.

The remaining significant 12 regression paths in Figures 7.18 were based on the results from the previous model (see Figure 7.16). First, whites had lower APACHEII scores (lower APACHEII scores represent lower levels of illness severity) (standardized beta= -.13, unstandardized beta= -.92, S.E. = .23, p<.001). Second, older individuals had higher APACHEII scores (higher APACHEII scores represent higher levels of illness severity) (standardized beta= .13, unstandardized beta= .07, S.E. = .02, p<.001). Third, individuals with higher levels of pessimism had more comorbidities (based on the

Charlson Comorbidity Index) (standardized beta= .17, unstandardized beta= .53, S.E. =

.13, p<.001). Fourth, females had less comorbidities (standardized beta= -.13,

unstandardized beta= -.47, S.E. = .12, p<.001). Whites had lower levels of optimism

(standardized beta= -.22, unstandardized beta= -.15, S.E. = .03, p<.001). Sixth,

individuals with higher levels of education had lower levels of pessimism (standardized

beta= -.22, unstandardized beta= -.09, S.E. =.02, p<.001). Seventh, individuals with

higher levels of income had lower levels of pessimism (standardized beta= -.22, unstandardized beta= -.05, S.E. = .01, p<.001). Eighth, whites had higher levels of

income (standardized beta= .26, unstandardized beta= 1.23, S.E. = .17, p<.001). Ninth, females had lower levels of income (standardized beta= -.16, unstandardized beta= -.77,

S.E. = .15, p<.001). Tenth, individuals with higher levels of education to had higher levels of income (standardized beta= .43, unstandardized beta= .71, S.E. = .06, p<.001).

Eleventh, whites had higher levels of education (standardized beta= .49, unstandardized

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beta= 1.39, S.E. = .08, p<.001). Twelfth, females had lower levels of education

(standardized beta= -.13, unstandardized beta= -.38, S.E. = .08, p<.001). Additionally

optimism was negatively correlated with pessimism was -.30 (p<.001).

The Development of the Autoregressive Latent Trajectory Hybrid Model with Predictors

The purpose of the Autoregressive Latent Trajectory (ALT) Hybrid Model is to combine the latent trajectory model with the autoregressive crosslagged model (for a review see the Data Analytic Strategy section of the dissertation). As a reminder, autoregressive paths are regression paths that go from the same variable measured at 1 time point (e.g., time 1) to the same variable measured at the next immediate time point

(e.g., time 2). The advantage of the ALT model is that the autoregressive crosslagged model is controlled for the trajectory of depression and physical functioning, while the latent trajectory model is controlled for the autoregressive and crosslagged effects of depression and physical functioning. The autoregressive component of the ALT model must be tested first to determine if the model needs to be altered. Specifically, models with large autoregressive standardized betas must be modified. Ideally, the standardized betas should be 0 in order to avoid modification of the model. The model that included autoregressive paths had small autoregressive standardized betas, therefore no further modifications were necessary to allow for model testing of crosslagged effects. It is expected that any bias introduce by the small autoregressive standardized betas will be negligible. Additionally, since the autoregressive paths were weak, it was not necessary

211

to keep them in the model to test the crosslagged effect model. It should be mentioned that the error terms associated with depression and physical functioning were correlated within each wave (e.g., the error terms of phyiscal functioning and depression at Day 30 were correlated).This model fit the data well (Chi square =360.66, df = 246, p<.001; TLI

= .97; CFI = .98; RMSEA= .02; RMSEA 90% CI = .02-.03). While this model fit the data well, the crosslagged effects were relatively weak.

Specifically, the model offers weak support that physical functioning predicts depression, but depression does not predict physical functioning. Three significant paths were found going from physical functioning to depression, but there were no significant paths from depression to physical functioning. The following four regression paths represent the causal paths from physical functioning to depression: 1) from physical functioning at discharge to depression at day 30 (standardized beta= .04, unstandardized beta= .01, S.E. = .01, p= .33); 2) from physical functioning at day 30 to depression at day

90 (standardized beta= .12, unstandardized beta= .04, S.E. = .01, p= .02); 3) from physical functioning at day 90 to depression at day 180 (standardized beta= .11, unstandardized beta= .04, S.E. = .02, p= .07); 4) from physical functioning at day 180 to depression at day 360 (standardized beta= .14, unstandardized beta= .04, S.E. = .02, p=

.02). The following four regression paths represent the causal paths from physical functioning to depression: 1) from depression at discharge to physical functioning at day

30 (standardized beta= .03, unstandardized beta= .09, S.E. = .07, p= .21); 2) from depression at day 30 to physical functioning at day 90 (standardized beta= .03, unstandardized beta= .09, S.E. = .09, p= .29); 3) from depression at day 90 to physical functioning at day 180 (standardized beta= -.03, unstandardized beta= -.11, S.E. = .12, p=

212

.36); 4) from depression at day 180 to at physical functioning day 360 (standardized beta=

.003, unstandardized beta= .01, S.E. = .11, p= .92). The following five intrawave

autocorrelations between the measurement errors of each physical functioning and

depression at each wave were: 1) at discharge (.16), 2) at day 30 (-.18), 3) at day 90 (-

.19), 4) at day 180 (-.02), 5) at day 360 (.02).

Since the crosslagged and the autoregressive paths were relatively weak, an

alternate more sophisticated way of fitting the model can be achieved by using the

specification search to identify the best fitting and most parsimonious model. Figure 7.23

represents the ALT hybrid model analyzed using the specification search. Sixteen paths

were set as optional (paths designated by thick arrows and labeled opt). In order to

analyze every combination of paths 216 or 65536 models would have to be tested.

Therefore, the eight autoregressive paths were first submitted to the specification search,

followed by the eight crosslagged paths. The results of the best 2 specification search for

the autoregressive and crosslagged paths were combined into a final specification search.

The scree plot (see Figure 7.24) as well as the list of best fitting models per number of parameters provided by the final specification search for the autoregressive and crosslagged paths suggested that the best fitting model with 100 parameters and 250 degrees of freedom. Additionally this model was the only model that had optional paths that met the .01 significance criterion used for penalizing random testing of paths. In other words any other models (e.g., the model with 101 parameters and 249 degerees of freedom) suggested by the specification search did not have any optional paths that met the significance criterion of .01. Four optional paths (three autoregressive paths and one crosslagged path) were found to be significant at the .01 level and added to model in

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Figure 7.18. The following optional paths were added: 1) from functionality at day 30 to functionality at day 90 (standardized beta= .07, unstandardized beta= .07, S.E. = .02,

Figure 7.23: The ALT Hybrid Bivariate Model of Physical Functioning and Depression

(Predictors not Shown; Thick Lines Marked by the Word “opt” are Optional)

Functioning Functioning Intercept 1 0 Linear Slope 1 1

1 1

1 3

6 2 1

Functioning opt Functioning opt Functioning opt Functioning opt Functioning Discharge Day 30 Day 90 Day 180 Day 360 0 0 0 0 0 Ea1 Ea2 Ea3 Ea4 ea5

o o o o t t t p p p p p t p t t t p t p o o o o Ec1 Ec2 Ec3 Ec4 Ec5 0 0 0 0 0 opt opt opt opt Depression Depression Depression Depression Depression Discharge Day 30 Day 90 Day 180 Day 360

1 3

1 1 1 1 2

1 1 0 Depression Depression Intercept Linear Slope

p<.001); 2) from depression at discharge to depression at day 30 (standardized beta= .12, unstandardized beta= .12, S.E. = .04, p<.01); 3) from depression at day 30 to depression at day 90 (standardized beta= .08, unstandardized beta= .08, S.E. = .03, p=.01); 4) from depression at day 90 to functionality at Day 180 (standardized beta= -.06, unstandardized beta= -.21, S.E. = .08, p<.01). While this model did show that depression at day 90 predicted functionality at day 180, this was an isolated (i.e., no other crosslagged paths were significant) and weak regression path that may have been biased by the weak but

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Figure 7.24: Scree Plot of Specification Search and List of Best Fitting Models per

Number of Parameters for the ALT Hybrid Bivariate Model of Physical Functioning and

Depression with Health Disparities, Optimism and Pessimism, and Clinical Measures as

Predictors

-0.7

-0.8

-0.9

-1

-1.1

-1.2

-1.3

-1.4 101 102 103 104 Number of Parameters

significant autoregressive path of depression at discharge to depression at day 30. Testing for this bias would involve testing a very complex model to determine that a very weak

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path with a standardized beta of -.06 is actually a weaker path that is not statistically significant. In other words, should a relationship that barely exists, be subjected to further testing to prove that a relationship does not exist. Since, this is beyond the scope of this dissertation, the decision was made not to test this model for bias. Figure 7.25 represents the final ALT model that was submitted to the specification search and fit the data well (Chi square =346.70, df = 250, p<.001; TLI = .97; CFI = .98; RMSEA= .02;

RMSEA 90% CI = .02-.03).

The remaining 23 regression paths in Figure 7.25 retained from the previous model (see Figure 7.18) were as follows. First, Older patients had lower initial physical functioning scores based on the intercept of physical functioning (standardized beta= -.29, unstandardized beta= -.33, S.E. = .03, p<.001). Second, females had lower initial physical functioning scores based on the intercept of physical functioning (standardized beta= -.08, unstandardized beta= -1.29, S.E. = .50, p=.01). Third, individuals with higher levels of pessimism had lower initial physical functioning scores based on the intercept of physical functioning (standardized beta= -.10, unstandardized beta= -1.37, S.E. = .57, p=.017). Fourth, individuals with more comorbidities had lower initial physical functioning scores, based on the intercept of physical functioning (standardized beta= -

.09, unstandardized beta= -.40, S.E. = .16, p=.01). Fifth, individuals with higher incomes had higher initial physical functioning scores, based on the intercept of physical functioning (standardized beta= .19, unstandardized beta= .62, S.E. = .12, p<.001).

