Effects of a Brief Character Strengths Intervention: a Comparison of Capitalization and Compensation Models Jerry V

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2013 Effects of a Brief Character Strengths Intervention: A Comparison of Capitalization and Compensation Models Jerry V. Walker III

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COLLEGE OF EDUCATION

EFFECTS OF A BRIEF CHARACTER STRENGTHS INTERVENTION:

A COMPARISON OF CAPITALIZATION AND COMPENSATION MODELS

JERRY V. WALKER, III

A Dissertation submitted to the Department of Educational Psychology and Learning Systems in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Degree Awarded: Summer Semester, 2013 Jerry V. Walker, III defended this dissertation on August 6, 2012. The members of the supervisory committee were:

Georgios Lampropoulos Professor Directing Dissertation

Thomas Joiner University Representative

Steven Pfeiffer Committee Member

James Sampson Committee Member

The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.

I dedicate this work to a man who lived by the tenets of positive psychology all the days I knew him—my grandfather, Ray C. Wilson. In any given morning, you could hear his gleeful singing voice bouncing throughout the house. Smiles were permanently etched in his well-worn face. And when cancer threatened to put an end to not only his positive demeanor but also his life, he fought back with astounding courage, and he won—four times. Time would not let him win a fifth. The message in this small piece of research echoes the lessons I learned from him: Live with a Zest for life Love everyone more than they deserve Have Courage even when the odds are stacked against you Persevere always And keep your Hope alive, no matter what.

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ACKNOWLEDGEMENTS

I would first and foremost like to acknowledge and express gratitude to my supervising professor, Dr. Georgios Lampropoulos, who honed my skills as a researcher through countless hours of individual instruction and mentorship. His dedication to my personal and professional development has made all the difference in my graduate education. I would like to thank Dr. Steve Pfeiffer for his support, guidance, and contribution to the development of my clinical skills and professional identity. He provided me with the knowledge and drive I needed to pursue the many opportunities that have led me to where I am today. I would like to extend to Dr. Jim Sampson a hearty thank-you for the CASVE cycle paper, which proved to me two things: I am capable of writing a 100+ page reflective paper, and I had no idea what I wanted to do with my degree. Through my drop-ins with Dr. Sampson and my practica at the Career Center, I am now blessed to feel 100% secure and enthusiastic in the direction my career is currently heading. I would like to thank Dr. Tom Joiner for guiding me toward this project (instead of the one I had originally planned). Without his input, wisdom, and foresight, this project may have never taken place, and that—to me—would have been a tragedy. I would like to thank Dr. Shane Lopez for all the materials and ongoing, unpublished research he sent to me for this project. It is my HOPE that I may someday return the favor. I sincerely thank Dr. Marty Seligman for his seminal and continuing work in the field of positive psychology; in short, he has made my research possible. I am also grateful for his willingness to take the time to speak briefly with an enthusiastic undergraduate student at the 2007 APA Convention in San Francisco. I would be remiss if I did not acknowledge and thank my family and friends, who shared the agonies and ecstasies of this project with me from start to finish. Your encouragement, love, and proofreading abilities were absolutely vital to the successful completion of this project, and I am grateful to have each and every one of you in my life. Finally, a HUGE THANK YOU to all of the organizations that participated in this study, especially all of my contact people within those organizations who hounded their fellow employees to complete the required surveys. I would also like to take this opportunity to apologize for the sheer length of the questionnaires that I asked you to complete.

TABLE OF CONTENTS

List of Tables ...... ix List of Figures ...... x Abstract ...... xi

CHAPTER ONE: INTRODUCTION ...... 1 Positive Psychology ...... 1 Character Strengths ...... 2 Character Strengths Research ...... 3 Capitalization vs. Compensation ...... 4 Positive Psychology Interventions in Organizational Settings ...... 5 Research Questions & Hypotheses ...... 7 Significance ...... 8

CHAPTER TWO: REVIEW OF THE LITERATURE ...... 9 Positive Psychology ...... 9 Theoretical Foundation and Core Constructs ...... 10 Research on Positive Psychology Interventions ...... 14 Character Strengths & Virtues ...... 20 Courage ...... 21 Humanity...... 22 Justice...... 24 Temperance ...... 25 Transcendence...... 26 Wisdom & Knowledge ...... 28 Signature Strengths ...... 30 Character Strengths Research ...... 31 Capitalization vs. Compensation ...... 39 Capitalization ...... 42 Compensation ...... 44 A Cautionary Note ...... 45 Positive Psychology Interventions in Organizational Settings ...... 46 The Role of Homework Assignments ...... 50 Summary, Research Questions, and Hypotheses ...... 52

CHAPTER THREE: METHODOLOGY ...... 55 Hypotheses ...... 55 Research Design ...... 56 Participants ...... 56 Population ...... 56 Sampling and Recruitment ...... 57 Participant Screening and Inclusion Criteria ...... 58 Characteristics of the Research Participants ...... 58

Power Analysis ...... 59 Variables ...... 60 Character Strengths ...... 61 Positive Variables ...... 62 Negative Variables ...... 63 Measures ...... 64 Values-In-Action (VIA) Survey of Character Strengths ...... 65 Positive and Negative Affect Schedule ...... 66 Behavioral Activation for Depression Scale ...... 67 Mental Health Continuum – Short Form ...... 68 Satisfaction with Life Scale ...... 69 Meaning in Life Questionnaire ...... 69 Rosenberg Self-Esteem Scale ...... 70 Center for Epidemiological Studies – Depression Scale ...... 70 Outcome Questionnaire – 45.2 ...... 71 Homework Rating Scale ...... 72 Ten-Item Personality Inventory ...... 72 Experimental Conditions ...... 73 Top Strengths ...... 74 Bottom Strengths ...... 75 Placebo ...... 76 Waitlist/Control...... 77 Procedure ...... 77 Procedures Prior to the Psychoeducational Intervention ...... 78 Procedures During the Psychoeducational Intervention ...... 79 Treatment Fidelity Check ...... 80 Procedures Following the Psychoeducational Intervention ...... 80 Delimitations ...... 81 Planned Data Analyses ...... 82

CHAPTER FOUR: FINDINGS ...... 83 Transformation of Dependent Variables ...... 83 Assumptions of ANOVA ...... 84 Normal Distribution of Dependent Variables ...... 84 Equality of Variances between Groups ...... 85 Independence of Observations ...... 85 Outliers ...... 87 Power Analysis ...... 88 Equality of Groups Prior to Intervention ...... 88 Demographics ...... 88 Pre-Treatment Outcome Measures ...... 89 Reliability of Measures in this Sample ...... 90 Treatment Fidelity ...... 90 Research Questions ...... 91 Research Question One ...... 91 Research Question Two ...... 92

Research Question Three ...... 93 Additional Analyses of the Research Questions ...... 93 ANCOVA with Age and Gender as Covariates ...... 94 ANCOVA with Big Five Personality Estimates as Covariates ...... 94 Alternate Data Analysis Using ANCOVA ...... 95 Alternate Data Analyses that Account for Homework Variables ...... 96 Removal of Homework Non-Completers ...... 96 Controlling for Homework Assignments ...... 96 OQ-45 Indices of Clinical Significance ...... 98 Additional Findings ...... 98 Compliance ...... 99 Differences between Conditions for Homework Rating Scale Items ...... 99 Impact of Compliance Variables on Outcome Measures ...... 100 Predictors of Compliance ...... 101 Relationships between Variable Sets ...... 101 Assumptions of Multiple Regression Analyses ...... 103 Assumption of Normality ...... 103 Linear Relationship between Predictor(s) and Criterion Variables ...105 Homoscedasticity ...... 105 Lack of Multicollinearity between Predictor Variables ...... 106 Sufficient Power to Carry Out Statistical Analyses ...... 106 Relationships between Demographic Data and Character Strengths ...... 107 Relationships between Demographic Data and Personality Estimates ...... 109 Relationships between Demographic Data and Outcome Variables ...... 111 Relationships between Character Strengths and Outcome Variables ...... 114 Relationships between Character Strengths and Personality Estimates ...... 119 Relationships between Personality Estimates and Outcome Variables ...... 121 Character Strengths Statistics ...... 123 Typical Top Strengths Profile of the Sample ...... 123 Typical Bottom Strengths Profile of the Sample ...... 123

CHAPTER FIVE: DISCUSSION ...... 125 Research Questions & Hypotheses ...... 125 Research Question One ...... 126 Research Question Two ...... 126 Research Question Three ...... 127 Additional Findings ...... 128 Compliance ...... 129 Demographic Analyses ...... 130 Relationships between Character Strengths and the “Big Five” ...... 131 Character Strengths Analyses ...... 132 Limitations ...... 133 Sampling ...... 133 Measures ...... 134 Treatment ...... 134 Data Collection ...... 136

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Data Analyses ...... 136 Implications ...... 137 Theory Development ...... 137 Practice ...... 138 Research ...... 140 Conclusion ...... 142

APPENDICES ...... 144 A. CHARACTER STRENGTHS & VIRTUES ...... 144 B. DEMOGRAPHIC QUESTIONNAIRE ...... 145 C. POSITIVE AND NEGATIVE AFFECT SCHEDULE ...... 146 D. BEHAVIORAL ACTIVATION FOR DEPRESSION SCALE ...... 147 E. MENTAL HEALTH CONTINUUM – SHORT FORM ...... 149 F. SATISFACTION WITH LIFE SCALE ...... 152 G. MEANING IN LIFE QUESTIONNAIRE ...... 153 H. ROSENBERG SELF-ESTEEM SCALE ...... 154 I. CENTER FOR EPIDEMIOLOGICAL STUDIES – DEPRESSION SCALE ...... 155 J. HOMEWORK RATING SCALE ...... 156 K. TEN-ITEM PERSONALITY INVENTORY ...... 158 L. TOP STRENGTHS PRESENTATION ...... 159 M. BOTTOM STRENGTHS PRESENTATION ...... 162 N. HEALTH & WELLNESS PRESENTATION ...... 165 O. TIMETABLE FOR PRESENTATIONS ...... 169 P. CHARACTER STRENGTHS ACTIVITIES HANDOUT ...... 170 Q. SAMPLE CUSTOMIZED TOP STRENGTHS ACTIVITIES HANDOUT ...... 174 R. SAMPLE CUSTOMIZED BOTTOM STRENGTHS ACTIVITIES HANDOUT ...... 175 S. HEALTH & WELLNESS ACTIVITIES HANDOUT ...... 176 T. POST-TREATMENT INSTRUCTIONS HANDOUT ...... 177 U. RECRUITMENT EMAIL TO EMPLOYERS ...... 178 V. ANNOUNCEMENT EMAIL TO EMPLOYEES ...... 179 W. CONSENT FORM ...... 180 X. THIRD PARTY RATING FORM ...... 182 Y. FOLLOW-UP EMAIL TO EMPLOYEES ...... 183 Z. FSU IRB HUMAN SUBJECTS APPROVAL MEMORANDUM ...... 184

REFERENCES ...... 214 BIOGRAPHICAL SKETCH ...... 226

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LIST OF TABLES

1 Demographic Data for Pre-treatment and Post-treatment Completers ...... 186

2 Pre-treatment and Post-treatment Means and Standard Deviations by Experimental Condition...... 187

3 Demographic Data for All Pre-treatment Completers ...... 189

4 Demonstrated Internal Consistency for Outcome Measures ...... 190

5 Third Party Rating Form Means and Standard Deviations by Experimental Condition .....191

6 Homework Rating Scale Means and Standard Deviations by Experimental Condition .....192

7 Means and Standard Deviations for All Pre-treatment Completers ...... 193

8 Means and Standard Deviations for All Character Strengths Data ...... 194

9 ANOVA for Gender and Character Strengths ...... 195

10 ANOVA for Income and Character Strengths ...... 196

11 Regression Analyses with Character Strengths Predicting Outcome Measures, Controlling for Age and Income ...... 197

12 Regression Analyses with Character Strengths Predicting Estimates of the "Big Five" Personality Traits ...... 205

13 Means and Standard Deviations for Participants’ Scores on the VIA Survey of Character Strengths ...... 208

LIST OF FIGURES

1 Flowchart of the Data Collection Procedure ...... 209

2 Pre-treatment and Post-treatment Means for the MHC-SF Emotional Well-being Subscale by Experimental Condition ...... 210

3 Pre-treatment and Post-treatment Means for the OQ-45.2 Symptom Distress Subscale by Experimental Condition ...... 211

4 Clinical Significance on the OQ-45.2 Total score by Experimental Condition ...... 212

5 Significant Relationships between Character Strengths and Outcome Measures ...... 213

ABSTRACT

The purpose of this study was to investigate the differential effects of the Capitalization vs. Compensation model applied to a brief, group-based intervention that focused on Character Strengths, as defined by Peterson and Seligman (2004). Traditional Character Strengths interventions in Positive Psychology apply a Capitalization model, in which individuals engage their top-ranked strengths of character, and this approach has amassed substantial empirical support. However, it is not known whether a Compensation model, in which individuals engage their bottom-ranked strengths, can offer similar benefits. One hundred and eighty-seven employees from eighteen small organizations were randomized at the group level to receive one of four psychoeducational interventions: Top Strengths, Bottom Strengths, Placebo (behavioral health), or a delayed-treatment Control. Participants completed the VIA Survey of Character Strengths and a pre-treatment battery of outcome measures that assessed both positive psychological variables, such as life satisfaction and psychological well-being, and negative life functioning variables, such as depression and negative affect. Post-treatment outcome measures and a compliance measure were completed approximately one month following the psychoeducational presentations. Results revealed few differences between experimental conditions for most measures; however, participants in the Bottom Strengths condition experienced a decrease in symptom distress and an increase in emotional well-being relative to those in the Placebo and Control conditions. Regression analyses revealed several interesting relationships between Character Strengths and outcome measures, with implications for applications in multiple fields. A discussion of methods to strengthen brief group-based interventions, as well as the possible future direction of Character Strengths research, concludes the paper.

CHAPTER ONE

INTRODUCTION The purpose of this study is to investigate the differential effects of the Capitalization vs. Compensation model in a short-term organization-based service delivery modality that focuses on Character Strengths as conceptualized by the field of positive psychology. By comparing the effects of strengths capitalization to strengths compensation, the practice of tailoring interventions to clients as a way to enhance positive emotionality and satisfaction with life may be explored and validated. In the ensuing sections, the field of positive psychology will be described, including a brief review of the relevant literature that supports the use of positive psychological interventions. The concept of Character Strengths will then be introduced, followed by a review of the empirical support that Character Strengths interventions have received over the years. Next, the Capitalization vs. Compensation model will be expounded upon and related to the current investigation. The general use of Character Strengths interventions with individuals in organizational settings will then be reviewed. Finally, specific research questions and hypotheses will be delineated, and the social significance of the current study will be explained.

Positive Psychology

Positive psychology (PP), a modern re-imagining of humanistic psychology, is a rapidly- growing field of psychotherapeutic intervention (Resnick, Warmoth, & Serlin, 2001). It may be conceptualized as a scientific field that investigates the nature and development of sustained happiness and all related constructs (Seligman, Steen, Park, & Peterson, 2005; Seligman, 2002). Its purpose is to assist individuals in their efforts to thrive by teaching them the skills and mood- enhancement techniques necessary to allow them to achieve pleasure, engagement, and meaning in life (Seligman, 2002; Seligman, & Csikszentmihalyi, 2000). From a psychotherapeutic standpoint, practitioners of positive psychology endeavor to cultivate in their clients positive emotions and positive character traits; from a broader perspective, they also seek to promote the positive, healthy functioning of organizations and institutions (Seligman & Csikszentmihalyi, 2000). Out of this tripartite focus—emotions, character, and institutions—have come a plethora of empirical investigations that have shed light on the various components that must necessarily

1 converge to enable lasting positive change. Based on these investigations, it appears that the development of positive character traits, specifically the traits of good character that are referred by positive psychologists as Character Strengths, are related most fundamentally to positive emotion, engagement, and satisfaction with—and quality of—life (Peterson, Ruch, Beermann, Park, & Seligman, 2007). Positive psychology has generally received strong empirical support; research on the effectiveness of positive psychological techniques has demonstrated that these interventions are effective not only in reducing the symptomatic distress of mental health problems, but also in improving quality of life, increasing positive emotions (such as happiness), enhancing resiliency, and improving overall psychological well-being (Mongrain & Anselmo-Matthews, 2012; Sin & Lyubomirsky, 2009). They also tend to be fairly enjoyable activities for clients (Seligman, Rashid, & Parks, 2006). Therefore, because positive psychology techniques have the potential to bestow real and desirable benefits upon clients who present with a variety of issues and concerns, they merit consideration for further development in different modes of intervention. It should be noted, however, that positive psychology does not intend to present itself as a new theoretical orientation. Instead, it should be conceptualized as a psychological perspective that differs remarkably from most other perspectives—which espouse the traditional medical model of psychotherapeutic intervention in adhering to a deficit-focused or illness approach—by focusing on the inherent strengths and potential of the individual (Maddux, 2008; Seligman & Csikszentmihalyi, 2000). By focusing explicitly on strengths, positive psychological interventions may be utilized as adjunctive techniques to be incorporated within traditional psychotherapeutic models, allowing for clients’ negative and positive symptomology to both be addressed (with the intention of reducing the former while/by increasing the latter; Rashid, 2009). It is for this reason that the PP Character Strengths intervention, in which individuals are led to discover their idiosyncratic strengths of character and build upon them by utilizing them more and in more novel ways, is an important component of positive psychology. Therefore, it is on the impact of strengths of character that the current investigation is based.

Character Strengths

Character Strengths are the favorable personality characteristics that support good and virtuous living in the individual, harmony in the community, and excellence in general society

(Park, Peterson, & Seligman, 2006). The utilization, or application, of strengths of character may be viewed as the process by which the content of virtue becomes evident (Peterson & Seligman, 2004). They are stable traits of personality that contribute to individual growth and the fulfillment of human potential; it is in this way that Character Strengths are critical to the cultivation of a life lived consistent with the tenets of positive psychology. In addition, a focus on Character Strengths in daily activities endows individuals with the potential for creating sustained change to both the positive and negative aspects of their daily lives. Peterson and Seligman (2004) developed a handbook for the organization of Character Strengths, organized along associated domains of virtues, that allows for comparative investigations of positive personality patterns within and between various groups. A classification such as this enables an explicit focus on client strengths within psychotherapy, a practice that has been shown to enhance positive emotionality in clients who seek treatment (Fitzpatrick & Stalikas, 2008a). For this and other reasons, the exercise of identifying and nurturing client strengths has become a mainstay of positive psychological intervention within individual, group, and organizational settings (Frederickson & Dutton, 2008; Seligman, Rashid, & Parks, 2006).

Character Strengths Research

A variety of investigations have been conducted on Character Strengths as identified by positive psychology. They have been examined for their predictive validity in their relationship with measures of engagement, meaning in life, satisfaction with life (Peterson et al., 2007), positive emotionality, and so on (Peterson, Park, & Seligman, 2006). They have also been utilized in psychotherapeutic contexts in order to promote change efforts directed at enhancing psychological health and well-being (Flückiger, Caspar, Holtforth, & Willutzki, 2009). Relevant research on Character Strengths will be further reviewed and critiqued in Chapter 2. A principle Character Strengths intervention, called Using Signature Strengths, involves the identification and application of an individual’s five highest rank-ordered strengths of character. By engaging in exercises consistent with their highest-ranked Character Strengths, individuals are essentially capitalizing on their positive characteristics and resources. This manner of Character Strengths intervention has been shown to both reduce depression symptoms and increase satisfaction with life (Seligman, Rashid, & Parks, 2006). However, what if

3 individuals were led to compensate for their relatively lowest-ranked Character Strengths and work to remediate these perceived weaknesses? This is the primary question of the current study, and it is based on the Capitalization vs. Compensation model.

Capitalization vs. Compensation

Both psychological researchers and clinicians have recognized the value of tailoring interventions in psychotherapy to client characteristics such as personality, strengths, resources, and preferences (Norcross & Wampold, 2011; Rude & Rehm, 1991). By recognizing these types of client traits, therapists may employ strategies that take advantage of client strengths to support recovery (Harkness & Lilienfeld, 1997). This is the central tenet behind all positive psychology interventions, and the Signature Strengths intervention serves as a good example of using clients’ strengths; in this technique, clients are led to discover their topmost Character Strengths and engage in therapeutic activities consistent with their greatest characterological preferences. In contrast, most non-positive psychological therapeutic interventions, and indeed most outcome studies, are directed primarily at the reparation of client deficits in cognition and behavior that are seen as maintaining the client’s current state of distress (Karwoski, Garratt, & Ilardi, 2006). This juxtaposition of foci in psychotherapeutic intentions and interventions is referred to as the Capitalization vs. Compensation model. The Capitalization principle of this model takes aim at developing and employing client strengths; alternatively, the Compensation principle is focused on remediating weaknesses (Wingate, Van Orden, Joiner, Williams, & Rudd, 2005). In a capitalization approach, the therapist highlights the inherent strengths of the client and encourages him to make use of his resources in order to affect change (Flückiger et al., 2009). There is some evidence that this method of tailoring to strengths enhances therapeutic outcome by matching clients to appropriate interventions to which they are predisposed based on aspects of their personality (Conoley, Padula, Payton, & Daniels, 1994; Beutler et al., 1991). However, capitalizing on client strengths openly neglects what may be considered relative weaknesses, and these weaknesses may be implicated in their contribution to psychopathology and general distress. Compensation, the opposing side of this line of reasoning, argues that individuals should, when appropriate, be led to examine their shortcomings in order to remediate any perceived limitations to effective functioning (Snow, 1991). Therefore, related back to the current investigation, it seems that

4 clients may theoretically derive differential benefits from capitalizing on their top-ranked Character Strengths versus compensating for their less well-developed Character Strengths. It remains to be seen whether there are differential effects, in terms of both positive and negative symptoms, of leading individuals to capitalize on their Character Strengths or to compensate for their relative character weaknesses. A preliminary investigation by Rust, Diessner, and Reade (2009) appears to be the only study that has come close to investigating the Capitalization vs. Compensation model with regard to a Character Strengths intervention. These researchers compared the standard capitalization approach in Character Strengths intervention to a combined capitalization/compensation intervention. They asked one group of research participants to work on only their Character Strengths while another group was asked to focus on both their Character Strengths and weaknesses. The results of this study indicated that both groups derived benefit from these disparate approaches; however, individuals who focused on their relative weaknesses in addition to their strengths may have experienced a greater increase in positive facets of psychological well-being, such as happiness and satisfaction with life, compared to individuals who worked only on their top-ranked Character Strengths (Rust, Diessner, & Reade, 2009). Because these researchers implemented a combined strengths and weakness intervention, it remains to be seen what effects a pure compensation approach may have compared to a pure capitalization approach in activating relative Character Strengths or weaknesses, and the associated benefits. For the purpose of the current study, a brief, therapist-directed, group Character Strengths psychoeducational intervention—that will vary based on an emphasis on either capitalization or compensation—will be given within an organizational setting. A self-help homework element, similar in nature to those that are incorporated within psychotherapy, will be included in this intervention.

Positive Psychology Interventions in Organizational Settings

The practice of identifying and building upon Character Strengths is readily-applicable to employees in organizations. In that Character Strengths interventions have the potential to promote health and well-being for individuals in an organizational setting, it is important to extend positive psychology research to this population (Harter, Schmidt, & Keyes, 2002). However, dissimilar from previous positive psychology studies that have focused on the

5 promotion of positive facets of effective organizational operations, the current study is focused on the impact of Character Strengths intervention with individuals within the organization, through an organizational service delivery modality. This spotlight on individuals directs the effects of interventions in organizational settings to motivate individuals to face challenges, to embrace change processes, and to lay the groundwork for self-improvement and enhancement of psychological well-being (Frey, Jonas, & Greitemeyer, 2003). Therefore, the current study will employ Character Strengths interventions in a therapist-guided, self-help organizational context from a psychoeducational perspective drawn from counseling psychology. Past research has demonstrated that merely aiding individuals to identify their Character Strengths serves as a primary intervention while working with individuals to utilize their Character Strengths by engaging in activities that are consistent with their Signature Strengths acts as a secondary intervention (Resnick & Rosenheck, 2006). One manner in which individuals may execute this secondary task may be to employ their Character Strengths within their organization (Harris & Thoresen, 2006). Therefore, a Character Strengths intervention in an organizational setting must direct individuals to not only identify their Character Strengths, but to also develop a strategy for cultivating their strengths in their daily lives and activities. Peterson and Park (2006) propose that organizational workshops that focus on Character Strengths and associated Character Strengths-activating interventions may lead to these positive effects for individuals within the organization. However, they advise that individuals must be taught to nurture their Character Strengths through ongoing practice, both on their own as well as within the context of the organization. Because relative Character Strengths are initially developed and maintained as a result of constant activation and continual practice, it may be argued that those who are encouraged to pursue activities that promote various Character Strengths may foster positive emotionality, engagement, and a sense of meaning in life (Peterson et al., 2007). Therefore, it is necessary to additionally provide guidance to individuals as to how they may utilize Character Strengths effectively throughout their daily lives. The prescription of homework assignments may accomplish this purpose. Because lasting change may only be affected through consistent practice and application of new knowledge and skills, it is important to accompany therapeutic interventions with real-life experiences (Broder, 2000). Additionally, positive psychological interventions are intended for adjunctive use in psychotherapeutic settings, making them exemplary candidates for use as homework

6 assignments (Walker & Lampropoulos, 2010). Therefore, individuals who learn to identify their Character Strengths in the current study will be asked to engage in strengths-consistent homework assignments in order to further develop and engage their Character Strengths. In this way, the Character Strengths intervention within organizational settings may be viewed as a method for planting a seed for lifelong change; however, the responsibility falls on individual participants to incorporate their Character Strengths into their daily lives.

Research Questions & Hypotheses

Given the tenets of the Capitalization vs. Compensation model as applied to the cultivation of Character Strengths, the following research questions and hypotheses are proposed for investigation in the current study: 1. Will individuals who focus on employing their top-ranked Character Strengths (capitalization model) experience an increase in positive variables and a decrease in negative symptoms? 2. Will individuals who focus on employing their relative character weaknesses (bottom- ranked Character Strengths; compensation model) also experience an increase in positive variables and a decrease in negative symptoms? 3. In which exercise (working on Character Strengths or character weaknesses) will individuals experience the greatest increase in positive variables and/or the greatest decrease in negative symptoms? Based on past Character Strengths research (e.g., Peterson, Park, & Seligman, 2006; Seligman, Rashid, & Parks, 2006; Seligman et al., 2005), it is hypothesized that individuals who nurture their top-ranked Character Strengths in the current study will experience a decrease in negative symptoms and an increase in psychological health. Some evidence from Rust, Diessner, and Reade (2009) indicates that a cultivation of bottom-ranked Character Strengths may also produce similar effects on positive and negative symptoms. However, as previously mentioned, this study used a mixed strengths/weaknesses group in lieu of a “pure” weaknesses condition. As there are no studies to date that have examined differences in Character Strengths interventions based on a comparison of employment of top-ranked versus (pure) lowest-rated strengths, it remains unclear which mode of intervention will produce the larger positive or negative effects.

Significance

A scientific investigation that compares strengths compensation to strengths capitalization fills a noticeable gap in the positive psychological literature. This is because most past empirical studies have focused on the use of Character Strengths as predictors of various indices of positive characteristics such as happiness, psychological health, and recovery from illness (e.g., Peterson et al., 2007; Pury & Kowalski, 2007; Peterson, Park, & Seligman, 2006). Only one study has investigated the effects of focusing on character weaknesses, though participants in all conditions in this study also worked to develop their Character Strengths, making any differential effects between the two approaches inextricable (Rust, Diessner, & Reade, 2009). Therefore, the current study may be viewed as an extension of past work that has questioned the utility of the compensation principle as applied to positive psychology’s concentration on strengths of character. The current study is also examining more broadly the potential effects of the Capitalization vs. Compensation question with respect to character strength interventions by incorporating several measures not used by prior researchers. In addition, the proposed investigation may serve to further validate the idea of tailoring interventions to the client by expanding opportunities for interventions; though capitalization on clients’ Character Strengths has heretofore been the most popular mode of intervention, there may be some instances in which compensating for relative character weakness are indicated in psychotherapy. Finally, the current study tests the effectiveness of a short-tem organizational intervention that may be practically applied in a variety of settings and with a variety of non- clinical clientele. It will also provide specific outcome data that may be used to understand the effects of positive psychological organizational consultation events on individuals within various organizations.

CHAPTER TWO

REVIEW OF THE LITERATURE In the following chapter, the field of positive psychology will be introduced and the relevant empirical investigations into the effects of positive psychology interventions will be reviewed. Character Strengths will be defined according to the classification delineated by Peterson and Seligman (2004), and research pertaining to the use of Character Strengths will be evaluated. Next, the Capitalization vs. Compensation model will be described as it pertains to the current investigation. The literature review will continue with a brief discussion on the use positive psychology interventions in organizational settings, and, because positive psychology interventions are meant primarily for adjunctive use in professional practice, the use of between- session (“homework”) assignments in psychotherapeutic intervention will be discussed as well. Finally, a summary of the literature review, as well as presentation of the research questions and hypotheses of the current study, will conclude this section.

Positive Psychology

The field of positive psychology emerged as a humanistic answer to the traditional medical model of psychopathological treatment (Maddux, 2008; Joseph & Linley, 2006). While earlier psychological research has focused on defining mental disorders and constructing etiological theories and concordant treatments for these deficit-oriented disorders, positive psychology strives to understand the factors that converge to allow the individual to achieve a full and satisfactory life (Seligman & Csikszentmihalyi, 2000). In other words, the bulk of general clinical research in the past few decades has, and appropriately so, focused primarily on the treatment and prevention of psychological disorders; in contrast, positive psychology endeavors not only to heal those with mental illness, but also to enable them thrive (Seligman, Rashid, & Parks, 2006; Seligman & Csikszentmihalyi, 2000). In this way, positive psychology espouses a shift from explicit foci on the treatment of depression, anxiety, and stress to those factors that not only promote psychological health but that also allow the individual to flourish (Ironson & Powell, 2005). Accordingly, the general goal of positive psychology may be seen as consistent with Abraham Maslow’s view of humanistic psychology as providing a means to self- actualization (Robbins, 2008; Maslow, 1943).

Theoretical Foundations and Core Constructs

Consistent with the original aspirations of the humanistic ideology, positive psychology’s aim is to enable individuals from all walks of life to enhance their current state of being by utilizing their strengths, talents, interests, and relationships to foster personal growth and a lasting sense of contentment (Lambert & Erekson, 2006; Snyder & Lopez, 2002). As most clients come to therapy wanting to become “happier,” PP strives to help them accomplish this goal (Rashid, 2009). To this end, the overarching objectives of positive psychology interventions are to enhance overall psychological well-being, to foster an enduring sense of contentment and satisfaction, to aid the client in developing hope and optimism for the future, to enhance quality of life, to maintain favorable interpersonal relationships, and to increase positive emotions— most notably happiness—within the individual (Seligman & Csikszentmihalyi, 2000; Seligman, Rashid, & Parks, 2006). To do this, individuals are encouraged to develop three domains of their lives, referred to jointly as “the good life” by Martin Seligman (2002): the pleasant life, the engaged life, and the meaningful life. The pleasant life involves everything that endows the individual with positive emotions and a sense of pleasure, and it does not need to necessarily be constrained to leisure activities (Seligman, Rashid, & Parks, 2006). Individuals engage themselves in the pleasurable life when they live in the moment and allow themselves to enjoy their current activity, whether it is something they are doing at their job, at home, or on their own time. The pleasurable life includes the enjoyment received from healthy living and from healthy relationships, and it is evident even in people who may not be able to devote a significant portion of their time specifically to leisure activities. The goal of this lifestyle is to encourage the presence and experience of positive emotions, including but not limited to joy, happiness, enjoyment, thrill- seeking, and pleasure (Snyder & Lopez, 2007). The engaged life involves a sense of immersion in everyday activities such that the individual is able to develop and cultivate a sense of flow (Csikszentmihalyi, 1997). Flow is essentially an absorption in the activities of daily life that allows the individual to develop a rhythm in which they are able to fully devote their efforts to a specific activity, free from distraction. This sense of engagement is typically applied to goal-directed activities; however, it may also be developed in any activity that would be elevated through increased attention and involvement. The goal for this type of lifestyle is to encourage the individual to live in the

10 moment, to experience all that life has to offer, and to not be distracted from their life experiences by superfluous or negative influences (Csikszentmihalyi, 1990). In this way, the engaged life enables us to achieve, to foster our personal strengths and build upon them, and to enhance our interpersonal relationships with others, because our immediate and interpersonal needs are being met and recognized (Snyder & Lopez, 2007). In addition, there is some evidence that accomplishing a sense of flow in our daily activities and experiences requires a social component; Walker (2010) found that while individuals could be led to experience flow while engaged in various experimental tasks in isolation, they were more likely to report sensations of flow—and flow that was more enjoyable—when engaging in social tasks. The meaningful life involves any and all activities that encourage the personal growth of the individual by endowing them with a sense of meaning or purpose in their lives (Seligman, 2002). Typically, individuals who report that their lives are meaningful feel that they are a part of, or actively contribute to, something greater than themselves (Seligman, Rashid, & Parks, 2006). They often describe an existential purpose, a specific calling, or see themselves as making a contribution to various entities (e.g., family, community, religious organization, etc.) through the giving of their time and/or resources. The meaningful life also allows individuals to feel that their actions matter to other people; the development of mutually-beneficial interpersonal and familial relationships is of vital importance in this domain (Snyder & Lopez, 2007). A correlational study conducted by Walker and Lampropoulos (2011) found that the number of hours individuals devoted on a weekly basis to positive psychological activities was weakly, yet positively, yet weakly, related to a sense of meaning in life, to satisfaction with life, and to general psychological well-being. Therefore, this sense of meaning, or purpose, often leads to both feelings of contentment or satisfaction with one’s life or with one’s direction in life, a key component of contemporary positive psychology. It is the pursuit, and not necessarily the attainment, of these three types of lives that allows an individual to obtain happiness in the fullest sense of the word (Seligman, 2002). Those who devote sustained effort toward those personally-fulfilling activities consistent with the pleasant life, the engaged life, and the meaningful life in near-equal parts are said to experience the good life. Individuals who have cultivated the good life tend to report more overall life satisfaction, regardless of their current life circumstances (Peterson, Park, & Seligman, 2005). In addition, and in contrast to popular belief, it appears that merely pursuing (but not necessarily

11 achieving) meaning and engagement in one’s life is highly correlated with not only higher ratings of satisfaction with life but also with lower scores on depression inventories (Huta, Peterson, Park, & Seligman, 2006), indicating that the pursuit of the pleasurable life is not as important to obtaining enduring happiness. In this same study, pursuit of pleasure was only modestly correlated with ratings of life satisfaction and depression, indicating that true “happiness” is not derived exclusively from the experience of positive emotions; instead, one must cultivate—or at the very least, pursue—a meaningful and purposeful life while indulging appropriately in the pleasurable side of life. While building upon positive emotion, engagement (flow), and a sense of meaning in life has been hypothesized to lead to subjective experience of the good life and to increase overall satisfaction with life (Seligman, Rashid, & Parks, 2006), Seligman (2011) recently expressed discontent with this traditional manner in which happiness has been described. He indicated, through the observation that happiness tends to be a largely momentary state, that individuals are rarely fully contented in their attainment of these three facets of life, suggesting that there may be other requisites of authentic and lasting happiness that are not contained by the previous definition of the good life. In a departure from positive psychology theoretical and experimental literature published over the primary years of the field, Seligman (2011) proposed that positive psychology should turn from investigations of the necessary components of “happiness” to an understanding of the structure of “flourishing,” a notion of well-being that encapsulates multiple aspects of individual functioning and quality of life. Flourishing may be understood as a theory of well-being that includes positive emotion (the pleasurable life), engagement (the engaged life), meaning (the meaningful life), accomplishment (the achieving life), and relationships (the connected life). With the addition of these latter two constructs, the concept of flourishing may now come to the forefront of positive psychology theory, research, and practice. However, as little current research has yet emerged on this new paradigm as it relates to positive psychology, the present study will continue to operate from the perspective of the elder definition of the good life while keeping an open eye to the impact of achievement-striving and positive relationships on the adaptive functioning and well-being of the individual. In order to develop pleasure, engagement, and meaning, and to therefore achieve a more complete state of being, PP focuses on enhancing one’s awareness and use of their perceived personal strengths, instead of focusing exclusively on their weaknesses or problems (Peterson &

Seligman, 2004). The main difference between PP interventions and those of other theoretical frameworks lies in the valuing process of one’s life events. Particular emphasis is given to acknowledging one’s past experiences in terms of the well-being, contentment, and satisfaction they provided so that the individual may develop behaviors that foster flow and happiness in the present and create realistic hope and optimism for the future (Seligman & Csikszentmihalyi, 2000). This is not to say, however, that current distress is ignored. Rashid (2009) exhorts practitioners to “develop an inclusive psychotherapy that examines both the strengths and weaknesses of our clients.” To this end, modern theorists recommend incorporating a PP component to traditional therapy that typically focuses on deficits and shortcomings. In this way, the primary focus on treatment may remain on the client’s current problems or life circumstances that brought them to counseling in the first place; however, this integration enables the practitioner to achieve a balanced focus between the client’s strengths and weaknesses. In that positive psychology interventions take aim at both enhancing happiness and relieving distress, utilizing these interventions as adjunctive techniques can be both a valuable and relatively unobstructive addition to therapy (Seligman et al., 2005). For these reasons, the integration of positive psychology theory into traditional models of psychotherapy holds promises as well as challenges. For example, Fitzpatrick and Stalikas (2008) argue that an emphasis on positive emotionality during the exploration of both positive and negative major life events for a client may facilitate client growth beyond simple negative emotional processing. By taking note of the positive emotional consequences of a resolution event or of a positive event, individuals experience secondary positive reinforcement for their contribution to the positive outcome and are likely to repeat the behavior that facilitated the favorable outcome. Consolidation of these events provides a window into the client’s experiences that provide them with positive emotion, engagement, and even meaning. In this way, positive emotions may be considered not only as outcomes of interventions but also as a means to achieve pleasure, engagement, and meaning (Stalikas & Fitzpatrick, 2008; Fitzpatrick & Stalikas, 2008a). It should be noted, however, that this focus on positive emotion does not ignore real client problems; therapists should be encouraged to take a similar positive emotionality approach to the investigation of negative life events while client explore the negative emotions that accompanied or followed such events (Lambert & Erekson, 2008). However, the danger inherent in a strict focus on positive emotionality is a neglect of the

13 exploration and amelioration of the conditions that support negative emotionality. Therefore, clinicians would be wise to address both positive and negative emotionality in psychotherapy. This dual focus provides an opportunity for positive psychology to support traditional psychotherapeutic approaches by incorporating a focus on the positive with the ever-present focus on the negative that comprises standard psychotherapy. From an integrative standpoint, several authors have expressed the sentiment that the field of positive psychology will ultimately become merged with traditional approaches to psychotherapeutic intervention (Diener, 2003; Linley, Joseph, Harrington, & Wood, 2006). From a conceptual standpoint, the mission of today’s therapeutic (or professional) psychology is not concerned only with the healing of mental illness but also with the facilitation of human potential (Seligman & Csikszentmihalyi, 2000). This renewed focus on positive aspects of mental health and adaptive functioning represent not a fundamental shift in the direction of applied psychology but rather a return to formative roots (Zarit & Robertson, 2006). Therefore, positive psychology should not be forever viewed as an optional adjunct to therapy; it should be considered a vital component of the client’s experience in their quest to health and wellness. In that positive psychology encourages practitioners to adopt an open and conscientious perspective of human nature through which to view their clients, a clear departure from the reductionistic bias and explicit focus on negativity that pervades current approaches to psychotherapeutic intervention, integration of positive psychological principles should not only be encouraged but required (Sheldon & King, 2001).

