Hope and Goal Outcomes: the Role of Goal-Setting Behaviors

Home , Goal setting, Hope (instrumental)

Hope and goal outcomes: The role of goal-setting behaviors

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Arts in the

Graduate School of The Ohio State University

Sara Anne Moss, B.A.

Graduate Program in Psychology

The Ohio State University

2018

Thesis Committee

Dr. Jennifer S. Cheavens, Advisor

Dr. Kentaro Fujita

Dr. Daniel R. Strunk

Copyrighted by

Sara Anne Moss

2018

Abstract

Hope Theory (Snyder, 1994; 2002) is a framework through which goal pursuit has been studied over the past two decades. According to Snyder (2002), individuals with higher levels of hope, as compared to their lower hope peers, set goals that are higher quality and are better able to generate routes to achieve their goals, predict and overcome obstacles, and effectively harness mental energy during goal pursuit. Hope Theory posits that these goal setting behaviors act as the mechanisms through which hope and goal attainment are related (Snyder, 1994; 2002). Empirical research supports the relation between hope and goal setting behaviors (Cheavens, Heiy, Feldman, & Rand, under review; Snyder et al., 1991) as well as the link between hope and goal outcome (e.g.,

Feldman, Rand, & Wrobleski, 2009; Guter & Cheavens, 2016). Furthermore, Goal

Setting Theory research links related goal properties (e.g., difficulty, specificity, importance) to goal attainment (Locke & Latham, 2006). However, the complete model in which the relation between hope and goal outcome is mediated by goal setting behaviors has not yet been tested. In this study, we sought to address this gap using a longitudinal design of goal setting and pursuit among a sample of college students (Study

1: N = 121; Study 2: N = 139). As predicted, hope significantly predicted goal outcome.

However, while there were positive, small-to-medium sized associations between hope and self-reported goal commitment, confidence, and perceived effectiveness of planned pathways, hope was not significantly related to coder-rated (i.e., “objective”) goal setting. ii

Furthermore, only self-reported goal commitment and confidence, not objective ratings of goal setting, significantly mediated of the relation between hope and goal outcome. Using exploratory analyses, we found that hope moderated the relation between goal quality and

2-month goal outcomes such that at lower levels of hope, individuals who set higher quality goals achieved their goals at rates indistinguishable from higher-hope individuals, while at higher levels of hope, goal achievement was not related to goal quality. If replicated, these findings suggest that the mechanisms of successful goal pursuit may differ at lower and higher levels of hope and that goal setting interventions focused on setting high quality goals and pathways may be of particular benefit to lower-hope individuals. In addition to providing information about the relations between hope, goal setting, and goal outcome, the results illuminate methodological considerations for future research.

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Dedication

In memory of Abby Shapiro, whose tenacious goal striving inspires me every day.

Acknowledgments

First, I owe an enormous thank you to my advisor and mentor, Dr. Jennifer

S. Cheavens. You provide me with the structure and support to grow as a researcher and professional, and I have benefited tremendously from your wisdom, insight, and perspective throughout this process. I would also like to thank my committee members, Dr. Daniel R. Strunk and Dr. Kentaro Fujita, for their time in providing thoughtful, comprehensive, and useful feedback to strengthen this project. I could not have completed this study without the help of my research assistants, Whitney Allen, Megan Crevar, Joling Hsing, Rabia Khan,

Taylor Thomas, Andrea Thompson, and Rachel Williams who spent countless hours training and coding goals. Finally, I would also like to thank my colleagues in the Mood and Personality Studies lab, Erin Altenberger, David Cregg, Kristen

Howard, Matt Southward, and Anne Wilson for their encouragement and support.

Vita

2004 – 2008 ...... Walter Johnson High School, Bethesda, MD

2010 – 2014...... Dickinson College, Carlisle, PA B.A., Summa Cum Laude, Psychology with Honors

2015 – Present ...... The Ohio State University, Columbus, OH Graduate Teaching Associate, Department of Psychology

Publications

Cardi, V., Ambwani, S., Crosby, R., Macdonald, P., Todd, G., Park, S., Moss, S. A., Schmidt, U., & Treasure, J. (2015). Self Help Aid and Recovery guide for Eating Disorders (SHARED): Theoretical rationale and protocol for a randomised controlled trial examining the effect of the Recovery MANTRA intervention for Anorexia Nervosa. Trials.