Sixth, individuals with more comorbidities had a weaker slope in physical functioning over the year, based on the freely estimated slope of physical functioning (standardized beta= -.17, unstandardized beta= -.30, S.E. = .10, p<.01). In other words, individuals

216

with more comorbidities had less recovery in functioning.

Seventh, individuals with higher levels of optimism have lower initial levels of

depression, based on the intercept of depression (standardized beta= -.39, unstandardized beta= -2.31, S.E. = .38, p<.001). Eighth, individuals with higher levels of pessimism have higher initial levels of depression, based on the intercept of depression (standardized beta= .33, unstandardized beta= 1.14, S.E. = .15, p<.001). Ninth, females had a stronger slope in depression over the year, based on the freely estimated slope of depression

(standardized beta= .18, unstandardized beta= .60, S.E. = .14, p<.001). In other words, females had less recovery from depression over the year. Tenth, individuals with higher levels of optimism had a stronger slope in depression over the year, based on the free estimated slope of depression (standardized beta= .25, unstandardized beta= 1.17, S.E. =

.37, p=.001). In other words individuals with higher levels of optimism had less recovery from depression, largely because they had lower initial levels of depression. Eleventh, individuals with more comorbidities had a stronger slope in depression over a year, based on the freely estimated slope of depression (standardized beta= .16, unstandardized beta=

.10, S.E. = .04, p<.01). In other words, individuals with more comorbidities had less recovery from depression.

Twelfth, whites had less illness severity as based on APACHEII scores

(standardized beta= -.13, unstandardized beta= -.92, S.E. = .23, p<.001). Thirteenth,

older individuals had more illness severity as based on APACHEII scores (standardized

beta= .13, unstandardized beta= .07, S.E. = .02, p<.001). Fourteenth, individuals with

higher levels of pessimism had more comorbidities (based on the Charlson Comorbidity

Index) (standardized beta= .17, unstandardized beta= .53, S.E. = .13, p<.001). Fifteenth,

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females had less comorbidities (standardized beta= -.13, unstandardized beta= -.47, S.E.

= .12, p<.001). Sixteenth, whites had lower levels of optimism (standardized beta= -.22,

unstandardized beta= -.15, S.E. = .03, p<.001). Seventeenth, individuals with higher

levels of education had lower levels of pessimism (standardized beta= -.22,

unstandardized beta= -.09, S.E. =.02, p<.001). Eighteenth, individuals with higher levels

of income had lower levels of pessimism (standardized beta= -.22, unstandardized beta= -

.05, S.E. = .01, p<.001). Nineteenth, whites had higher levels of income (standardized beta= .26, unstandardized beta= 1.24, S.E. = .17, p<.001). Twentieth, females had lower levels of income (standardized beta= -.16, unstandardized beta= -.77, S.E. = .15, p<.001).

Twenty-first, individuals with higher levels of education to had higher levels of income

(standardized beta= .43, unstandardized beta= .71, S.E. = .06, p<.001). Twenty-second, whites had higher levels of education (standardized beta= .49, unstandardized beta= 1.39,

S.E. = .08, p<.001). Twenty-third, females had lower levels of education (standardized

beta= -.13, unstandardized beta= -.38, S.E. = .08, p<.001). Additionally optimism was

negatively correlated with pessimism was -.30 (p<.001). The following five intrawave

autocorrelations between the measurement errors of each physical functioning and

depression at each wave were: 1) at discharge (.20), 2) at day 30 (-.23), 3) at day 90 (-

.26), 4) at day 180 (-.06), 5) at day 360 (.01). Inconsistencies in these scores are difficult to interpret and may be indicative of instability in the model, although scores are not

outside the range of possible scores.

As mentioned previously, when the constructs of intercepts and freely estimated

slopes of depression and physical functioning become endogenous by adding a set of

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predictors, any correlations among these constructs must occur through their disturbance

terms. The correlation between the disturbance terms of the intercept (individual’s initial

score for depression) and the freely estimated slope of depression is -.62 (p<.001). In

other words, higher initial levels of depression are associated with a line of trajectory that

represents a weaker slope of depression over time. Simply stated, individuals with higher

initial levels of depression have more recovery from depression. The correlation between

the disturbance of the intercept (individual’s initial score ) for physical functioning and

the freely estimated slope of physical functioning is -.50 (p=.02). In other words, higher initial levels of physical functioning are associated with a line of trajectory that represents a weaker slope of physical functioning over time. Simply stated, individuals with higher initial levels of functioning have less recovery from functioning over a year. Remember, that individuals who have high initial functioning score have little room to recover.

The correlation between the disturbance terms of the intercept of depression and

intercept of physical functioning was -.54 (p<.001). In other words, higher initial levels

of physical functioning are associated with lower initial levels of depression. The

correlation between the disturbance terms of the freely estimated slope of depression and

the freely estimated slope of physical functioning is -.49 (p=.03). In other words, the line of trajectory that represents a stronger slope of depression over time are associated with a line of trajectory that represents a weaker slope of physical functioning over time.

Simply stated, individuals with less recovery from depression also have less recovery

from functioning

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Figure 7.25: Standardized Results of the ALT Hybrid Bivariate Model of Physical Functioning and Depression with Health Disparities, Optimism and Pessimism, and Clinical Measures as Predictors .54 .71

Ea5 Functn .01 Depress Day 360 Day 360 .83 .56

Ea4

Functn -.06 Day 180 Day Depress Day 180 Day

.90 0 . .58 -

Ea3 Functn Day 90 -.26 Day 90 Depress .07 .08 2 .59 1 .73

.5 7

. 9

Ea2 .

Day 30 Functn Day 30 0 -.23 Depress .6

.12

.63 .83

7 4 6 .3 .

Ea1 Ec1 Ec2 Ec3 Ec4 Ec5 Functn .20 Depres s

Dis charge

0 Dis charge

.0 6

7 5

3 8

2 8 4 8

7 .

9 .

9 . .

9 8 . 0 . Slope 0 . Slope 5 . 9 1 . 9

. Depression

Functionality .11 Freely Estimated Freely Estimated

. 7

1 . - 5 .2

Intercept

9 0 . - 9 Depression Intercept

APACHEII 3 Charlson -. Comorbidity Functionality

3 8

3 1

1 3

7 . .

1 1

. .

- -

1 .

9 -

1 .

. -

3 8

0 . . -

3 lifech

lifecg .6

4 5

. .65

0 7 . lifecf lifecd

.78

Optimism 2 Pessimism 7 . -.2 6 lifece

lifecc 0

.4 2 2

. 2 5 - . . - lifecb lifeca

.43

3 1 6 . .2 - Education

16 Income 9 -. 4 .

Age

White

1 2 . Female 9 0 - .

1 1 .

220

The correlation between the disturbance terms of the intercept of depression and the freely estimated slope of physical functioning is .39 (p=.04). In other words, higherinitial levels of depression are associated with a line of trajectory that represents a stronger slope of physical functioning over time. Simply stated, individuals with higher initial levels of depression have more recovery from functioning, because those individuals also had low initial functioning scores. Therefore, more room to recover than individuals with low initial depression scores and high initial functioning scores. The correlation between the disturbance terms of the intercept of physical functioning and the freely estimated slope of depression is .35 (p<.01). In other words, higher initial levels of physical functioning are associated with a line of trajectory that represents a stronger slope of physical functioning over time. Simply stated, individuals with high initial functioning scores have less recovery from depression, because they had such low initial depression scores.

Figure 7.25 also shows the explained variance and the strongest loading factor for each wave of depression and physical functioning over time. The explained variance in depression over time is 63% for depression at discharge, 59% at day 30, 58% at day 90,

56% at day 180, and 54% at day 360. The standardized factor loadings represent how strong the intercept or freely estimated slope predicts each wave of depression. Overall, the intercept as compared to the freely estimated slope is the strongest predictor based on factor loadings at discharge (.79 vs. .00), at day 30 (.84 vs. .66), at day 90 (.85 vs. .64), at day 180 (.87 vs. .76), and at day 360 (.88 vs. .69).

The explained variance in physical functioning over time is 83% at discharge,

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72% at day 30, 90% at day 90, 83% at day 180, and 70% at day 360. Overall, the

intercept as compared to the freely estimated slope is the strongest predictor based on

factor loadings at discharge (.91 vs. .00), at day 30 (.94 vs. .37), at day 90 (.97 vs. .59), at

day 180 (.93 vs. .71), and at day 360 (.92 vs. .52).

A look at the unstandardized parametric estimates indicated similar trends in

unstandardized factor loadings as found in the previous latent trajectories models. The

unstandardized factor loadings (in units of depression) started at 0 for discharge, going to

1 at day 30, .95 at day 90, 1.10 at day 180, .98 at day 360. The unstandardized factor

loadings (in units of physical functioning) starting at 0 for discharge, 1 at day 30, 1.59 at

day 90, 1.99 at day 180, 1.44 at day 360. It should be noted that the means and variances are not provided for endogenous variables therefore the means and variances of the intercepts and slopes of depression and physical functioning are not provided. This also prevents the calculation of the lines of trajectory for depression and physical functioning.

While the line of trajectories cannot be calculated the unstandardized factor loadings follow the same pattern for depression as found in Figure 7.4 as well as physical functioning as found in Figure 7.9. Therefore the line of trajectories for the model represented in Figure 7.25 would be similar to the line of trajectories found Figure 7.5 for depression and Figure 7.10 for physical funcntioning.

Attempts to fit the contemporaneous ALT model were unsuccessful. Results were nonsensical. Two contemporaneous paths were significant, however the relationships did not make sense because the results suggested the higher levels depression caused higher levels of functioning. This is indicative of an attempt to fit a model that does not fit the data.