Research on Positive Psychology Interventions

As previously mentioned, a principle goal of positive psychology is to increase sustained or authentic happiness in the individual, which includes not only happiness in the present state but also trait-like happiness in the long-term (Seligman, 2002). Lyubomirsky, Sheldon, and Schkade (2005) propose that approximately 40% of the determinants of sustained happiness rely on the intentional activities of the individual, which is the only proportion of this model that they assert is under the individual’s direct control. Therefore, it is critical for the field of positive psychology to examine those interventions and activities that may allow individuals to take advantage of the level of happiness for which they are capable. A variety of clinical/outcome studies have investigated the effectiveness of PP interventions on decreasing the severity of various psychological disorders while also improving positive symptomatology. The primary 14 focus of these interventions has been on counteracting the negative effects of unipolar depression through various within-session and between-session modalities. The following section reviews a sample of the significant empirical investigations in the positive psychology intervention literature that have focused on the effects of PP interventions on both positive and negative symptoms. Seligman, Rashid, and Parks (2006) examined the effects of positive psychology techniques with moderately-depressed and severely-depressed participants in two separate studies. In the study with moderately-depressed individuals, participants were administered interventions in two counseling groups that consisted of a six-week program and that lasted for approximately two hours each. Six positive psychological techniques were given to the participants to complete over a six-week period of time: using Signature Strengths, thinking of three blessings, writing a positive obituary (i.e. positive reminiscence), going on a gratitude visit, active and constructive responding, and savoring. Participants discussed these homework assignments in their counseling groups and then received instructions for the next week’s assignment. Results indicated that most participants in the PP groups scored in the non-depressed range at post-treatment and that these results were maintained for a year following the intervention. This decrease in depressive symptoms was accompanied by a statistically- significant increase in satisfaction with life, compared to the scores of the control group. In the study with severely-depressed individuals, participants were either placed in a 14- week individual psychotherapy condition, received “treatment as usual,” or received “treatment as usual” in addition to psychotropic medication. The researchers found that participants in the individual positive psychology treatment condition experienced more improvement (reduction) in their depressive symptoms and greater increases in self-reported happiness compared to participants in either of the other two conditions by post-treatment. In addition, these gains were maintained for a year after the intervention. Researchers in China recently compared the effects of a positive psychology counseling group to a control group that did not receive treatment (He & Fan, 2010). Eighty college students, all from low-income families, were randomly assigned to one of several positive psychology counseling groups or to a wait-list control group. The researchers found that participants in the positive psychology groups experienced a significant decrease in both depressive and anxiety symptoms. They also reported enhanced self-esteem and increased

15 subjective happiness at post-treatment, compared to the control group. This reduction in negative symptoms and increase in positive dimensions of psychological health demonstrated resilience over a six-month period of time following the conclusion of the intervention. However, these results should be interpreted with caution because the authors did not adequately describe their procedure with respect to the positive psychological techniques implemented within the experimental groups. Seligman et al. (2005) conducted an online study to determine the effectiveness of various positive psychological interventions, removed from the therapeutic process. Participants were randomly assigned to complete one of five different positive psychology exercises or a placebo journaling intervention, on their own time and over the course of one week. Because these assignments were completed by the participants on their own, with no therapeutic contact or involvement in therapy from the researchers, this intervention simulates the effect of a homework assignment. At post-treatment, participants in all conditions, including the placebo condition, tended to report less depressive symptoms and increased happiness; however, participants who used their Signature Strengths or who had conducted a gratitude visit maintained their increases in happiness over a six-month period, relative to the placebo condition. These results should be interpreted with caution, however, because the researchers relied exclusively on participant self-report to substantiate the accurate completion of the assignments. In addition, participants in this study had been referred to the positive psychology website for inclusion in the study, so an expectancy effect may explain why there were no differences between any of the groups at immediate post-treatment. Because these participants self-referred to the website, they were also likely motivated to participate, which would positively affect the outcomes of the interventions; while this is not a substantial methodological flaw, it remains to be seen whether individuals who receive traditional psychotherapeutic services would experience the same benefit from these techniques. A similar online study randomly assigned participants to complete a positive psychology intervention, a problem-solving intervention, or a placebo-control intervention (Mitchell, Stanimirovic, Klein, & Vella-Brodrick, 2009). The results of this study indicate that those who completed the positive psychology exercise reported significantly more engagement and pleasure compared to the participants in the other conditions. However, substantially high attrition rates in this study (83% at 3-month follow-up) call into question the interpretation of the long-term

16 effects of the results, as it is possible that the participants who quit the study may have not perceived that they derived benefit from the interventions. Also, a lack of experimental control in this study may have diffused the effects of the intervention in that the experimenters could not be sure as to the extent or the frequency to which participants completed their assigned exercises. While this study presents promising results for future investigations, it also demonstrates the importance of controlling for the quality and frequency of interventions during treatment phases. Walker and Lampropoulos (2010) found, in a randomized controlled trial in which individual participants completed either behavioral, cognitive-behavioral, or positive psychological between-session assignments over a two-week period of time, that nearly all participants in the cognitive-behavioral and PP conditions experienced a clinically-significant reduction in depressive symptoms as post-treatment, compared to the control group. However, only participants in the PP condition also experienced a statistically-significant increase in both behavioral activation and positive emotionality. These results indicate that PP homework assignments may replicate the gains made in depressive symptom reduction by cognitive- behavioral homework assignments but with the added benefit of increasing happiness and behavioral activation. Sin and Lyubomirsky (2009) conducted a meta-analysis of 49 studies that sought to treat depressive symptoms with various PP interventions. They concluded that not only do PP interventions appear to be effective in treating symptoms of depression (r = .31), but they also tend to significantly enhance psychological well-being (r = .29). Well-being in this study was conceptualized as a construct that encompasses positive affect, satisfaction with life, happiness, and adaptive coping (Diener, 1984). While the individual studies examined in this meta-analysis focused on different aspects of the previous psychological well-being definition, Sin and Lyubomirsky (2009) noted that the convergence of these constructs represented overall well- being sufficiently. They also proposed that though clients’ explicit goals in therapy may be more directly focused on relieving current distress, clients are likely to also derive benefit from exercises that improve overall psychological well-being. Because several PP interventions have demonstrated the potential to remediate negative symptoms while improving overall quality of life and psychological well-being, including PP interventions in psychotherapy may enhance the effects of the work done within sessions.

Lyubomirsky, King, and Diener (2005) conducted a meta-analytic review of 225 published and unpublished cross-sectional, longitudinal, and experimental studies that investigated the short-term and the long-term consequences of positive affect. They analyzed a variety of outcomes including employment, quality of work, salary, job satisfaction, involvement in the community, social relationships, marital relationships, perceptions of social support, extraversion, sociability, engagement in activities, well-being, coping abilities, physical health, and mental health, etc. Results indicated that individuals who experienced or who were led to experience positive affect in the short-term tended to engage in behavior that supported both short-term and long-term success. Additionally, individuals who experienced “frequent positive affect,” or who had a tendency toward positive emotionality in the long-term, tended to be more successful in their work, tended to be better off financially, reported more positive relationships in both their friendships and romantic relationships, and were more physically and psychologically healthy. They also reported a generally favorable view of themselves and of others, indicated high sociability characteristics including perceived popularity, and engaged in prosocial and healthy behaviors. Consistent with evidence from Frederickson and Joiner (2002), those who reported frequent positive affect evidenced a strong capacity for coping with stress. In conclusion, the authors state that all available evidence indisputably indicates that positive affect impacts sociability, behavioral activity, affirmative views of the self and of others, healthy behaviors, general health, and resilience. However, it should be noted that a majority of the studies upon which these conclusions were drawn tended to be correlational in nature; therefore, it is difficult to determine whether these positive attributes were the direct consequence of positive affect or whether positive affect was derived from these favorable experiences and tendencies. It is likely that these constructs are intricately interrelated such that they mutually affect each other throughout the lifespan. In other words, positive social behaviors and general health may lead to the experience of positive affect in the short-term, which then leads to increased sociability and healthy behaviors, which then maintains and further enhances positive affect, and so on. In 2007, suicide was the tenth-leading cause of death for people in the United States, as approximately 35,000 individuals “successfully” ended their own lives (National Institute of Mental Health, 2010). This potentially avoidable outcome requires psychologists to understand suicidal phenomenon and to attempt to intervene appropriately. As one of the core purposes of

18 positive psychology interventions is to foster a sense of meaning in life, and because perceptions of personal significance and meaningfulness may mediate suicidal behavior, positive psychology holds promise in reducing the incidence of suicidal attempts. Wang, Lightsey, Pietruszka, Uruk, and Wells (2007) administered the Life Experiences Survey, the Coping Inventory of Stressful Situations, the Center for Epidemiological Studies Depression Scale, the Purpose in Life subscale of the Psychological Well-Being Scale, the Suicidal Behaviors Questionnaire-Revised, and the Reasons for Living Inventory for Young Adults to 479 college students. Out of this sample, fifty-four participants indicated previous suicidal ideation or behavior; therefore, the authors selected an additional 54 random participants from the remaining data set to serve as a comparison group. After examination of the intercorrelations between measures for both groups, the authors constructed a path model to explain the primary factors that impact and lead to suicidal ideation or attempts. This model indicated that the two greatest indices of suicide in the current study were depression (positively related to suicide attempts) and reasons for living (negatively related to suicide attempts). In addition, a sense of purpose in life as well as reasons for living were negatively related to depression symptoms. The authors acknowledged that emotion-oriented coping in response to stressful life events had a largely negative impact on the perception of purpose in life as well as reasons for living while simultaneously increasing depression symptoms. They noted that avoidance-oriented coping behavior was successful in reducing the risk for suicidal behavior by increasing reasons for living; task-oriented, or problem-solving, coping strategies was positively related to purpose in life and reasons for living, but it did not directly affect suicidal tendencies. These results indicate that positive coping strategies may lead to the development of a sense of meaning in life as well as reasons for living, which in turn reduce the likelihood of suicidal behavior. For positive psychology, this means that suicide prevention efforts should focus on increasing meaning in life as well as reasons for living through various interventions that foster gratitude, appreciation, positive reminiscence, hope, and optimism. In addition, Character Strengths may serve as an avenue for not only fostering meaning in life but also for increasing resiliency by providing positive coping strategies that are task-oriented.

Character Strengths & Virtues

Due largely to the medical model of psychopathological treatment, in which psychological practitioners have been historically educated and trained, much attention is given to perceived deficits while adaptive client resources are often overlooked (Maddux, 2008). Positive psychology espouses that a focus on strengths enables clients to not only overcome perceived deficits but to also learn to cope more effectively with future life stressors (Peterson & Seligman, 2004). A recent clinical study found that a focus on strengths increased participants’ perception of engagement and meaning in life (Seligman, Rashid, & Parks, 2006). Therefore, strengths-work within and between psychotherapeutic sessions is a veritable core component of positive psychological treatment and life enhancement. In addition, researchers and theorists are increasingly exhorting clinicians to incorporate and make appropriate use of client strengths in psychotherapy in order to enhance outcomes and facilitate client growth (e.g., Harris, Thoresen, & Lopez, 2007). Positive psychology’s aptly-termed Character Strengths offer a paradigm for utilizing client strengths in both clinical and non-clinical contexts. Character Strengths are personality characteristics that allow for virtuosity and righteousness to flourish; they promote personal growth, support a healthy lifestyle, enable supportive relationships, and foster a sense of community (Park, Peterson, & Seligman, 2006). The utilization of Character Strengths should be abundantly evident to others through the actions of the individual who is applying these most favorable qualities; in humanistic terms, they may be seen as actively engaging themselves in all of life’s duties and challenges such that they are taking the journey toward self-actualization (Peterson & Seligman, 2004). These individuals are constantly applying effort to projects that have the potential for personal fulfillment, and they do so because they are intrinsicly motivated to improve their own lives and the lives of those around them. As previously mentioned, “it is in this way that Character Strengths are critical to the cultivation of a life lived consistent with the tenets of positive psychology” (pg. 8) as those who pursue activities consistent with their Character Strengths will simultaneously pursue the pleasant life, the engaged life, and the meaningful life. Indeed, one may reflect on different key characters throughout the course of human history, who through their actions and their life’s work, exemplify each of the Character Strengths. Peterson and Seligman (2004) identified 24 Character Strengths and categorized them into 6 core Virtues: Courage, Humanity, Justice, Temperance, Transcendence, and Wisdom &

Knowledge (see Appendix A for a list of the Character Strengths organized by their associated virtue). Individuals do not necessarily possess all strengths within a particular virtue, but it should be noted that the overarching virtue for which individuals have two or more highly-rated Character Strengths is likely to be of particular importance to them. In addition, several of these identified Character Strengths are not necessarily wholly righteous in their standard definition; in other words, it is entirely possible for an individual to possess a character strength and to use it for malevolent purposes. One need only review the world history of serial killers, dictators and conquerors to see how even the best of otherwise valiant personality characters may be used for evil. However, the purpose of the current Character Strengths classification was to identify those characteristics that may be utilized in pursuit of the good life (Peterson & Seligman, 2004). For this reason, the Character Strengths should be understood from the perspective of their utility within a positive psychological framework. It should be noted also that although Character Strengths are considered to be personality traits, there remains considerable variability in the expression of each strength, indicating that some manner of social or moral environment may be required to activate an individual’s higher-rated strengths; as with other personality characteristics, the Character Strengths are not necessarily expressed in all situations in which the individual finds himself (Park, Peterson, & Seligman, 2006). Each of the core virtues and their associated Character Strengths, as identified by Peterson and Seligman (2004) will now be examined.

Courage

The virtue of Courage involves the motivation to work actively to accomplish goals despite both internal and external opposition. The emotional strengths that fall in line with this virtue deal explicitly with the exercise of willpower over some kind of difficulty, temptation, or maladaptive desire. It is in this way that the courage virtue consists not only of cognitive powers but emotional control as well, as the ability to moderate one’s emotions and learn to persevere in the face of opposition requires considerable fortitude of the mind. This virtue is comprised of the strengths of bravery, perseverance, honesty, and zest. Bravery is an admittedly convoluted construct whose presence is presumed through the observed actions of individuals. Bravery in action, therefore, is voluntary behavior taken in the clear presence of danger or personal risk to an individual despite his or her full knowledge and accurate judgment of the possible harmful consequences of their actions. It is an active strength 21 that requires a conscious appraisal of immediate situations, a mastery of the typical fear response, and an intrinsic motivation to act in contradiction to personal well-being for the greater good. At the same time, however, bravery is not inherent in foolish or careless actions; it is a planned response that is fueled by a moral or righteous desire to maintain order or justice in the world. Perseverance, also a voluntary action, is the persistence of goal-directed activity despite barriers to success, extrinsic discouragement, and/or repeated failure. Quite simply, it is the inability to quit. Determination and ambition allow the individual to resolve the difficulties they encounter by reminding them of the end-goal of the pursuit, and it is through this mastery of fears (of failure, of disappointment, etc.) that the persistent individual pushes onward in spite of recurring setbacks. To the extent that the grandest successes in the history of humankind were only achieved through lifelong and sometimes multi-generational persistence, determination, and ambition, this strength is highly-prized in most societies. Honesty involves the pervasive tendency to accurately reflect one’s true state of being, both to oneself and to important others. Those who demonstrate honesty and integrity in their dealings with others are sincere in representing their internal states, their emotional reactions, their intentions, their motivations, and their roles and responsibilities. They hold fast to their own convictions, not allowing their minds, their attitudes, or their actions to be swayed by popular influence or by the incongruent desires of significant others. To the extent that honesty reflects authentic emotionality, it also encompasses consistency in thought, emotions, and moral character regardless of circumstance. Zest is a vibrant strength that inspires energy and liveliness in the individual, and by doing so, it also energizes those who come into contact with the lively individual. People who possess this strength are goal-directed, ambitious, and spirited throughout their daily routine, and these characteristics tend to be evident in their interactions with others. Because their enthusiasm is contagious, and because their presence often encourages vigor in others, people tend to prefer their company. In addition, zest inspires the confidence to undertake multiple activities, and productivity is a readily-apparent consequence of these zestful endeavors.

Humanity

The virtue of Humanity consists of the interpersonal strengths, which focus on caring for and befriending others. As the name of the virtue implies, humanity involves a sense of empathy 22 and interconnectedness with all humankind to the extent that this emotional involvement motivates prosocial action. Individuals who possess two or more strengths associated with this virtue tend to concern themselves with the welfare of others, readily taking it upon themselves to serve, to uplift, and to ameliorate. Though acting upon these impulses may require sufficient effort as to be considered burdensome, these individuals do not see it as such; instead, they view humanity-consistent actions as part of their human obligation to watch out for and to improve the livelihood of their fellow man. Humanity is comprised of the strengths of love, kindness, and social intelligence. Love is a construct that has been debated, theorized, dismantled, reassembled, vilified, and exalted for millennia; throughout its history, love has been given many different definitions and categorizations. For the purposes of perceiving it as a strength of character, love involves desiring and valuing close, intimate relationships with others, especially when the warmth and affection of those relationships are not only appreciated but also returned. Mutual caring and reciprocated positive emotionality serve as the basis for this strength in that adoration is given as much as it is received. Though love may take many different forms depending upon the specific relationship, this character strength depends only on the sense of interdependence felt by those who willfully form cognitive, emotional, and behavioral attachments with others. Quite simply, it is “the capacity to love and to be loved” (Peterson & Seligman, 2004). Kindness may be viewed an attitudinal orientation, accompanied by action, that considers others as worthy of attention, respect, and care. It encourages the individual to embrace the humanity of others’ personal life situations by exposing the ability of the individual to contribute to the restoration of the needy others’ condition, thereby motivating action for its own sake. The undeniable differentiation of acts of kindness from other ameliorative actions lies in the absence of the expectation for reciprocity or repayment of some kind; indeed, kindness may be considered an altruistic motivation. Those who exemplify this strength often consider the needs of others as more important than their own, and it is from this perspective that they will generously give of their time, finances, and other personal resources. Social Intelligence as a trait may be conceptualized as emotional intuition. It refers to the ability of an individual to accurately perceive, analyze, and utilize emotional information in interpersonal interactions. This is a multi-faceted process; the individual must be able to identify emotion expressed in nonverbal channels, integrate emotional content to structure cognitive and

23 behavioral processes, conceptualize emotion in terms of the relationship, self-regulate his or her own emotional responses, and remain attuned to one’s own internal emotional processes. In mastering this complex sequence of events, those who possess social intelligence are astute in encouraging, advising, inspiring, and persuading others. They are adept at interpreting their own behavior and the behavior of others, and they can easily maintain social and individual control within a variety of complex social situations. Individuals with undeniable charisma likely posess the social intelligence character strength.

Justice

The virtue of Justice, similar in concept to the interpersonal strengths captured by the virtue of humanity, involves a sense of civic duty that is conducive to the promulgation of a healthy community and also to larger society. Actions consistent with this virtue enable social responsibility to fall on individual members of society such that they are motivated to sustain their idiosyncratic sociocultural institutions—including occupational organizations, social organizations, and governmental organizations—through active civic participation. Without members of the community who ascribe to this virtue, society would likely not function effectively; or, it would function solely for the benefit of a select group. The civic strengths that comprise the justice virtue are citizenship, fairness, and leadership. Citizenship reflects a sense of identification with a common goal that exceeds the wants and desires of the self. A pervasive feeling of personal responsibility to the good of the group, above the well-being of any one member, including the self, inspires a conviction to contribute actively in order for the group’s welfare to be attained and extended. Citizenship motivates the individual to work to improve the state of the group not only in the present, but also for the future generation. The hallmarks of this character strength, therefore, are loyalty and teamwork. Though citizenship refers most closely to civic obligation and duty, it extends also to participation within other social organizations. Fairness may be viewed as the highest level of moral development as it involves the manner in which one views all others in society. Those who ascribe to the notion of fairness have embodied the morality prescribed by the society in which they live such that social relations are viewed through the moral lens of justice and equity. In this way, they strive to discard any preconceived notions or biases of individuals or groups so that they may treat each person with the respect and impartiality that they are believed to deserved. These individuals tend to develop 24 a reputation for being compassionate toward others, for condemning social injustice in all its forms, and for working to rectify the wrongs done to others by the injustices of other members of society. Leadership is a socially-constructed characteristic that entails the motivation and direction of others within a group. Leaders foster an atmosphere of support and engagement that allows all members of a group to actively contribute to a common goal. Though their social power is occasionally derived solely through some measure of endowed authority, effective leaders will convey a sense of caring and personal involvement to the extent enabled by the organization. In this way, the members of the group are motivated to serve not only the purposes of the group, but the leader as well. To this end, individuals who embody leadership will facilitate the tasks of the group while maintaining the relations of members within the group. For this reason, leadership is a construct that also involves many of the other strengths categorized under the virtues of justice and humanity.

Temperance

The virtue of Temperance necessitates close self-monitoring and mastery of one’s internal and external states. By increasing awareness of one’s negative, self-defeating, or maladaptive states, and by developing the skills necessary to self-regulate these emotions as they present themselves, one may cultivate an empowering sense of personal control. In this way, the constellation of strengths that comprise this virtue have been labeled “the positive traits that protect us from excess” (Peterson & Seligman, 2004). Temperance therefore involves cognitive, emotional, and behavioral mastery, and it is comprised of the strengths of forgiveness and mercy, modesty and humility, prudence, and self-regulation. Forgiveness and Mercy may be understood as a propensity to transform the motivation to repay an offense to an attitude of compassion. In the face of clear transgressions, those who hold to the strength of forgiveness seek not revenge, but benevolence. In this way, the act of forgiveness becomes a state of mercy as those who have been transgressed upon will accept the faults of others and put aside all vengeful desires. It should be noted that the concept of partial forgiveness does not exist in this context; true forgiveness and mercy frees the individual and the transgressor of the emotional effects of the wrongdoing and allows for the future establishment of a prosocial, mutually beneficial relationship.

Modesty and Humility is more easily defined by what it is not rather than by what it is. As a character strength, humility is incompatible with pride and narcissism. It is not a self-referent sense of grandiosity or an exaggeration of one’s talents. At the same time, however, humility does not downplay personal achievements and accomplishments; instead, it allows an individual’s efforts to provide an accurate representation of their personal ability and value. In other words, they let their actions and accomplishments speak for them. Modest individuals do not seek recognition for their actions, they are willing to conduct an honest self-examination of their strengths and weakness, and they remain open to new ideas, information, and advice. Humility requires that the individual abstain from a constant focus on the self, and this allows for them to sustain personal growth by remaining open to critique and suggestions from others. Prudence involves an ever-present perspective of one’s current position in life as it relates to their future goals and aspirations. Prudent individuals maintain control over their present impulses by considering the effects that their actions may or will have for their future well-being. In making decisions, they consider both the immediate and extended consequences of various courses of action so that they may have a reasonable opportunity to attain their goals. Prudence therefore requires careful consideration of all choices that must be made, a tendency to avoid taking risks whenever possible, and a sense of caution in interpersonal relationships. As a trait, prudence manifests itself in all aspects of life, in both the public and private domains, and especially with respect to the construction of plans. Self-Regulation relates to the willpower to control thoughts, emotions, impulses, and behaviors in the pursuit of both internal and external goals. In that self-regulation involves constant awareness, and alteration when necessary, of one’s responses to impulses, this strength may be considered to be akin to the notion of self-control. Individuals who are able to master their own impulses are often described as self-disciplined; they are able to delay gratification, sometimes entirely, through the exercise of their mind and will. By moderating internal desires, the individual is able to live up to their own standards; by moderating external demands, they are able to conform to the norms and expectations of society.

Transcendence

The virtue of Transcendence may be viewed as a collection of traits that have at their core the aim of fostering a sense of connectedness, meaningfulness, and purpose of being. Unlike all of the previous virtues, transcendence is primarily concerned with promoting “connections to 26 the larger universe” (Peterson & Seligman, 2004). In doing so, individuals are able to not only establish their place in the world but also to develop a sense of purposefulness and meaning in life that has the capacity, when adequately facilitated, to supersede all other situational motivations through the provision of life-long direction. This virtue is comprised of the strengths of appreciation of beauty, gratitude, hope, humor, and spirituality. Appreciation of Beauty entails the constant awareness of and search for anything and everything that may be considered good, beautiful, excellent, or virtuous, or that is a product of similar endeavors. Admiration, wonder, and awe pervade daily experiences, with an undeniable sense of pleasure being derived from social interactions and/or observation of the physical world. Sensitized and intense responsiveness aesthetics, including but not limited to physical beauty, artistic performances, skills and talents, virtuosity, displays of morality, and demonstrations of humanity is common. The evaluation of these embodiments of beauty exceeds simple appreciation; individuals who possess this attribute notice beauty in all its many forms, and they personally value them for the sake of beauty and excellence, not merely as a means to an end. Gratitude involves an acute awareness of and responsiveness to the positive events that transpire in one’s life. Most commonly, gratitude-actions may be seen in response to the perception that one has acquired direct assistance, advantage, or benefit from the actions of another. Individuals who possess the strength of gratitude not only acknowledge the act(s) of kindness but they also express appreciation for the party responsible for the gift. In a larger sense, this strength incorporates an overarching attitude of thankfulness for all of life’s blessings. Those who identify with this trait will often reflect upon all of those things for which they are grateful, and they will make it known that they consider themselves blessed, regardless of their personal life circumstances. Hope signifies an orientation toward the future that espouses an optimistic mindset. However, it is not only the belief that one’s expected circumstances and planned achievements will come to fruition; it is also the motivation to exert considerable effort toward future goals with the belief that these goals are attainable. Individuals with hope and optimism keep a constant eye on the future, and they will formulate and revise plans for the future as necessary. They believe that, with the right attitude and work ethic, the best possible scenario is within their grasp, and it is not unusual for them to attempt to encourage a similar state of mind in others. In

27 this way, they respond to perceived failures and shortcomings with an inspired motivation to improve themselves so that they may succeed in the future. Humor may be understood as the capacity to perceive, create, and enjoy comedy and irony in its many forms. It involves a broad and open perspective of insight that takes into account the positive side of adversity, which enables positive emotionality despite sadness and heartache. Individuals who take pleasure in the humorous side of life typically derive benefit from sharing in comedy with others, and they enjoy making other people laugh and smile as much as they enjoy laughing and smiling with them; in this way, humor may be viewed as somewhat socially-dependent. As a coping mechanism, humor persuades individuals to see the comedy inherit in the condition of life, allowing them to embrace the lighter side of interpersonal situations. It is important to note, however, that the negative boundary of humor, which encompasses sarcasm and ridicule, should be considered as incongruous with the character strength of humor. Spirituality refers to all beliefs, practices, and rituals that are guided by a personally- relevant conception of the metaphysical world, as it is understood by the individual. The convictions associated with religious and spiritual beliefs tend to be pervasive in that they have the potential to affect cognitive processes, emotional responses, and behaviors across the lifespan and in response to situations that would ordinarily produce quite different reactions in non- religious individuals. To the extent that spirituality satisfies existential concerns, individuals who embrace a set of organized or personally-derived religious or spiritual principles will enjoy assurance of their place in the world, their purpose in life, and the general meaning of life for themselves and for all humankind. This set of beliefs has the potential to not only provide comfort in an otherwise uncertain world but to also encourage prosocial actions and civic participation.

Wisdom & Knowledge

Finally, the virtue of Wisdom and Knowledge entails the acquisition and use of knowledge for the practice of the good life; it is comprised of purely cognitive strengths that endow the seeker with the fundamental cognitive tools necessary for engaging oneself and others. Though occasionally knowledge is pursued for its own sake, this virtue implies that wisdom will be attained not through the mere acquisition of knowledge but through its application in improving the livelihood of one’s state of being and also that of others. This virtue 28 is comprised of the strengths of creativity, curiosity, judgment (open-mindedness), love of learning, and perspective. Creativity is the production of thoughts, ideas, or behaviors that are by their very nature original. It is not necessary that these ideas or behaviors be wholly novel or unusual; in fact, they may merely build upon the creative endeavors of others who have previously exercised creativity. The key aspect of this strength is that the creative products provide an adaptive solution or ingenuity to a problem or dilemma, and it is in this way that the creative strength positively serves the self and others. It is also in this way that inventiveness must be shared in order for it to benefit larger society; technological innovations such as computers, radios, and smartphones serve (albeit arguably) as an example of how creative enterprises have the potential to improve the lives of others. Curiosity represents the never-ending quest to engage, discover, and seek novelty and information for its own sake in response to challenging opportunities. The curious person has been endowed with a sense of openness and interest in themselves and in the world around them that is seemingly unquenchable. They strive to uncover new truths, to take part in new experiences, and to analyze new situations. Curiosity is rarely satisfied by surface-level explanations, and it serves as a strong, intrinsic motivation for exploring new topics, subjects, and opportunities. As a result, individuals with this strength tend to develop broad interests in a variety of interrelated—and sometimes completely unrelated—domains. Judgment (aka Open-Mindedness) involves a conviction to conduct a comprehensive search of all available information and to weigh that information without bias, to the extent that it is humanly possible. This strength enables individuals to set aside their personal beliefs and preferences in favor of a search for truth and understanding. Open-minded individuals allow their opinions (and indeed themselves) to be altered by new evidence derived from life experience, major life events, and other relevant external factors. A major component of this strength is the manner in which individuals approach new experiences; they view novel situations as opportunities for learning, allowing themselves to become truly enraptured by a new experience by setting aside any preconceived notions or value judgments. These individuals also display an advanced capacity for good judgment and critical thinking. Love of Learning is one of the most evident of all the Character Strengths, and it is one that is typically highly-valued in society. It may be understood either as a generalized absorption

29 with the acquisition of knowledge for knowledge’s sake or as a narrowly defined, yet intense, range of interest. Individuals who have developed a love of learning are motivated to be cognitively engaged in learning new information, developing skills, building upon older knowledge, and satisfying their own curiosity. These exploits are intrinsically rewarding, and they set the stage for sizeable impact on the lives of others, though not always immediately so. Because the concept of learning as a Character Strength requires a strong motivational component, it enables individuals to persist despite setbacks, challenges, and even perceptions of failure. In addition, it necessitates the utilization of acquired knowledge for the benefit of others. Perspective may be conceptualized as a construct akin to the popular notion of wisdom. It is not necessarily related to the ideas of intelligence or possession of a large knowledge base; however, those with the strength of perspective may enjoy both. This strength involves the ability to provide sagacious counsel and direction to others, enabled by a grounded view of various real-life situations, on the world in general, and on the larger culture-bound theory and idea of life. It is an overarching worldview of what is true and/or meaningful in the grander scheme of things, and it takes into account the relevant factors of a decision. This strength allows individuals to view a challenge or problem through the lens of both contextual and existential information such that resolution becomes evident for its contribution to all people involved, in the short-term and the long-term, as much as can be feasibly accomplished. It is for this reason that the character strength of perspective is so named.

Signature Strengths

The term Signature Strengths refers to an individual’s five most highly-ranked Character Strengths. The positive psychology Signature Strengths intervention is a prime example of the Capitalization principle (mentioned in Chapter 1 and expounded upon later in this chapter) in that individuals are led to focus on their most strongly-held Character Strengths and discover ways in which they may utilize them more in everyday life; hence, they literally capitalize on their strengths. In the standard positive psychology Signature Strengths intervention, clients complete the Values in Action (VIA) Survey of Character Strengths to identify their top-five Character Strengths. This free, 240-question Likert-type survey may be found online at the Authentic Happiness website or at http://www.viacharacter.org/ and is available in adult, child, and brief formats. Once the client’s top-five Character Strengths (Signature Strengths) are

30 identified, the clinician asks them to identify ways in which they can cultivate these strengths by using them more frequently or in new ways. Ostensibly, one could set aside a specific portion of their week or even some time in each day in which they engage in activities that are consistent with their most strongly-held Character Strengths. Clients typically identify various self- enhancement, self-improvement, pleasurable, or interpersonal activities that are consistent with their top strengths, but altruistic or service activities may also be applicable. Working with the therapist, clients can use this questionnaire to pinpoint their positive Character Strengths and to discuss specific, concrete ways to employ these strengths. They can also examine areas of their lives that may need improvement, relative to these strengths, and engage in activities that develop their Character Strengths to the fullest (Joseph & Linley, 2005). By examining their personal strengths and then utilizing those strengths more frequently, clients experience positive emotions and additionally gain access to positive reinforcement through both the favorable outcomes of their behaviors as well as the positive reactions of those who are recipients of their character-strength-related behaviors (Lambert & Erekson, 2008; Peterson & Seligman, 2004). In addition, Seligman et al. (2005) found that participants randomly assigned to use their Signature Strengths in new ways experienced a significant decrease in depressive symptoms, and that this decrease was sustained for at least six months. The specific utilization of client strengths holds the potential for effective integration within a large number of psychological modalities, as well as encouraging client involvement in the therapeutic process (Duckworth, Steen, & Seligman, 2005).