Ambwani, S., Thomas, K. T., Hopwood, C. J., Moss, S. A., & Grilo, C. M. (2014). Obesity stigmatization as the status quo: Assessment and prevalence among young adults in the U.S. Eating Behaviors: 15, 366-370.

Fields of Study

Major Field: Psychology

Table of Contents

Abstract ...... ii Dedication ...... iv Acknowledgments...... v Vita ...... vi List of Tables ...... viii List of Figures ...... ix Chapter 1: Introduction ...... 1 Chapter 2: Study 1 Method ...... 16 Chapter 3: Study 1 Results ...... 19 Chapter 4: Study 2 Method ...... 21 Chapter 5: Study 2 Results ...... 25 Chapter 6: Discussion ...... 35 References ...... 50 Appendix A: Tables ...... 59 Appendix B: Figures ...... 71 Appendix C: Goal Reporting Activities ...... 82

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List of Tables

Table 1. Operationalization and descriptive statistics of coder-rated goal descriptors...... 60

Table 2. Study 1: Average 2-month goal descriptor ratings ...... 61

Table 3. Component loadings and communalities for Principal Component Analysis with oblimin rotation of goal descriptors ...... 62

Table 4. Goal domains and examples ...... 63

Table 5. Hypothesis 1a: 2-week goal-setting variables at baseline: Descriptive statistics and correlations ...... 64

Table 6. Hypothesis 1a: 2-month goal-setting variables at baseline: Descriptive statistics and correlations ...... 65

Table 7. Hypothesis 1b: Hope Scale scores predicting 2-month goal progress and commitment, confidence, and perceived difficulty at T2 ...... 66

Table 8. Hypothesis 2a: 2-week goal setting variables predicting goal outcomes ...... 67

Table 9. Hypothesis 2b(i): 2-month goal setting variables predicting goal outcomes...... 68

Table 10. Hypothesis 2b(ii): 2-month goal setting variables predicting goal outcomes ...69

Table 11. Hypothesis 3a, 3b: Hope Scale scores predicting follow-up goal setting variables ...... 70

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List of Figures

Figure 1. The Hope Model ...... 72

Figure 2. Empirical support for Hope Theory from Hope and GST research...... 73

Figure 3. Mediating role of goal perceptions (self-reported) in explaining the relation between baseline Hope Scores and 2-week goal outcomes ...... 74

Figure 4. Mediating role of pathway perceptions (self-reported) in explaining the relation between baseline Hope Scores and 2-week goal outcomes ...... 75

Figure 5. Mediating role of goal properties (other-rated) in explaining the relation between baseline Hope Scores and 2-week goal outcomes ...... 76

Figure 6. Mediating role of goal perceptions (self-reported) in explaining the relation between baseline Hope Scores and 2-month goal outcomes ...... 77

Figure 7. Mediating role of pathway perceptions (self-reported) in explaining the relation between baseline Hope Scores and 2-month goal outcomes ...... 78

Figure 8. Mediating role of goal properties (other-rated) in explaining the relation between baseline Hope Scores and 2-month goal outcomes ...... 79

Figure 9. Hope Scores moderating the relation between coder-rated goal quality and 2- month goal completion ...... 80

Figure 10. Exploratory analysis: 2-month goal completion ratings as a function of Hope Scores and goal quality ratings ...... 81

Chapter 1: Introduction.

Goal pursuit is an ongoing, iterative process that is ubiquitous in daily life (Austin

& Vancouver, 1996; Snyder, 2002). Goals, which are broadly defined as “internal representations of desired states,” (Austin & Vancouver, 1996, p. 338) pervade every realm of life: Arriving to work on time, mastering a new skill, avoiding an argument with a partner, successfully delivering a speech, saving enough money to buy a home, and getting a promotion are all examples of goals. They guide behavior, direct resources and energy, and provide structure, purpose, and identity (Cheavens, Heiy, Feldman, & Rand, under review; Elliot, Sheldon, & Church, 1997; Snyder, 2002).