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Testing Optimism and Pessimism as Mediators of the Impact of Structural Disparities on

Depression and Physical Functioning using the Autoregressive Latent Trajectory Hybrid

Model

The final ALT crosslagged model (see Figure 7.25) was used as the base model for testing mediation. Since optimism and pessimism were the mediators, the ALT model needed to be developed to determine the predictors of optimism and pessimism, as well as to identify which intercepts and slopes of depression and physical functioning were predicted by optimism and pessimism. Ethnicity was the only predictor of optimism.

Education and income were the two predictors of pessimism. Optimism predicted the intercept and slope of depression. Pessimism predicted the intercepts of depression and physical functioning.

In order to test the mediation of optimism and pessimism the predictors of optimism and pessimism had to be set up as predictors of the same outcomes predicted by optimism and pessimism. The following six paths were added: 1) ethnicity to the intercept of depression; 2) ethnicity to the slope of depression; 3) education to the intercept of depression; 4) education to the intercept of physical functioning; 5) income to the intercept of depression; 6) income to the intercept of physical functioning.

Additionally, to test the strength of these paths optimism and pessimism was removed from the model in Figure 7.25.

Both models were compared to determine if the six additional paths became significant when optimism and pessimism were removed from the model. Table 6.2

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represents the standardized beta weights of the six paths tested for mediation. Three

nonsignificant paths when optimism and pessimism were present became significant when optimism and pessimism were removed from the model.

The most dramatic mediation occurred when the path from income to the intercept of depression was mediated by pessimism. The standardized beta for this path dropped

.09 when it was mediated by pessimism. In other words, income indirectly affects the intercept of depression through pessimism, such that the indirect relationship causes the direct relationship from income to the intercept of depression to drop. Simply stated, individuals with higher levels of income have lower levels of pessimism, which in turn causes lower initial levels of depression.

The next strongest mediation occurred when the path from ethnicity to the intercept of depression was mediated by optimism. The standardized beta dropped .07 when it was mediated by optimism. In other words, ethnicity indirectly affects the intercept of depression through optimism, such that the indirect relationship causes the direct relationship from ethnicity to intercept of depression to drop. Simply stated ,

African Americans have higher levels of optimism, which in turn causes lower initial levels of depression.

Finally the third strongest mediation occurred when the path from ethnicity to the slope of depression was mediated by optimism. The standardized beta for this relationship dropped .04 when optimism was introduced as a mediator. In other words, ethnicity indirectly affects the freely estimated slope of depression through optimism, such that the indirect relationship causes the direct relationship from ethnicity to the slope of depression to drop Simply stated, African Americans had higher levels of optimism,

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which in turn causes slower recovery from depression, because the initial levels of

depression are so low. Essentially there is little room to recover.

Table 7.1: Six regression paths tested for mediation

Testing Regression Path Testing Regression Path without Mediator with Mediator Standardized Standardized Regression Paths Beta p Beta p Optimism as a Mediator 1. Ethnicity Æ Intercept of .22 <.001 .15 .003 Depression *

2. Ethnicity Æ Slope of -.17 .005 -.13 .04 Depression*

Pessimism as Mediator 3. Education Æ Intercept of -.02 .71 .05 .27 Depression

4. Education Æ Intercept of .02 .71 -.007 .87 Physical Functioning

5. Income Æ Intercept of -.13 .01 -.04 .47 Depression*

6. Income Æ Intercept of Physical .21 <.001 .18 <.001 Functioning * Represents mediated paths

Summary

With regards to the relationship of optimism and pessimism to the outcomes of

physical health as measured by physical functioning and psychological well-being as

measured by depression, optimism and pessimism predicted both outcomes differently.

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Optimism impacted both initial levels and recovery from depression over a year, such that less recovery in depression occurred for those who had higher levels of optimism, because these individuals had such lower initial levels of depression, recovery was not as dramatic for these individuals. Optimism did not predict initial levels or recovery in physical functioning. Pessimism caused lower initial levels of physical functioning and higher initial levels of depression. Pessimism had not impact on recovery in physical functioning or depression.

Social structural disparities also impacted optimism and pessimism. Being

African American was associated with higher levels of optimism. While African

Americans are not the privileged social group, this suggests that their ability to successfully handle challenges is greater than whites. Lower levels of education and income were associated with higher levels of pessimism. No other social structural disparities impacted optimism and pessimism.

In examining if optimism and pessimism mediated the relationship between social structural disparities and physical functioning and depression. Testing if the influence of social disparities on physical functioning and depression is mediated by optimism and pessimism, also allows for the detection of control over these structural disparities on one’s life. Three paths from structural disparities to depression and physical functioning were mediated by optimism and pessimism. First, the relationship between income and initial levels of depression was mediated by pessimism. Specifically, individuals with higher levels of income have lower levels of pessimism, which in turn causes lower initial levels of depression. Second, the relationship between ethnicity and depression was mediated by optimism. Specifically, African Americans have higher levels of optimism,

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which in turn causes lower initial levels of depression. Third, the relationship between

ethnicity and the freely estimated slope of depression was mediated by optimism.

Specifically, African Americans had higher levels of optimism, which in turn causes

slower recovery from depression, because their initial levels of depression are so low.

Essentially there is little room to recover. Studying the mediation models, shows not

only the influence that the agent through optimism and pessimism controls one’s life, as

expressed by depression and physical functioning, but also shows how the influence of specific structural disparities on depression are controlled by the agent. Overall, looking at the big picture, agents are influenced by the structural disparities, but they also exhibit control over their life.

The latent trajectory results were more substantive and fit the data well. These latent trajectory models showed that the greatest change in recovery (post-hospitalization) occurring at day 30, with the most recovery at 180 days, before a slight decline and leveling off at day 360 for both physical functioning and depression. Overall, individuals with lower initial levels of depression and highest level of physical functioning having the least recovery because they did not have as much “room” to recover.

The latent trajectory model was further developed into a bivariate latent trajectory model for physical functioning and depression with a set of predictors. Younger individuals, males, individuals with more income, less pessimistic individuals, and those with less comorbidities had higher initial levels of physical functioning. Individuals with less comorbidities had more recovery in physical functioning. Individuals with low levels of optimism and high levels of pessimism had higher initial levels of depression. Finally, females, individuals with higher levels of optimism, and more comorbidities had less

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recovery from depression.

This model was further developed into an autoregressive latent trajectory (ALT) hybrid model. This model adds autoregressive and crosslagged or contemporaneous paths to the item indicators of the bivariate latent trajectory model with predictors. By adding testing the ALT model, the autoregressive and the latent trajectory components of the model are controlling or adjusting for the influence of each other in the model.

Results were nearly identical for all of the parameters that were tested in the previous model. Four relatively weak additional paths were added in testing the ALT models.

These paths were added because they helped to make the model fit the data better. Three relatively weak autoregressive paths were added: from physical functioning at day 30 to physical functioning at day 90; from depression at discharge to depression at day 30; from depression at day 30 to depression at day 90. Only one weak crosslagged path remained, higher levels of depression at day 90 were associated with less physical functioning at day

180.

Furthermore, testing if the influence of social disparities on physical functioning and depression is mediated by optimism and pessimism, also allows for the detection of control over these structural disparities on one’s life. Three paths from structural disparities to depression and physical functioning were mediated by optimism and pessimism. First, the relationship between income and initial levels of depression was mediated by pessimism. Specifically, individuals with higher levels of income have lower levels of pessimism, which in turn causes lower initial levels of depression.

Second, the relationship between ethnicity and depression was mediated by optimism.

Specifically, African Americans have higher levels of optimism, which in turn causes

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lower initial levels of depression. Third, the relationship between ethnicity and the freely estimated slope of depression was mediated by optimism. Specifically, African

Americans had higher levels of optimism, which in turn causes slower recovery from depression, because their initial levels of depression are so low. Essentially there is little room to recover. Studying the mediation models, shows not only the influence that the agent through optimism and pessimism controls one’s life, as expressed by depression and physical functioning, but also shows how the influence of specific structural disparities on depression are controlled by the agent. Overall, looking at the big picture, agents are influenced by the structural disparities, but they also exhibit control over their life.

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Chapter 8: Discussion and Conclusions

Verbrugge and Jette (1994) describe how hospitalized elders are often at risk for

functional decline that can lead to disability. This overall decline in health is referred to

as “The Disablement Process”. The decline in physical functioning is largely the result of

an illness that compromises a specific body system (e.g., muscoskeletal). This decline

can also contribute to one’s level of depression. As the individual’s level of functioning

and mental actions becomes more and more limited, the likelihood of disability increases.

Often a hospital stay for an elderly person with an acute illness or acute episode of a

chronic illness is the triggering event that will eventually lead to disability.

This dissertation investigated the course of recovery for elderly patients under the

overarching umbrella known as “The Disablement Process” as represented by the trajectories of physical functioning and depression over a year. Initial results of tracking the decline of physical functioning (i.e., the precursor to disability) over year demonstrated that overall a leveling off occurs after a peak at 6 months. Specifically, the line of trajectory for physical functioning (please refer to Figure 7.10) indicates that physical functioning post hospitalization continually increases to an additional 1.84 activities of daily living at 6 months (180 days) as compared to the number of

ADLl/IADLs at discharge. This may in large part be due to the beneficial effect that some patients may have received from the course of treatment during their hospital stay.

However, at 12 months (360 days) the number of ADL/IADLs decreased to 1.41 above the number at discharge. It should be noted that in the current study, that the trajectory of

functionality was only monitored for 1 year (a relatively short amount of time) and an

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assessment over a longer time period would be needed to establish a trajectory

representative of decline or disability. With this in mind the decline in ADL/IADLs from

1.84 at 6 months to 1.41 at 12 months may be the starting point for decline that eventually

leads to disability or just a leveling off due to sampling error.