Character Strengths Research

Most of the past empirical research on the Character Strengths has relied on correlational analyses to suggest relationships between the strengths and various positive and negative aspects of the general life condition. Comparatively fewer studies have utilized the Character Strengths in outcome research, either by assigning Character Strengths as independent variables or by asking individuals to employ their Character Strengths. While several of these studies exist, they are outnumbered by the more popular approach in scientific character strength investigation. However, as a number of researchers have investigated divergent aspects of the Character Strengths and their relationships with positive aspects of well-being, specifically satisfaction with life, many of the studies described below not only support the use of Character Strengths in

31 positive interventions but also serve as the basis for the current investigation. The extant psychological literature on Character Strengths, as relevant to the current study, will now be reviewed. As previously mentioned in this chapter and in others, Character Strengths are purported to be akin to personality traits; however, previous research has indicated that personality traits are to some extent heritable (Bouchard, 2004). Therefore, it stands to reason that if Character Strengths are in fact stable across time and do conform to contemporary definitions of personality characteristics, then they may also contain a heritable component. Steger, Hicks, Kashdan, Krueger, and Bouchard (2007) claim to have executed the first behavioral genetics investigation into the heritability of the Character Strengths. They obtained responses to the VIA Survey of Character Strengths from 336 monozygotic and dizygotic twins from the Minnesota Twin Registry and compared responses between twin sets for each of the 24 Character Strengths using biometric modeling procedures. Results indicated that correlations among monozygotic twins for each of the Character Strengths were generally medium to large (r = .22 to .59) while correlations between dizygotic twins generally ranged from small to medium (r = .02 to .36). Estimations of additive genetic factors attained a median estimate of 42% (range = 14-59%), which is purportedly similar to estimates obtained for other personality traits. In addition, concordance rates between twin sets achieved statistical significance for 21 of the Character Strengths, suggesting remarkable similarity between twins in the endorsement of Character Strengths. The authors concluded that the Character Strengths as measured by the VIA Survey of Character Strengths demonstrate adequate genetic influence to be considered heritable personality traits. However, they exhort readers to consider the influence of shared environment in this study and suggest that future studies should further investigate familial patterns of character strength heritability. While the original classification of the Character Strengths reports 24 strengths categorized into 6 factors (virtues; Peterson & Seligman, 2004), several other investigators have analyzed the factor structure of the VIA Survey of Character Strengths in order to assess for the fit of the instrument to the theoretical classification. As the VIA Survey was constructed as a direct attempt to measure the strengths according to Peterson and Seligman’s (2004) categorization, further factorial investigation is warranted. Shryack, Steger, Krueger, and Kallie (2010) reviewed past factor analyses by other authors (with most authors supporting either a four

32 or five-factor model) and then obtained completed VIA surveys from 332 respondents who returned a mailed version of the survey. The authors used a principle component analysis with a factor rotation in order to analyze the data. Original analyses yielded five factors with eigenvalues over 1.0; however, the authors noted from investigation of the scree plot and parallel analysis that only three factors were sufficient to explain the data, as incorporating additional factors seemed to account only for error variance. The three-factor model accounted for 48% of the variance in scores while the four-factor model accounted for 61% of the variance. However, in order to establish the applicability that a 6-factor model may have in explaining the data, as consistent with the model espoused by Peterson and Seligman (2004), the authors constructed theoretical models to fit the data that consisted of one factor, two factors, and so on, up to six factors; they also named these various factors according to heuristic associations with past factor-coherent titles. The results of this investigation indicated that a three-factor model would consist of factors labeled intellectual strengths, interpersonal strengths, and temperance strengths. A four-factor model retains the intellectual strengths and temperance strengths factors while creating two factors called social strengths and transcendence strengths; these categories correspond directly to the virtues of Wisdom and Knowledge, Temperance, Humanity, and Transcendence, though it may be argued that the social strengths factor encompasses the Courage and Justice virtue as well. The six-factor model consists of factors labeled intellectual strengths (Wisdom and Knowledge), social strengths (Humanity), spirituality (Transcendence), mercy (Justice), temperance strengths (Temperance), and agentic strengths (Courage). The authors summarize by explaining that this analysis does not necessarily undermine the factor structure of the VIA Survey of Character Strengths; rather, it serves to inform both researchers and practitioners of the manner in which the strengths are interrelated. However, they conclude by advising that either a three-factor or a four-factor model best explains the data, suggesting that future investigations should continue to explore the relationships of the strengths within and between these categories. Flückiger et al. (2009) conducted a small-scale investigation in which 36 participants in two separate studies were randomly assigned to one of two intervention groups in which therapists either worked with clients to identify and utilize their personal strengths as resources during the psychotherapeutic process or conducted psychotherapeutic treatment as usual. They found that recognizing and “activating” client strengths positively impacted the outcome of

33 short-term cognitive-behavioral treatment by significantly improving client self-esteem, fostering a sense of mastery over presenting issues, and enabling the client to feel that they were competent to affect change over their current state. In addition, these clients were more likely to indicate that their original goals for therapy had been achieved compared to those in the control group. The authors stated that working with client strengths has become an essential component of psychotherapy in a number a theoretical orientations, and this approach shows considerable promise for aiding the client’s success and general sense of self-efficacy with therapeutic interventions, at least in the early stages of therapy. In an extensive internet-based study conducted by Park, Peterson, and Seligman (2006), individuals from all fifty states in the United States completed the VIA Survey of Character Strengths. Demographic results indicated that the most commonly self-reported Character Strengths in the U.S. are kindness, fairness, honesty, gratitude, and judgment (open-mindedness); examination of the theoretical categorization of these strengths indicates that each are contained within a different core virtue, according to the classification established by Peterson and Seligman (2004), yet they are all remarkably interpersonal strengths. In addition, there were no significant geographic distributional differences in the endorsement of these top strengths, as this same rank-ordered profile appeared from responses from nearly every U.S. state. Conversely, the strengths under the virtue of temperance (specifically prudence, modesty, and self-regulation) tended to be the least-frequently endorsed of all the Character Strengths across the U.S. These results indicate that the Character Strengths, as they are presently defined, tend to be consistent across various U.S. populations, bolstering the argument that strengths of character are akin to personality traits. Although there is consensus that Character Strengths interventions are generally related to overall life satisfaction (Peterson, Park, & Seligman, 2006), a recent correlational investigation has found evidence that indicates that some Character Strengths are more strongly related to life satisfaction and general happiness than others (Peterson et al., 2007). These authors analyzed response sets from individuals who voluntarily signed on to the Authentic Happiness website and who completed the VIA Survey of Character Strengths, the Orientation to Happiness Scale, and the Satisfaction with Life Scale. They then analyzed correlational and regression analyses using the Character Strengths as predictors and formulated a path model to explain the variance in the construct of “satisfaction with life.” Consistent with the suppositions

34 of Seligman (2002) on his construction of the necessary facets that must converge for an individual to attain “the good life,” the authors found that self-reported scores on indices of pleasure, engagement, and meaning were strongly related to ratings of overall satisfaction with life. In the path model, the Character Strengths of zest, love, gratitude, and hope were moderately and statistically-significantly related to satisfaction with life. The construct of “pleasure” was composed of the Character Strengths of humor, appreciation of beauty and excellence, and hope while negatively related to spirituality. The construct of “engagement” was composed of the Character Strengths of zest, perseverance, curiosity, and creativity. The construct of “meaning” was composed of the Character Strengths of spirituality and perspective. The authors additionally found that general ratings of happiness did not appear to strongly moderate any of these relationships, and they concluded that there are likely other pathways to happiness beyond the sole pursuit of pleasure, engagement, and meaning; these findings appear to support the supposition that other factors, specifically achievement-striving and interpersonal connectedness—may also be related to happiness and flourishing (Seligman, 2011). Though the aforementioned Character Strengths appeared to be related to self-reported measures of happiness, the possibility that happiness in general may be a largely idiographic construct that is not as strongly tied to personality characteristics—such as the Character Strengths—as compared to other variables should not be ignored. For example, a measure of the extent to which an individual utilizes their highest-rated self-reported Character Strengths in daily life events may better explain orientations to happiness than the mere report of individual strengths. This proposal therefore encourages the current investigation in that it requires that the utilization of Character Strengths should be examined in relation to positive outcomes. Pury and Kowalski (2007) conducted one of the first investigations into the interrelatedness of Character Strengths and the factors that may be required for the transformation of the Character Strengths into action. They asked 298 college students to describe a recent situation in which they had acted courageously and then had them respond to various questions concerning the manner in which they had acted this way consistent with each of the 24 Character Strengths. Individual narrative responses were coded and placed in categories that represented the courageous action as demonstrating physical, moral, or psychological courage. Consistency between raters was relatively high (ranging from .85 to .92) and general linear models were used to analyze the data. Results indicated that the virtue of courage was

35 viewed by the participants as necessary for the enactment of the strengths of bravery, perseverance, and honesty. Courageous actions were also strongly related to the strengths of hope and perseverance, suggesting that these particular strengths may facilitate courageous behaviors, which in turn allow for other strengths (specifically bravery and honesty) to be expressed. From a conceptual standpoint, this study underscores the idea that several Signature Strengths are fundamentally related to each other, and that the specific strengths under the virtue of courage are internally consistent. From a psychotherapeutic standpoint, the results of this study indirectly promote the idea that encouraging individuals to employ certain Character Strengths, even those that are not highly ranked among their Signature Strengths, may have beneficial effects by allowing other Character Strengths to become activated. Therefore, it is crucial that future investigations examine the effects of asking individuals to work on Character Strengths other than their Signature Strengths. Peterson and Seligman (2003) investigated the hypothesis that Character Strengths may change over time in direct response to major sociohistorical events. They compared national samples of Character Strengths profiles that were completed both before and after the terrorist attacks on the World Trade Center towers that occurred on September 11, 2001. Their results indicated that, across nation-wide American samples, the strengths of faith, hope, and love increased significantly. This study serves as an indication that personally-relevant events in an individual’s life have the potential to impact the manner in which Character Strengths are endorsed and expressed such that certain strengths may rise in importance and utility to the individual as they are forced to cope with personal crises. From a broader perspective, these results suggest that some individuals may reconstruct their Character Strengths in response to major life events in an arguably adaptive manner that may serve as the foundation for positive coping strategies and/or resilience. Further investigation of this phenomenon may yield critical information concerning those factors that foster resilience in the face of trauma, and it certainly appears at present that adaptive responses (characterological changes) to such events may be critical to maintaining psychological well-being and staving the psychopathologies to which others who cannot appropriately adapt fall victim. In a related study, Peterson, Park, and Seligman (2006) asked participants who had completed the VIA Survey of Character Strengths and the Satisfaction with Life Scale on the Authentic Happiness website to report their most serious physical or psychological illnesses as

36 well as whether they had recovered from the specified malady. Through a variety of statistical analyses include multiple regression analyses, logistic regression analyses, and analyses of path models, the authors found that the Character Strengths of humor, kindness, and bravery appeared to partially moderate the relationship between physical illness and life satisfaction. Two Character Strengths, appreciation of beauty and love of learning, appeared to somewhat moderate the relationship between psychological disorders and satisfaction with life. However, the authors noted that the moderating effects of these strengths were relatively small and that they were likely noticeable in the statistical analyses purely due the large sample size of the investigation. In addition, those who reported that they had not yet recovered from their physical or psychological problems tended to report lower life satisfaction compared to those who indicated that they had recovered. However, there are several factors that limit the conclusivity of these results. For example, the authors refer to evidence from Joiner (2000) that indicates that certain psychological disorders such as depression may leave residual trauma or tendencies for recurrence of psychopathology even after successful treatment. Therefore, although individuals may consider themselves to have recovered from their specific maladies, the residual effects of the past illness continue to affect their construal of satisfaction with life. Additionally, despite these promising findings, the strict reliance on participant self-report in this study may complicate the purported relationships of the strengths to recovery from illness, as individuals may have selected only those illnesses from which they had already recovered, or conversely, may have indicated illnesses that are considered chronic and therefore cannot be considered to have gone into full remission. A common shortcoming of many studies of this nature, the authors restricted the range of information they could obtain by only asked participants to describe their most serious physical or psychological issue; as a result, there is no evidence to indicate that these or other strengths aided the recovery process in illnesses of lesser severity. Nonetheless, this study indicates that the utilization of certain Character Strengths holds the promise for improving the perceived quality of life by facilitating adaptive coping strategies with major life stressors. Given these results, the next level of inquiry is whether individuals who are encouraged to utilize these specific strengths of character may be able to further support their recovery from these issues, or at the very least increase their self-reported satisfaction with life. One of the first clinical studies on the impact of Character Strengths on negative symptomatology, Seligman, Rashid, and Parks (2006) treated 19 college students with mild to

37 moderate depression in six weekly group counseling sessions that focused on five different positive psychology interventions, including identifying and using Signature Strengths. Relative to the 21 individuals in the control group, these participants experienced a significant reduction in their depressive symptoms with a concordant increase in self-reported satisfaction with life. These gains were maintained up to a year following initial treatment. However, it should be noted that the mean reduction of depressive symptoms for participants in the experimental condition was relatively small (about six points on the Beck Depression Inventory-II, which is within the standard error of measurement). These results indicate that Character Strengths intervention may hold promise for adjunctive use in traditional psychotherapy by actively contributing to the reduction of negative symptoms and to the enhancement of psychological well-being; however, due to the small sample size, more research is needed in this regard in order to validate these assumptions. A similar study, conducted by Seligman et al. (2005), was a web-based investigation in which visitors to the Authentic Happiness website were recruited to take part in a research study that compared various positive psychology interventions. Participants were randomly assigned to complete a gratitude visit, to complete one of two positive reminiscence exercises, to identify their Signature Strengths and attempt to use them more, to identify their Signature Strengths and use them in novel ways, or to write about their earliest memories (placebo condition). Participants in all conditions reported an increase in positive emotionality and a decrease in depressive symptoms at the one-week posttest; however, those who were asked to use their Signature Strengths in new ways reported greater mean levels of happiness and significantly lower depressive symptoms at six months following intervention relative to the placebo condition. Only one other intervention condition, the “three good things” positive reminiscence intervention, produced similar positive and negative symptom changes in the long-term. These results indicate that working with clients’ strengths may be one of the strongest interventions in the positive psychology repertoire in terms of effects on both positive and negative symptoms; in addition, it was one of the most durable interventions in this study. Rust, Diessner, and Reade (2009) provide preliminary evidence for the possible effects of an intervention that focuses on relative weaknesses as opposed to relative strengths. They had 76 college students complete the VIA Survey of Character Strengths and the Satisfaction with Life Scale. They then assigned half of the students to focus on activities consistent with two of their

38 top-five Character Strengths (two of their Signature Strengths); the other half was asked to work on activities consistent with one of their top-five strengths and one of their bottom-five rated strengths (which comprised their weakest-endorsed Character Strengths). None of the experimental or control groups were significantly different in their life satisfaction scores or gender distribution at pretest. Participants in both experimental groups reported on their use of their respective strengths by completing a weekly strengths journal over a twelve-week period. Posttest results indicate that both intervention groups, relative to a control group, reported an increase in life satisfaction. While participants who worked on one strength and one weakness reported greater life satisfaction than participants in the two-strength condition, this effect was not statistically-significant. Unexpectedly, there was a slight gender effect; male participants in both experimental conditions reported a greater increase in life satisfaction at posttest compared to female participants. However, one major drawback for this study was the inability to randomize individual participants to treatment conditions, as participants were drawn from psychology courses and randomization occurred at the group level. In addition, the combination of exercises consistent with a strength and a relative weakness in the same condition may have impacted the internal validity of the study by diluting the ability of the authors to attribute gains in life satisfaction to working on relative strengths or weaknesses. However, it does appear that it is beneficial to encourage a dual perspective of focusing on both Character Strengths and weaknesses. In their conclusion, the authors state that future studies should investigate a pure weakness condition and compare the effects of such an intervention to a pure strengths condition (standard Signature Strengths approach). Fortunately, this is the purpose of the current investigation: to compare the effects of focusing on client strengths to focusing on relative client weaknesses. However, in order to understand the theoretical rationalization for a strengths and weaknesses comparison, a discussion of the Capitalization vs. Compensation paradigm is required.

Capitalization vs. Compensation

The central concept underlying the Capitalization vs. Compensation model is tailoring, or matching, interventions to clients in psychotherapy (Rude & Rehm, 1991). Both researchers and clinicians have recognized the utility of attending to client personality and other characteristics to plan treatment, and the most commonly-accepted form of prescriptive psychotherapy takes

39 advantage of clients’ strengths and resources to support therapeutic interventions (Norcross & Wampold, 2011). This technique is purported to “improve the efficacy, applicability, and efficiency of psychotherapy by tailoring it to the unique needs of the client” (Norcross, 1991). Therefore, targeting interventions to client strengths will not only make use of client personality characteristics that may facilitate change efforts, but it will also encourage clients to implement activities to which they are predisposed to prefer. In this way, tailoring encourages adherence to treatment interventions and sets the stage for the implementation of sustainable change. For these reasons, client personality characteristics should be considered during treatment planning (Anderson, 1998). The Capitalization principle is concerned primarily with developing and employing client strengths and resources; alternatively, the Compensation principle is focused on remediating perceived or real weaknesses, deficits, and/or deficiencies (Wingate et al., 2005). Fundamentally, individuals will either capitalize on their strengths or compensate for their weaknesses. A central concept in the capitalization/compensation literature is aptitude-treatment interaction (APT), which is the identification and utilization of client variables that predict response to treatment (Snow, 1991). While much of the evidence in APT is mixed and commonly subject to methodological errors that obscure sound conclusions (Dance & Neufeld, 1988), several studies have indicated that matching clients to treatment approaches based on their personality styles and preferences (capitalization) yields generally favorable outcomes. For example, a randomized controlled trial conducted by Zettle, Haflich, & Reynolds (1992) found that depressed participants responded best to therapeutic intervention (group or individual therapy) when they were matched to treatment based on aspects of their personality. This appears to be due to the fact that individuals tend to be more responsive to interventions that engage their personality preferences. Consistent with this supposition, Furnham (1981) believes that some personality traits may predispose individuals to prefer certain types of activities in their work and leisure. Particularly interested in the differential preferences of introverts versus extroverts, he gave 130 students the Eysenck Personality Questionnaire, asked them to list the leisure activities in which they had engaged over the past week, and had them rank-order these activities by preference. Results indicated a stark contrast in leisure activities both pursued and enjoyed by extroverts compared to introverts. These findings, when related back to psychotherapeutic intervention

40 efforts, highlight the utility of tailoring within-session interventions and between-session homework assignments to client preferences. However, not all standard approaches to psychotherapeutic intervention may be tailored to clients according to a match between interventions/activities and personality characteristics, preferences, or strengths. In fact, most theoretical orientations promote series of interventions that exclusively employ either a capitalization or a compensation approach, and both models have demonstrated utility. For example, Karwoski, Garratt, and Ilardi (2006) discuss the shared conceptual frameworks of positive psychology and cognitive-behavioral therapy (CBT). They argue that the relative success of some evidenced-based CBT treatments for depression, specifically behavioral interventions that accompany cognitive restructuring efforts, is owed to an unintentional capitalization on client strengths or other characteristics. In addition, because CBT involves an explicit examination and rectification of cognitive deficits that maintain depressive cognitive processes, CBT also operates under the compensation approach by working with clients’ relative weaknesses. In contrast, the theoretical foundation of positively psychology suggests that positive psychological interventions operate almost exclusively under a capitalization model. Similarly, individuals who are experts in certain domains such as golf, chess, and gymnastics, will purposefully practice on those skillsets within their proficient domain that are not yet particularly attuned; in this way, they employ a compensatory approach to skill development while simultaneously maintaining a capitalization approach by continuing to work within the larger domain in which they already experience considerable self-efficacy (Ericsson, 2006). The current study views the use of Character Strengths in psychotherapy as an example of tailoring interventions to the client. However, applying a capitalization or a compensation framework will differentially affect the manner in which these strengths may be utilized in psychotherapeutic contexts. To the extent that some personality characteristics may be implicated in their contribution to a client’s current state of distress on one hand, or may serve as a favorable, therapeutically-enabling support on the other, treatment should target both the client’s aspects of personality that may impede treatment and those that may enhance treatment (Harkness & Lilienfeld, 1997). However, the implementation of a capitalization model or a compensation model will likely depend on the relative impact of these personality characteristics on the well-being of the client, meaning that a one-size-fits-all approach to capitalizing on

41 strengths or compensating for weaknesses is not indicated. In order to attain and/or maintain a predetermined level of functioning consistent with the individual’s conception of the requisite personality and environmental characteristics that are necessary to achieve “the good life,” capitalization and compensation must be flexibly applied (Baltes & Freund, 2003). This perspective points out that individuals must recognize the impact of the changing environment on their actions and adapt accordingly; in other words, neither capitalization nor compensation will be appropriate 100% of the time, nor in every fathomable environment. In addition, effective life management that facilitates the adaptive use of one’s idiosyncratic strengths will incorporate situationally-relevant selection, optimization, and/or compensation of/for strengths (Baltes & Freund, 2003). Therefore, both capitalization and compensation have their place in psychotherapeutic intervention, and there may be situations or environments that require one over the other. The Capitalization principle and the Compensation principle will now be reviewed and compared.

Capitalization

Capitalization approaches to psychotherapeutic intervention attempt to take advantage of client strengths, including abilities, motivations, resources, and preferences (Flückiger et al., 2009). These authors assert that activating and utilizing client strengths increases the client’s propensity for experiencing positive reinforcement as a result of their efforts, thereby enhancing the therapeutic alliance, boosting client receptiveness and motivation, and augmenting the client’s personal perceptions of self-efficacy and mastery. Therefore, working with clients’ strengths may also improve their outlook for the future as they begin to see themselves as competent in implementing and sustaining change efforts. There is clear value in tailoring interventions to a client’s strengths. Beutler et al. (1991) investigated the hypothesis that personality variables of clients predispose them to prefer (and hence to derive more benefit from) different types of treatment. They examined patient-treatment interactions by assessing for coping styles at the outset of therapy and randomly-assigning depressed patients to different forms of therapy. Results indicated that individuals who ascribed to an externalizing coping style experienced the best gains in group cognitive therapy while individuals with an internalized coping style responded best to self-directed therapy (Beutler et al., 1991). A separate study in which 37 recordings of psychotherapy sessions were reviewed and coded for therapist matching of interventions to client variables revealed that clients were most 42 likely to follow through on therapist recommendations when the therapist related the advice to the client’s resources, past successes, and personal strengths (Conoley et al., 1994). These results suggest that capitalizing on client personality characteristics by matching them to appropriate interventions, and pointing out this congruity to the client, may improve treatment outcome. Though centrally directed on positive aspects of functioning and well-being, the tenets of positive psychology hold that an explicit focus on positive characteristics is not sufficient for ameliorating psychological distress; therefore, it is imperative that clinicians acknowledge clients’ weakness as well as strengths (Rust, Diessner, & Reade, 2009). However, possibly due to positive psychology’s persistent and popular contrast with the medical model of psychopathology treatment, most positive psychology interventions do not explicitly focus on client weaknesses; one possible explanation for this bias in focus may be the assumption that traditional psychotherapeutic approaches have already embraced this deficit-focused role and that positive psychology should serve to supplement traditional treatment with its added focus on well-being and psychological health (Seligman & Csikszentmihalyi, 2000; Sheldon & King, 2001). Concordantly, positive psychology focuses primarily on bolstering the strengths and positive attributes of an individual, while in isolation neglecting negative characteristics and behaviors. This is most evident in the previously-described Signature Strengths intervention, which is considered a core technique in the positive psychology repertoire and appears to enhance positive attributes such as engagement and satisfaction with life (Seligman et al., 2005). By asking clients to identify and focus on implementing their top-five Character Strengths, clinicians employ a capitalization approach; indeed, this practice may be construed as the most literal form of strengths capitalization. However, it remains to be seen what effects a compensation process in Character Strengths interventions may have on client outcome. Only the aforementioned study by Rust, Diessner, and Reade (2009) has attempted to determine the utility and effectiveness of incorporating therapeutic work on relative character weaknesses—with the perspective that ideal psychotherapeutic treatment will incorporate both a capitalization and a compensation approach to intervention. As the current literature in Character Strengths intervention is conspicuously lacking the application of the compensation principle, this line of comparative research begs scientific inquiry.

Compensation

Though tailoring interventions to clients in psychotherapy has long been advised, this does not always mean that interventions should target client strengths. In contrast to capitalization models, compensation models are directly focused on remediating weaknesses. Baltes and Fruend (2003) view compensation as a process by which the individual engages in either the activation of unutilized resources or the acquisition of new resources, and there is some evidence that this approach is not only effective but that in some cases it is also necessary. As shown above, some cognitive-behavioral interventions for depression specifically target cognitive and behavioral deficiencies and encourage the client to work to remediate these perceived shortcomings until the weaknesses either become normalized or evolve into relative strengths (Rude & Rehm, 1991). Focusing on client weaknesses is therefore aligned with the medical model of psychotherapeutic treatment, which has a long and definitive place in the history of psychology. Besides, if a deficit-orientation did not work to improve the condition of clients in distress, why would traditional intervention models continue to employ this approach? That the compensation principle conforms to the medical model explains the relative neglect of positive psychology to implement compensatory approaches in psychotherapeutic intervention. Wingate et al. (2005) examined compensation and capitalization approaches in a problem-solving treatment study with young adults who had recently attempted suicide. A hierarchical linear regression of the data suggested that interventions that were tailored to participants’ self-reported problem-solving abilities prior to treatment were most effective in reducing suicidal ideation; participants who reported poor problem-solving ability responded best to treatment that focused on problem-solving skills, while participants who reported more developed problem-solving ability responded best to treatment as usual. These results indicate that a compensation approach should be preferred over a capitalization approach with suicidal individuals who express problem-solving deficits. Therefore, it is possible that a compensation model applied to perceived deficits of favorable personality characteristics (Character Strengths) may also garner favorable outcomes. In this way, the compensation model also has the potential to alter maladaptive patterns of behavior that stem from issues of personality. Instead of focusing on those positive and adaptive characteristics that are already well-developed, the therapist may align psychotherapeutic efforts with developing various adaptive and potentially-enabling aspects of

44 personality. In doing so, personality traits or styles that may be considered maladaptive (think Character Strengths in reverse) are altered and remediated as much as possible so that they are unable to maintain the client’s current state of distress. This approach is validated by evidence that suggests a relationship between negative aspects of personality with current and future psychopathology; several longitudinal and conceptual studies have implied that certain personality traits, specifically enduring aspects of negative emotionality such as neuroticism, are linked to future psychopathology (e.g., Krueger, Caspi, Moffitt, Silva, & McGee, 1996; Watson, Clark, & Harkness, 1994). However, the application of the compensation model to Character Strengths in the current study focuses not on the remediation of negative characteristics but on the development of potentially positive personality traits that may be considered relative weaknesses for the individual. Nonetheless, the rationale for investigating relative character weaknesses depends heavily on the compensation principle in that these character “strengths” may be likened to a skill or set of skills that the individual has not yet had the opportunity to develop or has not yet allowed to be developed through exposure to enabling life experiences. In addition, preferential decisions for engagement in work, leisure and relational activities may have also stifled the growth of the individual’s relatively weaker Character Strengths. Therefore, a compensation model applied to the Signature Strengths paradigm—which may be termed a non-Signature Strengths intervention—will give individuals the opportunity to focus on their perceived characterological weaknesses, begin to develop them more fully, and may ultimately turn a former deficit into a personality asset.

A Cautionary Note

Edwards and Cronbach (1952) encourage the use of the aptitude-treatment interaction principle in experimental methodologies by arguing that simple comparison of the effects of two treatments may not adequately explain the true impact of the interventions if they do not take into account personality variables that affect preference and propensity. Such empirical designs attempt to average individual differences, but these studies are typically unable to account for within-group variability. Nonetheless, this is a common approach in comparative treatment research (Anderson, 1998). Future research may be able to identify those personality characteristics that indicate whether a capitalization approach or a compensatory approach is differentially indicated for various clients, and Anderson (1998) argues that the Five-Factor 45 model of personality may be particularly useful in this regard. However, the efficacy and utility of compensation and capitalization models in general practice requires preliminary examination before such an investigation may be executed. For ease in applicability to real settings and for generalizability to real clients, capitalization and compensation approaches to Character Strengths must be tested in the real world. Therefore, the current study plans to implement Character Strengths interventions that embrace either a capitalization model or a compensation model in an organizational (group) setting, using a brief psychoeducational intervention format. The remaining sections will review the available literature on the implementation of positive psychology and Character Strengths interventions within an organizational context and discuss how positive psychology interventions as homework assignments support the application and activation of Character Strengths.

Positive Psychology Interventions in Organizational Settings

As more recent attention in the field of positive psychology has been given to the development of positive institutions, the practice of identifying and building upon Character Strengths has been increasingly extended to employees in organizations. Providing brief, time- limited interventions that focus on promoting the health and well-being of individuals within in an organizational setting is cost-effective and has the potential to enhance individuals’ quality of life as well as their work performance (Harter, Schmidt, & Keyes, 2002). However, it should be noted that the focus of the current study is not centered on improving work performance, employee relations, or other facets of effective organizational operation; instead, the current study is aimed at improving the relative quality life of the individuals within the organization. This is an important distinction between organizational interventions and interventions presented in an organizational context. The latter entails a group format for intervention that retains a focus on the individual as the entity who receives service, rather than the organization. To this end, Frey, Jonas, and Greitemeyer (2003) maintain that such positive interventions in organizations may be devised and implemented in group-type formats, and that these psychoeducational approaches have the potential to motivate individuals to face challenges, to embrace change processes, and to lay the groundwork for self-improvement and enhancement of various facets of quality of life and well-being. Additionally, Sheldon and King (2001), argue that positive interventions that focus on the functioning and well-being of the individual should be examined

46 in a realistic human context. Therefore, the current study will employ Character Strengths interventions in an organizational context while maintaining a counseling-oriented psychoeducational approach. This manner of positive psychological intervention with employees in organizational settings has been examined by several authors and will now be reviewed. As positive psychology has recently experienced renewed interest in the individual, relational, and environment factors that allow people to flourish in their various life domains (Tan, 2006), increased attention has been given to the context of the organization as a domain that may serve as a primary environment for cultivating social and individual growth (Frederickson & Dutton, 2008). Foster and Lloyd (2007) see the application of positive psychological principles in organizations, specifically through the identification and use of employee strengths, as a means through which positive emotion, prosocial interactions, and generativity/creativity may be bolstered among the employees of an organization. This “social synergy” described by Csikszentmihalyi (1997) in the workplace not only positively impacts employee relationships and the larger organization, but it also has the potential to increase Seligman’s (2011) conceptualization of the connected life and the achieving life. Concordantly, organizational settings may provide opportunities for individual growth. One way to accomplish this goal may be to encourage individuals to employ their Character Strengths in the workplace (Harris & Thoresen, 2006). There are many ways in which this may be done. Snyder and Lopez (2002) propose that the general application of positive psychology principles may occur along emotional, cognitive, and interpersonal processes. These different modes of expression of positive traits may all be utilized within an organizational setting with positive character traits as the guiding principles behind positive behavior (Luthans & Youssef, 2007). These authors state that utilizing Character Strengths as a means to foster self-efficacy may also positively impact work performance and facilitate positive perceptions of the self in individual employees, and these outcomes may both indirectly lead to favorable organizational contribution by employees. To the extent that an organization may be conceptualized as a community of like-minded individuals, the advancement of strengths-based activities may allow for an atmosphere of positive emotionality to flourish, and this encourages positive behavior in the individual (Schueller, 2009; Tan, 2006). In an article on the manner in which school psychologists may cultivate a sense of fulfillment, satisfaction, and happiness within themselves in their occupation, the roles of

47 fostering positive emotion and positive traits were projected to determine these positive perceptions (Miller, Nickerson, Chafouleas, & Osborne, 2008). Insomuch as cultivating positive character traits is a process that tends to lead to positive emotionality, Character Strengths interventions within organizations may support the goals of the individual to improve his or her overall quality of life. Strengths-based interventions of this nature in school-based interventions have demonstrated efficacy in not only increasing a personal sense of fulfillment, but also in enhancing happiness and satisfaction with life (Park & Peterson, 2008). There is evidence that merely identifying Character Strengths may lead to positive outcomes. In a study with patients at a Veterans Administration hospital, participants who took the VIA Survey of Character Strengths and received individualized feedback on their Signature Strengths reported increased positive perceptions of themselves and a renewed sense of efficacy in accomplishing strengths-related goals (Resnick & Rosenheck, 2006). These results indicate that Character Strengths interventions with individuals in organizations may provide two separate levels of impact: first, by helping individuals to identify their Character Strengths (a primary intervention) and second, by then working with them to develop activities consistent with their Character Strengths (a potentially enduring secondary intervention). Both modes of intervention are planned in the current study. Peterson, Park, Hall, and Seligman (2009) investigated the relationship of Character Strengths with work satisfaction and general satisfaction with life. Through a correlational study that assessed Character Strengths, work-life satisfaction, and satisfaction with life via online questionnaires with 9,803 employed adults from all over the world, these authors found that one particular character strength—zest—acted as a predictor of satisfaction with work as well as satisfaction with life. Additionally, scores on the Zest subscale of the VIA Survey of Character Strengths were moderately and significantly correlated with perceptions of work as a calling. These results indicate that some Character Strengths may be related to not only satisfaction with life but also with individuals’ perceptions of their occupations, as well as their perspective of their place within the larger organization. Because it may be argued that Character Strengths cannot become central components of core aspects of personality without constant activation, it may be assumed that those who pursued activities that promoted the character strength of zest were more likely to derive satisfaction from their work as well as from the current state of their lives. This study suggests that the promotion of strengths-based interventions in organizations

48 has the potential to positively impact individuals’ perceptions of themselves as well as their place within organizations, an effect that is likely to foster positive emotionality, engagement, and a sense of meaning in life. Results of preliminary investigations reported by Peterson and Park (2006) indicate that some Character Strengths may predict positive behavior of individuals within various organizations. For example, they found that some Character Strengths are related to high academic performance, organizational and group leadership, good physical and mental health, longevity, work satisfaction, reluctance to retire, and voluntary occupational retention. In addition, they propose that special workshops within organizations that focus on Character Strengths and associated Character Strengths-activating interventions may lead to these positive effects for individuals within the organization. However, they caution that such a “one-shot” approach to positive characterological intervention will not alone be sufficient to enact sustained change; in addition to these Character Strengths workshops, individuals must be taught to nurture their Character Strengths through ongoing practice, both on their own as well as within the context of the organization. Therefore, it is necessary to additionally provide guidance to individuals as to how they may utilize Character Strengths effectively throughout their daily lives. The methodology of the proposed intervention in the current study is consistent with this conceptualization of organizational workshops that focus on Character Strengths and associated Character Strengths activities as a means to promote the principles of positive psychology within the lives of individuals. In addition to providing a presentation and workshop on Character Strengths, the principle investigator will encourage individuals to activate their respective Character Strengths through various strengths-consistent activities and behaviors, through the use of homework assignments, that promote psychological well-being and personal growth. The convergence of this literature indicates that interventions with individuals in the group format allowed by organizational settings has the potential to build upon positive emotions and a sense of engagement as well as improve perceived quality of, and satisfaction with, life. However, as with any brief, self-help intervention, it is not enough to comprehend basic life lessons and insightful truths; individual must have the opportunity to enact real change in their own lives, on their own time (Broder, 2000). Fortunately, therapists may facilitate the process by which individuals translate therapist-directed self-help interventions into practice through the use of homework assignments. In the current study, homework will not only allow for individuals to

49 apply the positive psychology and Character Strengths lessons they learn during the intervention, but it will also encourage activities that support the purposes of Character Strengths intervention. Indeed, homework assignments are the actions that support the intervention, and it is through strengths-congruent activities that individuals will be able to gain full benefit from the proposed Character Strengths interventions. Therefore, a review of the role of homework assignments in the current investigation is necessary.

The Role of Homework Assignments

Between-session assignments, commonly referred to as “homework assignments,” are a mainstay of psychotherapeutic practice (Ledley & Huppert, 2007; Kazantzis, Deane, & Ronan, 2000). The effectiveness of homework assignments in individual psychotherapy has been well- validated over the past several decades, and guidelines as well as manualized interventions for the implementation of homework assignments abound (Beck & Tompkins, 2007). Homework has its roots in learning processes; these assignments enable clients to become engaged in the therapeutic process outside of regular therapy sessions, supplementing the work done within sessions by fostering interpersonal growth, personal exploration, and behavioral experimentation (Kazantzis & L’Abate, 2007). Due to their malleability and adaptability, homework assignments often take many shapes and forms, and in practice they are typically tailored to the client’s needs and goals. Because clients do not necessarily make lasting changes to their methods of processing external and internal information or to their typical behavioral patterns during the therapeutic process, it is important to accompany gains made in session with participation in real-life experiences (Broder, 2000). In other words, clients must have an opportunity to act out the specific techniques they learn during therapy (Persons, 1989). For this reason, client engagement with and completion of homework assignments is just as important as the psychotherapy sessions, if not moreso (Broder, 2000). By allowing clients to try out new patterns of behavior in the real world, and by teach clients to view their typical life experiences in a more adaptable way, homework assignments serve a valuable therapeutic purpose. Homework assignments fit well within a positive psychology framework. As positive psychology interventions are considered to be adjunctive techniques that have the potential for integration into traditional counseling models, they may be presented to individuals as

50 homework activities (Seligman, Rashid, & Parks, 2006). In a randomized controlled trial conducted by Seligman et al. (2005), most participants not only improved clinically but also reported that they enjoyed the activities so much that they continued to engage in the positive psychological exercises for up to six months following the conclusion of the study. A randomized controlled trial conducted by Walker and Lampropoulos (2010) found similar results; in this study, participants with mild to moderate depression were randomly assigned to complete various types of homework assignments consistent with different theoretical orientations over a two-week period. Not only did most of the participants in the positive psychology condition report a “positive” experience after completing the exercises on their own, but nearly all of the participants in the PP homework condition self-reported a decrease in depression symptoms consistent with the depressive symptomatic reduction of participants in the other groups, accompanied by an increase in positive emotionality that was not present in participants in the other groups. Therefore, positive psychology interventions hold considerable promise not only for their added benefit to the therapeutic process and for their transferability to real-life experiences, but also for their agreeable nature as evidenced by client enjoyment and continued engagement. In the current study, homework assignments will be utilized to encourage activities that are consistent with individuals’ Character Strengths. These assignments will be tailored to participants depending on their randomization to a capitalization or a compensation approach. Detweiler and Whisman (1999) propose that matching homework assignments to client characteristics is a major consideration that could potentially enhance homework outcomes, and in line with this supposition, the current study will align Character Strengths interventions with associated Character Strengths “homework” activities. In addition, participants will each receive an individualized form that will allow them to choose from various activities to complete as part of their homework assignments. During each presentation, participants will be asked to circle these activities and to write out a plan for completing these activities that include when, how, with whom, etc. This manner of assigning homework allows for specificity of assignments, for participants to be involved in selection of assignments, and for participants to have a written copy of their assignment procedures. Lampropoulos and Moore (2010) found, in a national survey of practicing psychologists, that clients were most likely to complete their psychotherapeutic homework when there was a specified time for completion, when the

51 assignments were personalized to the client and tailored to their strengths, and when clients were provided with written instructions for the homework. In line with these findings, the homework assignments given in each experimental condition will include all of these elements in order to enhance participant adherence and compliance.

Summary, Research Questions, and Hypotheses

In closing, positive psychology and its associated interventions have demonstrated considerable efficacy and applicability for enhancing psychological well-being, happiness, engagement, meaningfulness, and satisfaction with life. The positive psychology intervention in which individuals identify their Character Strengths and engage in strengths-consistent activities as a means to activate and develop favorable personality characteristics holds great promise for not only decreasing negative psychopathological symptoms such as depression, but also for increasing positive dimensions of psychological health. In the Signature Strengths intervention, individuals focus their attention on their top-five ranked Character Strengths; this is an example of a capitalization model in which strengths and resources are exploited for the betterment of the individual. A compensation model, in contrast, would support the idea that individuals should work to remediate their perceived weaknesses, and this means focusing specifically on cultivating the lowest-rated Character Strengths; however, this model has not heretofore been applied to either positive psychology research or practice. Positive psychological interventions have recently been implemented with individuals in organizational settings. Character Strengths workshops allow individuals to identify their positive personality traits and understand their values, motivations, and preferences more fully. A critical component of this method of intervention is the use of homework assignments, through which individuals may plan ways to utilize their Character Strengths more fully in their daily lives. However, the Capitalization vs. Compensation model remains untested with individuals within organizations. Therefore, the current study plans to investigate and compare the positive and negative effects of identification and activation of top-ranked versus bottom-ranked Character Strengths with individuals in organizations. In review, and based on the above review of the relevant literature surrounding Character Strengths interventions and the Capitalization vs. Compensation model, the following research questions and hypotheses are proposed for investigation in the current study:

Research Question #1: Will individuals who focus on employing their top-ranked Character Strengths (Capitalization model) experience an increase in positive variables and a decrease in negative symptoms? Hypothesis #1: It is hypothesized that participants who identify and activate their top- ranked Character Strengths will experience a reduction in negative symptoms with a concordant increase in positive variables. Past research on the correlates and use of ranked strengths in psychotherapeutic modalities has indicated that utilizing Character Strengths is effective in not only reducing depressive symptoms but also in enhancing positive emotionality and satisfaction with life (Peterson, Park, & Seligman, 2006; Seligman, Rashid, & Parks, 2006; Seligman et al., 2005). Therefore, it is hypothesized that all individuals who identify their relative Character Strengths and engage in activities that are consistent with those strengths will experience both an increase in positive variables as well as a reduction in negative symptoms, and this effect will not occur in participants who are randomized to the placebo control condition. Research Question #2: Will individuals who focus on employing their relative character weaknesses (lowest or bottom-ranked Character Strengths; Compensation model) also experience an increase in positive variables and a decrease in negative symptoms? Hypothesis #2: It is hypothesized that participants who identify and activate their top- ranked Character Strengths will experience a reduction in negative symptoms with a concordant increase in positive variables. While no previous studies have investigated the utility of focusing on lowest-ranked Character Strengths only, the compensation principle supports the idea that individuals may be able to reduce their subjective distress by remediating perceived weaknesses or shortcomings (Wingate et al., 2005). In addition, there is some evidence that a focus on lowest-rated strengths achieves positive benefits, though this is not conclusive (Rust, Diessner, & Reade, 2009). Therefore, it may be reasoned that individuals who work on their bottom-ranked Character Strengths may derive similar benefits compared to those who focus on their top-ranked Character Strengths. Research Question #3: In which exercise (working on Character Strengths or character weaknesses) will individuals experience the greatest increase in positive variables and/or the greatest decrease in negative symptoms? Hypothesis #3: It is tentatively hypothesized that individuals in the compensation condition (bottom-ranked strengths) will experience the greatest reduction in negative symptoms

53 while individuals in the capitalization condition (top-ranked strengths) will experience the greatest increase in positive variables. As there are not currently any published studies that have compared the effects of focusing on top-ranked versus bottom-ranked strengths, it remains unclear which mode of intervention will produce the largest effects. However, based on the capitalization model and the compensation model, remediation of weaknesses (traditional psychotherapy) has been shown to lead to a decrease in negative symptoms while a focus on strengths (positive psychology) is associated with an increase in positive dimensions of psychological health.

CHAPTER THREE

METHODOLOGY Several authors have described the manner in which positive psychology interventions may be implemented within organizations (Harris & Thoresen, 2006; Peterson & Park, 2006; Luthans & Youssef, 2007), and they encourage interventions that focus explicitly on Character Strengths in therapist-directed, short-term interventions given in a group format. These previous studies informed the methodology that will be described in this chapter, which will review the process by which the impact of Character Strengths interventions described in the aforementioned chapters was empirical investigated, based on the best practices of psychological outcome research. Specific research hypotheses will be presented first, followed by a description of the research design—based on the Character Strengths literature provided in Chapter 2—that was implemented to test the hypotheses. Then, participant demographics will be presented and attrition reviewed. Next, the variables under investigation will be identified and conceptualized, leading to a report on the psychological measures that may best quantify these variables for the purpose of this investigation. A description of the various experimental conditions to be utilized in this study will follow, including the step-by-step procedure for the investigation.

Hypotheses

Though the specific hypotheses for the current investigation were initially derived through the author’s personal experience in the provision of strengths-based interventions with individuals within an organizational setting, they have been further honed by the most current theoretical and empirical research on the psychotherapeutic effects of positive psychological interventions. Therefore, based on prior research on the impact of short-term positive psychological interventions as well as the use of Character Strengths for psychotherapeutic purposes, and considering the shortcomings and gaps in the literature in this regard, the following hypotheses are set forth for the current study: 1. Participants in the Top Strengths condition will experience a significant increase in positive variables (positive affect, meaning in life, satisfaction with life, psychological well-being, behavioral activation, self-esteem) as well as a decrease in negative

symptoms (depression, negative affect, general negative psychological symptoms) relative to participants in the control condition. 2. Participants in the Bottom Strengths condition will experience a significant increase in positive variables as well as a decrease in negative symptoms relative to participants in the control condition. 3. It is currently unclear which groups of participants (top-ranked strengths or bottom- ranked strengths) will experience the greatest increase in positive variables and/or the greatest decrease in negative symptoms. The current study, therefore, may be considered an exploratory investigation in this regard.

Research Design

The current investigation implemented a 4-way between-subjects experimental design in which participants were randomly assigned to one of four conditions: waitlist/control, placebo, bottom strengths, and top strengths. These last two conditions (bottom strengths and top strengths) varied according to the focus certain strengths in the participants’ rank order list of Character Strengths, and (accordingly) the specific types of post-intervention behaviors and activities in which the participants was asked to engage. Each of these experimental conditions will be described in the Experimental Conditions section of this chapter. Participants in each of the three experimental conditions completed a battery of outcome questionnaires (pre-treatment) prior to attending a psychoeducational presentation; they were then asked to engage in activities discussed at the presentation over a period of one month, at which time they would complete another battery of outcome questionnaires (post-treatment). Participants in the waitlist/control condition completed both sets of questionnaires, but they did not receive the psychoeducational intervention until after they had completed the post-treatment battery.

Participants

Population

Because the focus of the current study was on the impact of an intervention within a consultatory framework that educated individuals within organizations on the practical applications of their Character Strengths, the population consisted of employees of various small (10-40 people) organizations. Therefore, this study would ideally generalize to employees, managers, and supervisors who work in various small organizations throughout the United 56

States. See the Characteristics of the Research Participants section for more information on the demographic generalizability of the study.

Sampling and Recruitment

The goal in sampling a population for use in a psychological research study is optimum generalizability; we want the sample for the current study to be as closely representative to the larger population as possible. In the current study, this meant recruiting from a variety of different types of organizations as well as attending to the balancing of the participant demographic characteristics between each experimental condition. Participants were recruited from various organizations within a large metropolitan city in the Southern United States and from a medium-large metropolitan city in the Southeast United States. Managers or supervisors were contacted by phone or email, according to preferred method of contact as specified by the organization’s website, by the primary investigator with a proposal for providing a free “mental health and wellness” presentation to employees at the organization. In return, employees would be asked to complete several online questionnaires both prior to the psychoeducational presentation as well as a month following the presentation. All employees were informed that they would be participating in a research study, and were given the option to not participate (a few people in nearly every organization elected for this option). The following different types of occupations were accrued in the sample: administrators, nurses, educators, lawyers, pastoral workers, engineers, volunteers, fitness instructors, advertisers, professors, veterinarians, administrative assistants, accountants, creativity and design executives, caretakers, researchers, realtors, a wide range of business professionals, homemakers, and recent retirees (the latter two were invited to participate by their working spouses). Participating organizations included a communications and advertising firm, a hospice, faculty and administrative individuals at two large public universities, two religious organizations of different denominations, a local middle school, a local high school, a national health and physical fitness club, a realty firm, a management firm, a community volunteer organization, and several interdisciplinary business firms.

Participant Screening and Inclusion Criteria

There were not any inclusion criteria in the proposed study, which made screening participants prior to the intervention unnecessary. The only requirement for inclusion resided within the organization’s willingness to require participants to complete pre-intervention and post-intervention online questionnaires; this was generally not a problem, as most participants who attended the presentation at their organization had filled out the pre-treatment battery of outcome questionnaires and the VIA Survey of Character Strengths (except for those in the placebo condition, who were not required to fill out the VIA Survey of Character Strengths). On a separate note, it seems that many people are genuinely excited to fill out a questionnaire if it means getting feedback on their “personality.”

Characteristics of the Research Participants

A total of 27 organizations were recruited for participation in this study, not the organizations whose managerial/supervisory staff were either unavailable for a meeting or who declined before hearing the full proposal. Out of these organizations who listened to my proposal, 18 (66.7%) agreed to participate in the study. Employees were forwarded a recruitment email (Announcement Email to Employees; Appendix V) by the supervisory individual, which contained instructions for completing the pre-treatment battery of questionnaires and the VIA Survey of Character Strengths. Two hundred and sixty-nine individuals completed all of the questionnaires in the pre-treatment battery (an additional six individuals completed the VIA Survey of Character Strengths but not the pre-treatment battery) and attended the psychoeducational presentation. Out of this group, 187 participants also completed the post- treatment battery of questionnaires (32% attrition). Demographic information will be presented on these 187 participants (Table 1), because the current study is examining pre/post-treatment differences (Table 2). However, as regression statistics were also conducted with scores from pre-treatment respondents (see Additional Findings section of Chapter 4), demographics for all pre-treatment completers are presented in Table 3. Additionally, Figure 1 displays a flowchart for the progress of participants through the data collection stages of the study. Despite attempts to sample a variety of organizations and occupation types, the sample was fairly homogenous in respect to the demographics measures. Individuals who indicated their race/ethnicity as “White/Caucasian” comprised 89.9% of the sample (168 participants), while the

58 remaining 10.1% consisted of individuals self-identifying as “Hispanic/Latino (11), “African- American/Black” (4), “Asian-American” (3), and “Pacific Islander” (1). Mean age of the sample was 48.35 (SD = 16.53) with ages ranging from 18 to 88. As for gender, the sample was overwhelmingly female (72.2%). Reports of annual income demonstrated unequal groups as well; over 1/3 of participants in the sample indicated their annual income exceeded $100,000 per year (68 individuals; 36.3%), compared to 34 individuals (18.3%) in the $75,000-99,000 range, 36 individuals (19.3%) in the $50,000-74,999 range, 33 individuals in the $25,000-49,999 range, and 11 individuals (5.8%) in the “Less than $25,000” range. An additional 5 participants declined to share their income estimate. While a heterogeneous demographic sample would have been preferable, randomization at least ensured that the groups were roughly equivalent on these variables.

Power Analysis

An a priori power analysis was conducted in order to discern the number of participants required for the total sample in order to establish statistically-significant results for between- group effects, provided that these effects truly occur. A meta-analytic review of published positive psychology research by Sin and Lyubomirsky (2009) indicates that PP interventions in psychotherapeutic settings demonstrate medium effect sizes in enhancing psychological well- being (r = .29) and in decreasing depression symptoms (r = .31). However, a majority of the studies considered in this analysis utilized a 4-12 week intervention structure; because the current investigation implemented a brief, therapist-directed self-help one-time intervention with participants expected to engage in activities consistent with their Character Strengths on their own over a one-month period of time, we anticipated an effect size directly between small and medium (f = .175, according to the criteria set forth by Cohen, 1998). The power analysis was conducted using the statistical power analysis program G*Power 3 (Faul, Erdfelder, Buchner, & Lang, 2009; Faul, Erdfelder, Lang, & Buchner, 2007). Alpha error probability (false positive rate) was entered as α = .05 and Beta error probability (power) was entered as β = .80; these values are consistent with the guidelines for statistically comparing treatments in outcome research in the behavioral and social sciences (Kazdin & Bass, 1989). The proposed study compares between-group effects for two Character Strengths intervention groups, a placebo group, and a control group, bringing the total number of comparison groups to four. Measurement of the dependent variables will take place on two occasions, and zero correlation is 59 hypothesized between dependent variables. The above criteria were entered into an F-test (ANOVA: repeated measures, between-factors) a priori power analysis to determine the number of participants needed in order to examine between-group differences. Base on these parameters, in order to establish statistically-significant differences between groups (provided that true differences exist), the proposed study requires at least 184 participants, which translates to 46 participants in each of the four experimental conditions. In the current study, our total sample size of participants who completed both pre- treatment and post-treatment batteries of questionnaires as well as attended their respective psychoeducational presentation was 187, an additional three participants above the 184 individuals required for the study as determined by the a priori power analysis. In addition, we had at least 46 participants in 3 out of the 4 conditions: Top Strengths = 49, Bottom Strengths = 46, Placebo = 40, Waitlist/Control = 52 (see Figure 1 and Table 1).

Variables

As previously described in the Characteristics of the Research Participants section, demographic information was collected from participants in order to ensure generalizability of outcome data. Participants responded to open and force-choice questions concerning their age, gender, race/ethnicity, income, and occupation. Age refers to the participant’s number of years of life, and was asked via an open-ended question (“How old are you?”). Gender refers to the participant’s biological sex, and was assessed using a forced-choice response that included options of either male or female; to the principle investigator’s knowledge, there were no transgender individuals who participated in the study. Race/ethnicity refers to the sociobiological and associated cultural group to which the participant belongs and required a forced-choice response that included options of African-American/Black, Asian-American, Caucasian/White, Hispanic/Latino, Middle-Eastern, Native American, Pacific Islander, Biracial/Multiracial, or an open-ended option for “Other.” Income refers to the participant’s average annual income, and for purposes of sensitivity, was offered with forced-choice response that offered categories with a fixed difference interval of $25,000. Occupation refers to the participant’s current occupational title, and was assessed using an open-ended question (“What is your occupational title?”). Of prime importance to the current investigation is measuring the psychotherapeutic impact of the interventions according to the psychological constructs that are related theoretically

60 to the philosophic purpose of positive psychology. Because positive psychological techniques purport to enhance positive emotionality and favorable views of one’s life as well as decrease negative symptoms associated with traditional views of psychopathology, both “positive” and “negative” variables require attention. In this perspective, the dependent variables in question may be categorized based on what they add to or detract from the relative baseline of psychological health; for example, depression is a “negative” variable, because it detracts from an individual’s quality of life, while behavioral activation is a “positive” variable, because it contributes to activity and well-being. For this reason, dependent variables will be presented in terms of their positive or negative qualities. Character Strengths will also be assessed in the current investigation, but only for the necessity of procuring a rank-ordered list of strengths in order to facilitate differential interventions. Because Character Strengths are purported to be, like personality traits, fairly stable over time, it is not hypothesized that the brief interventions of the current study will have a substantial effect in altering the extent to which participants identify personally-relevant Character Strengths. Therefore, Character Strengths will not be measured as a dependent variable. Nonetheless, a basic understanding of the dimensions of Character Strengths is required, so they will be briefly described first, followed by the positive and negative variables of interest.

Character Strengths

Derived as Positive Psychology’s answer to the medical model of mental health and illness, Character Strengths may be conceptualized as stable characteristics or traits of individuals that hold the intrinsic potential for enabling health and well-being, broadly-defined (Maddux, 2008; Peterson & Seligman, 2004). As individual differences, they are personality characteristics that are valued in cultures and societies across geographies and across the recorded history of mankind (Steger et al., 2007). As the psychological community has identified a number of undesirable personality characteristics that purportedly contribute to psychopathology, as interpreted by the DSM-IV-TR (American Psychiatric Association [APA], 2000), Character Strengths are considered antithetical to the maladaptive personality characteristics and disorders described in the DSM, as they contribute to self-actualization and lasting fulfillment. Peterson and Seligman (2004) give several criteria for the qualification of a personality dimension as a character strength; they must: 61

1. Contribute to various fulfillments that constitute the good life, for oneself and for others. 2. Be morally valued in their own right, even in the absence of beneficial outcomes. 3. Not diminish other people in the vicinity by their use. 4. Not be considered undesirable when phrased in terms of their opposite. 5. Manifest in a range of an individual’s behavior, generalizing across situations and maintaining stability over time. 6. Be distinct from other positive traits in the Character Strengths classification. 7. Be represented in consensual ways, and hence valued, across cultures. 8. Be embodied by prodigies who possess a superior version of the trait. 9. Not be present in all people. 10. Be fostered by the institutions and socialization processes of the larger society. After a critical examination of the major positive personality characteristics represented in the psychological literature, the authors of the Character Strengths and Virtues handbook (Peterson & Seligman, 2004) selected 24 traits, organized by 6 virtues, which met the above criteria and may therefore be considered Character Strengths.

Positive Variables

Positive Affect may be conceptualized as an emotional state that encompasses positive and optimistic feelings; it may be operationalized as “the extent to which a person feels enthusiastic, active, and alert” (Watson, Clark, & Tellegen, 1988). Individuals with an abundance of positive affect tend to be energetic, happy, confident, and in a state of pleasurable engagement. Behavioral Activation refers to a state in which an individual is actively involved within his immediate environment such that he is able to derive response-contingent positive reinforcement as a direct result of his activity (Zeiss, Lewinsohn, & Muñoz, 1979). Individuals who have high levels of behavioral activation exhibit goal-directed activity and tend to not avoid challenging situations. They are active in all areas of their lives, including work/school, homelife, and interpersonal relationships; in addition, they tend to have lower scores on measures of depression symptoms (Kanter, Mulick, Busch, Berlin, & Martell, 2007). Psychological Health may be conceptualized as the positive dimensions of mental health, and it encompasses various constructs related to positive emotionality and adaptive functioning in everyday life; it is “a syndrome of symptoms of an individual’s subjective well-being” (Keyes, 62

2002). This general concept may be separated into three further dimensions: emotional well- being, psychological well-being, and social well-being. Emotional well-being refers to positive affect such as happiness, contentment, and satisfaction with life, and it implies the absence of the opposing states of negative affect. Psychological well-being refers to positive functioning, self- acceptance, a sense of purposefulness of one’s life, and autonomy. Social well-being involves social acceptance, a sense of belongingness to one’s community and/or social group, and contribution to the larger society. Satisfaction with Life refers to a global assessment of the fit between one’s current life circumstances with their subjective standard for an adequate quality of life (Diener, Emmons, Larsen, & Griffin, 1985). It is therefore through a cognitive, evaluative process that individuals compare their current life situation with their own subjective criteria. As a construct related to overall psychological well-being, satisfaction with life involves a sense of contentedness with the state of one’s existence. While Meaning in Life has been defined inconsistently throughout the psychological literature, for the intent of the current investigation it will refer to the sense of purpose felt by an individual when they have fulfilled their needs for self-worth, self-efficacy, goal-directedness (Steger, Frazier, Oishi, & Kaler, 2006). In this way, the construct of meaning may be considered synonymous with the idea of purpose. Individuals with a sense of meaning in life not only possess long-term and short-term goals in various life domains, but they also demonstrate an innate drive or motivation to achieve these goals, particularly in ways in which they can actualize their potential. They may see themselves as a part of something greater than themselves, and/or they may report that they have a “calling” in life. Self-Esteem refers to a stable sense of self-worth, or an attitude of worthiness, held by an individual; moreover, it is a construct that is closely connected to general psychological well- being (Rosenberg, Schooler, Schoenbach, & Rosenberg, 1995). Unlike Albert Bandura’s (1977) self-efficacy, self-esteem does not directly affect performance, and it is not constrained to specific domains of interest. Rather, it is a global attitude that transcends specific domains and is focused primarily on self-acceptance as well as respect for one’s self (Rosenberg et al., 1995).

Negative Variables

Negative Affect may be conceptualized as an emotional state that includes a variety of undesirable or negative feelings, including anger, fear, guilt, disgust, and anxiety (Watson, Clark, 63

& Tellegen, 1988). Individuals who are experiencing negative affect may report depression and/or anxiety, and they are typically in a state of subjective distress and displeasure. Depression refers to a constellation of symptoms consistent with the DSM-IV-TR (APA, 2000) Major Depressive Disorder diagnostic category, specifically symptoms that are indicative of a depressive episode. These symptoms include dejected affect, anhedonia, reduced energy, abnormal fluctuations in appetite and/or weight, significant changes to sleep patterns, feelings of worthlessness, feelings of hopelessness, and trouble concentrating. In this sense, depression may be conceptualized as a continuum of the relative quantity and severity of these symptoms that ranges from relative absence to relative presence/severity, with those who score on the higher end of the continuum considered to experience more debilitating depressive symptoms. General Negative Psychological Symptoms may be defined as a collection of various undesirable physical, behavioral, cognitive, and emotional states that are related to psychological distress (Tally & Clack, 2006). Because this grouping of symptoms reflects a sampling of various psychiatric domains, the psychological distress reflected by this construct refers to nonspecific syndromes and therefore purposefully intends to be general in nature. These symptoms may be roughly organized as (1) symptoms that reflect anxiety, depression, and substance abuse; (2) problems in interpersonal relationships; and (3) problems with family, work, and leisure activities (Schulenberg, 2004). For the purposes of the current study, general negative psychological symptoms will serve as an umbrella term for all other facets of psychopathology unrelated to depression.

Measures

In this section, we present the outcome measures that were used to assess for psychotherapeutic change (pre-treatment minus post-treatment) in the variables described above, as a result of the psychotherapeutic interventions. Participants also completed a brief Demographic Questionnaire (Appendix B) after they completed the outcome questionnaires in the pre-treatment battery. Participants’ Character Strengths data was provided by the VIA Institute on Character in the form of raw scores ranging from 0.0 to 5.0 on the VIA Survey of Character Strengths that were then rank-ordered by the principle investigator. Both positive and negative affect were measured by the Positive and Negative Affect Schedule (PANAS). Behavioral activation was measured using the Behavioral Activation Scale for Depression

(BADS). Psychological well-being and general mental health were measured using the Mental Health Continuum – Short Form (MHC-SF). Satisfaction with life was assessed using the Satisfaction with Life Scale (SWLS). Meaning in life was measured using the Meaning in Life Questionnaire (MLQ). Self-Esteem was measured using the Rosenberg Self-Esteem Scale (RSES). Depression was measured using the Center for Epidemiological Studies – Depression Scale (CES-D). General negative psychological symptoms were measured using the Outcome Questionnaire – 45.2 (OQ-45). Finally, and only within the follow-up battery of questionnaires, participants in the Top Strengths, Bottom Strengths, and Placebo conditions completed a brief, adapted compliance measure (Homework Rating Scale; HRS) that asks them to report on the activities that they completed consistent with their Character Strengths or health and wellness activities. These questionnaires were organized into the pre-treatment and post-treatment batteries and were then converted to an online format for ease of completion by the participants. Below is a description of each of the measures used in the current investigation.

Values in Action (VIA) Survey of Character Strengths

The VIA Survey of Character Strengths (Peterson & Seligman, 2004) was developed to assess the degree to which individuals possess various Character Strengths as defined by the most current classification of human strengths. The earliest version of the VIA Survey of Character Strengths consisted of a large number of items that were presumed to uniquely reflect each of the separate Character Strengths such that they would only be endorsed by those who possessed each of the appropriate strength constructs. Further development of the scale involved eliminating and rewriting individual items until each of the 24 Character Strengths scales attained internal consistency coefficients that were greater than or equal to .70. The most current version of the survey consists of 240 statements that require respondents to indicate the extent to which they feel that the statement describes themselves, based on a 5-point Likert scale with the following response options: “Very much like me,” “Like me,” “Neutral,” “Unlike me,” or “Very much unlike me.” Each of the 24 scales is comprised of ten items (three of which are reverse- scored) for each of the 24 Character Strengths such that scoring yields a raw score from 10-50 for each of the Character Strengths. Normative data for the VIA Survey of Character Strengths was acquired through online administration to individuals who accessed the Authentic Happiness website associated with the University of Pennsylvania; therefore, while the majority of respondents who comprise the normative group are college students, there is a broad 65 representation of adults from across the United States as well. Assessment of the test-retest reliability for the measure over a 4-month period (α ≥ .70) indicates good stability over time. There is some evidence for the convergent validity of the measure. All of the Character Strengths (except Wisdom and Knowledge) are strongly correlated with life satisfaction; individuals who have rebounded from physical or psychological issues tended to score more highly on the Character Strengths of Gratitude, Hope, and Appreciation of Beauty; and individuals who considered their work and leisure activities as rewarding tended to report that they viewed their Character Strengths as being consistently utilized within those environments. In addition, an exploratory factor analysis of the 24 scale scores on the VIA survey yielded five factors, and four of these factors appeared, at face value, to correspond directly with four of the five personality factors measured by the NEO Personality Inventory-Revised (NEO-PI-R; Costa & McCrae, 1992), a questionnaire informed by the Five-Factor Model of Personality (Peterson & Seligman, 2004). This measure will not be included in the Appendices due to copyright issues.

Positive and Negative Affect Schedule

The Positive and Negative Affect Schedule (PANAS; Appendix C) was developed to measure affective states that could be broadly categorized as either positive affect, which “reflects the extent to which a person feels enthusiastic, active, and alert,” or negative affect, which “is a general dimension of subjective distress and unpleasurable [dis]engagement” (Watson, Clark, & Tellegen, 1988). The PANAS lists thirty-five adjectives of both positive and negative emotional states and asks participants to rate, on a 5-point Likert scale, the extent to which they have experienced those emotional states within the past two weeks. Higher scores indicate greater experience of the respective emotive category over the past two week. Responses may be given according to the following rating scale: 1 = “Very slightly or not at all,” 2 = “A little,” 3 = “Moderately,” 4 = “Quite a bit,” and 5 = “Extremely.” In constructing the current version of the PANAS, Watson, Clark, and Tellegen (1988) conducted a varimax-rotated factor analysis on the items, which yielded a scale for negative emotions (19 items, possible scores = 0- 95) and a scale for positive emotions (16 items, possible scores = 0-80). Factor loadings for each descriptor ranged from .52 to .75, with minimal loading on the non-representative scale. In addition, convergent correlations of descriptors with their appropriate scale ranged from .89 to .95, with discriminant correlations between descriptors and the opposite emotive scale ranging from -.02 to -.18. Internal consistency for the positive affect scale ranged from .86 and .90 while 66 internal consistency for the negative affect scale ranged from .84-.87. Test-retest reliability for the positive affect scale was found to be .68 while reliability for the negative affect scale was found to be .71. With respect to convergent validity, the negative affect scale was positively correlated with the Beck Depression Inventory – II (r = .58), the Hopkins Symptoms Checklist (r = .74), and the Anxiety subscale of the State-Trait Anxiety Scale (r = .51), while the positive affect scale was mildly negatively correlated with each of these measures (r = -.36, -.19, and -.35, respectively). Additionally, these authors demonstrated construct validity by providing factor correlations with similar measures of positive and negative affect, which ranged from .88 to .92 for the negative affect scale and from .81 to .94 for the positive affect scale. This data indicates that the PANAS is both a reliable and valid measure of state-like positive and negative affect.

Behavioral Activation for Depression Scale

The Behavioral Activation for Depression Scale (BADS; Appendix D) was constructed with the purpose of quantifying changes in maladaptive behavioral patterns related to depression, ideally during psychotherapeutic intervention (Kanter, Mulick, Busch, Berlin, & Martell, 2007). The BADS is a 25-item questionnaire that asks participants to rate, on a 7-point Likert scale, the extent to which they have engaged in various behavioral activities or exercises consistent with behavioral activation over the past week. Response options include: 0 = “Not at all,” 2 = “A little,” 4 = “A lot,” and 6 = “Completely.” There are no descriptions or definitions for numbers 1, 3, or 5, but it is assumed that they fall between their respective anchors. Scoring yields a cumulative total score as well as scores on four subscales: Activation (7 items), Avoidance/Rumination (8 items), Work/School Impairment (5 items), and Social Impairment (5 items), with higher scores on the 0-150 range indicating more behavioral activation and less subjective impairment. These subscales were initially derived from an exploratory factor analysis and later validated with a confirmatory factor analysis (Kanter et al., 2007). In this same study, internal consistency (Cronbach’s alpha) of the BADS total score was .79 with the internal consistency of the subscales ranging from .78 to .87. Administration of the BADS in a clinically- depressed population has yielded high internal consistency for the total score (α = .92) as well as for the subscales (.75-.85; Kanter, Rusch, Busch, & Sedivy, 2009). Test-retest reliability, assessed through successive one-week administrations, yielded a correlation of .74 for the total score and between .60 and .76 for the BADS subscales. The BADS total score demonstrates good construct validity as it is highly correlated with the Beck Depression Inventory – II (r = - 67

.67; Kanter et al., 2007) and with the Center for Epidemiological Studies – Depression Scale (r = -.72; Kanter et al., 2009). In terms of predictive validity, the authors theorize that the avoidance/rumination subscale will predict the onset, length, and severity of depressive episodes, though there is no current data to support these suppositions (Kanter et al., 2007). The BADS appears to be both a valid and reliable measure of behavioral activation along several relevant dimensions.

Mental Health Continuum – Short Form

The purpose of the Mental Health Continuum – Short Form (MHC-SF; Appendix E) is to evaluate subjective well-being and general psychological health with an explicit focus on positive functioning and psychological well-being (Keyes, 2002). The MHC-SF is a 14-item form that asks participants to rate, on a 5-point Likert scale, the frequency with which they have experienced various “positive” symptoms; response options include: 0 = “Never,” 1 = “Once or twice,” 2 = “About once a week,” 3 = “About 2 or 3 times a week,” 4 = “Almost every day,” and 5 = “Every day.” Scoring of the measure yields a cumulative total score as well as scores for three subscales: emotional well-being, social well-being, and psychological well-being. Possible scores range from 0-70 with higher scores indicating the more frequent experience of positive emotions and cognitions associated with adaptive facets of overall subjective well-being (Keyes, 2005). An exploratory factor analysis with an oblimin rotation revealed three distinct factors that contained factor loadings between .34 and .80 and cross-loadings on other factors between .01 and .30. A confirmatory analysis revealed a good fit for the three-factor model, which contributed 56.9% to the global Chi-Square (Lamers, Westerhof, Bohlmeijer, ten Klooster, & Keyes, 2011). In this sample, the total score of the MHC-SF demonstrated good internal consistency (α = .89) with the Cronbach’s alpha for the subscales ranging from .74 to .83. Test- retest reliability coefficients for the total score ranged from .65 to 70 and for the subscales ranged from .45 to .56. Construct and convergent validity were assessed by correlating the MHC-SF total score and subscales with various related psychological measures. The MHC-SF total score was significantly negatively correlated with measures of mental illness (r = -.33). The emotional well-being subscale was significantly correlated with the Satisfaction with Life Scale (r = .49), measures of happiness (r = .49) and the positive affect scale of the PANAS (r = .24). The psychological well-being subscale was significantly correlated with measures of self-esteem (r = .33). The social well-being subscale was significantly correlated with measures of social 68 engagement and political participation (r = .21 and .17, respectively). Taking the above data into consideration, the total score of the MHC-SF appears to be both a reliable and valid measure of general psychological health, and although the subscales appear to be valid, they demonstrate sub-optimal reliability.

Satisfaction with Life Scale

The Satisfaction with Life Scale (SWLS; Appendix F) was developed to assess the narrow construct of global life satisfaction without simultaneously assessing related positive constructs (Diener et al., 1985). The measure consists of five statements to which the respondent indicates their relative agreement or disagreement on a 7-point Likert scale, with possible scores ranging from 5-35. The SWLS was developed from an original list of 48 self-report items; items that did not load onto a single factor of life satisfaction with a factor loading greater than .60 were removed, as were items that were judged to be too similar to higher-loading items. In later trails, the final five-item scale demonstrated a two-month test-retest reliability coefficient of .82 with high internal consistency (α = .87; Diener et al., 1985). Convergent validity was assessed by correlating the SWLS with other measures of subjective well-being; the total score of the SWLS was positively and moderately correlated with the Fordyce Emotions Questionnaire (r = .58) and the Bradburn Positive Affect Scale (r = .50). Later studies demonstrated similar levels of internal consistency and convergent validity with college student and elderly samples (Pavot, Diener, Colvin, & Sandvik, 1991). The SWLS therefore appears to be both a valid and reliable measure of the construct that it purports to represent, satisfaction with life.

Meaning in Life Questionnaire

The authors of the Meaning in Life Questionnaire (MLQ; Appendix G) designed the instrument to be an improved “measure of therapeutic outcome and personal growth” by specifically assessing for the degree to which an individual experiences his or her life as personally and socially significant (Steger et al., 2006). The MLQ is a 10-item questionnaire that asks participants to rate, on a 7-point Likert scale, the extent to which they endorse various statements that refer to their subjective evaluation of their lives as meaningful. A factor analysis of the items revealed two factors, labeled Search and Presence, which demonstrated the best fit of the data to the two-factor model; possible scores for each of the two scales range from 5-35, and there is not a total score derived from the MLQ. Internal consistency for the Search and

Presence scales of the MLQ were .87 and .86, respectively, with all items loading onto their respective factor with coefficients between .70 and .84 and not significantly loading onto the other factor. One-month test-retest coefficients were found to be .70 for the Presence scale and .73 for the Search scale. Steger et al. (2006) report good convergent validity with the MLQ scales, finding that the Presence scale was positively correlated with life satisfaction (.46), positive emotions (.40-.49), intrinsic religiosity (.30), and negatively correlated with depression (-.48). The Search scale was positively correlated with fear (.25), sadness (.26), and depression (.36). For these reasons, the MLQ appears to be both a reliable and valid measure of a sense of, and search for, meaningfulness in life.

Rosenberg Self-Esteem Scale

The Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1989; Appendix H) is a 10-item questionnaire that asks respondents to indicate, on a 4-point Likert scale (Strongly Agree, Agree, Disagree, Strongly Disagree), the extent to which they feel that each of the ten self-referent statements is representative of their own attitudes. Sample statements include “On the whole, I am satisfied with myself,” “At times I think I am no good at all,” and “I feel that I have a number of good qualities.” Half of the items are framed in the positive while the other five items are framed in the negative, and the latter are therefore reverse-scored. Scoring of the measure yields a cumulative total score that ranges from 10-40, with higher scores indicating greater levels of self-esteem. McCarthy and Hoge (1982) found internal consistency of the measure to be α = .74, and test-retest reliability achieved .77 over a one-year period of time. With respect to convergent validity, there are some indications that scores on the Rosenberg Self-Esteem Scale are related to scores on the Harter’s Self-Perception Profile for Adolescents (Hagborg, 1993) as well as scores on the Tennessee Self-Concept Scale (Bridle, 1984). This measure was used in this study and is reproduced in the Appendices with permission.