While “The Disablement Process” is the broader framework for understanding the recovery of patients after hospitalization, this project looks at the deeper underlying processes that may contribute to or delay the onset of “The Disablement Process” by examining the role of social structural disparities, optimism and pessimism, as well as the clinical condition (evaluated by the Charlson Comorbidity Index and the Apache II) on the trajectories of physical functioning and depression.

How does the Proposed Study Contribute to the Literature?

Using conflict theory and Settersten’s work on structure and human agency to provide a sociological foundation, the proposed study contributes to the literature in four primary ways. The first two contributions will revisit the results based on testing the six hypotheses . The third contribution examines the dimensionality of the LOT scale. The fourth contribution regards testing the causal relationship between physical functioning and depression using longitudinal analytic techniques.

The First Contribution

The first contribution concerns two health-related consequences of optimism and pessimism. This study expands the dearth of research conducted on the relationship

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between optimism and pessimism and psychological well being. Additionally, the study

tested the relationship between optimism and pessimism and physical health in

hospitalized elders, which has never been tested in the literature. The two general

hypotheses that tested these relationships were as follows:

H1: Higher levels of optimism will be associated with higher levels of physical health (physical functioning) and lower levels of psychological well- being (depression). H2: Higher levels of pessimism will be associated with lower levels of physical health and psychological well-being.

These hypotheses focus on optimism and pessimism’s impact on elder patients’ recovery

from a hospital stay. In order, to gain a better understanding of how a hospitalization may impact the recovery process the following review on institutionalization and the ecological model will be revisited.

Goffman’s Asylum (1961), and Kahana’s, (1974), and Lawton’s (1982, 1989)

work has not only provided a theoretical basis, but also empirical proof of the negative

side effects of institutionalization. In the case of the current study these approaches could

be also be applied to hospitalization, a form of institutionalization. Of special interest is

Goffman’s, and Kahana’s and Lawton’s focus on ecological models based on person-

environment fit to understanding the hospital environment’s role in patient recovery.

Goffman’s work on total institutions has three components that are similar to the

acute hospital setting. First, all aspects of life are conducted in the same place under a

single authority. Second, all daily activities are tightly scheduled. Finally, the activities

are brought together in a single rational plan designed to fulfill the official aims of the

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institution. These three aspects, according to Goffman (1961) work together as process of depersonalization that begins as soon as the patient is admitted and autonomy is restricted. Hospitalization strips the patient of their personal belongings and forces the patient to become a number being treated as a case, and not a person forced to follow the hospital routine that may clash with personal routines.

Kahana’s (1974) and Lawton’s (1982, 1989) work further refine the issue concerning congruence in person-environment fit by applying the ecological model to gerontological samples. Kahana, Liang, & Felton (1980) offer a theoretical lens to look at how the hospital setting plays a role in a patient’s outcomes. Specifically, the ecological model focuses on how an elderly person’s level of functioning and well being is a function of their personal background, physical and social environment. Emphasis is placed on the needs and capacities of the older person and the supplies and demands from his/her environment.

Examples of how the hospital environment places demands on a compromised individual include: 1) Muscle deconditioning from bedrest (e.g., Hoening & Rubinstein,

1991); 2) Partial starvation before diagnostic procedures (e.g., Sullivan, Patch, Walls, &

Lipshitz, 1991); 3) Confusion or persistent sedation induced by medication (e.g., Lamay,

1990). Additionally, procedures such as urinary catheterization, physical restraints, enemas, and endoscopic procedures can be uncomfortable, may prolong bedrest, prevent exercise, and cause injury (Lofgren, Macpherson, Graneiri, Millenbeck, Sprafka, 1989).

Since most patients are in a compromised position to begin with, the added demands of the hospital setting may hinder a patients recovery. However, when a patient returns to the familiarity of his/her home, that individual’s level of health and well being should

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improve. Patients with higher levels of optimism and lower levels of pessimism may

have additional resources that improve their level of competence to handle the

environmental press of the hospital setting.

While this section has focused on the negative effects associated with

hospitalization, it is also important to realize that hospitalization can also have some

positive effects based on treatment. In many cases the treatments help individuals

overcome the stress and demands associated with being placed in a hospital. For

examples, physical and occupational therapies under professional supervision may

strengthen a patient to overcome the environmental press from the institution.

Additionally these treatments may provide a long term lasting effect that will enable the

patient to overcome the environmental press associated with returning home.

Testing the impact of hospitalization on patients’ outcomes is not possible during

the current study, because a non-hospitalized sample was not part of the study. A non-

hospitalized sample would have allowed comparison between the two groups. This study

focuses on the impact of optimism and pessimism as well as other predictors on the

recovery (based on the lines of trajectories) in physical functioning and depression

starting at discharge through 360 days post-hospital discharge. With regards to physical

functioning, the patients level of functioning was lowest at discharge, but improved over

the year. With regards to depression, patients had the highest levels of depression at discharge, but improved over the year. For both, physical functioning and depression, the greatest improvements occurred in the first 30 days, with maximum benefit occurring at day 180 and a leveling off at day 360.

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With this in mind, does optimism and pessimism play a role in recovery from

hospitalization? Within Lazarus and Folkman’s (1984) stress model, optimism and

pessimism would contribute to making secondary appraisals. Specifically, secondary

appraisals take into account not only one’s coping options when faced with a challenge,

but the likelihood that a chosen coping mechanism will handle a challenge, as well as the

likelihood that one can use these coping strategies effectively to handle a challenge.

Since, optimism and pessimism develop based on one’s success and failures in handling

challenges, optimism and pessimism may serve as the personality trait that contributes to

managing one’s ability to make secondary appraisals.

In many ways, Scheier and Carver’s conceptualization parallels Lazarus and

Folkman’s work. Higher levels of optimism and lower levels of pessimism may serve as a personal resource for overcoming the stresses associated with hospitalization. People view the world in different ways. Optimists view the world through “rose-colored” glasses, while pessimists view the world through “dark-colored” glasses. Optimists have a favorable outlook on life and expect good things to happen to them (Scheier & Carver,

1985). Pessimists on the other hand have an unfavorable outlook on life and expect bad outcomes (Scheier & Carver, 1985). Optimism and pessimism are considered as stable personality traits that can influence behaviors, including behaviors related to health

(Scheier & Carver, 1985).

The behavioral-self regulation model, according to Scheier and Carver (1985) explains how optimism and pessimism impacts behavior. Optimism and pessimism develops in individuals based on their ability to successfully handle challenges. For an optimist, the more success an individual has had in handling challenges the more likely

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the individual will be in renewing efforts to handle future challenges. Conversely, for a pessimist, the more failures the individual has had in dealing with difficulties the less likely the individual will be willing to face future difficulties. With this in mind an optimistic patient, is more likely to put forth renewed effort to recover from a hospitalization than a pessimistic patient. From an individual’s standpoint, the knowledge of past successes and failures contributes to one’s optimism and pessimism which may serve as the stable trait-like mechanism that drives one’s ability to make a secondary appraisal.

With regards to recovery in the current study (please refer to figure 7.13), in testing H1, optimism was not associated with initial levels or the trajectory of physical functioning. In other words, optimism did not impact physical functioning initially or recovery in physical functioning. Optimism was associated with lower initial levels of depression and less recovery from depression. Essentially, individuals who had high levels of optimism had such low initial levels of depressions, that they could not recover from depression as quickly as those with lower levels of optimism, who also had higher initial levels of depression. Basically, if an individual is not depressed, how can they recover from depression . However another interpretation of this finding is that the affect on lack of recovery is due to a methods artifact.

In testing H2, pessimism was associated with lower initial levels of physical functioning, but not the slope of physical functioning. In other words, individuals with higher levels of pessimism also had lower levels of initial functioning or were more disabled at discharge. Pessimism did not impact recovery in physical functioning as measured by the slope of physical functioning. With regards to depression, individuals

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with higher levels of pessimism also had higher initial levels of depression. Pessimism

did not impact the recovery from depression as measured by the freely estimated slope of

depression.

The Second Contribution

The second contribution refers to the antecedents of optimism and pessimism. I t

should be noted that optimism and pessimism are two distinct concepts and not opposite

sides of the same concept The antecedents of interest are based on social structural

disparities represented by sociodemographic variables (gender, ethnicity, age, income and

education). This will expand the knowledge base of social influences on optimism and

pessimism. In their prior research, Scheier and Carver (1985) only refer to social

processes that impact one's expectations for success and failure which, in turn, contribute to the individual’s level of optimism and pessimism. The two general hypotheses testing the relationship between social structural disparities and optimism and pessimism are as follows:

These two hypotheses address the question, do social structures impact one’s level of optimism and pessimism? The model of behavioral self-regulation as described by

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Scheier and Carver (1985) focuses on the individual, but does not take into account the macro level social processes associated with the disparities in social structures that influence individuals. (It should be mentioned that disparities in social structures are typically represented by sociodemographics.) Specifically, adding the social structural component to the behavioral self-regulation model allows for the testing of the impact of disparities social structures on determining success or failure in facing challenges.

Conflict theory, based on the works of Marx, offers a foundation for looking at the disparities that exists within classes or social structures and how these disparities impact one’s level of optimism and pessimism. At the most basic levels, social classes can be broken down into privileged and disadvantaged groups based on who controls the power and resources (Dahrendorf, 1959, Ritzer, 1988, Skaff, 1999).

Dannefer’s (2003) work on cummulative advavntage/disadvantage (CAD) provides further support on how disparities in social structures are perpetuated and maintained over a lifetime. CAD in its simplest form refers to how “success breeds success” (Huber, 1998) and “the rich get richer, the poor get poorer” (Entwistle,

Alexander, & Olson, 2001). Dannefer (2003) argued that CAD is a systemic tendency that is not based on an individual’s position of origin in a structural group, but advantage occurs based on the interaction of a complex of forces within a specific structural group.