Center for Epidemiological Studies – Depression Scale

The Center for Epidemiological Studies – Depression Scale (CES-D; Appendix I) was designed to measure the severity of depressive symptomatology with an emphasis on the affective component of depressive episodes (Radloff, 1977). The CES-D is a 20-item questionnaire that asks respondents to rate, on a 4-point Likert scale, the frequency of occurrence of 16 negative depressive symptoms and 4 positive states (which are reverse-scored) that are

70 based on the DSM-IV-TR (APA, 2000) criteria for a depressive episode. Respondents are asked to endorse the frequency of depressive symptoms over a one-week period using the following scale: 0 = “Rarely or none of the time (less than 1 day),” 1 = “Some or a little of the time (1-2 days),” 2 = “Occasionally or a moderate amount of the time (3-4 days),” and 3 = “Most or all of the time (5-7 days).” Possible scores on this measure range from 0-60; cumulative scores between 10 and 24 indicate mild depression, while scores greater than 24 indicate moderate to severe depression (Radloff, 1977). Internal consistency for this measured has been reported at α = .85 in the general population and .90 in patient samples. Test-retest reliability coefficients for the CES-D were only moderate, ranging from .51 to .67, which the author attributes largely to the purpose of the CES-D for measuring current depressive symptoms, stating that it is expected for this type of symptomatology to vary over time. Additionally, the CES-D discriminates well between psychiatric inpatient samples and individuals in the general population, indicating good discriminant validity. The CES-D was also significantly correlated with the Hamilton Rating Scale for depression with psychiatric inpatients on admission (r = .44) and highly correlated after four weeks of treatment (r = .69). Therefore, the CES-D appears to be a valid and reliable measure of depressive symptoms.

Outcome Questionnaire – 45.2

The Outcome Questionnaire – 45.2 (OQ-45; Lambert et al., 1996) is a 45-item questionnaire that purports to measure general psychological distress and has been publicized as an outcome measure that may provide evidence for the effectiveness of psychological interventions in the reduction of general negative symptoms (Talley & Clack, 2006). It is therefore most commonly implemented as an outcome measure. For this questionnaire, respondents rate, on a 4-point Likert scale, the extent to which they have experienced general negative cognitive, emotional, and behavioral symptoms over the past week. Scores are given relative to the frequency of endorsed symptoms over a one-week period with 0 = “Never,” 1 = “Rarely,” 2 = “Sometimes,” 3 = “Frequently,” and 4 = “Almost Always.” The OQ-45 produces a cumulative total score (from 0-180) as well as scores on three subscales: symptom distress, interpersonal problems, and social role, with higher scores indicating more subjective impairment. The OQ-45 is frequently used in psychotherapeutic settings to assess therapy outcome, and has demonstrated high internal consistency for the total score (α = .95) as well as moderate to high internal consistency for the subscales (.68-.93; Mueller, Lambert, & 71

Burlingame, 1998). Test-retest reliability coefficients for the total score and subscales range from .78 to .84. Scores on the OQ-45 were highly correlated with scores on the Behavior and Symptom Identification Scale; in addition, there is a negative relationship between the OQ-45 total score and the Global Assessment of Functioning clinician rating at intake (Schulenberg, 2004). Overall, the validity and reliability of the OQ-45 has been well-established, indicating that it is a useful outcome measure for demonstrating the effectiveness of psychological interventions (Tally & Clack, 2006), and for this reason it may serve as an efficacious measure of general negative psychological symptoms for the purposes of the current investigation. The OQ-45.2 is not included in the Appendices due to issues of copyright.

Homework Rating Scale

The Homework Rating Scale (HRS; Appendix J) was designed to allow therapists to assess for between-session homework compliance as a product of the various issues clients generally encounter in completing homework assignments (Kazantzis, Deane, & Ronan, 2004). The HRS is a 12-item questionnaire that asks participants to assess various aspects of the homework assignments they completed on a 5-point Likert scale. The questionnaire asks participants to respond to items that concern the quantity and quality of homework assignments completed, as well as assignment clarity, collaboration with the therapist in activity planning, task difficulty, barriers to completion, and task enjoyment, etc. All twelve questions on the HRS are intended to be examined at the item level. No reliability or validity data are currently available for this questionnaire; however, Walker and Lampropoulos (2010) found, in a randomized clinical trial, that the twelve items together demonstrated good internal consistency (α = .73). This questionnaire was adapted for use in the current study, and the present version includes an open-ended question that will allow the principle investigator to assess for relative completion of homework assignments following the organizationally-based intervention.

Ten-Item Personality Inventory

An additional measure, the Ten-Item Personality Inventory (TIPI; Appendix K), was added to the pre-treatment battery of questionnaires as part of an exploratory analysis. It serves as an extremely brief measure of the broad domains of the Five Factor Model of Personality, also known as the “Big Five”: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness (John & Srivastava, 1999). For this particular assessment instrument, the Neuroticism

72 domain is actually the antithesis of its traditional definition; this scale is referred to as “Emotional Stability,” and purportedly measures the opposite of the traditional Big Five domain of Neuroticism. As the name implies, the TIPI consists of ten items that ask the respondent to rate the extent to which they describe themselves as a set of two-adjective descriptors (for example, “Extraverted, enthusiastic”) using a 7-point Likert scale that ranges from “Disagree Strongly” (1) to “Agree Strongly” (7). The TIPI yields a score from 2 to 14 for each of the Big Five domains by adding the two appropriate items together. Half of the items on the TIPI are reverse-scored, one for each domain. Gosling, Rentfrow, and Swann, Jr. (2003) found good evidence for convergent validity of the TIPI domains when correlating these scale scores with their counterparts on the Big Five Inventory (r = .65 - .87). In the same study, 6-week test-retest reliabilities for the TIPI scales were as follows: Extraversion, r = .77; Agreeableness, r = .71; Conscientiousness, r = .76; Emotional Stability, r = .70; Openness to Experience, r = .62. While internal consistency analyses were conducted, these statistics are difficult to interpret with a two- item scale. For the purposes of this study, we will rely on the test-retest reliability coefficients as indicators of the TIPI’s reliability. Overall, the TIPI demonstrates good reliability and validity, especially as a brief measure of personality traits, for assessing the Big Five domains. In this study, it will be correlated with scores on the VIA Survey of Character Strengths in order to assess for possible relationships between the Character Strengths and the Big Five personality traits.

Experimental Conditions

There are four experimental conditions proposed for the current study: Top Strengths, Bottom Strengths, Placebo, and Waitlist/Control. All participants in either of the two Character Strengths conditions received one of the presentations on Character Strengths (Top Strengths Presentation, Appendix L; Bottom Strengths Presentation, Appendix M), with the principle investigator as the sole presenter, which was based on the principles of Positive Psychology and the Character Strengths intervention. Participants in the Placebo condition received a neutral, general physical health and wellness presentation given by the principle investigator (Health and Wellness Presentation; Appendix N). Participants in the Waitlist/Control condition completed all outcome measures associated with the study and then received the Character Strengths presentation, following completion of data collection with this group. No further data were

73 collected from these participants. Each of these presentations was psychoeducational in nature and was programmed to last approximately one hour. Each presentation was organized and scripted as much as was feasibly possible in order to ensure similarity between presentations offered to different organizations, prevent researcher/presenter bias, and maintain intervention fidelity. However, a brief amount of additional time was allotted at the end of each presentation to accommodate questions and to facilitate a brief discussion between participants and the presenter (see Timetable for Presentations in Appendix O). The primary experimental manipulation specific to the research questions and hypotheses in this study involves focusing on specific classes of rank-ordered Character Strengths that are specific to the individual participants, consistent with the Capitalization vs. Compensation model as described by Wingate et al. (2005). Participants were encouraged to utilize either their five top-ranked strengths, referred to by Seligman (2002) as “Signature Strengths,” or their five bottom-ranked strengths for a month following the psychoeducational presentation. In this way, the proposed experiment may be considered an extension of the previous work conducted by Rust, Diessner, and Reade (2009) that investigated the differential effects of developing and employing Character Strengths compared to developing relative character weaknesses, though these authors compared a combined strengths/weaknesses conditions to the Signature Strengths intervention as usual. Each of the four experimental conditions will be further described below.

Top Strengths

Participants in this condition completed the VIA Survey of Character Strengths as part of the primary intervention. They then received the standard one-hour Character Strengths Presentation and workshop on the definition and use of Character Strengths in everyday life (Appendix L). The focus of this presentation was on practical application of the participants’ top- five (most highly-ranked) Character Strengths (a.k.a. Signature Strengths) in daily interactions with friends, family members, coworkers, etc. as well as in their personal and free time. The rationale for this intervention is consistent with the Capitalization principle described more fully in Chapter 2. At the beginning of the presentation, participants were introduced to the PERMA model (Seligman, 2011), which describes the five aspects of human experience that purportedly contribute the most significantly to “the good life”: Positive Emotions, Engagement, Relationships, Meaning, and Achievement. Participants were then given a brief overview of the 74 field of Positive Psychology and some of the applied research that has shown how engaging these five areas contribute to psychological well-being. Participants were then informed on the research that has shown that employing personal strengths through regular engagement in Signature Strength-consistent activities has the potential to improve satisfaction with life, to increase general happiness, and to reduce negative symptoms. Next, the presentation delved into the 24 Character Strengths, separated by their associated Virtue (see Appendix A), with the presenter explaining the meaning and significance of each. Finally, the presentation ended with a discussion in which participants were encouraged to share their relevant Character Strengths with others within the group and to work together to think of activities in which they already engaged or in which they could plan to engage that are consistent with their Character Strengths. In this way, participants were led to take an active role in the discussion of Character Strengths and practical applications. At the beginning of the presentation, participants were given an individually-customized handout that included specific activities (adapted from Peterson, 2006 and Peterson & Seligman, 2004) that utilize each of their own top-five Character Strengths (Character Strengths Activities Handout; Appendix P). The handout also reminded them that they were being asked to engage in as many activities consistent with their top-five Character Strengths as they would like in the following month. An example of the Character Strengths Activities handouts given to one of the participants in the Top Strengths condition in this study is shown in Appendix Q. At the end of the presentation, participants were given a handout with specific instructions on how to complete the post-treatment questionnaires a month later (Post-Treatment Instructions Handout; Appendix T).

Bottom Strengths

Participants in this condition also completed the VIA Survey of Character Strengths as part of the primary intervention. They then received the same one-hour Character Strengths Presentation and workshop given to participants in the Top Strengths condition. However, the focus of this presentation was instead on practical application of the participants’ bottom-five (most lowly-ranked) Character Strengths in daily interactions with friends, family members, coworkers, etc. as well as personal and free time (Bottom Strengths Presentation, Appendix M). The content of this presentation for participants in this condition was nearly identical to that given to participants in the Top Strengths condition (Appendix L). Participants in this condition 75 were still educated on the PERMA model, Positive Psychology and relevant research, and the 24 Character Strengths. However, unlike in the Top Strengths condition, participants in the Bottom Strengths condition were informed that research has shown that working on our problem areas and taking steps to improve areas of relative personal weakness have the potential to improve satisfaction with life, to increase general happiness, and to reduce negative symptoms. The rationale for this method of intervention was that remediating our personal weaknesses and working on our problem areas has the potential for personal achievement and fulfillment, consistent with the Compensation principle described in Chapter 2. In addition, the discussion at the end of the presentation focused on ways participants could engage in activities that were consistent with their bottom-ranked Character Strengths. At the beginning of the presentation, participants were given the individually-customized handout that included specific activities for engaging their own five lowest-ranked Character Strengths (Character Strengths Activities Handout; Appendix P). The handout reminded them that they were being asked to engage in as many activities consistent with their bottom-five Character Strengths as they would like in the following month. An example of the Character Strengths Activities handouts given to one of the participants in the Bottom Strengths condition in this study is shown in Appendix R). At the end of the presentation, the participants received a handout that contained specific instructions on how to complete the post-treatment questionnaires a month later (Post-Treatment Instructions Handout; Appendix T).

Placebo

Participants in this condition did not complete the VIA Survey of Character Strengths as part of the primary intervention, because simply identifying Character Strengths by completing the questionnaire has been shown to have therapeutic effects (Resnick & Rosenheck, 2006). Rather, in order to control for the effects of receiving an intervention (expectation effect), participants in the placebo group were given a separate psychoeducational presentation on behaviors that support general physical health and wellness (Health and Wellness Presentation; Appendix N). This subject was chosen for the placebo intervention because it is not only unrelated to the general field of positive psychology, but it also hypothetically should not substantially affect the variables of interest in the current study. This presentation, which was created through collaboration with officials working within the Florida State University Health Center, should allow us to control for expectation effects. The amount of time spent on key 76 content pertaining to health and wellness was equalized as much as possible with the amount of time used to present Character Strengths content in the Character Strengths Presentation (Timetable for Presentations; Appendix O). All other differences—beside content, of course— between the Health & Wellness presentation and the Character Strengths presentation have been kept to a minimum, as much as feasibly possible, and participants in all three conditions who received one of these two presentations engaged in approximately the same amount of interaction and discussion of presentation material, though the content for each presentation differed. Participants in this condition were given the Health and Wellness Activities Handout (Appendix S) at the beginning of the presentation so that they could write down the specific activities in which they planned to engage over the next month. At the end of the presentation, they also received the Post-Treatment Instructions Handout (Appendix T).

Waitlist/Control

Participants in this condition completed all of the pre-treatment measures and were informed that they would be asked to fill out more questionnaires in one month. Following this time period, they then completed the post-treatment measures and the VIA Survey of Character Strengths, after which they received the Character Strengths presentation and associated homework intervention consistent with the Top Strengths condition, which has the greatest quantity and quality of current empirical support. However, no further data was collected from these participants following the Character Strengths presentation. Therefore, the waitlist/control group acted as a delayed intervention condition in order for the effects of each of the other three conditions to be compared against a non-intervention control.

Procedure

The experimental procedure may be best understood by separating portions of data collection into discrete phases that occurred prior to, during, and following the psychoeducational intervention. A flowchart of the data collection procedure is presented in Figure 1.

Procedures Prior to the Psychoeducational Intervention

The first step of data collection involved recruiting potential organizations for the main psychoeducational intervention. A list of various organizations was accumulated based on their representation of different sectors of employment. Individual organizations were recruited either with through a personalized recruitment email (Recruitment Email to Employers; Appendix U), which was sent to managing personnel at the local organization, or through a phone call that relied on the language of the Recruitment Email. The research project was presented as an opportunity for the organization to receive a no-cost, one-hour employee health and wellness presentation based on psychological principles, with the only requirement being a commitment from participating employees to complete a battery of questionnaires both before and after the presentation. Upon acceptance of these terms, the principle investigator arranged the logistics of the presentation, including the day and time, the location of the presentation (which was always held at the organization’s place of business), and the availability of supporting media for the presentation (which required a projector and screen). The computer on which the PowerPoint presentation was displayed and all handouts associated with the presentation were provided by the principle investigator. Randomization occurred at the group (organizational) level prior to first contact with the organization; although group-level randomization is not ideal from a statistical perspective, not only was this procedure most feasible, but it also guarded against cross-over or contamination effects of the intervention. The principle investigator sent the contact person from the organization an announcement email to distribute amongst employees that introduced the research project and identified the terms of participation (Announcement Email to Employees; Appendix V). Instructions within this email led employees/future participants to an online survey website that included the study’s Consent Form (Appendix W), pre-treatment outcome questionnaires, and the demographic questionnaire. A separate link and set of instructions within the Announcement Email to Employees led participants to the website where they took the VIA Survey of Character Strengths. Consent for all procedures of the study was obtained on the first page of the pre-treatment outcome questionnaire battery, in which participants indicated their consent by entering their email address and proceeding to the next page. Within this battery, participants were only asked to identify the name of their organization and provide their email address.

For participants whose organizations were randomly assigned to the Top Strengths, Bottom Strengths, or Waitlist/Control conditions, the principle investigator obtained VIA Survey of Character Strengths raw data from the VIA Institute on Character for each person within the organization and created a personalized Character Strengths Activities Handout for them that was comprised of either their top-five or bottom-five ranked Character Strengths, as applicable. This form was given to participants during the Character Strengths presentation.

Procedures During the Psychoeducational Intervention

Presentations were given based on the criteria for the experimental conditions previously described: Top Strengths presentation for participants in the Top Strengths condition, Bottom Strengths presentation for participants in the Bottom Strengths condition, Health & Wellness presentation for participants in the Placebo condition, and Top Strengths presentation for participants in the Waitlist/Control condition. In the Top Strengths and the Bottom Strengths conditions, participants were given oral and typed instructions to complete activities consistent with their highest-ranked strengths or their lowest-ranked strengths, as applicable for the experimental condition, over the course of the following month. These participants received a worksheet that outlined these instructions, provided definitions for the five Character Strengths on the page, and gave examples of activities consistent with their Character Strengths (Character Strengths Activities Handout; Appendix P). Each of these handouts was personalized for each of the presentation attendees based on their responses to the VIA Survey of Character Strengths; participants in the Top Strengths condition received a Character Strengths Activities Handout that contained only their top-five Character Strengths while participants in the Bottom Strengths condition received a Character Strengths Activities Handout that contained only their bottom-five Character Strengths. During the presentation, these participants were asked to circle, underline, and/or write down activities on this personalized list that they thought they might want to complete over the next month. In the Placebo condition, participants were given a handout during the presentation upon which they were asked to record several healthy exercises, consistent with the content of the presentation, to complete over the next month; this handout also included contact information for the principle investigator (Health and Wellness Activities Handout; Appendix S). Participants in all three of these conditions were given a separate form with instructions for completing the post-treatment questionnaires one month after the presentation (Post- 79

Treatment Instructions Handout; Appendix T). Any questions concerning participant procedures were answered at the end of the presentation hour. Treatment Fidelity Check. In all conditions measuring the effect of the intervention (not the Waitlist/Control condition), and in order to control for differences in the presentations between organizations, the principle investigator asked a neutral third party (typically the contact person at the organization supervisor) to rate him on various aspects of the presentation as well as his personal presentation style; this person also completed a checklist on this form that mapped the content areas of each presentation (Third Party Rating Form; Appendix X). This was done in order to ensure that the three intervention conditions were structurally equivalent and that the intended content for each treatment was administered as planned. The principle investigator did not meet with participants in the Waitlist/Control condition at this time; they were given the Character Strengths presentation following completion of post- treatment and VIA Survey of Character Strengths measures.

Procedures Following the Psychoeducational Intervention

One month following the psychoeducational presentation, the principle investigator sent follow-up emails (Follow-Up Email to Employees; Appendix Y) either to the participants within the organization who attended the presentation or to the contact person for dissemination to the employees; this was left to the discretion of the contact person. This email reminded participants to complete the online post-treatment battery of questionnaires, which was identical to the first battery of online pre-treatment questionnaires but swapped out the demographic information questions for the Homework Rating Scale (Appendix J), which was completed at the beginning of the battery. This reminder email was sent a second time, approximately a week following the first emailing, to participants who still had not yet completed the post-treatment questionnaires, unless they had indicated (via email) to the principle investigator that they were declining to participate. For participants in the Top Strengths, Bottom Strengths, or Placebo conditions, this concluded their participation in the study. Following Waitlist/Control participants’ completion of the post-treatment questionnaires and the VIA Survey of Character Strengths, the principle investigator arranged to give the Character Strengths presentation to that organization.

Delimitations

There are several delimitations of the current study that should be addressed. First, the study design did not allow for a blind administration of the interventions; the principle investigator was aware of the specific condition to which the participants had been randomized, and this may have led to researcher bias. As previously mentioned, in order to guard against this bias, the psychoeducational presentations were standardized as much as possible between all experimental conditions (Timetable for Presentations; Appendix O). In addition, a manipulation check was devised to attempt to control for any differences both within and between conditions in the quality of presentations given to organizations (Third Party Rating Form; Appendix X). Results of the analysis of this manipulation check (presented in the next section) indicated that there were not any differences of this nature between conditions. Second, there was a variable amount of attrition between conditions; however, as 187 participants completed post-treatment measures out of the original 275 who completed pre- treatment measures and/or VIA Surveys, nearly 70% percent of the original sample was retained. This is an acceptable level of participant dropout for this type of research design. Third, compared to other types of randomized controlled trials in which interventions are administered individually, the design of the current study presented the primary intervention at the group level. This procedure violates one of the assumptions of ANOVA, independence of observations. While statistical procedures attempted to control for this difficulty, it remains that the statistical analyses may not have been adequate to compare differences between groups. For this reason, several ANOVA and ANCOVA analyses were run with participant data, and the results were interpreted using a convergence of the available evidence. Fourth, the recruitment, data collection, and participant reminder procedures relied almost exclusively on online media, particularly email. Instructions for completion of pre- treatment questionnaires and the VIA Survey were provided only in email form, while instructions for completing the post-treatment questionnaires were given in written form as well as through individual emails. Data was collected exclusively using online, third-party survey tools. For these reasons, participants who do not regularly check their email or who may not have been proficient in the use of internet technology, may have been at a relative disadvantage in completing the questionnaires.

Finally, there was likely an inconsistent amount of time that elapsed between participants’ completion of pre-treatment and post-treatment outcome measures. However, as this amount of time seemed to vary equally for participants between all conditions, it is unlikely that these discrepancies in time between the completion of the outcome measures varied systematically between levels of the independent variable, or at least not enough to substantially affect analyses. Planned Data Analyses The difference in scores between pre-treatment outcome measures and post-treatment measures was calculated for each participant and entered into a One-Way ANOVA as the primary method for determining differences between experimental conditions. However, because this metric is subject to limitations in some analyses (discussed at length in the next section) alternate statistical procedures will also be employed. This includes conducting a series of One- Way ANCOVAs that compare post-treatment means between experimental conditions while controlling for pre-treatment means, ANOVA and ANCOVA analyses that take homework variables such as compliance into account, and examining clinically-significant changes in scores. Post-hoc tests were performed in order to reveal specific between-group differences. Dependent variables were also tested to ensure that the assumptions of ANOVA were met. Additionally, a One-Way ANOVA was calculated to compare the four experimental groups prior to the intervention on all scores for outcome measures, as well as for demographic variables, in order to ensure a priori equivalency between groups.

CHAPTER FOUR

FINDINGS In this section, we present the statistical findings of this study. We begin by specifying how the outcome data collected at pre-treatment and post-treatment will be transformed in order to be used in statistical analyses. Because all of the research questions can be answered through One-Way analysis of variance (ANOVA) and post-hoc analyses, we ensured that the assumptions of ANOVA have been satisfied. Next, we will review all three of the research questions and hypotheses, providing information from the statistical analyses as appropriate in an effort to answer each research question. Several additional, and some unexpected, findings were revealed through a series of regression analyses, and these will be presented after the research questions have been addressed.

Transformation of Dependent Variables

The dependent variables (scale and subscale raw scores) yielded by the outcome measures at pre-treatment and post-treatment were transformed into new variables that are amenable to ANOVA analysis. We created a set of dependent variables for statistical analysis by calculating the difference between participants’ scores on all measures from pre-treatment to post-treatment, a technique that is commonly implemented under pre-treatment/post-treatment designs (Dimitrov & Rumrill, Jr., 2003). These scores are constructed by subtracting participants’ post-treatment scores from their pre-treatment scores (for negative variables, as well as the RSES) and participants’ pre-treatment scores from their post-treatment scores (for positive variables). However, it should be noted that this set of variables is subject to ceiling and floor effects; examination of pre-treatment means and standard deviations (Table 2) reveals that, in general, participants initially scored fairly highly on measures of psychological well-being and mental health while scoring fairly lowly on measures of psychological distress and negative symptoms. In other words, these outcome measures may not be sensitive enough to changes in the constructs under investigation simply because there is not much room for improvement in participants’ raw scores on the outcome questionnaires. The following tests for the assumptions of ANOVA will be conducted on these difference scores as dependent variables.

Assumptions of ANOVA

ANOVA statistical procedures operate under a set of specific assumptions; if these assumptions are not satisfied, then the results of further analyses may be comprised, either in distorting the true relationship between variables or failing to reveal them. The following assumptions of ANOVA will be reviewed in the subsequent section: normality of dependent variables, equality of variances for all dependent variables between groups, and independence of observations (Howell, 2010; Glass & Hopkins, 1996). We will then identify any potential outliers that might skew the data analyses, as well as ensure that there is sufficient power to carry out ANOVA statistics. We will also examine the experimental groups for any demographic of pre-treatment outcome measure differences that might have existed prior to the intervention (in order to establish equality between the groups prior to the intervention), the internal consistency of the measures used in this sample, and the relative equality of the psychoeducational interventions provided to each group (treatment fidelity analysis).

Normal Distribution of Dependent Variables

In order to test whether all continuous variables are normally-distributed, we employed three tests advocated by Osborne and Waters (2002): skewness, kurtosis, and visual inspection of the data plots with an overlaid normal curve. Any variable that failed two or all three of these tests were considered to have non-normal distributions, while variables that passed two or all three tests were considered to have satisfied the requirement for a normal distribution. This leniency was implemented for cases in which an outlier or two might have distorted the kurtosis estimate of normality, which was then supported or ruled out by visual inspection of the variable histogram. This leniency in normality was implemented because Glass and Hopkins (1996) indicate that it is acceptable to have minor violations of normality in ANOVA analyses, provided the sample includes a large number of cases. For difference scores, all but six of the 20 variables met the above criteria for satisfying the assumption of normality; however, the RSES total score, the MHC-SF Social Well-being subscale, the BADS Avoidance/Rumination subscale, the BADS Social Impairment subscale, the BADS total score, and the OQ-45 Interpersonal Problems subscale failed to meet these criteria. Visual examination of the distributions for these measures indicates that they likely failed the assumption of normality because their curves were leptokurtic; regardless, nearly all of the other

84 scales appeared to have a similar normal distribution with the majority of scores centered around the mean. No further transformation of the difference scores will be necessary, as they approximate the normal distribution. However, further examination of the histograms for each dependent variable indicated that outliers may be compromising the normality statistics for at least seven of the dependent variables. Outliers will be investigated further in a later section.

Equality of Variances between Groups

Because there are unequal numbers of participants in the experimental conditions in the sample, we cannot assume equality of variance between groups. For ANOVA analyses, Levene’s tests the assumption of homogeneity of variance (Glass & Hopkins, 1996). Only two difference scores did not meet this assumption according to Levene’s test: the MHC-SF Emotional well- being subscale (p = .02) and the BADS total score (p = .02); however, the BADS total score satisfied this assumption after a few outliers were removed (which will be addressed in a later section). Therefore, we must be cautious when interpreting any between-group differences for the MHC-SF Emotional well-being subscale.

Independence of Observations

ANOVA statistics require that all data observations collected be independent from each other; this means that data points must not be influenced by each other (Howell, 2010). Nonindependence of data commonly occurs when treatments are group-administered and involve interactions between participants (Glass & Hopkins, 1996). Unfortunately, the experimental design of the current study operated under a group intervention procedure in which the presentations were given to organizations, though data was collected at the individual level with employees of those organizations. This makes the violation of the independence of observations assumption a valid concern. Therefore, we must investigate any possible differences between all organizations within each experimental condition. Not only will this allow for us to somewhat fulfill this assumption, but it will also ensure that no single organization within an experimental condition is skewing the data for the entire group. To this end, we conducted a series of One-Way ANOVAs (one for each of the four experimental conditions) on pre-treatment/post-treatment difference scores, with each organization within their respective experimental condition as a separate level of the independent variable. For example, as there were 6 different organizations who received the Top Strengths

85 intervention, there will be 6 levels of the independent variable in the One-Way ANOVA that investigates any between-organization differences within the Top Strengths experimental condition. Below are the results of the One-Way ANOVA’s with each of the four experimental conditions. Because there are large differences in the number of participants in the Top Strengths condition (n = 3, 6, 6, 7, 8, and 19), homogeneity of variances becomes a concern with this analysis. However, Levene’s test indicated that all 20 difference scores met this requirement. The ANOVA revealed a significant difference between organizations for the SWLS, F(5, 43) = 2.90, p < .05. No other significant differences between groups were found. Tukey post-hoc analysis revealed that both group #3 (p < .05) and group #4 (p < .05) had a larger change score than group #2 on the SWLS (mean difference between difference scores was 5.92 and 6.33, respectively). However, it should be noted that group #2 only consisted of three individuals who completed both the pre-treatment and post-treatment, which may not be an adequate number of cases by which to estimate the mean change in SWLS from pre-treatment to post-treatment. In the Bottom Strengths condition, there were groups of sizes n = 2, 10, 10, and 24. Both the SWLS and the CESD failed Levene’s test, indicating that variance is not equivalent between organizations in the Bottom Strengths condition for these measures. There were again significant differences between organizations for the SWLS score, F(3, 42) = 3.32, p < .05. There were also differences between organizations for the MLQ Presence scale, F(3, 42) = 4.56, p < .01; the OQ- 45 Symptom Distress subscale, F(3,42) = 4.01, p < .05; the OQ-45 Social Role subscale, F(3, 42) = 3.00, p < .05; and the OQ-45 Total Score, F(3,42) = 4.49, p < .01. SWLS post-hoc tests were non-significant, and we may be able to account for the inequality of variance between groups to explain this finding. Tukey post-hoc tests revealed that both group #1 (p < .01) and group #3 (p < .05) experienced a greater difference score than group #2 on the MLQ Presence scale. Participants in group #2 experienced a greater change in OQ-45 Symptom Distress scores (μ = 25.00) than all three of the other groups (p < .05) and a greater change in OQ-45 Total Scores (μ = 37.50) than groups #3 and 4 (p < .01). Participants in group #1 experienced slightly greater change in thier OQ-45 Social Role score compared to those in group #3 (p < .05). Again, most of these differences can be attributed to the low n in group #2 (2 participants); examination of these participants’ difference scores revealed that both experienced large reductions in their OQ-45

86 scores from pre-treatment to post-treatment; these results, however, should not significantly alter the statistics of the larger experimental condition (in which n = 49). In the Placebo group, there were also large differences in group size (n = 7, 8, and 25); however, Levene’s test demonstrated that the assumption of equality of variances between groups had been met for all difference scores. There were no significant differences between organizations for any of the difference scores in this experimental condition. As for the Waitlist/Control group, which consisted of 5 groups of sizes n = 4, 10, 10, 12, and 16, homogeneity of variances was only a problem for the MHC-SF Total Score (p < .05). However, there were not any differences in difference scores between organizations in this condition that achieved statistical significance. Though we cannot ignore the nonindependence of the data in this study, we have at least demonstrated that there are not substantial differences between organizations within each condition in this data sample. This means that we can feel confident that the data will not vary significantly within different levels of the independent variables in later One-Way ANOVA analyses that compare scores between experimental conditions. It also means that within-group variance is likely within normal ranges for the Mean Square and F-statistics to render the analyses capable of demonstrating statistically significant differences, should they exist for any of the difference score variables between experimental groups.

Outliers

To identify outliers, z-scores were calculated for all variables. Data cases with z-scores greater than 2.0 or smaller than -2.0 indicates that they are more than two standard deviations from the group mean for that variable. In a sample size of 187, and assuming a normal distribution for each dependent variable, we would expect approximately 9 cases to exceed this criterion, so we expected to find several data points for each measure with scores outside this range. Results of this transformation showed that each difference scores had between 7 to 16 data points with z-scores outside of the ± 2.0 SD range, which is an acceptable number of outliers in a sample of this size. However, there were several data points that had much greater magnitude z- scores; for the purposes of this study, any data point with a z-score outside of the ± 3.0 SD range (less than 99.7% occurrence in the normal population, assuming a normal distribution) was automatically removed from further data analyses because of their extreme impact on measures of variance. This removed the following number of outlying data points from each measure: 87

SWLS - 3, MLQ Presence – 4, MLQ Search – 3, RSES – 6, MHC-SF Emotional well-being – 2, MHC-SF Social well-being – 1, MHC-SF Psychological well-being – 1, MHC-SF Total score – 1, PANAS Negative affect – 5, PANAS Positive affect – 3, CESD – 4, BADS Activation – 1, BADS Avoidance/Rumination – 2, BADS Work/School Impairment – 3, BADS Social Impairment – 2, BADS Total score – 3, OQ-45 Symptom Distress – 3, OQ-45 Interpersonal – 4, OQ-45 Social Role – 2, and OQ-45 Total score – 4. These outliers did not seem to be concentrated in any one condition; rather, they seemed to be fairly evenly spread between conditions, and varied in both positive and negative valence. Removal of these outliers should enhance the normality of all dependent variables.

Power Analysis

Recall from Chapter 3 that, anticipating an effect size of f = .175, which is exactly halfway between small and medium, our a priori power analysis recommended 184 total participants for the study. Our current sample includes 187 participants; though with the above outliers removed, a few measures will have slightly fewer cases with which to operate. However, removing these outliers should actually strengthen the statistical analyses, so being a few cases short of 184 is acceptable in this regard. If a small-medium effect size (or greater) truly exists between these groups with these measures, ANOVA should have sufficient power to reveal those differences.

Equality of Groups Prior to Intervention

In order to investigate whether all of the experimental groups were virtually identical prior to the intervention (psychoeducational presentations), we ran two separate analyses. The first analysis examined any demographic differences between experimental conditions. The second analysis examined any differences in the means of (pre-treatment) outcome measures between conditions prior to the intervention. Assuming the groups are roughly equivalent prior to the experimental condition will allow us to isolate the impact of the experimental intervention as much as possible with respect to its effect(s). Demographics. Table 1 displays the demographic data of all 187 participants, separated by experimental condition, who completed both the pre-treatment and post-treatment outcome measures. A One-way ANOVA compared all four experimental conditions on the demographics of Age, Gender, and Income (which, for the purposes of this analysis, was treated as a

88 continuous variable even though it is actually a 5-category interval variable). There were significant differences between groups for Age, F(3, 182) = 11.56, p < .001 as well as Gender, F(3, 183) = 3.06, p < .05, but not for Income. Tukey post-hoc tests revealed that individuals in the Placebo condition were significantly older than those is all other conditions, with a mean difference of 12.83 years older than those in the Control group (p < .01), 11.14 years older than those in the Bottom Strengths group (p < .01), and 19.02 years older than those in the Top Strengths group (p < .001). This effect is likely explained by two organizations in the Placebo condition that consisted of a number of individuals of advanced age. Post-hoc tests for Gender— which became a percentage score for the purposes of this ANOVA analysis—found that there were significantly more men in the Control condition (20 men out of 52 total participants) than in the Placebo condition (6 men out of 40 participants, p < .05). A multinomial logistic regression analysis, which was conducted to compare all four experimental groups on Race/Ethnicity, did not find any differences between groups on this demographic. However, it should be noted that the small sampling (only one person in a specific Race/Ethnicity demographic in some conditions) may have adversely affected the analysis’ integrity. Pre-Treatment Outcome Measures. Means and standard deviations for all outcome measures at pre-treatment and post-treatment, separated by experimental condition, are shown in Table 2. In order to demonstrate that there were no differences between groups on any of the outcome measures prior to the intervention, we conducted a One-way ANOVA on pre-treatment means using experimental condition as the independent variable. All dependent variables passed Levene’s test for homogeneity of variances, and the ANOVA only found one statistically significant difference between groups: the OQ-45 Social Role subscale, F(3, 183) = 5.21, p < .01. Post-hoc tests revealed that participants in the Top Strengths condition had a 2.68-point higher mean than participants in the Placebo condition (p < .01) and 1.95 points higher than those in the Bottom Strengths condition (p < .05) at pre-treatment. The Control group also exhibited a 1.98-point higher raw score over the Placebo group (p < .05) on this measure. Though these differences may be statistically-significant, the difference is not so grand as to preclude further analyses with this measure. For all intents and purposes, these four groups were equivalent on the outcome measures prior to the intervention.

Reliability of Measures in this Sample

Internal consistency statistics (Cronbach’s alpha coefficients) for all scales and subscales are presented in Table 4 in three categories; this table compares the internal consistency coefficients reported in the published psychometric articles for each scale and subscale with the internal consistency found in the current study at both pre-treatment and post-treatment. As examination of the table will demonstrate, reliability for these measures met or exceeded that of the published coefficients for nearly every scale and subscale. In addition, all but two scales achieved at least a Cronbach’s α = .80, the designated cutoff for preferred reliability according to Kline (2000), except for the BADS Work/School Impairment Scale (pre-treatment and post- treatment α’s = .77) and the OQ-45 Social Role Scale (pre-treatment α = .64; post-treatment α = .65), which purport to measure similar constructs. However, the reported internal consistency coefficients for these two scales is also reportedly low (α’s = .78 and .68, respectively), so these results are considered consistent with known reliability estimates and are therefore acceptable.