Additionally, CAD is viewed as component of populations or cohorts, not of the individual. This is best exemplified by how some individuals are in a better position to move “up the ladder” in an organization, yet this opportunity is not equally available for all employees. Conversely, individuals who do not receive a timely promotion or who are shifted to a lower track at school are at a distinct disadvantage for advancement

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(Dannefer, 2003).

Symbolic interactionism can also be used to explain how an individual internalizes the beliefs, views, and perspectives of a specific social structural group.

Rosenberg and Pearlin (1978) describe this process as reflected appraisals which refer to

Cooley’s (1902) and Mead’s work (1934) on how people see themselves as how they

believe others see them. Specifically, Cooley’s concept of the looking-glass self addresses how we imagine others see us; how we imagine their judgment of us; and how we develop self-feeling based on how we believe other’s judge us. Mead develops the concept of the self even further through role-taking.

The first step in developing one’s self-image is the termed play. In this phase an individual as an infant assumes the perspective of a small number of individuals such as one or two significant others (Turner, 1991). As the individual matures, they move to the

next phase known as the game, which refers to an individual’s ability to assume multiple

self images presented to them a group participating in a coordinated activity. The final

phase occurs when an individual is able to take on the role of the “generalized other”, which refers to the ability of an individual to assume the general beliefs, values, and norms of a community (Turner, 1991). With regards to social class, the generalized other reflects one’s ability to take on the on the overall perspectives of a specific social class.

In interpreting how the significant individuals in one’s life judge him/her by his/her social class, one becomes aware of how others see him/her (Rosenberg and

Pearlin, 1978). Rosenberg and Pearlin state that how individuals see themselves from others’ status based viewpoints is how social status will impact one’s self-esteem. For example, if an individual sees himself/herself as a member of a disadvantaged class

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because other’s see him/her that way and being a member of a disadvantaged social

structural group has negative connotations, then this will impact his/ her self esteem.

When applying this approach to social structures and optimism/pessimism, if individuals

see their successes and failures tied to social structures because valued others’ opinions

see them in those terms, then the successes and failures tied to social status will impact

one’s level of optimism/pessimism.

With this in mind, individuals who are in a position of power or who have more resources (privileged class) are also more likely to have successful results when dealing with difficult circumstances as compared to individuals who are not in a position of power or who have limited resources (disadvantaged class). In other words, individuals in the privileged class have a distinct advantage because of their ability to control and mobilize the resources to handle challenges.

Scheier and Carver (1985) argued that individuals who become more successful at handling challenges are also more optimistic about their ability to do so. On the other hand, pessimism develops when individuals are unsuccessful at handling difficulties.

Disparities with access to power and resources may give a member of a privileged status class a distinct advantage in successfully handling problematic situations; continued success, in turn contributes to the development of optimism within that individual. For example, higher levels of education and income can contribute to one’s ability to accumulate the necessary resources needed to dealing with challenges. Conversely, a member of a disadvantaged status class lacks the power and resources, therefore they are already at a burden when attempting to handle a difficult situation successfully.

Continued failures may contribute to the development of pessimism in individuals.

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Based on this perspective, social structures are antecedent to optimism and pessimism.

Pearlin’s (1989) work offers the theoretical underpinnings for understanding the mechanisms that place individuals from lower structural positions at a disadvantage to individuals from higher structural positions. Specifically, Pearlin focused on how structured arrangements, people’s lives, and the repetition of experiences from these structures impact one’s well-being. Understanding how these mechanisms contribute to the disadvantages in social structures may help explain how pessimism develops in individuals.

Women are typically recognized as being members of a disadvantaged status group, therefore they would be more likely to have higher levels of pessimism and lower levels of optimism. Specific mechanisms that place women at a disadvantage include: how gender can affect stressors; how gender modifies the impact of a stressor on an outcome; how personal and social resources are impacted by gender; how stress outcomes are impacted by gender (Pearlin, 1985). From a sociological perspective, gender impacts stress in different ways. First, from a functional theoretical perspective, men and women have different social roles (e.g., Aneshensel & Pearlin, 1987). Second, from a conflict theoretical perspective, women are often in less valued social roles (e.g., Barnett &

Baruch, 1987). Third, from a symbolic interactionist perspective, gender interpretation differences in the same roles impact stress (Aneshensel & Pearlin, 1987). Fourth, from a life course perspective, gender roles may be impacted differently based on different stages in the life course (e.g., Moen, 1997).

In the current study (see Figure 7.14) being a female does not directly impact optimism or pessimism. It also does not impact optimism indirectly. However, being a

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female indirectly impacts level of pessimism through education, through income, and income through education. In other words, females have lower education and those individuals with lower levels of education have higher levels of pessimism. Females also have lower levels of income and those individuals with lower income levels have higher

levels of pessimism. Additionally, since females have lower education levels, results

show that individuals with low education levels have lower income levels, and

individuals with lower income levels have higher levels of pessimism.

African Americans are typically considered to be members of a disadvantaged

group and therefore should have lower levels of optimism and higher levels of pessimism.

Specific mechanisms that place African Americans at a disadvantage focus on how

whites as members of the privileged group work to maintain a system that keeps African

Americans at a disadvantage. McAdoo (1986) studied the mundane extreme

environments imposed by whites forcing African Americans to live under “mundane”

daily pressures through rejection of identity, value and economic opportunities. Stoller

and Gibson (2000) state that the hierarchies used to create disadvantage for African

Americans can also create privileges for whites. Meyers (2003) points out that prejudice

is manifested by structural measures and not individual measures, subjective assessments

and not objective assessments, and daily discrimination events or hassles and not major

life events that impact everyone.

In the current study, disparities in ethnicity directly impact optimism and

indirectly impact pessimism. Interestingly, whites have lower levels of optimism.

African Americans may have faced and successfully handled more challenges over a

lifetime than whites. This is in direct opposition to the hypotheses that the

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disadvantageed group should have higher levels of optimism. The speculated higher success rates in African Americans may contribute to their higher levels of optimism.

Additionally, this relationship may be based on a survivor effect. African Americans typically have a shorter life expectancy than whites, the remaining African Americans who have lived longer may be hardier and have successfully faced life and health challenges. With regard to pessimism, being white indirectly impacts level of pessimism through education, through income, and income through education. Whites have higher levels of education and those individuals with higher levels of education lower levels of pessimism. Whites also have higher levels of income and those individuals with higher income levels have lower of pessimism. Additionally, since whites have higher education levels, results show that individuals with high education levels have higher income levels, and individuals with higher income levels have lower levels of pessimism. Conversely,

African Americans have lower education levels, have lower income levels, and individuals with lower income levels have higher levels of pessimism.

Inequality between age cohorts seems to focus around issues of power and resources, developmental basis, economics, and ageism. Henretta (1988) states that age is a prerequisite for many social structural arrangements that leads to competition for power and resources with the older elderly being at greater risk because of illness and frailty. (With regards to optimism and pessimism, age probably will not have an impact since optimism and pessimism should not change over time. However, optimism and pessimism may have been affected by age if a specific age cohort (cohort affect) may have faced some particular hardships, such as the Depression or war, during the time optimism and pessimism were developing.) From a developmental perspective, Kahana

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and Kahana’s (1996) work looks at how major developmental stressors including illness,

losses, and P-E fit can place an older elder as compared to a younger elder at a

disadvantage to aging successfully. From an economic point as potential employment

opportunities declines, Easterlin (1980) and Henretta (1988) point out that older adults

are forced into early retirement to make room for younger workers. Additionally,

Neugarten (1996) addresses the issue of ageism. Specifically, Neugarten points out that

the old are at risk to becoming victims in the fight for age rights. Finally, Neugarten

described how inequality can occur between young-old cohorts and the old-old cohorts

because the old-old are more likely to face prejudice based on stereotypes of the old-old

elderly being feeble, frail, sickly, poor and desolate.

While there are disparities in age cohorts, these disparities do not impact one’s level of pessimism and optimism in the current study. It should be noted that the individuals in the sample ranged in age from 70 to 100 and this age range may not have been large enough to identify disparities across age. Perhaps, if the sample had included

individuals as young as 20, then the disparities in age may have impacted optimism and

pessimism, largely because these age cohorts have experienced different life events (e.g.,

the Depression, World War II) that may have impacted the development of optimism and

pessimism.

Many mechanisms (such as, CAD) contribute to the inequalities found in social

structures. These mechanisms may contribute not only to inequalities in social structures,

but also to the development of pessimism for the disadvantaged and optimism for the

advantaged. While the relationship between disparities in SES and optimism and

pessimism has never been established, Houses’ (1981) work shows that SES and

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personality are linked through social comparison, reflected appraisals, self-perception theory, and psychological centrality. Lynch, Kaplan, & Salonen’s work (1997) linked parents SES level with adult children’s SES level and risky health behavior (e.g., drunken bouts, highest pack-years exposure to cigarettes, and poorer diets). With regard to optimism and pessimism, if children’s SES are linked to their parent’s SES, then SES is antecedent in time to optimism and pessimism. While no links from SES to optimism and pessimism have been studied, SES provides resources (e.g., through finances) to help overcome difficulties. Overcoming difficulties is a foundation for the development of optimism. With this in mind, it stands to reason that individuals who lack resources because of existing in a lower SES level will have lower levels of optimism and higher levels of pessimism, because lacking the proper resources may make it more difficult to be successful in handling challenges.

Education and income both predict one’s level of pessimism, but not optimism.

Income directly impacts pessimism, such that individuals with higher income levels have lower levels of pessimism. Education impacts pessimism directly and also indirectly through income. Individuals who have higher levels of education have lower levels of pessimism. Education also impacts pessimism indirectly, because individuals with higher levels of education have higher levels of income, and those individuals with higher levels of income have lower levels of pessimism.