Treatment Fidelity

In order to maintain treatment fidelity both within and between experimental conditions as much as possible, one participant from each organization filled out a Third Party Rating Form (Appendix X) immediately following the psychoeducational presentation. This form contained a checklist for specific content areas to be covered in the presentation, specific to either the Character Strengths or Health & Wellness presentation, in addition to six items that the organization’s representative answered on a 5-point Likert scale that ranged from “1 = Strongly Disagree” to “5 = Strongly Agree.” These questions were concerned with presenter enthusiasm, presenter expertise, the extent to which the presenter encouraged questions, the presenter’s ability to facilitate a discussion, the clarity of the information presented, and the clarity of the homework assignments given based on the presentation. Keeping in mind the small sample size that this manipulation check represents (13 organizations in each of three experimental conditions, not include the Waitlist/Control condition), a One-Way ANOVA failed to find any differences between experimental conditions for any of the six items, or for a total score that was created by summing the scores on these six items. Visual inspection of the scores confirms that any possible differences were minimal; in fact, those who filled out the measure tended to respond to each item with the highest possible level of agreement. However, one trend was

90 readily apparent; the principle investigator, who was also the sole presenter for each of the presentations given in this study, consistently scored more lowly on the items concerning the clarity of information presented and the clarity of the homework assignments (more 4’s than 5’s on these two items; see Table 5). However, this trend was consistent in all experimental groups. This procedure was designed to eliminate researcher bias (and the measure, itself, might have been able to assist in controlling for researcher bias, were it found necessary to do so). The results of this analysis indicate that we may generally assume equivalency between experimental groups for the psychoeducational presentations in terms of presenter variables. Now that we have, to the greatest extent possible, met the assumptions of ANOVA, ensured that each experimental group was roughly equivalent prior to the intervention, and determined that there were not any biases or relative differences in the delivery of the interventions between groups, we proceed with answering the research questions.

Research Questions

All three research questions require a One-Way ANOVA and post-hoc tests in order to support their associated hypotheses. Therefore, an ANOVA was used to analyze potential differences between experimental conditions using pre-treatment/post-treatment difference scores for all dependent variables. Results of these analyses, including post-hoc tests, will be presented within the research questions below as appropriate.

Research Question One

The first research question was as follows: “Will individuals who focus on employing their top-ranked Character Strengths experience an increase in positive variables and a decrease in negative symptoms?” We hypothesized that participants in the Top Strengths condition would experience both an increase in positive variables (positive affect, meaning in life, satisfaction with life, psychological well-being, behavioral activation, and self-esteem) as well as a decrease in negative symptoms (depression, negative affect, and general negative psychological symptoms) relative to participants in the Waitlist/Control condition. This research question was informed by the Capitalization model, and is the basis for the Signature Strengths positive psychology intervention (Peterson, 2006; Seligman, 2002). The One-way ANOVA found statistically significant differences between groups for the following two difference scores: the MHC-SF Emotional well-being subscale, F(3, 181) = 2.81,

91 p < .05 and the OQ-45 Symptom Distress subscale, F(3, 180) = 3.26, p < .05. The OQ-45 Social Role subscale also approached statistical significance (p = .06). However, Tukey post-hoc analyses revealed that difference scores for participants in the Top Strengths condition were not statistically different from those of participants in any of the other conditions on either of these measures.

Research Question Two

The second research question asked: “Will individuals who focus on employing their relative character weaknesses (bottom-ranked Character Strengths) experience an increase in positive variables and a decrease in negative symptoms?” We similarly hypothesized that participants in the Bottom Strengths condition would experience both an increase in positive variables (positive affect, meaning in life, satisfaction with life, psychological well-being, behavioral activation, and self-esteem) as well as a decrease in negative symptoms (depression, negative affect, and general negative psychological symptoms) relative to participants in the Waitlist/Control condition. This research question was informed by the Compensation model and is based on preliminary evidence from Rust, Diessner, and Reade (2009). As previously stated, the One-way ANOVA found statistically significant differences between groups for the MHC-SF Emotional well-being subscale and the OQ-45 Symptom Distress subscale. Tukey post-hoc analyses revealed that participants in the Bottom Strengths condition reported a greater increase in scores on the Emotional well-being subscale than participants in the Placebo condition (p < .05) with a medium effect size (Cohen’s d = .56), as shown in Figure 2. However, recall that this particular scale failed to satisfy Levene’s test for equality of variance between experimental conditions, indicating that there is likely greater within-group variance in some groups compared to others that may compromise the integrity of the ANOVA analyses. For this reason, we conducted a T3 post-hoc test with this measure, which does not assume equality of variance between groups; this analysis failed to find a statistically significant difference between the Bottom Strengths and Placebo conditions for pre- treatment/post-treatment difference scores on the Emotional Well-being scale (p = .07). Considering also that the mean difference (in difference scores) between these groups is a mere 1.10 points (meaning that, on average, Bottom Strengths participants experienced a 1.10-point greater increase in MHC-SF Emotional well-being score by post-treatment than did Placebo

92 participants), a true difference between these two groups for this variable is likely minimal, if it exists at all. In addition, participants in the Bottom Strengths condition experienced a greater reduction in OQ-45 Symptom Distress scores than did participants in the Placebo (p < .05, Cohen’s d = .52) and Waitlist/Control (p < .05, Cohen’s d = .56) conditions, with both mean differences constituting a medium effect size, as shown in Figure 3. On average, participants in the Bottom Strengths condition experienced a 4.35-point greater reduction in OQ-45 Symptom Distress scores compared to participants in the Placebo condition, and a 4.11-point greater reduction compared to participants in the Waitlist/Control condition by post-treatment.

Research Question Three

The third and final research question asked: “In which experimental condition—Top Strengths or Bottom Strengths—will individuals experience the greatest increase in positive variables and/or the greatest decrease in negative symptoms? This research question compared the outcomes of the Capitalization model to the outcomes of the Compensation model. We avoided concretely predicting which group would outperform the other in this study, as research to-date has not isolated the effects of working on bottom-ranked Character Strengths, only. However, as participants in each of these conditions would be working on Character Strengths (regardless of whether those strengths were that person’s top five or bottom five), we could propose that there will not be much difference between the two conditions relative to each other. This is exactly what we found. Not only did the One-way ANOVA only find significant differences between experimental conditions on two separate difference scores, but post-hoc tests did not find any differences (on those two difference scores) between the Top Strengths and Bottom Strengths conditions. In other words, it appears that participants in the Top Strengths condition did not experience any more of an increase or decrease in positive variables or negative symptoms than did participants in the Bottom Strengths condition, and vice versa.

Additional Analyses of the Research Questions

There are other statistical methods available for comparing the outcomes between experimental groups in this study. In this section, we will conduct several of these alternative methods and compare them to the original statistical analysis. This will allow us to rely on the convergence of available evidence for interpretation of the data in this study.

First, we re-ran the ANOVA described above while controlling for age and gender as covariates, using an ANCOVA; then, we did the same while controlling for Big Five Personality estimates. Next, we ran a series of ANCOVAs in which post-treatment scores were compared by experimental condition while controlling for pre-treatment scores. Then, we removed all participants from the Top Strengths, Bottom Strengths, and Placebo conditions who indicated on the Homework Rating Scale that they did not complete any homework assignments, and then we re-ran the original One-Way ANOVA analysis on difference scores. We also controlled for the number of activities and the amount of time participants spent on homework assignments using an ANCOVA to measure between-group differences in pre/post difference scores (all participants were included in this particular analysis). Finally, we examined clinically significant changes in scores on the OQ-45 Total score between experimental conditions. Again, the convergence of all these analyses will create a much more complete picture of the real differences between groups than would any one statistical procedure, alone.

ANCOVA with Age and Gender as Covariates

In order to control for the effects of age and gender in the analyses, the analysis on difference scores was re-run with both age and gender as covariates. Though age was not significantly related to any of the difference scores in the outcome measures, gender was significantly related to the BADS – Activation Scale, F(1, 149) = 6.81, p < .05; and the BADS Total Score, F(1, 149) = 5.22, p < .05. After controlling for age and gender, there were statistically significant differences between experimental conditions on only one of the outcome measures, the MHC-SF Emotional Well-Being Subscale, F(3, 149) = 5.21, p < .01. However, the OQ-45.2 Symptom Distress subscale (p = .06), OQ-45.2 Social Role subscale (p = .06), and RSES Total Score approached significance (p = .14).

ANCOVA with Big Five Personality Estimates as Covariates

In order to control for the effects of personality variables in the analyses, the analysis on difference scores was re-run with TIPI scores for Openness, Conscientiousness, Agreeableness, Extraversion, and Emotional Stability as covariates. After controlling for these estimates of personality, there were statistically significant differences between experimental conditions for the following difference scores on outcome measures: the MHC-SF Emotional Well-Being Subscale, F(3, 147) = 3.59, p < .05; the OQ-45.2 Symptom Distress subscale, F(3, 147) = 3.06, p

< .05; and the OQ-45.2 Social Role subscale, F(3, 147) = 2.81, p < .05. These results are consistent with previous analyses, indicating that measurable aspects of personality pertaining to the Big Five model may not significantly impact the benefit derived from working on your top- ranked or bottom-ranked Character Strengths.

Alternate Data Analysis using ANCOVA

In outcome research in which the same measures are given to participants at pre- treatment and post-treatment, it is acceptable to run a series of One-Way ANCOVAs with the post-treatment measures as the dependent variables and the pre-treatment dependent variables as covariates (Dimitrov & Rumrill, Jr., 2003; for an example, see McCloskey, Noblett, Deffenbacher, Gollan, & Coccaro, 2008). These statistics were run for all 20 outcome variables, which each met Levene’s test criteria for equality of variances. The ANCOVAs found significant differences between experimental conditions for three post-treatment outcome measures, after controlling for pre-treatment outcome measures: the RSES total score, F(3, 182) = 2.98, p < .05, partial ETA² = .05, observed power = .70; the MHC-SF Emotional well-being subscale, F(3, 182) = 2.70, p < .05, partial ETA² = .04, observed power = .65; and the OQ-45 Symptom Distress subscale, F(3, 182) = 3.78, p < .05, partial ETA² = .06, observed power = .81. The ANCOVA with the OQ-45 Total score approached, but did not achieve significance in these analyses (p = .09). Tukey post-hoc tests indicated that participants in the Bottom Strengths conditions reported more self-esteem at post-treatment (as evidenced by a reduction in RSES scores), after controlling for pre-treatment self-esteem scores, compared to participants in the Waitlist/Control condition (p < .05, Cohen’s d = .39). Post-hoc tests were unable to find any significant differences between experimental groups for the MHC-SF Emotional well-being subscale. However, post-hoc tests for the OQ-45 Symptom Distress subscale indicated that individuals in the Bottom Strengths condition reported a lower score on the OQ-45 Symptom Distress subscale at post-treatment than individuals in the Placebo condition (p < .05, Cohen’s d = .32) after controlling for pre-treatment scores. These results indicate that individuals in the Bottom Strengths condition experienced an increase in self-esteem and a decrease in symptom distress from pre-treatment to post-treatment, and these changes exceeded those experienced by other groups on the same measures. This finding with the OQ-45 Symptom Distress subscale is consistent with previous ANOVA analyses; however, this is the first statistical analysis that has 95 found a significant difference between groups for the RSES Total score. These results should be interpreted in light of all the other analyses that compared outcome measures between experimental conditions.

Alternate Data Analyses that Account for Homework Variables

The Homework Rating Scale contains two open-ended questions that ask participants to indicate the number of homework assignment (Character Strengths or Health & Wellness) activities they completed over the past month, as well the number of hours they spent on Character Strengths or Health & Wellness activities. We can take their responses to these items into account when analyzing the differences from pre-treatment to post-treatment between experimental conditions in two ways: (1) removing the scores of those who did not complete any homework assignments (Character Strengths or Health & Wellness activities), or (2) controlling for the number of assignments and the amount of time spent on assignments by making them covariates in an ANCOVA analysis. Removal of Homework Non-Completers. Several participants indicated “0” for one or both of these aforementioned compliance categories (# of hours or # of activities): 15 participants in the Placebo condition, 18 participants in the Bottom Strengths condition, and 14 participants in the Top Strengths condition. These cases were removed and the One-Way ANOVA was re-run with only those participants who completed at least one activity and more than 0 hours of homework in their respective experimental condition (but still including Waitlist/Control participants, who did not complete the HRS. Results were non-significant except for the OQ-45 Symptom Distress Scale, F(3, 133) = 4.15, p < .01, which produced roughly the same results as the previous ANOVA that included all participants. Tukey post-hoc tests revealed that participants in the Bottom Strengths condition experienced a greater decrease in OQ-45 Symptom Distress scores than did participants in the Waitlist/Control condition (p < .05) and in the Top Strengths condition (p < .01), mirroring the effects found in the previous analysis. This ANOVA also approached significance for the RSES difference scores (p = .06), the MHC-SF Emotional well-being subscale difference scores (p = .053), and the MHC-SF Social well-being subscale difference scores (p = .09). Controlling for Homework Assignments. We also wanted to see if we could control for these homework quantity estimate variables by making them covariates. To this end, we ran two ANCOVA’s—both of which were identical to the One-Way ANOVA run earlier with the 96 difference scores at dependent variables, but this time with the number of activities and the number of hours variables entered as covariates—with all 187 participants. The first ANCOVA, which controlled for the number of hours participants spent on homework assignments in their respective experimental conditions, found differences between groups as evidenced by the omnibus test, F(51, 403) = 1.45, Wilks' lambda = .61, p < .05. The equality of variances assumption was met for all variables except for the MLQ Search scale. After controlling for the number of hours participants spent in the past month on homework activities, there were significant differences between experimental conditions for the amount of pre-treatment/post- treatment change experienced by participants for the following scores: the MHC – Emotional well-being subscale, F(3, 151) = 3.60, p < .05, partial ETA² = .07, observed power = .78; the OQ-45 – Symptom Distress subscale, F(3, 151) = 3.03 p < .05, partial ETA² = .06, observed power = .70; and the OQ-45 Social Role subscale, F(3, 151) = 2.77, p < .05, partial ETA² = .05, observed power = .66, consistent with the results of the One-Way ANOVA used to answer the research questions. The second ANCOVA, which controlled for the number of activities participants completed consistent with the psychoeducational presentation for their respective experimental conditions, also passed the omnibus test, F(51, 403) = 1.43, Wilks' lambda = .61, p < .05. The equality of variances assumption was met for all variables in this analysis. After controlling for the number of activities participants completed in the past month on homework activities, there were significant differences between experimental conditions for the amount of pre- treatment/post-treatment change experienced by participants for the same scales as in the other ANCOVA: the MHC – Emotional well-being subscale, F(3, 151) = 3.66, p < .05, partial ETA² = .07, observed power = .79; the OQ-45 – Symptom Distress subscale, F(3, 151) = 2.88 p < .05, partial ETA² = .05, observed power = .68; and the OQ-45 Social Role subscale, F(3, 151) = 2.77, p < .05, partial ETA² = .06, observed power = .69. These results were replicated in a MANCOVA with experimental condition as the predictor and number of hours spent on homework activities and number of activities were entered as covariates. The results of these ANCOVA analyses provide further support for the previous findings; there appear to be differences between experimental conditions for the MHC-SF Emotional well- being subscale, the OQ-45 Symptom Distress subscale, and the OQ-45 Social Role subscale, with participants in the Bottom Strengths condition experiencing the greatest change from pre-

97 treatment to post-treatment compared to participants in the Top Strengths, Placebo, and/or Waitlist/Control conditions.

OQ-45 Indices of Clinical Significance

Often, statistical measures of significance can be misleading; for this reason, we also examined a measure of clinical significance. The OQ-45 manual (Lambert et al., 1996) presents specific criteria for establishing clinically-significant change with the OQ-45 Total score. Individuals whose total score does not vary more than 14 points from pre-treatment to post- treatment are considered to be basically unchanged (“No change”). Those whose scores decrease by at least 14 points are considered “Improved” if their total score remains above 63 after this 14+ point reduction or if their pre-treatment score was less than 63. Finally, individuals with a pre-treatment score of 63 or higher who experienced at least a 14-point reduction in their total score that now drops their total score below the 63-point cutoff are considered to have “Recovered.” For the purposes of this study, any participant whose total score increased by 14 or more points were considered to have “Declined.” In this sample, prior to removing any outliers, 22% of participants in the Bottom Strengths condition either improved or recovered (6 and 4 participants, respectively) compared to 18% of participants in the Top Strengths condition (7 and 2), 15% of participants in the Placebo condition (5 and 1), and 12% of participants in the Waitlist/Control condition (6 and 0). These results are depicted in Figure 4.

Additional Findings

A wealth of data was collected in this study in a quest to answer the primary research questions. However, there are many other questions that could also potentially be answered, or hypotheses that could be supported, by this data as well. For the purposes of this study, we will examine two distinct areas of interest: homework compliance and relationships between variable sets. The Compliance section will examine items on the Homework Rating Scale between experimental conditions, investigate relationships between these items and participants’ scores on the outcome measures, and probe for possible personality-based predictors of compliance. The Relationships between Variable Sets section seeks to identify the relationships between four sets of variables: Character Strengths scores, TIPI scores, pre-treatment outcome measures, and demographic data. Relationships between demographic data and the other three sets of variables will be analyzed in this section first so that any significant relationships involving demographics

98 may be identified and controlled in later regression analyses. Finally, the Character Strengths Statistics section will present participants’ mean scores for each of the Character Strengths and attempt to present the “typical” Top Strengths and Bottom Strengths profiles of Character Strengths for participants in the study.

Compliance

The following analyses examine participants’ responses to the Homework Rating Scale. In this section, we will examine any differences in compliance or homework variables between conditions. Then, we will correlate the HRS items with the difference scores to examine the relative impact homework have had on changes in participants’ scores. Finally, we will investigate Character Strengths and TIPI scores’ relationship with assessments of compliance. Differences between Conditions for Homework Rating Scale Items. Means and standard deviations for all individual items and a summed total score on the HRS, separated by the three experimental conditions in which homework was assigned (Top Strengths, Bottom Strengths, and Placebo), are shown in Table 6. A One-Way ANOVA revealed significant differences between groups for the following HRS items: Assignment Clarity, F(2, 132) = 9.67, p < .001; Difficulty, F(2, 132) = 7.57, p < .01; Specificity of Activity, F(2, 132) = 7.00, p < .01; Rationale for Activity, F(2, 132) = 6.88, p < .01; Collaboration, F(2, 132) = 5.64, p < .01; and Obstacles, F(2, 132) = 4.26, p < .05. However, there do not appear to be any substantive differences between conditions for the number of activities completed or the number of hours spent on these activities. This supports the internal validity of the study, suggesting that participants in all conditions were equally able to engage in homework related to their experimental condition. Tukey post-hoc tests will be interpreted with HRS items grouped by similarity. This analysis revealed that participants in the Placebo condition rated Assignment Clarity significantly more highly than did participants in the Top Strengths (p < .001) or Bottom Strengths conditions (p < .01). Similarly, Placebo condition participants rated Specificity of the Activities more highly than did participants in the Top Strengths (p < .01) or Bottom Strengths conditions (p < .05). In addition, Placebo condition participants indicated a higher understanding of the Rationale for the Activities than did participants in either the Top Strengths (p < .01) or Bottom Strengths (p < .01) conditions. These results indicate that participants in the Placebo group reported more clarity, specificity, and understanding of the assignments than did 99 participants in the Character Strengths conditions. This is understandable, given that homework assignments in both of the Character Strengths conditions were more complex than those in the Placebo condition. For Difficulty of assignments, participants in the Bottom Strengths and Placebo conditions indicated greater difficulty in completing the activities than did individuals in the Top Strengths condition (p < .01 for both differences). With respect to the Obstacles encountered by participants in completing the assignments, individuals in the Bottom Strengths condition reported more interference than did Top Strengths individuals (p < .05). These results indicate that participants in the Bottom Strengths condition likely experienced the most difficulty and/or obstacles in completing the assignments out of the three groups in which homework was assigned; however, this is to be expected, as the participants in this group were asked to work on their relative weaknesses (Compensation model), which should theoretically be more difficult than working on relative strengths. As for Collaboration, participants in the Placebo condition indicated more control over planning the assignments than did participants in the Top Strengths (p < .01) and Bottom Strengths (p < .05) conditions. Impact of Compliance Variables on Outcome Measures. In this section, we examined whether the number of hours and/or the number of activities participants reported for homework assignments could be used as predictors of the difference scores on outcome measures. However, in order to explore this relationship and others between compliance and outcome, we first conducted correlational analyses between all HRS items and outcome measure difference scores. This analysis was conducted on both the data set that included all participants in the three homework assignment conditions (Top Strengths, Bottom Strengths, and Placebo) as well as the data set in which all participants who indicated a “0” for number of activities or number of hours on the HRS were removed from statistical analyses. Correlational analyses between HRS items (including number of hours and number of activities) and outcome measure difference scores for both of these data sets did not reveal any noteworthy relationships between these variables. Only a few HRS items were weakly, positively correlated with difference scores to a statistically significant degree, and most of these were uninterpretable. There does not appear to be a relationship between these variable sets in this sample; therefore, regression analyses using HRS

100 items, including the number of hours spent on activities and/or the number of activities completed, to predict outcome measure difference scores were not conducted. Predictors of Compliance. In this section, we hoped to explore any possible relationships between stable personality traits (TIPI scores and Character Strengths) and activity compliance (number of hours, number of activities), using the former constructs as predictors. To do this, we first correlated the number of hours and number of activities variables with TIPI scores and Character Strengths scores. Results indicated that the following TIPI domains and Character Strengths were correlated with the number of activities: Teamwork (r = .28, p < .01), Modesty (r = -.22, p < .05), and TIPI-Openness (r = .19, p < .05). In addition, the following TIPI domains and Character Strengths were correlated with the number of hours: Self-Regulation (r = .25, p < .05), Perspective (r = .23, p < .05), Bravery (r = .23, p < .05), Perseverance (r = .22, p < .05), Judgment (r = .22, p < .05), Love of Learning (r = .21, p < .05), and TIPI- Conscientiousness (r = .17, p < .05). Regression analyses were conducted based on the relationships shown above. By way of a series of hierarchical analyses that controlled for the effects of all possible predictors in the model, both Teamwork (β = .36, p < .01) and Modesty (β = -.30, p < .01) ultimately emerged as significant predictors of the number of activities completed, F(2, 90) = 8.98, p < .001. This model explained 17% (R² = .166) of the variance in participant estimates of the number of hours dedicated to homework assignments over the past month. These results indicate that as personality constructs such as Teamwork increase and Modesty decrease, compliance (in terms of the number of activities completed) somewhat increases. After conducting another series of hierarchical analyses to control for the effects of all of the possible predictors in the second model, only Self-Regulation (β = .25, p < .05) was a significant predictor of the number of hours spent on homework assignments, F(1, 91) = 5.94, p < .05. However, this model only explained 6% (R² = .061) of the variance in participant estimates of the number of hours they spent on homework assignments over the past month. Surprisingly, Self-Regulation was only weakly related to the amount of time participants spent on assignments.

Relationships between Variable Sets

The pre-treatment questionnaire battery and the VIA Survey of Character Strengths were completed at the same time, prior to the primary intervention. Because a large number of 101 participants (263) fully completed the pre-treatment battery of questionnaires (with an additional 12 providing partial data), and because Character Strengths data was retained for 126 of these participants in the sample, we have a great opportunity to learn about the inter-relationships between multiple variables in the study. To do this, we divided the variables of interest into four main categories: (1) demographic data, (2) scores on the Ten-Item Personality Inventory (TIPI; Gosling, Rentfrow, & Swann, 2003), which yields estimates for each of the “Big Five” personality traits, (3) Character Strengths, and (4) pre-treatment outcome measures. Demographic data included age, a continuous variable; gender, a binary categorical variable; ethnicity, a categorical variable; and income, an interval variable used to estimate socioeconomic status. Scores on the TIPI are continuous variables that offer an estimate for Extraversion, Agreeableness, Conscientiousness, Emotional Stability (the inverse of Neuroticism as measured by the NEO-PI-3), and Openness. Character Strengths scores are continuous variables that calibrate the influence of each of 24 Character Strengths on an individual’s daily life and perception of themselves. Outcome measures (at pre-treatment, prior to any experimental manipulation) include all the scales and subscales of the mental health (both positive and negative) questionnaires introduced in the Method section: the Satisfaction with Life scale total score; the Meaning in Life Questionnaire’s Presence scale and Search scale; the Rosenberg Self- Esteem Scale total score; the Mental Health Continuum total score and subscales Emotional well-being, Social well-being, and Psychological well-being; the Positive and Negative Affect Schedule’s Negative Affect scale and Positive Affect scale; the Center for Epidemiological Studies – Depression total score; the Behavioral Activation for Depression Scale total score and subscales Activation, Avoidance/rumination, Work/School impairment, and Social impairment; and the Outcome Questionnaire – 45.2 total score and subscales Symptom Distress, Interpersonal Problems, and Social Role. Demographic data for the participants who provided these data can be found in Table 3. Means and standard deviations for outcome variables and the TIPI for the pre-treatment sample are presented in Table 7. Means and standard deviations for Character Strengths are available in Table 8. In this section, we will first cover the assumptions of linear regression; then, we will investigate demographic data that may act as moderating variables, which may influence the relationships between other variables. Next, we will conduct a series of correlational, multiple regression, and hierarchical linear regression analyses in order to derive the most parsimonious

102 and efficacious relationships between these four sets of variables. It should be noted that, with some demographic data, a series of One-Way ANOVAs will be required, as we will be dealing with categorical data as predictors. In these instances, the statistical assumptions for conducting One-Way ANOVAs are identical to those of linear regression, with the addition of the expectation of homogeneity of variances between groups (Glass & Hopkins, 1996), as mentioned earlier in this chapter. Because the various categorizations of participants into separate groups automatically creates a disparity of group sizes with some groups being substantially larger than others (e.g., gender, income, ethnicity, etc.), testing for equality of variances is essential. This additional assumption for ANOVA will be covered in appropriate sections, as relevant. Assumptions of Multiple Regression Analyses. Before any correlational or regression analyses can be conducted, we need to ensure that the assumptions for multiple linear regression are satisfied. The primary assumptions are as follows: (1) all continuous variables are normally- distributed, (2) there is a linear relationship between the predictor(s) and the criterion variable, (3) error variance in the relationships between variables exhibit homoscedascticity, (4) there are no issues with multicollinearity between predictor variables, and (5) we have sufficient power for the statistical procedures (Howell, 2010; Osborne & Waters, 2002). These will be covered in the ensuing sections. Please note that although the requirements for fulfilling the assumption of normality were covered earlier in this chapter when discussing the assumptions for ANOVA, those statistics were conducted on the difference scores of outcome measures; in the current regression analyses, we plan on utilizing the pre-treatment outcome measures, which warrants additional examination in order to verify that these variables meet the normality assumption.

Assumption of Normality

We employed the same method described earlier for verifying normality in the pre- treatment outcome variables. Normality was assessed by analyzing skewness and kurtosis statistics as well as visually examining a histogram of the variable’s distribution with an overlaid normal curve. Variables must pass at least two out of these three tests in order to satisfy the assumption of a normal distribution. According to these criteria, the following continuous predictor variables failed to meet the assumptions for normality: the Character Strength of Teamwork, which appeared to have a normal distribution, and for which kurtosis and skewness statistics may have been compromised by two outliers; the Character Strength of Forgiveness and Mercy, which also appeared to have a 103 normal distribution, but also may have had kurtosis and skewness violated due to two outliers; the Character Strength of Perseverance, which barely failed kurtosis and skewness criteria; the Character Strength of Leadership, which also barely failed kurtosis and skewness criteria; the Character Strength of Spirituality, which was skewed to the right; the TIPI score of Extraversion, which was very slightly skewed to the right; and the TIPI score of Conscientiousness, which was skewed to the right. These findings are not troublesome, however; the Character Strengths and Virtues Handbook (Peterson & Seligman, 2004) indicates that in general population samples, scores on all Character Strengths tend to be slightly skewed to the right, but they still show sufficient variation for statistical analyses. This is consistent with findings of the normality of Character Strengths in the current study. The demographic variable Age passed skewness and visual inspection but failed kurtosis, most likely due to the fact that its leftmost tail was cut off (no participants under the age of 18). It otherwise appears to be a normal distribution. Altogether, 23 out of 30 continuous criterion variables met the normal distribution assumption; out of the 7 that failed this assumption, only Teamwork and Leadership did not show up as significant predictors in later analyses. In addition, the higher scores on the TIPI scales of Extraversion and Conscientiousness may indicate a tendency for respondents to generally rate themselves positively. This tendency to rate oneself in a generally positive manner evidently carried over into the outcome questionnaires. Because this sample was drawn from the general population, instead of a clinical population, scores on several of these measures (particularly the CES-D, OQ-45.2, and BADS) tended to fall on the more positive end of the spectrum compared to scores that might have been derived from a depressed population, for example. In other words, respondents in this sample tended to rate themselves highly on positive variables such as satisfaction with life and lowly on negative variables such as depression. This may be attributed to the fact that the general population is (generally) psychologically healthy. For these reasons, distributions of the outcome measures in this study will likely be skewed in a positive direction—to the left for all negative variable scales and subscales, and to the right for all positive variable scales and subscales, with the exception of the RSES, in which low scores are desirable—which also creates a ceiling effect for positive scales and a floor effect for negative scales. Taking this justification into consideration, the following criterion variables failed to meet the assumptions for normality:

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SWLS total score, which was slightly skewed in the positive direction; MLQ – Presence scale, which was skewed in the positive direction; RSES total score, which was skewed in the positive direction; MHC-SF emotional well-being subscale, which was skewed in the positive direction; MHC-SF psychological well-being subscale, which was skewed in the positive direction; PANAS – Negative scale, which was skewed in the positive direction; CES-D total score, which was skewed in the positive direction; BADS avoidance/rumination subscale, which was skewed in the positive direction; BADS work/school impairment subscale, which was skewed in the positive direction; BADS social impairment subscale, which was skewed in the positive direction; BADS total score, which was slightly skewed in the positive direction; OQ-45.2 symptoms distress subscale, which was skewed in the positive direction; OQ-45.2 interpersonal problems subscale, which was skewed in the positive direction; and OQ-45.2 total score, which was slightly skewed in the positive direction. In sum, only 6 out of the 20 outcome variable scales and subscales fulfilled the assumption of normality. Though most of the variables are not extremely skewed, this may somewhat affect regression analyses.

Linear Relationship between Predictor(s) and Criterion Variables

In order to confirm that a linear relationship did in fact exist between our predictor and criterion variables of interest (as opposed to a curvilinear relationship or no relationship), we examined scatterplots, histograms of standardized residuals, and the P-P Plot of standardized residuals for each regression analysis conducted. Using these visual criteria, all of the statistically-significant relationships proposed in the following sections demonstrated evidence of a linear relationship between the variables, with a few minor exceptions, which will be addressed in later sections as necessary. It should be noted that in all of the cases in which this assumption was failed, one or more of the variables in the regression equation had also violated the assumption of normality. Because these variables had skewed distributions to begin with, it was all the more likely that a linear relationship between them would not be found, even though a linear relationship may be demonstrable with an alternate sample that is capable of producing a normal distribution of scores for the measure.

Homoscedasticity

Homoscedasticity, a fancy and fun-to-say label for constant variance of errors, is the assumption that residuals do not vary systematically in any way. In order to test this assumption,

105 we examined scatterplots of standardized residuals by the regression standardized predicted values (Y'). This visual examination indicated that all regression analyses satisfied the assumption of independence of error variance with the exception of one (Perseverance predicting TIPI-Conscientiousness); this particular regression analysis also failed the assumption of linear relationship, as one of the variables in the equation had a markedly non-normal distribution, and this will be addressed in a later section. A few scatterplots showed very mildly fan-shaped arrangements, but slight heteroscedasticity is acceptable in a sample of this size (Tabachnick & Fidell, 1996).

Lack of Multicollinearity between Predictor Variables

Multicollinearity occurs when two or more predictor variables are correlated to such a degree that their influence on the variance explained in the model overlaps considerably, thereby increasing the standard error of a regression coefficient and decreasing its t-value (Howell, 2010). For this reason, problems with multicollinearity may obfuscate the true relationships between variables in a regression equation. In order to test for multicollinearity, VIF and Tolerance statistics were examined in each regression equation in which more than one predictor variable was entered. In addition, stepwise analyses were performed with all regression equations with multiple predictors in order to ascertain the relative contribution to the model by each predictor. All predictors that overlapped significantly with other predictors and that did not explain a significant amount of variance in the model past their correlated counterparts were removed from the regression equation in order to yield the strongest, most parsimonious model for the relationships between variables. After this trimming of predictor variables, all VIF and Tolerance statistics remained in an appropriate range to satisfy the assumption of non- multicollinearity.