In summary testing H3, optimism was directly predicted by being African

American. This may seem counterintuitive, but optimism develops through having success in handling challenges. African Americans may have successfully faced more challenges than whites, or older African Americans may have survived through adversity

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that may have compromised the lives of those younger African American who have died, therefore building their level of optimism. No other structural disparities predicted optimism.

In summary testing H4, pessimism was directly predicted by education and income. Lower levels of education and income are associated with higher levels of depression. Pessimism was indirectly predicted by being an African American or a female through education, income, and education through income. Individuals who are

African American or female have lower education levels and lower income levels, and individuals with lower levels of education and income have higher levels of pessimism.

African Americans and females also have lower education levels that cause lower income levels, which in turn cause higher levels of pessimism. Disparities in age did not impact pessimism.

Additionally, because this investigation is testing the relationship of optimism and pessimism as well as its antecedents and consequences, it can also test whether or not optimism and pessimism mediate the relationship between social disparities and psychological well-being and the relationship between social disparities and physical health. Previous literature has shown that social disparities or inequalities impact psychological well-being (e.g., George, 1996; La Gory & Fitzpatrick, 1992) and physical health (e.g., Clark and Maddox, 1992; George, 1996). The two general hypotheses for the mediating arrows are as follows:

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Sociologists often take the macro approach of looking at how social structures shape human lives (Settersten, 1999), practically relegating the person to the role of a passive participant being ruled by external structures. This sociological view, suggested by Settersten (1999), overlooks the roles of personality traits and characteristics, desires, aspirations, motivations, and expectations. Settersten refers to this model as structure without agency (as italicized by Settersten). From this perspective social structures would be the only contributor to psychological well-being and physical health.

On the other hand, the psychological approach, also suggested by Settersten

(1999), usually ignores the powerful social and historical forces that can impact one’s life

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by enhancing or constraining development. Settersten refers to this model as agency

without structure (as italicized by Settersten). From this perspective only one’s level of

personal control represented by optimism and pessimism impacts one’s psychological

well-being and physical health.

Settersten offers a third model of agency within structure (as italicized by

Settersten) as a means of bringing the psychological and sociological perspectives into a

single model. This model returns to his original argument for the bridging of social structures and human agency. Settersten (1999) argues that one must take into account aspects of the agency without structure and structure without agency models in order to understand the lives of individuals. Simply stated, the agency within structure model views people as being proactive in creating or constructing their own lives, yet these individuals remain interactive with their environments. When trying to understand how structures may influence human agency, Settersten states that one should be aware of both the constraining and enabling effects that structures have on individual lives. This perspective, not only explains how an individual can contribute to his/her own life through his/her level of optimism and pessimism, but also how social structures can influence an individual’s level of optimism and pessimism. This approach can also be used to explain how individuals who are proactive and successful in creating their own lives can develop optimism even when dealing with the adversity associated with existing in a lower class structure. While it may seem possible that optimism and pessimism may impact income and education, Salonen’s (1997) work suggest that one’s SES is linked to one’s parents SES, therefore serving as an antecedent to optimism and pessimism.

Essentially parental SES has such a strong impact on an individuals SES, therefore the

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most plausible causal ordering is that one’s SES affects their optimism and pessimism.

With regard to testing if optimism and pessimism serves as the agent bridging the

gap between the influences of structural disparities on one’s life as expressed through

physical functioning and depression (the agency within structure model), Figure 7.25

provides us with the answers. The first step is to test if any structural disparities

influence well being (depression) and physical health (physical functioning). Individuals,

who are older, female, poorer have lower initial levels of physical functioning. Structural

disparities do not directly impact physical functioning recovery over a year. With regard

to depression, structural disparities do not impact the initial level of depression, however

being female is a hindrance to recovery from depression. This section shows that disparities in the social structures in one’s life impact the agent. However, what is important in understanding the agency within structure model is how the agent uses

mechanisms such as optimism and pessimism to control one’s life.

The second step is to identify if optimism and pessimism impact one’s life (as

expressed by physical functioning and depression). Optimism impacts initial levels of

depression and the recovery from depression over the year. Optimists have lower initial

levels of depression and have less recovery from depression. As stated previously, this

may seem counterintuitive, however optimists have such low initial levels of depression

that there is no “room” to recover. Whereas, individuals with low levels of optimism

have higher levels of depression, and therefore more room to recover from depression.

With regards to pessimism, individuals with high levels of pessimism have higher

initial levels of depression and lower levels of physical functioning. In taking control of

one’s life, individuals with low levels of pessimism have lower initial levels of

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depression and higher initial levels of physical functioning. This section shows that the agent can take control of their life.

Furthermore, testing if optimism and pessimism mediate the influence of social disparities on physical functioning and depression, also allows for the detection of control over these structural disparities on one’s life. Optimism and pessimism mediated three paths from structural disparities to depression and physical functioning.

In testing H5, among the privileged groups, whites were the only group that were associated with optimism, but whites actually had lower levels of optimism as compared to African Americans. This finding goes against the hypotheses, but remember that optimism develops when individuals are successful in handling their challenges. A possible explanation is that African Americans, as compared to whites, may have experienced more challenges across a lifetime and had more success in handling challenges across a lifetime than whites. Another possible explanation is that older

African Americans may have higher levels of optimism which enable them to survive longer than younger African Americans who may have already died The relationship between being African American and the initial levels of depression as well as recovery in depression over the year was mediated by optimism. African Americans have higher levels of optimism, and individuals with higher levels of optimism have lower initial level of depression. This may in large part be due to a methods artifact. Optimism did not mediate the relationship between any other privileged group and depression and physical functioning.

In testing H6, among the privileged groups, the better educated and those with higher income levels were associated with lower levels of pessimism. Pessimism was

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associated with the initial levels of depression and physical functioning. Therefore, the following four relationships were tested using pessimism as a mediator:1) education to initial levels of depression; 2) education to initial levels of physical functioning;

3)income to initial levels of depression; 4) income to initial levels of physical functioning. Pessimism only mediated the relationship between income and initial levels of depression. In other words, individuals with higher levels of income had lower levels of pessimism, individuals with lower levels of pessimism also had lower initial of depression. Pessimism did not mediate the relationship between any of the other privileged groups and depression and physical functioning.

Studying the mediation models, shows not only the influence that the agent through optimism and pessimism controls one’s life, as expressed by depression and physical functioning, but also shows how the influence of specific structural disparities on depression are controlled by the agent. Overall, looking at the big picture, agents are influenced by the structural disparities, but they also exhibit control over their life.

The Third Contribution

The proposed study's third contribution to the current literature focuses on identifying the dimensional nature of optimism/pessimism as measured by the Life

Orientation Test (LOT) (Scheier & Carver, 1985). Traditionally this measure has been treated as having a single dimension. Recent literature, however, has shown a bidimensional nature to the scale. (e.g. Chang, Dzurilla, Maydeu-Olivares, 1994). This study is the first to extensively test the dimensionality of the LOT by exploratory factor

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analysis, confirmatory factor analysis, the strength of the correlations between the two dimensions, and a test for a distinct pattern of correlations between the two dimensions and a set of external variables.

The factor structure of an 8-item LOT appears to be bidimensional. Factor analysis of the eight Life Orientation Test (measured at admit) items yielded a two-factor solution. Based on their content, the decision was made to name the two LOT factors optimism and pessimism. Both the optimism and pessimism factors consisted of four items. All factors had items with primary factor loadings of at least .40. The items factored cleanly without cross loading.

Based on the EFA results, the life orientation test items were submitted to a confirmatory factor analysis. Both the CFI (.98) and the TLI (.98) exceed the .90 value considered to represent a model with excellent fit. Similarly, the RMSEA (.03) falls below the range of .05—i.e., under the generally designated level for good fit. The correlation between the two factors (corrected for measurement error) was small (-.21).

This correlation was not large enough to suggest that the LOT measures should be treated as unidimensional.

Testing for a distinct pattern of correlations between the two dimensions and a set of external variates was conducted using Figure 7.25. Specifically, whites had lower levels of optimism, but being white did not impact level of pessimism. The better educated and those with higher income levels had lower levels of pessimism, yet education and income level had no impact on optimism. Pessimism did not impact recovery from depression, but it was related to initial levels of depression, however the standardized beta weights for optimism and pessimism to initial levels of depression were

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different from each other. Higher levels of pessimism were also associated with, more comorbities, and lower initial levels of functioning, while optimism was not associated with comorbities or lower initial levels of functioning.

The findings of the current study support treating optimism and pessimism as separate (distinct) dimensions of personality (i.e., support the construct validity of these two hypothesized dimensions of personality). The results of our confirmatory factor analysis indicate that these measures of personality form two separate constructs with only modest correlations (-.21) between the factors. Second, the results of the final model

(Figure 7.25) show that optimism and pessimism have different predictors as well as have different consequences. In other words, optimism & pessimism behave as if they are not just opposite ends of a single underlying dimension of personality.

Is optimism or pessimism a trait or a state?

Once the factor structure of the LOT was identified, it was also necessary to determine whether or not optimism and pessimism should be treated as a trait or a state.

This was an important step needed to identify the logical causal placement of optimism and pessimism into the final model. If optimism and pessimism are trait-like than causally they should serve as predictors of physical functioning and depression. If optimism and pessimism are state-like than the proper causal ordering, or at the least correlational relationships, with physical functioning and depression must be determined.

These relationships needed to be identified before a model could be tested.

A look at the literatrute shows that Scheier and Carver (1985, 1993) believe that

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dispositional optimism is a global personality characteristic that reflects general

expectancies of outcomes. While expectancies can be specific in nature (e.g., “Will I be

able to get out of bed today?”) expectancies can also be very general (e.g., “Do usually

good things happen to me?”). Both specific expectancies and state like characteristics

vary greatly depending on the circumstances, are often hard to identify, and can change

over time (Scheier et al., 1989). On the other hand, both general expectancies and trait

like characteristics are more general in scope, more stable and don’t vary depending on

the circumstances, and are stable over time (Scheier et al., 1989). Scheier et al. (1989)

believe that dispositional optimism is trait-like and refers to expectations that good

outcomes will generally occur across many challenges and circumstances.

In general the literature supports treating optimism as a personality trait. Stability

has been shown in test-retest reliabilities for 1 month (Billingsley, Waehler, & Hardin,

1993; Scheier & Carver, 1985) and over three years by Karen Matthews (Scheier &

Carver, 1993) and Robinson-Whelen et al. (1997). Additional support can be found in

the Plomin et al’s (1992) work on a behavioral genetic study of optimism and pessimism

on fraternal and identical twins raised together and apart. Plomin et al. (1992) found that

correlations for hereditability of optimism and pessimism were within range for

correlations testing genetic influence on personality. Shifren and Hooker (1995) attempted to study state optimism (questions focused on level of optimism for that specific day) and found that state optimism varies on a daily basis. However, dispositional optimism (questions focusing on general optimism) was not measured on a daily basis.

The current study supports the use of optimism and pessimism as a stable trait-

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like measure. Two attempts were made to fit a bivariate autoregressive model of

optimism and pessimism (see Figures 6.1 & 6.2). Both models fit the data poorly. The

first model tested the autoregressive coefficients for optimism and pessimism as well as

the intrawave correlations between the disturbance terms of optimism and pessimism for

the same time wave. The second model added autocorrelations of measurement errors

from one wave to the next wave. This was an attempt to stabilize the model and produce

a better fitting model. In the first model, autoregressive coefficients across waves were

extremely high for optimism (.82-.95) and extremely high on pessimism (.75-.96).

Intrawave correlation between the disturbance terms of optimism and pessimism were so

unstable that these ranged from (-.08 to –1.61). These large coefficients suggest that

optimism and pessimism were so highly correlated with each other that they were not

different form one another over time. Additionally, the wildly discrepant intrawave

correlations indicate that the large autoregressive weights did not allow for variance in the

model and needed to be adjusted in the autocorrelations of the disturbance terms. The

large autoregressive coefficients that cause instability in fitting the model are indicative of

measures (such as optimism and pessimism) that are stable over time and trait-like in

nature. The second model (see Figure 6.2) had similar results to the first model (i.e, large

autoregressive coefficients and discrepant intrawave correlations between the disturbance terms of optimism and pessimism.

The Fourth Contribution

The fourth contribution takes advantage of the longitudinal nature of the proposed study to provide an empirical test of the causal ordering between psychological well-

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being (as measured by depression) and physical health (as measured by physical functioning). Traditionally, physical health has predicted psychological well-being

(Bradburn, 1969; Lawton, 1983; Lawton, Moss, Kleban, Glicksman, & Rovine, 1991), but “longitudinal” tests of this relationship have been generally weak. Therefore, to empirically test the predictive direction of the true relationship between these variables it is imperative to study their relationship across time. This study provided the opportunity to accomplish this.

Two attempts were made to identify the proper causal ordering of physical health and psychological well-being. Both crosslagged (from one time wave to the next time wave) and contemporaneous models (within the same time wave) were tested. This was accomplished by looking at either the crosslagged or contemporaneous effects of psychological well-being as a consequence of physical health while simultaneously testing physical health as a consequence of psychological well-being. The results at best, weakly (standardized betas <.20) and inconsistently supported physical functioning predicting depression (see Figures 6.7 and 6.8) . For both models depression was predicted at day 30 and day 360, but not at day 90 and day 180. These models were not developed with a set of predictors, because causality between physical functioning and depression was not established.

Additionally, latent trajectory analyses can be used to test the longitudinal nature of the relationships between physical functioning and depression. Specifically, latent growth trajectory models were developed and tested on physical functioning and depression. Latent trajectory models (LTM) test the “developmental” processes of the longitudinal data. In other word, LTMs can be used to track the trajectory or the changes

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of a variable over time. While both linear and quadratic models were tested, the best

fitting models for both depression and physical functioning were nonlinear based on their

freely estimated slopes. In interpreting the slopes, sometimes it’s easier to think about the

change in slopes in terms of recovery. For depression (see Figure 7.5) and physical

functioning (see Figure 7.10) the most recovery occurred within the first thirty days of

discharge, both reached peak levels of recover at 6 months post discharge before a slight

decline and leveling off at 12 month.

The LTMs for depression and physical functioning were combined into a bivariate

latent trajectory model (see Figure 7.11). When testing the bivariate LTM the latent

constructs representing the intercepts and slopes of depression and physical functioning

are correlated. Higher initial levels of physical functioning were associated with less

recovery from physical functioning (a lesser need for recovery for higher functioning

individuals), lower initial levels of depression, and less recovery from depression,

because higher functioning individuals have lower initial levels of depression in which to recover. Higher initial levels of depression were associated with more recovery from depression, and have more recovery in physical functioning, because individuals with higher initial levels of depression have lower initial levels of physical functioning, therefore, these individuals have more “room” to recover. Finally, more recovery in physical functioning is associated with more recovery in depression.

The bivariate LTM (see Figure 7.18) was developed into a model with a set of predictors that represent social structural disparities, optimism and pessimism, and clinical measures. Younger individuals, males, individuals with more income, less pessimistic individuals, and those with less comorbidities had higher initial levels of

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physical functioning. Individuals with less comorbidities had more recovery in physical

functioning. Individuals with low levels of optimism and high levels of pessimism had

higher initial levels of depression. Finally, females, individuals with higher levels of optimism, and more comorbidities had less recovery from depression.

The bivariate LTM with predictors (see, Figure 7.18) was developed into an

autoregressive latent trajectory (ALT) hybrid model. The ALT model allows for the

simultaneous testing of the autoregressive and latent trajectory models of longitudinal

analyses. Essentially, controlling the effect of the autoregressive model on the LTM as

well as controlling for the effect of the LTM on the autoregressive model. This was

accomplished by adding auto regressive paths between the indicators of physical

functioning as well as between the indicators of depression. Additionally, crosslagged

paths were added from physical functioning to depression as well as from depression to

physical functioning. Figure 7.23 shows how these paths were added to a bivariate LTM.

Figure 7.25 shows the results of the specification search testing the ALT hybrid

model with predictors. Three relatively weak autoregressive paths were added: from

physical functioning at day 30 to physical functioning at day 90; from depression at

discharge to depression at day 30; from depression at day 30 to depression at day 90.

Only one weak crosslagged path remained, higher levels of depression at day 90 were

associated with less physical functioning at day 180. One should be cautious in

interpreting causality because this pattern was weak and inconsistent, only occurring at

one crosslagged time point. Additionally, the significant albeit weak autoregressive path

from the first time point (discharge) of depression to depression at Day 30 may introduce

bias in the ALT model (i.e.,a significant autoregressive path form the first time point to

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the next time point, according to Curran and Bollen (2001) may cause bias in other parts

of the model). Testing for this bias involves a very complex set of analyses beyond the realm of this dissertation. All other regression paths from the predictors to the intercepts and slopes of depression and physical functioning (see Figure 7.25) were nearly identical to those found in Figure 7.18.

Implications of Findings

Overall, from a gerontological perspective, Settersten’s argument for studying agency within structure models to understand the lives of individuals, applies to research on recovery from hospitalization. The agency without structure model (the psychological perspective) and the structure without agency model (the sociological perspective) only take into account small parts of the model of recovery. Neither model fully explains the processes involved in recovery from hospitalization. Clearly, the agency within structure model which views people as being proactive in creating or constructing their own lives, yet remaining interactive with their environments plays a role in recovery. Individuals’ personality, well-being, and physical health are influenced by social structural disparities. However, individuals through their level of optimism and pessimism can impact their initial levels of physical functioning and depression and recovery in depression. Gerontologists and other social scientists should be aware of both agency and structural influences in understanding recovery from hospitalization.

Another important finding is identifying the factor structure of optimism and pessimism. These measures are not just opposite sides of the same coin, but are two

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distinct constructs. Optimism and pessimism have different antecedents and consequences. Optimism is mostly influenced by ethnicity, while pessimism is mostly influenced by education and income. Pessimism’s greatest impact is on initial levels of depression and physical functioning, while optimism not only impacts initial levels of depression. Social scientist need to become more aware that optimism and pessimism are distinct and can be used as distinct constructs in research to gain a better understanding of psychological well-being and physical health.

With this in mind, clinical interventions to reduce depression among patients recovering from hospitalization should focus on persons who score lower on optimism and higher on pessimism. Additionally, clinical interventions to increase physical functioning should focus on those who score higher on pessimism. Other clinical interventions could address issues of social structural disparities and clinical measures.

For instance, clinical interventions to increase initial levels of physical functioning could be directed to the older elderly, females, those with lower incomes, and those with more comorbidities. Regarding better recovery in physical functioning, clinical interventions could focus on those with more comorbidities. With regards to recovery from depression, clinical interventions should focus on females and those with more comorbidities.

Simply stated, a health care provider’s awareness to these preexisting risk factors to depression and decline in physical functioning will enable the health care provider to take appropriate actions to ensure the best quality of care for their patients who are at risk.

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Limitations

As with any study, there are limitations associated with it. This also is true for this dissertation. The first limitation is that the current study was completed in 1997, results may not be generalizable to today’s standards in hospitalization. While there is no reason to believe that current trends in hospitalization would impact recovery from hospitalization, nonetheless shorter hospital stays, medical expenses, cost of medicine, cutbacks in insurance coverage, and cuts in hospital staffing associated with current hospitalizations may have some impact on patient’s initial levels as well as recovery in depression and physical functioning. The second limitation is that the current study was conducted at a large academic hospital and results may not be generalizable to nonacademic hospitals, smaller hospitals or community hospitals. The third limitation is that this study was conducted on elders hospitalized for an acute illness or an acute episode of a chronic illness, results may not be generalizable to elders hospitalized for other conditions.