Sufficient Power to Carry Out Statistical Analyses

We conducted a power analysis in order to ensure that each regression equation would have sufficient cases to arrive at statistically-significant results, should they exist. Using G*Power 3 software (Faul, Erdfelder, Buchner, & Lang, 2009), we conducted an a priori power analysis of linear multiple regression for a fixed model, based on the R² deviation from zero. Based on correlations and preliminary regression analyses, we expect at least a medium effect size, and the standard .05 error probability rate and .80 power threshold. The maximum number

106 of predictors used in regression analyses between Character Strengths, TIPI scores, and/or outcome variables at any point in time was 6; with this value entered as the number of predictors, the anticipated minimum number of cases needed was 98. Because the lowest number of cases used in any of the regression equations that were conducted was 125, the regression analyses maintain sufficient power. We also conducted an a priori power analysis for repeated measures ANOVA with an expected medium effect size, a .05 error probability rate, and a .80 power threshold. The maximum number of groups in any of our independent variables was 5 (levels of the variable Income), meaning that our sample would require at least 105 cases in order to demonstrate differences between groups. Because the lowest number of cases used in any of the regression equations that were conducted was 125, the ANOVA analyses maintain sufficient power. With the assumptions of linear regression and ANOVA satisfied, we may proceed with an explanation of the findings. For the purposes of brevity and salience, only statistically- significant and clinically meaningful results will be reported in these sections. Relationships between Demographic Data and Character Strengths. The following section inspects Character Strengths scores for any differences based on demographic data. This information will demonstrate how (or whether) Character Strengths differed by age, gender, ethnicity, or socioeconomic status, in the current sample. In addition, these analyses may indicate certain demographic variables whose influence may need to be removed (controlled for) in future regression analyses involving Character Strengths. Bivariate correlations were conducted between Age and all 24 Character Strengths. The following Character Strengths scores were significantly, though mildly, positively correlated with participant Age: Gratitude (r = .30, p < .01), Modesty (r = .25, p < .01), Perseverance (r = .24, p < .01), Zest (r = .23, p < .01), Self-Regulation (r = .21, p < .01), Fairness (r = .21, p < .05), Forgiveness and Mercy (r = .21, p < .05), Spirituality (r = .20, p < .05), and Honesty (r = .18, p < .05). There were not any statistically significant negative relationships between the Character Strengths scores and Age. These results indicate a subtle increase in positive traits as one ages, specifically those Character Strengths that are organized according to the Virtues of Courage, Temperance, and Transcendence (see Appendix A). In fact, several other Character Strengths within these categories approached, but did not achieve statistically significance, such as Bravery (Virtue of Courage; p = .06), Appreciation of Beauty and Excellence (Virtue of Transcendence; p

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= .06), and Prudence (Virtue of Temperance; p = .07). However, the small magnitude of these correlations indicates that these increases in scores are only minimally explained by age, and therefore should be interpreted cautiously. On a separate note, contrary to popular belief, Humor was not negatively correlated with age; in fact, there appears to be no relationship between the two variables whatsoever (r = .01, p = .95). A One-Way ANOVA was conducted with Gender as the independent variable and Character Strengths as the dependent variables (see Table 9). Equality of variances was confirmed with non-significant Levene’s tests. Only two Character Strengths varied significantly by Gender: Appreciation of Beauty and Excellence, F(1, 124) = 4.39, p < .05, Cohen’s d = .41, and Kindness, F(1, 124) = 4.43, p < .05, Cohen’s d = .41, both of which differed in the favor of the female gender. However, the mean difference between genders for these Character Strengths was slight enough to be negligible: Appreciation of Beauty and Excellence (μfemale – μmale =

0.29), Kindness (μfemale – μmale = 0.19). Recall that the possible range for the Character Strengths is 5.0; this mean difference between groups is therefore likely due to the large sample size used in the statistical analysis. However, these findings are consistent with Peterson and Seligman’s (2004) findings that “women score higher than men on all of the Humanity [Virtue] strengths” (p. 631), including Kindness. A One-Way ANOVA was conducted with Ethnicity as the independent variable and Character Strengths as the dependent variables. Despite the wildly unequal group sizes for this demographic in the sample, equality of variances was confirmed with a non-significant Levene’s test for each Character Strength. Consistent with Peterson and Seligman (2004), no significant differences were found for any the Character Strengths between racial/ethnic groups. However, in the current study, we may be unable to say whether true differences exist; statistical procedures are compromised due to very large and very small between-group sizes. For example, 240 participants self-identified as “White/Caucasian” while only 30 participants identified as minorities (the remaining 5 participants declined to indicate race/ethnicity). Further, there were two categories—Pacific Islander and Native American—that consisted of only one participant each. A One-Way ANOVA was conducted with Income as the independent variable and Character Strengths as the dependent variables (see Table 10). Equality of variances for all Character Strengths was confirmed with non-significant Levene’s tests with the exception of

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Fairness (p < .05) and Spirituality (p < .01); this test is particularly important for this analysis because there are large differences in sample size, which will augment the variance for some groups and shrink it for others. Participants varied significantly by their reported income category on the following three Character Strengths: Spirituality, F(4, 120) = 7.20, p < .001; Gratitude, F(4, 120) = 4.23, p < .01; and Forgiveness and Mercy, F(4, 120) = 2.97, p < .05. Tukey post-hoc tests revealed non-significant differences between groups for Forgiveness and Mercy. Because Spirituality violates the assumption of homogeneity of variances, its differences should be interpreted with caution; using a T3 post-hoc test (and a stricter T2 post-hoc test to confirm), which assumes inequality of variances between groups, it appears that individuals who report earnings between $75,000 to $100,000 annually tend to score on average 0.71 points higher on the VIA Survey of Character Strengths scale of Spirituality than their peers who reported earning between $25,000 to $50,000 annually (p < .05, Cohen’s d = .88). In addition, individuals who reported earning more than $100,000 per year scored an average of 0.82 points higher on Gratitude scores compared to those who reported earning less than $25,000 per year (p < .05, Cohen’s d = 1.67), though this result was not replicated with T2 post-hoc tests, likely due to the fact that only 6 individuals comprised the smaller category in these analyses. In that a very large effect size was still found between the groups despite one of them having so few members indicates that this mean difference between groups (over 1/5 the range of the Gratitude variable) likely exists. Visual inspection of the mean scores between income groups confirms a trend of increasing Gratitude scores as income increases. Relationships between Demographic Data and Personality Estimates. We also investigated relationships between our demographic variables and the estimates of the Big Five personality traits as measured by the TIPI. We did this in order to examine the effects that belonging to a particular group may have on personality, as well as to explore any possible changes in broad personality constructs throughout the lifespan. Bivariate correlations were conducted between Age and the five TIPI scores. Only the TIPI scales of Agreeableness (r = .15, p < .05) and Emotional Stability (r = .19, p < .01) were significantly correlated with age. This indicates a possible weak relationship between aging and increases in Agreeableness and Emotional stability as personality traits. A One-Way ANOVA was conducted with Gender as the independent variable and the TIPI scores as dependent variables. Equality of variances for all TIPI scores except

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Conscientiousness (p < .05) was confirmed with non-significant Levene’s tests. The construct of Agreeableness was the only TIPI score that varied significantly by Gender, F(1, 268) = 11.31, p < .01, Cohen’s d = .46, while Emotional Stability approached significance (p = .053). The mean difference between genders for Agreeableness was 1.04, with women having higher average scores than men. As the range for scale scores on the TIPI is 12, and because this difference attained a medium effect size, it appears that women in this sample had a slightly higher score on Agreeableness than did men. This is consistent with findings in the standardization sample reported in the NEO-PI-R manual that there are minor gender differences on some domains and facets of the NEO personality inventories, and these differences are distinct enough to warrant separate norms for respondents by gender (Costa & McCrae, 1992). This is consistent also with Peterson and Seligman’s (2004) assertion that women generally score higher on those Character Strengths within the Virtue of Humanity (Appendix A), which has demonstrated relationships with the Big Five construct of Agreeableness. A One-Way ANOVA was conducted with Ethnicity as the independent variable and TIPI scores as dependent variables. Not surprisingly, there were no significant effects between groups for any of the TIPI scores, likely due to disparate group sizes in the different levels of the Ethnicity variable. However, racial/ethnic group differences on measures of the “Big Five” may not actually exist anyway. A One-Way ANOVA was conducted with Income as the independent variable and TIPI scores as dependent variables. Equality of variances for all Character Strengths was confirmed with non-significant Levene’s tests, except for—once again—Conscientiousness (p < .05). Extraversion was the only TIPI score that varied significantly by Income, F(4, 250) = 2.93, p < .05. Tukey post-hoc tests indicated that individuals who report earning more than $100,000 per year score an average of 1.66 points higher on TIPI’s Extraversion scale than individuals who report earning between $50,000 to $75,000 per year (p < .05, Cohen’s d = .51). Though there were no other significant differences between other income groups for Extraversion, it is likely that a significant difference between the highest income group and the lowest income group was not found purely due to group sizes; despite a 1.77 mean difference between the two groups, the lowest income group consisted of only 17 participants compared to the highest income group’s 94 participants, which likely contributed to a larger standard deviation and post-hoc mean

110 difference 95% confidence interval for the smaller group, preventing statistical significance from being attained. Relationships between Demographic Data and Outcome Variables. This series of analyses was conducted in order to investigate any systematic differences in outcome variables based on specific demographic information. These results will not only inform the curious mind, but more importantly will serve to guide future regression analyses that may need to take into account the relative contribution of demographic data in proposed relationships with these outcome variables. In other words, by examining relationships between demographics and outcome measures, we will be able to control for the effects of specific demographic contributions in future analyses. Bivariate correlations were conducted between Age and the 20 outcome variable scales and subscales that measure positive and negative aspects of mental health. The following outcome variables were significantly positively correlated with age: the BADS Work/School impairment subscale (r = .27, p < .001), the BADS Avoidance/Rumination subscale (r = .20, p < .01), the BADS total score (r = .19, p < .01), and the MHC-SF Psychological well-being subscale (r = .13, p < .05). The following outcome variables were significantly negatively correlated with age: the OQ-45.2 Social Role subscale (r = -.29, p < .001), the MLQ Search scale (r = -.27, p < .001), the PANAS Negative Affect scale (p = -.22, p < .001), the RSES total score (r = -.16, p < .05), and the CES-D total score (r = -.15, p < .05). These results indicate an interesting pattern of (weak) relationships with age. As age in the sample increases, a search for meaning in life decreases, negative affect and frequency of depressive symptoms decrease, avoidance and rumination decrease, and work (and/or school) impairment and stress associated with work decrease. Meanwhile, self-esteem, psychological well-being, and overall behavioral activation increase very mildly. However, it should be noted that these relationships may not be strictly linear; some research indicates that there may be a U-shaped relationship between age and positive variables such as well-being (Blanchflower & Oswald, 2008) happiness, and life satisfaction (Frijters & Beatton, 2012). If this is the case, then these correlation coefficients, which assume a linear relationship, likely underestimate the degree to which these variables are related to each other. A One-Way ANOVA was conducted with Gender as the independent variable and the outcome measures’ scales and subscales as dependent variables. Equality of variances was

111 confirmed with non-significant Levene’s tests for all outcome variables with the exception of the SWLS total score (p < .05) and the MLQ – Presence scale (p < .05), though neither of these measures demonstrated significance in the ANOVA analyses. In fact, only two outcome variables differed significantly by Gender: the BADS Activation subscale, F(1, 262) = 5.41, p < .05, Cohen’s d = .34, and the OQ-45 Social Role subscale, F(1, 262) = 4.83, p < .05, Cohen’s d = .29. The mean difference between genders for BADS Activation was 2.32, with men scoring slightly higher than women, on average. The mean difference between genders for OQ-45 Social Role was 1.19, with men again scoring slightly higher than women, on average. Due to the small mean differences and small effect sizes of this statistically-significant difference, the true difference between men and women on these two measures is likely very slight. A One-Way ANOVA was conducted with Ethnicity as the independent variable and the outcome measures as dependent variables. As with the previous two statistical analyses, which looked for ethnic group differences on Character Strengths scores and TIPI scores, there were no significant effects between groups for any of the outcome measures. Possible reasons for this finding include the nonexistence of true group differences, excellent questionnaire design that eliminates bias against specific ethnic groups, and/or the disparate sizes of ethnic groups in the current study’s sample. A One-Way ANOVA was conducted with Income as the independent variable and outcome questionnaire scores as dependent variables. Unlike in prior analyses with Income, equality of variances was a major concern for half of the outcome variables. The SWLS total score (p < .05), the MLQ Search scale (p < .05), the MHC-SF Emotional well-being subscale (p < .05), the MHC-SF Total score (p < .05), the CES-D Total score (p < .01), the BADS Work/School impairment subscale (p < .001), the BADS Social impairment subscale (p < .01), the BADS Total score (p < .05), the OQ-45 Symptom Distress subscale (p < .01), and the OQ-45 Total score (p < .05) all failed Levene’s test, indicating that the assumption of homogeneity of variances required for ANOVA has not been met for these variables. These variables will therefore not be interpreted. For the remaining ten variables that did meet this assumption, there were significant differences between at least two Income categories for the following outcome variables: the RSES Total score, F(4, 250) = 5.89, p < .001; the PANAS Positive Affect scale, F(4, 250) = 4.97, p < .01; the MLQ Presence scale, F(4, 250) = 4.87, p < .01; the BADS Avoidance/Rumination subscale, F(4, 250) = 3.95, p < .01; the MHC-SF Social well-being

112 subscale, F(4, 250) = 3.37, p < .05; and the BADS Activation subscale, F(4, 250) = 3.16, p < .05. The OQ-45 Interpersonal Problems subscale (p = .054), the MHC-SF Psychological well-being subscale (p = .06), and the PANAS Negative Affect scale (p = .07) all approached, but did not achieve, statistical significance for differences between Income categories. Tukey post-hoc tests were used to discern significant differences between income groups for each of the above (statistically significant) outcome variables. For the total score on the RSES (in which a lower score is indicative of higher self-esteem), individuals who reported earning more than $100,000 annually scored an average of 2.82 points lower than individuals in the $25,000 to $50,000 group (p < .05, Cohen’s d = .51), 2.89 points lower than individuals in the $75,000 to $100,000 group (p < .05, Cohen’s d = .57), and 5.29 points lower than individuals in the $25,000 or less group (p < .01, Cohen’s d = .95). For the PANAS Positive Affect scale, individuals who reported earning more than $100,000 annually scored an average of 11.35 points higher than individuals in the $25,000 or less group (p < .001, Cohen’s d = 1.02), while individuals in the $50,000 to $75,000 range scored an average of 8.33 points higher than those in the $25,000 or less group (p < .05, Cohen’s d = .77). For the MLQ Presence scale, individuals who reported earning more than $100,000 annually scored an average of 3.58 points higher than individuals in the $25,000 to $50,000 group (p < .001, Cohen’s d = .73), while individuals in the $50,000 to $75,000 range scored an average of 2.90 points higher than those in the $25,000 or less group (p < .05, Cohen’s d = .56). For the MHC-SF Social well-being subscale, individuals who reported earning more than $100,000 annually scored an average of 3.23 points higher than individuals in the $25,000 to $50,000 group (p < .05, Cohen’s d = .58). For the BADS Avoidance/Rumination subscale, individuals who reported earning more than $100,000 annually scored an average of 6.76 points higher than individuals in the $25,000 or less group (p < .01, Cohen’s d = .84). Finally, for the BADS Activation subscale, individuals who reported earning more than $100,000 annually scored an average of 5.66 points higher than individuals in the $25,000 or less group (p < .05, Cohen’s d = .77). In all of these cases, the differences between these groups achieved an effect size that ranged from medium to very large, and these effect sizes are echoed by the large mean differences in questionnaire scores between these groups. These results may indicate several differences between income groups for positive and negative variables that contribute directly to quality of life.

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Though half of the outcome questionnaire scales or subscales could not be interpreted due to violation of the assumption of equality of variances, nearly all of them were statistically significant, and T3 post-hoc tests (which do not assume equal variances between groups) revealed a similar pattern of relationships as described above. Specifically, for the CES-D, the remaining BADS scales including the Total score, and the SWLS, the common theme was that there were significant differences between the scores of groups on the higher end of the income spectrum than those of groups on the lower end of annual earning, always in favor of the higher earners. Now that we have identified demographic data that are statistically, significantly related to our variables of interest, we can control for its impact in all future regression analyses by using hierarchical regression procedures in which both age and income are entered together in the first block. This will allow us to essentially remove their influence on the relationship between our variables of interest. In particular, Age and Income seemed to have both the greatest frequency of relationships with other variables as well as the strongest relationships with the outcome, Character Strengths, and TIPI variables, compared to the substantially lower or nonexistent impact of the other demographic variables, Gender and Ethnicity. Therefore, it will be prudent to control for both Age and Income in future statistical analyses of correlative/regression relationships involving these outcome measures in order to enhance the precision of measured relationships between other variables (and so that the amount of variance explained by other predictors is not overestimated). Relationships between Character Strengths and Outcome Variables. The purpose of the statistics in this section is to determine how and to what extent personality traits (Character Strengths) affect current states (outcome measures). Though the predominance of variance in an affective, behavioral, or cognitive state is likely determined predominantly through transient environmental causes, there tends to be a fairly noteworthy contribution to the current state by individuals’ personality-borne predispositions (Weiss, Bates, & Luciano, 2008). For this reason, we investigate the contribution of Character Strengths to individuals’ mental health and psychological well-being. A correlation matrix demonstrated that each of the Character Strengths, except for a select few such as Modesty and Prudence, are highly correlated with the outcome measures used in this study (.18 < |r| < .67, significant correlations only). Possible Character Strengths

114 predictors of outcome variables were selected from this correlation matrix if they had a correlation coefficient equal to or of a greater absolute magnitude than .30, ensuring that each predictor had at least a weak to moderate relationship with the criterion variables. Multiple regression analyses were conducted for each outcome variable, and predictors were pruned according to their relative contribution to the regression equation (β and p-value). Additionally, predictors that violated the assumption of multicollinearity (overlapped with other predictors in the regression equation) were separately examined; those predictors that explained more of the variance in the equation were kept, and the overlapping variables that explained less of the variance were rejected. These procedures ultimately yielded a parsimonious regression equation for each outcome variable that consisted of no more than three Character Strengths predictors. Stepwise regression techniques were then employed with regression equations that contained more than one predictor variable in order to determine the relative contribution of each predictor in the model; ΔR² scores and associated p-value were used to determine which predictors explained a significant amount of variance in the criterion variables, beyond what was already explained by the other predictor(s) in the model. Several non-significant, substantially overlapping predictors were then dropped from various regression equations, though most were retained. These procedures yielded robust and straightforward relationships between specific Character Strengths and scores on the outcome measures. These multiple regression analyses were repeated for each outcome measure, this time as hierarchical linear regression with the demographic variables Age and Income entered in the first block and the Character Strengths predictor(s) entered in the second block. This was done to control for the effects of Age and Income on the relationship between Character Strengths and the outcome measures, as Age has demonstrated a correlation with several of these variables and Income has demonstrated systematic variance a number of these variables. These procedures produced a statistically-significant (p < .001) regression equation with one or more Character Strengths as predictors for all but one of the outcome variables (MLQ – Search); see Table 11 for the significant regression analyses and Figure 5 for a simplified visual explication of the results of these regression analyses, which are delineated below. For the criterion SWLS total score, only the Character Strength of Hope was a significant predictor (β = .32, p < .001), after controlling for Age and Income, F(3, 121) = 15.59, p < .001.

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Together, these three variables explained 28% of the variance in general life satisfaction scores; ΔR² indicated that Hope accounted for an additional 9.8% of variance beyond Age and Income. For MLQ - Presence, both Spirituality (β = .56, p < .001) and Zest (β = .27, p < .001) were significant predictors after controlling for Age and Income, F(4, 120) = 31.38, p < .001. This four-predictor model explained 51% of the variance in Presence of Meaning in Life scores, with ΔR² indicating that Spirituality and Zest accounted for a combined 44% of variance beyond that contributed by Age and Income. As was the case with SWLS scores, Hope (β = -.53, p < .001) was the only significant predictor for RSES scores after controlling for Age and Income, F(3, 121) = 26.46, p < .001. This model explained 40% of the variance in scores of self-esteem, with ΔR² indicating that Hope explained 27% of the variance in life satisfaction scores beyond Age and Income. Zest (β = .37, p < .001) and Love (β = .26, p < .01) were significant predictors of the MHC-SF Emotional well-being subscale, after controlling for Age and Income, F(4, 120) = 12.87, p < .001. These variables explained a total of 30% of the variance in the MHC-SF Emotional well-being subscale scores; ΔR² indicated that Zest and Love together accounted for 28% of the variance in the model beyond Age and Income. For MHC-SF Social well-being subscale scores, both Zest (β = .39, p < .001) and Perspective (β = .21, p < .05) were significant predictors after controlling for Age and Income, F(4, 120) = 13.40, p < .001. This four-predictor model explained 31% of the variance in social well-being scores, with ΔR² indicating that Zest and Perspective accounted for a combined 27% of variance beyond that contributed by Age and Income. Perspective (β = .38, p < .001) and Hope (β = .31, p < .01) were significant predictors of the MHC-SF Psychological well-being subscale, after controlling for Age and Income, F(4, 120) = 20.05, p < .001. These variables explained a total of 40% of the variance in psychological well- being scores; ΔR² indicated that Perspective and Hope accounted for 36% of the variance in the model beyond Age and Income. As Zest, Perspective, and Hope were significant predictors of MHC-SF subscales, it seems logical to expect them to be significant predictors of the MHC-SF Total score, and they were (β = .27, p < .05; β = .26, p < .01; and β = .22, p < .05, respectively) after controlling for Age and Income, F(5, 119) = 18.68, p < .001. This model explained 44% of the variance in

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MHC-SF Total scores, with ΔR² indicating that these three Character Strengths explained 40% of the variance in scores beyond Age and Income. Only Hope (β = -.27, p < .01) was a significant predictor of PANAS Negative Affect scores after controlling for Age and Income, F(3, 121) = 6.53, p < .001. However, this model only explains 14% of the variance in negative affect, with ΔR² indicating that Hope accounts for exactly half of the variance explained in this model, while Age and Income account for the other half. For PANAS Positive Affect scores, Zest (β = .33, p < .01), Hope (β = .25, p < .05), and Perseverance (β = .20, p < .05) were significant predictors after controlling for Age and Income, F(5, 119) = 21.51, p < .001. This five-predictor model explained nearly half of the variance (48%) in positive affect, with ΔR² indicating that Zest, Hope, and Perseverance accounted for a combined 42% of variance in the model beyond Age and Income’s collective contribution. Hope (β = -.34, p < .001) was the sole predictor of CES-D scores, after controlling for Age and Income, F(3, 121) = 9.01, p < .001. Together, these three variables only explained 18% of the variance in depression scores, with ΔR² indicating that Hope accounted for 11% of the variance in the model beyond Age and Income. However, there were a few problems with this statistical analysis that likely interfere with the interpretability of this data. First, the CES-D scores failed the assumption of normality, being significantly skewed to the left. Second, and partially as a result of this skewedness, it seems that the relationship between CES-D scores and Hope may not be strictly linear. Visual examination of the standardized residuals histogram and the P-P plot are slightly off, but more importantly, visual examination of the scatterplot of Hope and CES-D scores indicates that the relationship between the two variables is possibly asymptotic rather than linear. Zest (β = .36, p < .001) and Self-Regulation (β = .31, p < .01) were significant predictors of the BADS Activation subscale, after controlling for Age and Income, F(4, 120) = 16.33, p < .001. These variables explained a total of 35% of the variance in behavioral activation scores; ΔR² indicated that Zest and Self-Regulation together accounted for 30% of the variance in the model beyond Age and Income. With the BADS Avoidance/Rumination subscale, only Perseverance (β = .27, p < .01) was a significant predictor after controlling for Age and Income, F(3, 121) = 6.46, p < .001. This model explained only 14% of variance in the model, however, with ΔR² indicating that

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Perseverance accounted for half of this variance and Age and Income together accounted for the other half. Examination of the scatterplot of standardized residuals with Y' and the P-P Plot points to the possibility that the non-normal distribution of both the criterion variable and the predictor variable is negatively impacting the alleged linear relationship between these variables; this means that if a true linear relationship exists between Perseverance and the BADS avoidance/rumination subscale, then it may be more robust than these statistics indicate. A normally-distributed sample of both scores will be required to fully test this hypothesis. For now, based on other data including a scatterplot of the two scores in which data points tended to cluster in the top right of the plot, we hesitantly endorse a linear relationship between these two variables and propose that the relationship may be stronger than our data suggest. Perseverance (β = .44, p < .001) and Zest (β = .18, p < .05) were significant predictors of the BADS Work/School impairment subscale, after controlling for Age and Income, F(4, 120) = 19.08, p < .001. These variables explained a total of 39% of the variance in work/school impairment scores, with ΔR² indicating that Perseverance and Zest accounted for 27% of the variance in the model beyond Age and Income. Love (β = .31, p < .01) was the only statistically-significant predictor of the BADS Social impairment subscale after controlling for Age and Income, F(3, 121) = 4.79, p < .01. However, Love only accounted for 9% of variance in social impairment scores, with Age and Income together only accounting for an additional 1%. This finding is likely a direct result of the non- normal distribution of the criterion variable; as with the BADS Avoidance/Rumination subscale, if a true linear relationship exists between Love and the BADS Social impairment subscale, then a normally-distributed sample for both variables might better elucidate the strength of the relationship. Visual examination of the scatterplot was inconclusive in determining a linear relationship due to the clustering of BADS Social impairment scores near the ceiling of the plot. For BADS Total scores, Zest (β = .32, p < .001) and Perseverance (β = .30, p < .01) were significant predictors after controlling for Age and Income in the first step, F(4, 120) = 15.80, p < .001. This model explained 35% of the variance in BADS Total scores, while ΔR² indicates that Zest and Perseverance together accounted for 26% of the variance in the model beyond Age and Income. Hope (β = -.43, p < .001) was the sole significant predictor of OQ-45 Symptom Distress subscale scores after controlling for Age and Income, F(3, 121) = 9.74, p < .001. This model

118 explains a total of 19% of the variance in symptom distress; ΔR² indicates that Hope accounts for 17% of the variance explained in this model beyond the contribution of Age and Income. Hope (β = -.37, p < .001) was also the sole significant predictor of OQ-45 Interpersonal problems subscale scores after controlling for Age and Income, F(3, 121) = 7.28, p < .001. This model explains 15% of the variance in interpersonal problems scores, with ΔR² indicating that Hope accounts for 13% of the variance explained in this model beyond Age and Income. Zest (β = -.36, p < .001) was the only significant predictor of OQ-45 Social Role subscale scores after controlling for Age and Income, F(3, 121) = 10.68, p < .001. This model explains 21% of the variance in social role scores (which are similar in theory to the BADS work/school impairment construct); ΔR² indicates that Zest accounts for 12% of the variance explained in this model, with Age and Income accounting for the other 9%. Once again, Hope (β = -.45, p < .001) is the only significant predictor in a regression model, this time with the OQ-45 Total score, after controlling for Age and Income, F(3, 121) = 11.89, p < .001. This model explains 23% of the variance in the OQ-45 Total scores, with ΔR² indicating that Hope accounts for nearly a fifth (19.4%) of the variance explained in this model, beyond the contribution of Age and Income. Relationships between Character Strengths and Personality Estimates. The purpose of this section is to determine how and to what extent Character Strengths are related to TIPI scores (estimates of the “Big Five” personality domains). A correlation matrix demonstrated that all of the Character Strengths were significantly correlated with at least one estimate of the Big Five domains, and many were correlated with more than one of these constructs (.18 < |r| < .58, significant correlations only). Possible Character Strengths predictors of TIPI scores were selected from this correlation matrix if they had a correlation coefficient equal to or of a greater absolute magnitude than .20, ensuring that each predictor had at least a weak relationship with the criterion variables. The same regression analysis procedures that were employed in the previous section, in which Character Strengths scores were used to predict scores on the 20 outcome variables, were employed with the relationships between Character Strengths and the TIPI. These procedures yielded robust and fairly straightforward relationships between Character Strengths and TIPI scores (Table 12).

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Compared to previously demonstrated relationships with Character Strengths and the outcome variables, demographic data was not substantially related to the TIPI scores. While Age was weakly positively correlated with two TIPI scores (Agreeableness and Emotional Stability), while there was a statistically significant Gender difference on Agreeableness scores, and while Extraversion varied between Income groups, all these relationships and differences were miniscule. However, because demographic data was still fairly significantly related to Character Strengths data, which here will be used as predictors, we will continue to control for the two most salient demographic variables, Age and Income. For the Extraversion estimate, Modesty (β = -.48, p < .001) and Social Intelligence (β = .39, p < .001) were significant predictors after controlling for Age and Income in the first step, F(4, 120) = 16.76, p < .001. This model explains 36% of the variance in Extraversion scores, while ΔR² indicates that Modesty and Social Intelligence together account for 33% of the variance in the model, beyond the influence of Age and Income. It should be noted that when this analysis is run without controlling for Age and Income, Zest becomes a significant predictor in conjunction with Modesty and Social Intelligence, though this model also explains 36% of the variance in Extraversion scores. It seems that a lack of modesty and a wealth of social intelligence are fairly strongly related to extraversion, and energy and enthusiasm is also related to extraversion, at least to some extent. Kindness (β = .32, p < .001) and Forgiveness and Mercy (β = .21, p < .05) were significant predictors of Agreeableness after controlling for Age and Income, F(4, 120) = 9.16, p < .001. This model explains 23% of the variance in Agreeableness scores, with ΔR² indicating that Kindness and Forgiveness account for a combined 19% of the variance in the model, beyond Age and Income. Kindness, but not necessarily Forgiveness and Mercy, has been found by previous research to be related to the Big Five factor of Agreeableness (Peterson & Seligman, 2004). Perseverance (β = .55, p < .001) was the only significant predictor of Conscientiousness after controlling for Age and Income, F(3, 121) = 22.65, p < .001. This model accounted for 36% of the variance in Conscientiousness scores, and Perseverance was responsible for 29% of the variance when the influence of Age and Income was removed. However, these results should be interpreted extremely cautiously, as both Perseverance and Conscientiousness failed to meet the assumption of normal distribution of scores. Likely as a direct result of this non-normality, it

120 appears that a linear relationship between the two variables does not exist in this sample. Furthermore, the relationship between these variables failed the homoscedasticity assumption, as the scatterplot of the standardized residuals appears to be fan-shaped, indicating unequal levels of variance in Conscientiousness scores at opposite ends of the Perseverance scores. This also is likely due to the skewed Conscientiousness and Perseverance distributions. Hope (β = .33, p < .01) was the only statistically significant predictor of Emotional Stability after controlling for Age and Income, F(3, 121) = 7.21, p < .001. This model explained 15% of the variance in Emotional Stability scores, with ΔR² indicating that Hope accounts for about 10% of the variance in the model beyond Age and Income, suggesting that there are likely many other factors (not necessarily measured in this study) that have an impact on emotional stability. This finding is consistent with previous research in which Hope was inversely correlated with Neuroticism (Peterson & Seligman, 2004), which is essentially the same finding in this study (Hope is positively related to opposite-Neuroticism). Finally, there were four Character Strengths that together predicted scores on the TIPI estimate of Openness, after controlling for Age and Income, F(6, 118) = 12.80, p < .001: Creativity (β = .33, p < .001), Love of Learning (β = .30, p < .001), Prudence (β = -.26, p < .01), and Modesty and Humility (β = -.21, p < .05). This six-factor model accounted for 39% of the variance in Openness scores, with 38% of the variance in the model explained directly by Creativity, Love of Learning, Prudence, and Modesty. The aforementioned study by Peterson and Seligman (2004) also found that Creativity and Love of Learning were significantly related to Openness. These results indicate that there is some degree of overlap between Character Strengths and the Big Five personality domains as measured by the TIPI, and the results of this study are generally supported by what little prior research has investigated these relationships. From a theoretical standpoint, the Character Strengths draw some influence from the Big Five model, so some correlations between these constructs are to be expected. Relationships between Personality Estimates and Outcome Variables. The final possible construction of relationships between the four sets of variables, that of the relationships between the Big Five personality factors as estimated by the TIPI and the outcome measures in this study, will not be conducted, for two reasons. First, both the TIPI scales and the outcome scales tended to violate the assumption of normality, which is a requirement for most statistical

121 procedures. This is acceptable when either the predictor(s) or criterion variable are only slightly skewed; however, it would not be prudent to proceed with regression analyses when both the predictor(s) and criterion variables are skewed. Second, this type of analysis is beyond the scope of this study, the purpose of which is to focus on the relative impact of Character Strengths on other variables of interest. However, despite these objections, a few preliminary analyses were conducted. Review of the correlations between the TIPI scales and the pre-treatment outcome measures reveals that, in a normally-distributed sample, there may be some rather robust relationships between estimates of NEO-PI traits and the outcome variables used in this study. For example, consistent with theoretical prognostications, Emotional Stability appears to be inversely related to constructs such as depression (CES-D total score; r = -.50, p < .001) and negative affect (PANAS negative affect scale; r = -.55, p < .001) while Extraversion appears to be positively related with various measures of psychological and social well-being (the MHC-SF scales; .17 < r < .27; p < .01) and positive affect (PANAS Positive Affect scale; r = .32; p < .001), and so on. In addition, because there does not seem to be overwhelming overlap between the contributions of Big Five personality traits and Character Strengths to the regression equations (as would otherwise render them multicollinear), it would be interesting to test the extent to which these divergent measures of personality would explain the variance in the outcome questionnaires used in this study, as combined predictors. As a preliminary investigation, we ran a hierarchical regression analysis in which Age and Income were entered in the first block and the Character Strength of Hope and the TIPI estimate of Emotional Stability (inverse of the Big Five domain of Neuroticism) were entered in the second block. The results were striking. Emotional Stability (β = -.39, p < .001) and Hope (β = -.21, p < .05) both came out as significant predictors of the CES-D total score, after controlling for Age and Income, F(4, 120) = 13.67, p < .001, with the entire model accounting for nearly 1/3 of the variance in CES-D scores (31%) while Emotional Stability and Hope together accounted for nearly 1/4 of the variance in CES-D scores (24%) after partitioning the influence of Age and Income. Recall that Hope, alone, only accounted for 11% of the variance in CES-D scores after controlling for demographic data. These results indicate that fairly fixed variables such as Age, Income, Emotional Stability, and scores on the Character Strength of Hope may determine up to 1/3 of an individual’s score on a

122 depression inventory at any given point in time. We will interpret these findings through the lens of Positive Psychology research and applications in the Discussion section.

Character Strengths Statistics

Overall means and standard deviations for participants’ scores for each of the 24 Character Strengths on the VIA Survey of Character Strengths are presented in Table 13. It should be noted that the participants in this sample do not necessarily represent the normal population; rather, they represent mostly Caucasian, female individuals working in human- service organizations in the South. This is an important distinction, as Peterson and Seligman (2004) have found different “average” profiles of Signature Strengths (Top-five ranked Character Strengths) in different cross-sections of society, differing by such aspects as gender and occupation. Interestingly, only the Character Strength of Spirituality has been shown to significantly differ based on geographic location in the United States (Park, Peterson, & Seligman, 2006). Below are the top-ranked and bottom-ranked Character Strengths for participants in this sample. Normative data for the VIA Survey of Character Strengths were not available for comparison. Typical Top Strengths Profile of the Sample. The five Character Strengths with the largest means in this sample were: Spirituality (μ = 4.22, SD = .82), Gratitude (μ = 4.18, SD = .51), Honesty (μ = 4.14, SD = .45), Love (μ = 4.08, SD = .49), and Kindness (μ = 4.07, SD = .47). Consistent with findings by Park, Peterson, and Seligman (2006) that people tend to endorse the Character Strength of Spirituality more highly in the Southern U.S., this sample’s topmost Character Strength was Spirituality and Religiosity. Furthermore, the strengths of Honesty, Gratitude, and Kindness also comprise the “typical” Signature Strengths profile for individuals residing in the United States (Park, Peterson, & Seligman, 2006). Interestingly, the remaining four Character Strengths in this list seem to correlate with interpersonal characteristics. In addition, two of these Character Strengths are categorized in the Humanity virtue, while two others are categorized in the Transcendence virtue, possibly indicating that these two groupings of Character Strengths are more readily accessible to individuals in the study. Typical Bottom Strengths Profile of the Sample. In contrast, the five Character Strengths with the smallest means in this sample were, in order from lowest-ranked to highest- ranked: Creativity (μ = 3.48, SD = .71), Modesty and Humility (μ = 3.53, SD = .70), Self- 123

Regulation (μ = 3.56, SD = .60), Appreciation of Beauty and Excellence (μ = 3.58, SD = .71), and Love of Learning (μ = 3.72, SD = .61). Two of these Character Strengths are found in the Temperance virtue, while two others are found in the Wisdom & Knowledge virtue. Based on these findings, it is possible that individuals in this sample would report that their behavior is typically more subject to impulses and immediate gratification. Furthermore, they may not see the Wisdom & Knowledge virtue’s Character Strengths as easily accessible to them, possibly due to the common view of these traits as being innate and not characteristics that can be developed.

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CHAPTER FIVE

DISCUSSION In this study, we compared the effects of two brief Character Strengths interventions, which were based on either a Capitalization or Compensation philosophy, with each other as well as against a placebo intervention and a waitlist/control condition. These interventions were presented in a group format with small organizations, whose employees completed a series of measures of positive variables and negative symptoms both prior to the intervention as well as a month following the intervention. The results of this study demonstrates tentative, partial support for previous research on Character Strengths interventions that found enhancement of certain aspects of quality of life (Mongrain & Anselmo-Matthews, 2012; Seligman et al., 2005) with a concordant reduction of symptoms of mental illness (Sin & Lyubomirsky, 2009; Seligman, Rashid, & Parks, 2006). However, these interventions, still in their infancy, require further development and research before they will be ready for broad-scale dissemination or integration into existing mental health service protocols. In the remaining sections, we will interpret the results of the statistical analyses in the context of the research questions and hypotheses. Next, we will expound upon the additional findings and offer a perspective on the relationships found between the variable sets. We then discuss the limitations of the study and offer suggestions for strengthening the impact of the Character Strengths interventions employed in the current study. Finally, we submit implications of the study for theory development, future research, clinical practice, and education and training.

Research Questions & Hypotheses

The Capitalization vs. Compensation model (Flückiger et al., 2009; Wingate et al., 2005; Rude & Rehm, 1991) provided the speculation for the formulation of these hypotheses. The Capitalization theory states that individuals can tackle their problems and assert personal control by employing their strengths, hence capitalizing upon them (Conoley et al., 1994). The Compensation theory, by contrast, argues that addressing personal weaknesses and rectifying shortcomings is a more effective approach (Baltes & Fruend, 2003). Both theories have accrued considerable evidence bases for their abilities to address specific problems; however, it remains to be seen which of these theories best applies to the Character Strengths paradigm. It should be

125 noted, however, that positive psychology’s well-validated “Signature Strengths” intervention— like much of positive psychology—is based on a Capitalization perspective (Peterson & Seligman, 2004; Seligman, 2002). Therefore, the impetus was on the Bottom Strengths condition to demonstrate statistical and clinical improvement. Each research question and associated hypothesis is restated below. A series of ANOVA and ANCOVA analyses were conducted in order to answer these questions, the idea being that a convergence of evidence should provide the best indications of statistical outcomes of the study.

Research Question One

The first research question investigated the impact of employing idiographic top-five rank-ordered Character Strengths, with the expectation that, consistent with past research (Peterson & Seligman, 2004), individuals who did so would experience an increase in positive variables—such as positive affect, satisfaction with life, and psychological well-being—and a reduction in negative symptoms—such as negative affect, depression symptoms, and general negative psychological symptoms relative to individuals in the Waitlist/Control and Placebo groups. This research question was informed by the Capitalization model, and is the basis for the empirically-validated Signature Strengths positive psychology intervention (Peterson, 2006; Seligman, 2002). Contrary to expectations, there were no differences between the scores of individuals who focused on their Top Strengths compared to individuals in the other conditions. In fact, visual comparison of Top Strengths participants’ pre-treatment and post-treatment means for each outcome measure (Table 2) reveals little, if any, tangible differences. Both quantitatively and qualitatively, it seems that there were few effects for the Character Strengths intervention with individuals in this experimental condition; or, if there was an effect, it dissipated by a month following the intervention. These results seem to contradict previous studies on the effects of Character Strengths interventions (e.g., Park, Peterson, & Seligman, 2004).