With regards to data, there are several additional limitations. First, as with any longitudinal study, attrition is a problem. In some cases data had to be removed because data were collected outside of the window for a follow-up time period. Ideally, there could have been fewer dropouts and data could have been collected in a timelier manner in the study. Additionally, missing data may have impacted the power of the study.

Fortunately, AMOS allows analysis of incomplete data using Full Information Maximum

Likelihood (FIML) estimation. Second, using the shortened version of the CESD to

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assess depression may have reduced variability and information on depression. The full

CESD would have been a more accurate tool for assessing depression. Third, data collection in equal time periods (i.e., every month or 2 months, instead of uneven increments of 1,3,6, and 12 month intervals) would have introduced less bias into the autoregressive models. Fourth, regarding the ALT hybrid model, bias may have been introduced into the model, because the autoregressive path between depression at discharge to depression at day 30 was non zero. This may have resulted in the weak crosslagged path from depression at day 90 to physical functioning at day 180. Advance analyses beyond the realm of this dissertation are needed to assess for bias. Nonetheless, the crosslagged path is relatively weak, and does not substantively contribute to the model. With these caveats in mind, let’s look to the future implication of the study.

Implications for Future Research

This model fit the data for patients recovering from a hospitalization for an acute illness or an acute episode of a chronic illness. The final model should be tested and developed on other populations, such as elderly individuals admitted for a chronic disease

(such as cancer, arthritis, diabetes or heart disease) or for those hospitalized for surgery including joint replacement. Additionally, this model could be tested in nonacademic hospitals, smaller hospitals, or community hospitals for purposes of identifying if this model is generalizable across hospital settings. Future studies can also be directed toward tracking recovery for a longer period than just one year, expanding to 2 to 5 year follow-ups.

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Studying the impact of other measures in the model can expand future research on the final model. Basically, four types of variables can be added to the model: social structural disparities, personality or trait-like measures, psychological well-being, and physical health. Social structural disparities can be expanded to take into account other ethnic groups (such as, Hispanics and Asian American), urban vs. rural locations, former occupations, living arrangements, type of insurance, and access to healthcare. Optimism and pessimism were the trait-like measures that reflected the agent or the individual.

Other personality traits, such as neuroticism and extroversion could be tested and added to the model. These traits may act as a mediator, like optimism and pessimism, between social structural disparities and physical health as well as psychological well-being.

Other psychological well-being measures, such as cognitive life satisfaction, quality of life, zest, and anxiety, could be tested in the model. Additionally, the CESD sub-scales

(depressive affect, positive affect, and somatic issues) could also be tested. Other measures of physical health, such as subjective health and physical performance tests, could be tested in the model.

From an analytic standpoint, the length of hospital stay may be included as a covariate with the Charlson Comorbidity Index and the APACHE II Clinincal Severity scale, representing the severity of disease. However, length of stay should not be substituted for the impact of hospitalization on an individual, which can only be tested with a comparative non-hospitalized control group.

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Conclusions

Understanding how individuals recover from hospitalization is a process that is intricately connected to the agent, as well as to the structures found in society. While structural disparities not only impact the individual’s level of optimism and pessimism, they also impact one’s physical health and psychological well-being. However, structural disparities are only a part of the picture; an individual can also shape one’s life through optimism and pessimism, as indicated by optimism and pessimism’s impact on one’s physical health and psychological well-being. In fact, some of the social structural disparities that affect physical health and psychological are mediated by optimism and pessimism. In other words, one’s level of optimism and pessimism can diminish the impact of social disparities on physical health and psychological well-being. Researchers studying recovery in hospitalized elders need to be aware of the sociological (e.g., structural disparities) as well as the psychological (e.g., agent) influences that play such an integral part in the recovery process.

From a practical point of view, clinical interventions for post-hospitalization recovery can be targeted to individuals who have low levels of optimism and high levels pessimism. Additionally, clinical interventions can also be designed to focus on the older elderly, females, those with lower incomes, and those with more comorbidities, since these groups are at a disadvantage for recovery. From a health care providers perspective these same individuals are at a risk for decline instead of recovery. Health care providers, not only should be aware of the circumstances that hinder recovery, but be prepared to

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recommend additional services that can assist in recovery to the more disadvantaged.

Overall, the final model suggests that optimism and pessimism not only plays a

role in recovery in physical health and psychological well-being, but may serve to

decrease the impact of social structural disparities on these outcomes. Further testing of

this model over a longer recovery time, across different health conditions (e.,g, chronic

diseases), and surgical conditions needs to be examined. Modifications to the model

through testing additional or alternative measures of social disparities, traits

representative of agency, physical health, and psychological well-being may provide

additional information to gaining a better understanding of the recovery process.

Furthermore, this information can be used to develop clinical interventions to assist the

disadvantaged through the recovery process. As the aging population continues to increase in size, it is inevitable that hospitalizations due to age related illnesses will also increase, understanding the intricacies of the recovery process may help individuals to

regain and maintain their independence when faced with the challenges of recovering

from a hospitalization.

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Appendix A:

Sociodemographic Measures

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Sociodemographic Measures

Gender 0 MALE 1 FEMALE

Marital Status 0 SINGLE 1 MARRIED 2 SEPARATED/DIVORCED 3 WIDOWED

Race 1 BLACK 2 ASIAN 3 HISPANIC 4 WHITE 5 NATIVE AMERICAN 6 OTHER SPECIFY ______)

How many years of school did you complete?

0 0 - 8 YEARS 1 9 - 11 YEARS 2 12 YEARS (HIGH SCHOOL GRADUATE) 3 13 -15 YEARS (SOME COLLEGE OR TECHNICAL COLLEGE) 4 16+ YEARS (COLLEGE GRADUATE) 8 DK 9 REFUSED TO ANSWER

Total household income:

01 (A) 0 - 4,999 02 (B) $ 5,000 - 9,999 03 (C) $10,000 - 14,999 04 (D) $15,000 - 19,999 05 (E) $20,000 - 29,999 06 (F) $30,000 - 39,999 07 (G) $40,000 - 49,999 08 (H) $50,000 OR MORE 98 DK 99 REFUSED

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Appendix B:

Dispositional Characteristic Measures

299

Dispositional Characteristic Measures

LIFE ORIENTATION TEST

"Now I'd like to read you some statements about how people feel about what goes on in their lives. There are no right or wrong answers."

1. Please tell me if you strongly agree, agree, SA A N D SD DK REFUSED are neutral, disagree, or strongly disagree with the following: a. In uncertain times, I usually expect the best. 4 3 2 1 0 8 9 b. If something can go wrong for me, it will. 0 1 2 3 4 8 9 c. I always look on the bright side of things. 4 3 2 1 0 8 9 d. I'm always optimistic about my future. 4 3 2 1 0 8 9 e. I hardly ever expect things to go my way. 0 1 2 3 4 8 9 f. Things never work out the way I want them to. 0 1 2 3 4 8 9 g. I'm a believer in the idea that "every cloud has a silver lining". 4 3 2 1 0 8 9 h. I rarely count on good things happening to me. 0 1 2 3 4 8 9

* - Items A,C,D,G are indicators of Optimism.

** - Items B,E,F,H are indicators of Pessimism.

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Appendix C:

Psychological Well-Being Measures

301

Psychological Well-Being Measures

DEPRESSION (Short Version CES-D)

"We are interested in how you've been feeling during the past week. For each of the following, tell me if you felt that way much of the time during the past week."

Would you say yes or no? Much NO YES DK REFUSED of the time during the past week: a. I felt depressed. 0 1 8 9 b. I felt everything I did was an effort. 0 1 8 9 c. My sleep was restless.. 0 1 8 9 d. I was happy 0 1 8 9 e. I felt lonely. 0 1 8 9 f. People were unfriendly. 0 1 8 9 g. I enjoyed life. 0 1 8 9 h. I felt sad. 0 1 8 9 i. I felt that people disliked me. 0 1 8 9 j. I could not get "going". 0 1 8 9

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Appendix D:

Physical Health Measures

303

Physical Health Measures

ACTIVITIES OF DAILY LIVING (ADLS)

"Now I would like to ask about how you took care of yourself at this time. Each question is about some activity of daily living, things we all need to do as part of our daily lives."

1. At this time YES NO NA DK REFUSED a. Did you need help washing or 1 0 8 9 bathing yourself? b. Did you need help dressing and 1 0 8 9 undressing? c. Did you need help eating, Tube including cutting food? 1 0 fed 8 9 7 d. Did you need help getting in and out of the bed and a chair? 1 0 8 9 e. Did you need help cleaning cath & yourself for either bowel or 1 0 col. 8 9 bladder functions? 7

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INSTRUMENTAL ACTIVITIES OF DAILY LIVING (IADLS)

“I'd like you to continue thinking about how you take care of yourself at this time.

1. At this time, do you do ON the following on your own, OWN/ SOME UNABLE NA DK REFUSED with some help, or are you NO HELP unable to: HELP a. Use the telephone, including looking up and 0 1 2 8 0 dialing numbers, and answering the phone? b. Get to places out of walking distance by using 0 1 2 8 9 public transportation or driving your car? c. Shop for groceries or 0 1 2 8 9 clothes? d. Prepare, serve, and 0 1 2 8 9 provide meals for yourself? e. Do light housework, such as dusting or doing 0 1 2 8 9 dishes? f. Take pills or medicines no in the correct amounts and 0 1 2 med 8 9 at the correct times? s, NH 7 g. Handle your own money, including writing 0 1 2 8 9 checks and paying bills?

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