Research Question Two

The second research question explored the effects of engaging one’s relative character weaknesses (bottom-five rank-ordered Character Strengths) with the proposition that individuals in this condition would experience an increase in positive variables and a decrease in negative symptoms, and this change would exceed that of participants in the Waitlist/Control condition (at

126 the very least) and possibly exceed that of participants in the Placebo condition. This research question was based on the Compensation model and was informed by the only study to-date that has investigated the least of the rank-ordered Character Strengths (Rust, Diessner, & Reade, 2009). The results of the ANOVA and ANCOVA analyses were admittedly mixed. For example, all analyses agreed that participants who focused on their Bottom Strengths experienced a reduction in their OQ-45 Symptom Distress scores by post-treatment relative to those in the Waitlist/Control condition (and possibly those in the Top Strengths and Placebo conditions as well). However, only about half of the analyses revealed significant reductions in OQ-45 Social Role scores and increases in MHC-SF Emotional well-being scores for the Bottom Strengths group relative to the other groups. One analysis even found a significant increase in self-esteem for participants in this condition, but only one other analysis came close to replicating this finding. In addition, the Bottom Strengths condition boasted the highest percentage of participants who met improvement or recovery specifications for the OQ-45 Total score. However, there were not any significant differences between conditions for the other 15 scales and subscales measured in the study. These results tentatively support the hypothesis by Rust, Diessner, and Reade (2009) that individuals who focus on engaging their bottom-ranked Character Strengths will experience an increase in positive variables (self-esteem and emotional well-being) with a concordant reduction in negative symptoms (symptom distress, social role problems, and general negative psychological symptoms as measured by the OQ-45) relative to individuals who do not.

Research Question Three

The final research question sought to investigate the differences between the Top Strengths and Bottom Strengths conditions in terms of the changes in positive variables and negative symptoms from pre-treatment to post-treatment, essentially comparing the Capitalization theory and the Compensation theory. Without sufficient evidence from the Character Strengths literature to confidently support one condition over the other, we avoided prediction by merely stating that both groups would experience benefits and that there may not be much difference between the two conditions on the outcome measures relative to each other (only relative to the Placebo and Waitlist/Control conditions).

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Based on the outcomes of the first two research questions, it appears that Bottom Strengths outperformed Top Strengths on several outcome variables. However, only one post- hoc test from one of the ANOVA analyses found a significant difference specifically between these two groups (OQ-45 Symptom Distress, in favor of the Bottom Strengths condition). Regardless, these results may still serve as an indication of the extent of the differences between these conditions. Because we know that the Signature Strengths intervention has in the past demonstrated a medium-level effect size on the outcome variables used in the current study (Sin & Lyubormisky, 2009), and because we saw that the Bottom Strengths intervention had some (albeit minor) effects on these variables, we may postulate that the interventions implemented in this study may not have had sufficient strength to reveal differences between these conditions. The fact that some differences were found in the Bottom Strengths condition anyway speaks directly to the promise of Bottom Strengths-focused interventions; at the very least, there is evidence to suggest it can mildly reduce symptoms of psychological distress and increase emotional well-being. These findings are consistent with the study that compared a combined Character Strengths/Weakness group to the traditional Signature Strengths intervention, in which participants who worked on both their Top Strengths and Bottom Strengths experienced an increase in psychological well-being, life satisfaction, and scores of happiness by follow-up that exceed the gains made by participants in the Top Strengths-only condition (Rust, Diessner, & Reade, 2009). Because Bottom Strengths participants in the current study appeared to experience an increase in emotional well-being, and possibly self-esteem as well, we therefore find support for these authors’ combined strengths/weakness model while advocating separately for continued empirical investigation of an isolated Bottom Strengths intervention.

Additional Findings

Several analyses were conducted on the collected data that was not necessarily relevant to the primary research questions but which is nonetheless interesting and informative for the general field of positive psychology. These supplementary results will be interpreted in light of the primary focus of the current study to the greatest extent possible, and they hopefully will serve as fodder for future studies investigating similar relationships. The first set of analyses dealt with issues of compliance, while the second set of analyses concentrated on the

128 relationships between all of the variables assessed by the pre-treatment measures, prior to any experimental intervention.

Compliance

An examination of the individual items on the Homework Rating Scale, including participant estimates of the number of activities completed and the number of hours spent on the activities consistent with their experimental group (Top Strengths, Bottom Strengths, or Health & Wellness) revealed that none of these variables were related to changes in the outcome measures. In other words, homework assignment variables did not appear to play a role in any of the differences between groups in outcome variables; this is not surprising, however, as there weren’t many differences between groups on the outcome variables. There were indications that participants in the Placebo condition reported more clarity and specificity in their homework instructions than did participants in either of the Character Strengths conditions. In addition, they indicated a better understanding of the rationale behind the assignments as well as a greater sense of collaboration in planning the assignments than did participants in the two Character Strengths conditions. These differences likely correspond to the nature of the presentation given in the Placebo condition; behavioral health information concerning diet, exercise, and sleep tends to be more concrete and activity-focused than Character Strengths information, which tends to be more abstract and prone to individual differences in interpretation and application (Peterson & Seligman, 2004). However, these differences in homework assignment clarity, specificity, rationale, and collaboration likely did not affect participant outcomes in the study. Separate analyses found that the Character Strengths of Teamwork, Self-Regulation, and Modesty (negative relationship) were significant predictors of participant estimates of the number of activities or hours spent on homework assignments. In a clinical setting, high scores on Teamwork and Self-Regulation and low scores on Modesty may facilitate between-session homework compliance. Therapists may be able to use clients’ scores on these three Character Strengths to gauge potential compliance as well as work with clients on developing these strengths in order to bolster compliance. TIPI scores (estimates of Big Five personality domains) were not related to these two measures of compliance (estimates of number of hours/activities), indicating that these broader personality characterizations likely do not have as much of a relative impact on compliance compared to these three Character Strengths. These findings are 129 consistent with a recent randomized controlled trial that found that Big Five personality traits, as measured by the NEO-PI-R, failed to predict participant compliance with outpatient homework assignments (Lampropoulos & Walker, 2011).

Demographic Analyses

Two demographic variables, Age and Income, displayed interesting relationships with other variables in the study. For example, correlations between participant age and the pre- treatment outcome variables indicated that older participants in the sample tended to report less negative affect, less depressive symptomatology, less avoidance and/or rumination, less subjective problems related to work or school, and lower scores pertaining to a search for a meaning in life. In addition, older participants displayed higher scores on self-esteem, psychological well-being, and behavioral activation than their younger counterparts. Prior research has demonstrated that constructs such as hope, life satisfaction, and a sense of a purpose in life tend to vary by age (Bronk, Hill, Lapsley, Talib, & Finch, 2009), and these variations are likely related to the other age-bound relationships found in this study. There were also weak relationships between age and personality variables; as age increased, so did scores on TIPI estimates of Agreeableness and Emotional stability. The most interesting finding, however, was that age was weakly correlated with several Character Strengths, including Gratitude, Modesty, Perseverance, Zest, Self-Regulation, Fairness, Forgiveness and Mercy, Spirituality, and Honesty. These particular Character Strengths are categorized predominantly into three Virtues: Temperance, Transcendence, and Courage (see Appendix A), possibly indicating a broad change process that occurs as we age. In addition, several of the other Character Strengths contained in these three virtues approached statistically significant correlations with age in this sample. However, the small magnitude of these relationships seem to indicate that these changes in strengths of character, measures of positive variables, and measures of negative symptoms with age may not be a direct resulting of the aging process but rather what age represents. For example, our accumulation of life experiences and life lessons (including both successes and failures) lead us to be more appreciative (Gratitude), teach us the value of Honesty and Fairness, increase the importance of existential connections (Spirituality), and help us learn to maintain control over the manner in which we live our lives (Modesty, Forgiveness and Mercy, and Self-Regulation).

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Differences in certain scores were also found between levels of annual income. In the higher income categories, individuals generally reported higher self-esteem, more positive affect, greater social well-being, more behavioral activation, less avoidance and rumination, and a greater sense of a meaning in life than lower income category participants. These differences likely reflect the advantages that financial security can afford, including freedom to pursue opportunities that directly impact these variables. For example, Johnson and Krueger (2003) found that the perception of one’s financial situation as well as perceived control over finances seemed to mediate the relationship between income and these positive assessments, indicating that money does not automatically lead to a better quality of life. Wealth does not guarantee happiness.

Relationships between Character Strengths and the “Big Five”

Regression analyses unveiled several relationships between Character Strengths and estimates of Big Five personality domains. Extraversion was predicted by the Character Strengths of Modesty and Social Intelligence; Agreeableness was predicted by Kindness and Forgiveness and Mercy; Conscientiousness was predicted by Perseverance; Emotional Stability (the converse of Neuroticism) was predicted by Hope; and Openness was predicted by Creativity, Love of Learning, Modesty, and Prudence. Several of these relationships were consistent with previous findings by Peterson and Seligman (2004), including the predictors of Agreeableness; however, these results must be considered preliminary due the fact that they were assessed in the current study using two-item estimates for each Big Five domain. Another study investigating the factor structure of the VIA Survey of Character Strengths and its relationship with the Big Five also found a moderate relationship between the Character Strength of Hope and the Big Five domain of Neuroticism (Macdonald, Bore, & Munro, 2008). However, because these authors chose to restructure the Character Strengths’ organizational scheme given by Peterson and Seligman (2004; Appendix A) by conducting a factor analysis with their data and then analyzing relationships between this new factor structure and scores for the Big Five, it is difficult to compare findings between this particular study and the current investigation. Regardless, these relationships indicate that there is some overlap between traditional measures of personality and positive personality characteristics as measured by the VIA Survey of Character Strengths. Future studies should examine Character Strengths as predictors of Big Five constructs using full-scale measures such as the NEO-PI-3. 131

There is some evidence that the combination of Big Five typology and Character Strengths may serve as viable predictors of individuals’ scores on several positive variables and negative symptoms (such as those measured in this study). For example, after controlling for demographic variables, the Character Strength of Hope and the TIPI estimate of Emotional Stability together accounted for nearly 1/3 of the variance in depression scores in the sample, implying a relatively stable predisposition for scores on measures of depression. These same analyses could be used to uncover relationships between these personality characteristics and positive variables, especially because recent empirical investigations have determined that we may have an idiosyncratic baseline—accounted for by genetics—for certain desirable states including positive emotion (Lyubomirsky, 2007; Lykken, 2000), emotional and psychological well-being (Kendler, Myers, Maes, & Keyes, 2011), mental well-being (Keyes, Myers, & Kendler, 2010), constructs of subjective well-being such as happiness (Røysamb, Tambs, Reichborn-Kjennerud, Neale, & Harris, 2003) and satisfaction with life (Johnson & Krueger, 2006). Because these positive variables have demonstrated a fair amount of genetic influence and appear to be significantly related to stable constructs of personality, analyses using Big Five and Character Strengths profiles to predict these positive variables may be particularly useful for aiding practitioners in assessing their clients’ genetically and personality-derived dispositions on these positive states.

Character Strengths Analyses

On a similar note, regression analyses with Character Strengths as predictors of outcome variables indicated that these positive personality characteristics seem to play a role in a large number of positive and negative states. Rather than delineate each of the uncovered relationships (see Chapter 4 for specific data on these analyses), a summary of these relationships is presented in Figure 5. As this table demonstrates, the resounding theme of these analyses was that four Character Strengths (Hope, Zest, Perspective, and Perseverance) appear to have a profound impact on both the positive and negative aspects of mental health investigated in this study. Unfortunately, Paul McCartney was a little off…but I guess “All you need is love” is a much better song lyric than “All you need is hope, zest, perspective, and perseverance.”

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Limitations

There were several unexpected circumstances that may have impacted the outcomes in this study or that may moderate our interpretation of the findings. These limitations are presented with respect to sampling, measures, treatment, data collection, and data analyses.

Sampling

The sample consisted of a sufficient number of cases to meet the requirements of the statistical procedures, assuming a small-medium effect size. However, there were not enough cases in the current study to render the analyses capable of identifying potential differences between groups that constituted a smaller effect size. While the ANOVA conducted on difference scores found medium effect sizes for the differences between groups on the MHC-SF Emotional well-being scale (Bottom Strengths > Placebo) and the OQ-45 Symptom Distress subscale (Bottom Strengths > Placebo; Bottom Strengths > Waitlist/Control), several of the alternate analyses found small effect sizes (Cohen’s d = .39 and .32) for these and other measures. It is possible that a larger sample is required to fully reveal differences between these groups for these measures. In addition, demographic inequities may limit the generalizability of the study (external validity). An overwhelming percentage of the sample identified as White/Caucasian (90%) and female (72%). Additionally, though the numbers of participants in the middle three income categories ($25K to $50K, $50K to $75K, and $75K to $100K) were roughly equivalent, many more participants identified their income as “$100,000 or more” (36%) than those who identified it as “Less than $25,000” (6%). While these demographic variables tended to be split fairly evenly across experimental conditions, they might jointly represent a skewed sample of individuals from the larger population. For this reason, the results of the study will generalize particularly to White females who report a higher than average annual salary. In addition, the mean age of participants in the Placebo condition was significantly higher than that of participants in the other conditions, especially the Top Strengths condition. This effect likely occurred because two of the organizations randomized to the Placebo condition consisted primarily of “experienced” business executives while a large number of participants in the Top Strengths condition were younger teachers and administrators at a local school. Because of the breadth of types of organizations and occupations that were sampled in the current study, it does

133 not appear that biased sampling procedures occurred that might have otherwise excluded racial minorities or men from participation. One group in the Placebo condition reported that they had recently experienced a workplace tragedy, and they anticipated that this may have affected the manner in which they responded to the pre-treatment questionnaires. Therefore, differences in pre-treatment and post- treatment might have been slightly exaggerated for this organization compared to others in the same condition. However, One-Way ANOVA comparisons between groups within each of the experimental conditions failed to find differences between organizations in the Placebo condition for any of the difference scores.

Measures

All of the outcome measures in this study demonstrated acceptable internal consistency or at least reliability that was consistent with past precedent for those measures (see Table 4). However, the use of the TIPI to estimate Big Five personality domains might have limited the internal validity of relationships between these estimates and other variables, such as Character Strengths. Future studies should investigate some of the preliminary relationships found with this measure while using a more comprehensive measure of Big Type typology such as the NEO-PI-3 (McCrae, Costa, & Martin, 2005). Additionally, we used participant estimates of the number of activities and the number of hours spent on activities to serve as a measure of compliance. Perusal of these estimates indicates that some participants may have been poor self-reporters, possibly overestimating or underestimating these variables. For example, several participants in Character Strengths conditions reported 60 or more Character Strengths activities over the past month and/or over 50 hours spent engaging their Character Strengths. Because about 1/3 of participants in one of the three experimental conditions requiring homework assignments reported “0” activities/hours, it is possible that several participants did not fully realize which of their daily or work activities qualified as Character Strengths-consistent or Health/Wellness-consistent.

Treatment

With some organizations, there was difficulty arranging the day and time of the psychoeducational presentations due to the busy schedules of the organizations. While many groups were able to schedule the presentation within a week or two after initial recruitment, a

134 few had to schedule it a couple of months in the future. This created some variability in the time elapsed between the completion of pre-treatment measures and the reception of the intervention. It also effectively lengthens the amount of time that elapsed between completion of the pre- treatment and post-treatment outcome measures. While this could not be helped, it would have been ideal for participants to have completed the pre-treatment outcome measures as close the day of the intervention as possible. Of prime relevance to this study, many outcome measures may have failed to reach levels of significant differences between groups due to the relative mildness of the intervention. Authors of previous research on similar Character Strengths interventions within organizations (Peterson & Park, 2006) recommend a series of interventions involving Character Strengths that include multiple workshops and reminders to participants. These authors specifically stated that, as a stand-alone intervention, we cannot expect to see sustained behavioral change or long-term effects in positive or negative psychological variables as a result of a single psychoeducational intervention. Instead, individuals must be led to nurture their Character Strengths through continued practice, both with others in the organization as well as on their own (Peterson & Park, 2006). If participants did experience short-term improvements as a result of the intervention, the Peterson and Park study (2006) might explain why this effect had mostly dissipated by a month or less following the intervention. These results might have been different if the research protocol involved multiple Character Strengths workshops and/or other methods in place to support maintenance of the Character Strengths intervention. For example, occasional reminders of individuals’ Character Strengths or arranging for weekly check-ins with participants on their progress in employing their Character Strengths might have improved compliance and sustained changes in the outcome variables; these procedures tend to have positive effects in increasing compliance of homework assignments in psychotherapy (Beck & Tompkins, 2007; Kazantzis, Deane, & Ronan, 2004), and there is reason to believe that these same procedures would be at least partially effective in this type of organizational intervention protocol. Scores on both the manipulation check (Third Party Rating Form) and scores on the Homework Rating Scale indicated that individuals in the Character Strengths conditions reported a relative lack of clarity on the assignments they were being asked to complete. Therefore, a more explicit focus on the practical application of Character Strengths should be amended to the current presentations for future interventions/research. This could be accomplished by dedicating

135 more time to a discussion of specific activities consistent with Character Strengths, providing more examples of activities for each Character Strength, and tailoring the Character Strengths activities to individuals as much as possible.

Data Collection

Two obstacles in data collection may have affected responses to surveys in this study. First, a third-party online survey tool failed to function in the days leading up to an intervention, keeping several participants from being able to fill out pre-treatment questionnaires. In addition, the website used for collection of outcome measure data was terminated in the middle of the study, requiring a new website for outcome measure data collection and an update of all forms and emails to include the new web address and instructions for completing these questionnaires. Second, there were a few problems that created difficulty in obtaining participants’ results of the VIA Survey of Character Strengths. For example, the third-party company that provided the principle investigator with these scores changed their website layout halfway through the study, requiring the author to give his participants different instructions for filling out the survey. Additionally, a large number of participants either forgot to enter the research code or incorrectly entered the research code that connected their VIA Survey results to their participation in this study. This meant that many participants who took the VIA Survey did not have their scores provided to the principle investigator, which excluded those scores from analyses involving Character Strengths scores; this is why there were only Character Strengths data for 126 out of the original 275 participants.

Data Analyses

Using a sample from the general population might have made finding significant differences between groups difficult, because the general population tends to be fairly (mentally) healthy. This is reflected in low pre-treatment scores of negative symptoms and high pre- treatment scores of positive psychological functioning (see Table 2). As a result, difference scores may not have been sensitive enough to pick up changes from pre-treatment to post- treatment; in some cases, a ceiling effect may have limited room in the measure for improvement, which may partially account for the low number of significant differences found with analyses of difference scores. It is possible that different results may occur in samples with specific mental health problems; for instance, a depressed sample might be more capable of

136 demonstrating statistical improvement in the measures used in this study, assuming they have higher scores on negative symptoms and lower scores on positive measures than did individuals in the current sample. The manner in which income was measured in this study might have limited the statistical analyses by creating unequal conditions that compared scores from a few people to scores from a larger group of people. Measuring income with a categorical model was done to protect sensitive participant information and encourage them to at least provide an estimate (though five participants were still uncomfortable providing this information). However, an ideal metric for this data would be as precise an estimate as possible; for example, if income were assessed using an open-ended prompt that required a continuous variable response, the statistics would be more precise. Alternatively, combining the two lowest income categories into one category might have improved the various analyses from a statistical standpoint, though this also could potentially further defuse differences between income categories.

Implications

Despite the delimitations and limitations of this study, there are a number of implications that can be deduced from the results, including practical applications. In this final section, we will explain what this investigation means for theory development, practice, and future Character Strengths research.

Theory Development

Though the effects found for the treatment conditions were minor, this study provides tentative, preliminary evidence in support of the Compensation model over the Capitalization as applied to Character Strengths. It seems that addressing one’s relative shortcomings on a number of positive personality variables has the potential to lead to tangible gains that may include a reduction in negative psychological symptoms and an increase in emotional well-being and self- esteem. These findings are consistent with previous research that discovered benefits in the engagement of bottom-ranked Character Strengths (Rust, Diessner, & Reade, 2009), though this study implemented a combined condition that included both top-ranked and bottom-ranked strengths. The current study, instead, partitioned the impact of Top Strengths and Bottom Strengths approaches in order to compare the two groups discretely. For this reason, these results were able to indicate there may be some value in working toward the development and

137 improvement of lesser-ranked Character Strengths. This hypothesis is certainly consistent with current positive psychological thought, which advocates holistic self-improvement and activities that promote self-actualization (Snyder & Lopez, 2007; Peterson, 2006). In one set of regression analyses, we used stable traits (Character Strengths) to predict transitory states (pre-treatment outcome measures), and the outcomes demonstrated that a combination of various Character Strengths were able to account for nearly half of the variance in measures of negative symptoms and/or psychological well-being. This is consistent with other studies that have found a link between genes, personality, and subjective well-being (Weiss, Bates, & Luciano, 2008), proposing that, to some extent, our transitory positive and negative states may have a set baseline or predisposition. In fact, happiness has been consistently shown to depend to a certain degree on such relatively stable characteristics as our personality and genes (Seligman, 2011; Lyubomirsky, 2007; Lykken, 2000). These findings and previous research point to a complex nature/nurture interaction that directly impacts our positive and negative emotional states; however, it appears that most of the variance in these relationships still comes from environmental causes (Lyubomirsky, 2007). In other words, individuals may be able to behaviorally control the majority of variance in these states. This supposition is consistent with the tenets of cognitive-behavioral therapy (Beck, 2011; Beck, 1976) in which it is not the negative events that happen to us that determines our resultant emotional state (such as depression or anxiety), but rather our cognitive and behavioral reactions to those events.

Practice

In the current study, we implemented a consultation model of service provision for the Character Strengths interventions. Based on the results of this study and others that provided the basis for the intervention (Peterson, Stephens, Park, Lee, & Seligman, 2010; Harris & Thoresen, 2006; Peterson & Park, 2006), we suggest that this remains an appropriate avenue for educating the general public on the use and utility of Character Strengths. At the individual level, this study serves as an indication that some clients in individual psychotherapy may benefit from focusing on their bottom-ranked strengths, possibly in addition to their top-ranked strengths, for a balanced Capitalization/Compensation approach consistent with that described by Rust, Diessner, and Reade (2009). While positive psychology interventions have shown some promise for integration into existing psychotherapy protocols as homework assignments (Walker & Lampropoulos, 2010), the interventions implemented in the 138 current study require enhancements and further research before they will be ready for broader dissemination. Theoretically, however, many of the shortcomings of the current study (e.g. absence of individualized treatment planning, homework assignments follow-up, multiple psychoeducational events, etc.) could potentially be remediated in an individually-administered format, though this assumption requires practice-based research for validation. Interested clinicians should see Harris, Thoreson, & Lopez (2007) for a practice-oriented article on the integration of Positive Psychology with counseling. Clinicians should also pay close attention to the Character Strengths of Zest and Hope (and to a lesser extent, also Perspective and Perseverance) with their clients, as these Character Strengths were particularly robust predictors of a number of outcome variables in this study. These findings are consistent with prior research that has demonstrated the wide-reaching potential of these two traits in a variety of domains. For example, previous authors have found that Zest is related to work satisfaction (Peterson et al., 2010) as well as life satisfaction and a sense of one’s occupation as a calling (Peterson, Park, Hall, & Seligman, 2009), a lack of boredom or anxiety and frequent experience of engagement (Peterson et al., 2007), higher frequency in exercise and other healthy habits, more autonomy, and to some extent, emotional resilience (Peterson & Seligman, 2004). The Character Strength of Hope has also demonstrated relationships with work satisfaction (Peterson et al., 2010), life satisfaction (Bailey, Eng, Frisch, & Snyder, 2007), resilience (Peterson et al., 2007), job performance (Peterson & Byron, 2008), a conscientious orientation to the future (Peterson & Seligman, 2004), better physical health, and a lower incidence of depression (Seligman, 1990). This evidence seems to indicate that both Zest and Hope are necessary components of psychological well-being and mental health, making them of particular relevance to the clinician. Generally, clinical practitioners may benefit from knowledge of their clients’ Character Strengths and Big Five personality characteristics, as both of these constructs appear to be related to predispositions for a number of positive variables and negative symptoms that were the subject of investigation in this study. Several brief measures that can be used to screen Big Five personality domains (NEO Five-Factor Inventory-3: McCrae & Costa, 2007; TIPI: Gosling, Rentfrow, & Swann, Jr., 2003) and Character Strengths (Brief Strengths test available free-of- charge at www.authentichappiness.com). The results of these screening measures may be used in individual psychotherapy to estimate a client’s Character Strengths profile as well as to provide

139 an indication of their personality-based predispositions for positive variables and negative symptoms (according to the measures used in this study), and this information can assist in the identification of target areas for improvement as well as the provision of strengths-based, client- tailored treatment plans.

Research

Due to constraints in the “strength” of the Character Strengths presentations, the intervention protocol should be updated per the recommendations in the Limitations section so that the research questions and hypotheses of the current study may be retested. A replication and extension of the current study would allow us to have greater confidence in the results and could serve to further guide the manner in which Character Strengths interventions are provided at the large group level. Specifically, the following revisions to the current protocol are recommended: 1. Plan a series of psychoeducational events. The content of the current study’s Character Strengths presentations may have covered too much information in too little time for participants to derive lasting benefit (or to remember what was discussed, at all, weeks later). Breaking up the content into three or four separate segments that allow for more depth of education in each section would allow for better audience retentions. By having one small(er) information session once a week for three to four weeks, participants would be able to reflect what they learned and then be reminded of it later. This would also enable a week-to-week follow up with participants to remind them of their Character Strengths and activities, to check their understanding of and compliance with homework assignments, and to help them overcome any barriers to completion of the assignments. 2. Facilitate more discussion with participants on their Character Strengths. Due primarily to time constraints, the discussion section of the intervention was frequently cut short, and/or participants indicated that they would have appreciated more time to discuss the Character Strengths among those within their organization. We recommend an approximate 50/50 split of psychoeducation/discussion as the preferred model for future interventions of this kind. 3. Provide more customization of Character Strengths to activities. One of the most common questions during the discussion portion of the intervention pertained to troubleshooting how to plan for interventions based on individual Character Strengths. Providing an enhanced list of activities may help, but it would be better to have an extended discussion period to work with participants individually on planning activities to match their Character Strengths. 140

4. Arrange organizational support in data collection process. Ideally, completion of the pre-treatment outcome measures would happen immediately prior to the preliminary intervention as well as at set time frames throughout the duration of the interventions. Organizational support, both at the head of the organization and with individual employees, is essential in collecting outcome data. With the right a priori agreements, it may be possible to set an established time during which all participants may complete the outcome measures together, rather than asking them to complete the measures at random intervals (on their own time). Furthermore, interventions that target the development and use of the Character Strengths of Zest and Hope should continue to be the subject of empirical investigation. Future practice- oriented research should consider developing and testing Character Strengths interventions that specifically target these Character Strengths (rather than focusing exclusively on an individual’s top or bottom five Character Strengths) in an effort to bolster resiliency, psychological well- being, and various other facets of mental health. This empirical focus on Zest and Hope may be extended to a wide range of psychotherapeutic activities, including individual psychotherapy, group-based interventions, career counseling, and larger inoculative prevention efforts. As specific populations that are considered at-risk for mental health problems (e.g., first-year college students, pre-deployment military, individuals with a recurrent depression, individuals from low SES groups, individuals with stressful lifestyles or occupations, etc.) may benefit from broader Character Strengths interventions that promote the development of the strengths of Zest and Hope, more research is required. A preliminary discussion of strategies to improve Hope and Zest may be found in the Character Strengths and Virtues handbook (Peterson & Seligman, 2004). Based on this study’s evidence for the interrelatedness of the Hope and Zest Character Strengths with attributes of positive and negative mental health, interventions that strengthen these Character Strengths should be the target of future research into their effectiveness and potential applications. The regression analyses described in the Additional Findings section require further investigation, specifically the relationships between the Big Five personality domains and Character Strengths, as we have provided preliminary evidence in this study for possible relationships between the two personality-based measures. Further examination of the components that relate these scales, as well as structural equation modeling to demonstrate the complex interrelations that inevitably exist between these constructs, may allow us to understand

141 how these two separate explanations of personality (broadly defined) can be applied to future psychological interventions. Taking personality into account to this degree (e.g. relying on information gleaned from both Big Five personality data as well as Character Strengths data) may enhance the tailoring of interventions to client predispositions and strengths, providing them with bolstered probability for success in treatment. In addition, several preliminary analyses in the current study demonstrated that combining Character Strengths and Big Five personality data may enhance the predictive value of models that assess for psychological states. For example, recall that the Character Strength of Hope and the estimate of Emotional Stability (reverse of Neuroticism) together accounted for nearly 1/3 of the variance in participants’ depression scores prior to any intervention. These types of relationships should be explored further as well as extended to other measures of mental health and psychological well-being so that the unique contribution of both of these personality categorization models to our predispositions to health and happiness may be revealed.

Conclusion

Due largely to methodological issues, the effects of the interventions presented in this study produced mild between-group differences on only a few outcome variables, failing to replicate the findings of previous authors with interventions of this nature. However, taking these flaws into consideration, it is remarkable that individuals in the Bottom Strengths condition still evidenced improvement on certain positive and negative measures over individuals in the other groups. Therefore, the results of this study, combined with those of Rust, Diessner, & Reade (2009), tentatively support a Compensation model approach to Character Strengths interventions while simultaneously acknowledging the benefits of the traditional Capitalization model-based Signature Strengths intervention. We suggest to future researchers that the limitations and delimitations of this study be addressed, the research protocol be amended to include multiple interventions over time, and this study be replicated with a more generalizable sample. This study also provides a wealth of evidence to indicate that the Character Strengths of Hope and Zest are extremely closely linked with both negative variables (such as symptom distress, negative affect, and depressive symptoms) and positive variables (life satisfaction, positive affect, self-esteem, and psychological well-being). Clinicians would be wise to address these two Character Strengths with their clients and work with them to improve their

142 predispositions for cognitive, emotional, and behavioral action consistent with these strengths. For clients who possess high levels of these Character Strengths, a Capitalization approach to treatment may be indicated. Furthermore, for all clients in therapy, we advocate a strengths- based, collaborative treatment planning process. Future research should investigate interventions with clients that increase Hope (optimism and future-mindedness) and Zest (enthusiasm, excitement, and energy), as these studies would have direct applications for clinical practice. In summary, it appears that engaging in activities consistent with top-ranked or bottom- ranked Character Strengths may benefit individuals along both positive and negative outcome dimensions. In fact, it is possible that merely engaging any of the 24 Character Strengths, without regard for which ones are most consistent with idiographic personality, may have beneficial effects. It remains to be seen, however, whether there truly are differences between the benefits of engaging your Top Strengths versus your Bottom Strengths. Given these conclusions, and considering that all the Character Strengths are positive and pro-social personality variables, we generally endorse Character Strengths work for integration into daily life.

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APPENDIX A

CHARACTER STRENGTHS AND VIRTUES

VIRTUE CHARACTER STRENGTH COURAGE Bravery Perseverance Honesty Zest HUMANITY Love Kindness Social Intelligence JUSTICE Citizenship Fairness Leadership TEMPERANCE Forgiveness and Mercy Modesty Prudence Self-Regulation TRANSCENDENCE Appreciation of Beauty and Excellence Gratitude Hope & Optimism Humor Spirituality WISDOM & KNOWLEDGE Creativity Curiosity Judgment (Open-mindedness) Love of Learning Perspective

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APPENDIX B

DEMOGRAPHIC QUESTIONNAIRE

The purpose of the following questions is to gather basic information that will be relevant to the generalizability of the study. All personal information will remain confidential.

Your email address is required in order to give you feedback on the surveys you have completed, as well as to remind you when it is time to complete the second set of questionnaires. You will not receive any “spam” messages, and your email address will not be given to any other parties.

E-mail Address: ______

Age: _____

Gender (circle one): Male Female

Ethnicity: African-American Asian-American Caucasian/White

Hispanic/Latino Middle-Eastern Native American

Pacific Islander Biracial/Multiracial

Other: ______

Income: Please indicate your annual household income.

____ Less than $25,000 ____ $25,000 - $50,000 ____ $50,000 - $75,000 ____ $75,000 - $100,000 ____ Greater than $100,000

Name of organization/employer: ______

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APPENDIX C

POSITIVE AND NEGATIVE AFFECT SCHEDULE

This scale consists of a number of words that describe different feelings and emotions. Read each item and then mark the appropriate answer in the space next to that word. Indicate to what extent you have felt this way in the past two weeks. Use the following scale to record your answers:

1 2 3 4 5 very slightly a little moderately quite a bit extremely or not at all

___ interested ___ irritable ___ distressed ___ alert ___ excited ___ ashamed ___ upset ___ inspired ___ strong ___ nervous ___ guilty ___ determined ___ scared ___ attentive ___ hostile ___ jittery ___ enthusiastic ___ active ___ proud ___ afraid ___ competent ___ egotistic ___ stupid ___ effective ___ resourceful ___ conceited ___ efficient ___ inadequate ___ shame ___ confident ___ smart ___ incompetent ___ worthless ___ self-centered ___ boastful

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APPENDIX D

BEHAVIORAL ACTIVATION FOR DEPRESSION SCALE

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APPENDIX E

MENTAL HEALTH CONTINUUM – SHORT FORM

Please answer the following questions are about how you have been feeling and how you have been functioning during the past month. Place a check mark in the box that best represents how often you have experienced or felt the following:

NEVER ONCE ABOUT ABOUT ALMOST EVERY During the past month, OR ONCE 2 OR 3 EVERY DAY how often did you feel … TWICE A TIMES DAY WEEK A WEEK

1. happy

2. interested in life

3. satisfied

4. that you had something important to contribute to society

5. that you belonged to a

149 community (like a social group, or your neighborhood)

6. that our society is becoming a better place for people like you

7. that people are basically good

8. that the way our society works makes sense to you

9. that you liked most parts of your personality

10. good at managing the responsibilities of your daily life

11. that you had warm and trusting relationships with others

150

12. that you had experiences that challenged you to grow and become a better person

13. confident to think or express your own ideas and opinions

14. that your life has a sense of direction or meaning to it

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APPENDIX F

SATISFACTION WITH LIFE SCALE

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APPENDIX G

MEANING IN LIFE QUESTIONNAIRE

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APPENDIX H

ROSENBERG SELF-ESTEEM SCALE

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APPENDIX I

CENTER FOR EPIDEMIOLOGICAL STUDIES – DEPRESSION SCALE

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APPENDIX J

HOMEWORK RATING SCALE

HRS

This questionnaire consists of 12 questions regarding your homework assignments from the past two weeks. Please read each question carefully and pick out the one response that best describes your experience. Place a check mark beside the response you have picked. If several statements apply equally well, circle the lowest number for that group. Be sure that you do not choose more than one response for any question.

1. Quantity: How many activities consistent with 6. Comprehension: How well did you understand your character strengths were you able to complete? what to do?

______Activities ___ (0) Not at all. ___ (1) Somewhat. 2. Time: About how many hours did you spend on ___ (2) Moderately. all of the activities consistent with your character ___ (3) Very. strengths? ___ (4) Completely.

______Hours 7. Rationale: How well did you understand the reason for engaging in these activities? 3. Quality: How well did you do the assignment? ___ (0) Not at all. ___ (0) Not at all. ___ (1) Somewhat. ___ (1) Somewhat. ___ (2) Moderately. ___ (2) Moderately. ___ (3) Very. ___ (3) Very. ___ (4) Completely. ___ (4) Extremely. 8. Collaboration: How much involvement did you 4. Difficulty: How difficult was it for you to have in planning the activities? engage in character strength activities? ___ (0) None. ___ (0) Not at all. ___ (1) A little. ___ (1) Somewhat. ___ (2) Some. ___ (2) Moderately. ___ (3) A lot. ___ (3) Very. ___ (4) Extensive. ___ (4) Extremely. 9. Specificity: How specific were the guidelines on 5. Obstacles: How much did obstacles interfere how to complete these activities? with these activities? ___ (0) Not at all. ___ (0) Not at all. ___ (1) Somewhat. ___ (1) Somewhat. ___ (2) Moderately. ___ (2) Moderately. ___ (3) Very. ___ (3) Very. ___ (4) Extremely. ___ (4) Completely.

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10. Pleasure: How much did you enjoy these Briefly, what kind of character strengths activities activities? did you perform?

___ (0) Not at all. ______(1) Somewhat. ______(2) Moderately. ___ (3) Very. ______(4) Extremely. ______

11. Mastery: How much did working on your ______character strengths help you gain control over ______your life? ______(0) Not at all. ______(1) Somewhat. ___ (2) Moderately. ______(3) Very. ______(4) Extremely. ______12. Post-Participation: How likely do you think ______you will be to continue engaging in these activities consistent with your character strengths on your ______own? ______

___ (0) Not at all. ___ (1) Somewhat. ___ (2) Moderately. ___ (3) Very. ___ (4) Extremely.

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APPENDIX K

TEN-ITEM PERSONALITY INVENTORY

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APPENDIX L