Understanding what makes individuals more or less likely to attain their goals is an important part of the research agenda in many psychological sub-disciplines including social, clinical, industrial-organizational, and educational psychology (Austin &

Vancouver, 1996). Empirical findings consistently support that there are certain individual characteristics, as well as specific goal properties, that are associated with goal attainment (Locke & Latham, 2002; Snyder, 2002). For example, individuals who have higher levels of grit (Sheldon, Jose, Kashdan, & Jarden, 2015) and self-esteem (Tang &

Reynolds, 1993) are more likely to be effective in their goal pursuits, and goals that are specific (Ingledew, Wray, Markland, & Hardy, 2005; Locke & Latham, 1990) and personally important (Beattie, Hardy, & Woodman, 2015) are more likely to be attained.

Snyder’s Hope Theory (1994; 2002) is a framework through which goal pursuit has been studied over the past two decades. With this theory, Snyder posits that individuals’ levels of hope, conceptualized as a combination of perceived ability to generate workable routes to achieve goals (pathways thinking) and determination to use those routes (agency thinking), predicts goal attainment. Empirical studies have consistently supported the link between hope and personal goal attainment (e.g.,

Feldman, Rand, & Wrobleski, 2009; Guter & Cheavens, 2016) and between hope and goal setting behaviors (e.g., Cheavens et al., under review; Snyder et al., 1991), though there has been considerably less attention paid to empirically examining the specific behaviors that are theorized to account for the relation between hope and goal outcome.

In this study, we aim to test the model in which the relationship between hope and goal outcome is indirectly accounted for by goal setting behaviors using a longitudinal design

(Figure 1).

Defining hope

Though the term “hope” is often used colloquially to describe a general sense that positive things will happen (Cheavens & Ritschel, 2014), academic conceptualizations dating back to the 1930s have focused more specifically on individuals’ expectations that their goals will be reached (Snyder et al., 1991). Early theoreticians did not include hypotheses regarding the means through which goals are pursued, a gap which Snyder and colleagues (1991) sought to fill by drawing together relevant goal constructs to create a unifying framework to understand goal pursuits. They stated, “the Hope model involves reciprocal action between an efficacy expectancy reflecting the self-belief that one can

2 achieve goals (agency) and an outcome expectancy reflecting the perception of one or more available strategies for achieving those goals (pathways; p. 571).” Snyder (2002) believed that one’s goal-related learning history creates trait-like styles of pathways and agency thoughts, which transact through subsequent development: successful goal pursuits early in life help to reinforce self-perceptions about ability to achieve future goals (Choma, Busseri, & Sadava, 2014; Snyder, 2002).

Hope researchers posit that both clear definitions of desired outcomes and a sense of self-efficacy are necessary for successful goal striving, and that neither alone is sufficient (Snyder et al., 1991; Snyder, Sympson, Michael, & Cheavens, 2001). Thus, although hope is related to other outcome expectancy constructs including self-efficacy and optimism, it is clearly differentiable. The constructs load onto separate factors

(Gallagher & Lopez, 2009; Magaletta & Oliver, 1999; Rand, 2009), and are associated with unique variance in predicting aspects of flourishing and well-being (for a review see

Cheavens & Ritschel, 2014; Snyder et al., 2001). Furthermore, when compared to optimism, hope appears to have a stronger, more consistent relationship with academic performance (Feldman & Kubota, 2015; Feldman, Davidson, & Margalit, 2014), positive adjustment and coping (Snyder et al., 2001), and life satisfaction (Bailey, Eng, Frisch, &

Snyder, 2007).

Hope and goal outcomes

Through the decade following development of the Hope Scale (Snyder et al.,

1991) and publication of Snyder’s (1994) seminal book, much of hope literature concentrated on either describing its theoretical underpinnings (e.g., Cheavens & Snyder,

2002; Snyder, 2002; Snyder et al., 1997) or establishing hope’s association with a host of positive outcomes. Hope is associated with adaptive psychological variables including constructive conflict responses and relationship maintenance behaviors (Merolla, 2014), psychological functioning (Feldman & Snyder, 2005), and life satisfaction and employee well-being (Reichard, Avery, Lopez, & Dollwet, 2013). It serves as a protective factor for individuals at increased risk for suicide (Anestis, Moberg, & Arnau, 2014; Davidson,

Wingate, Rasmussen, & Slish, 2009), emergency department professionals at risk for

PTSD and burnout (Ho & Lo, 2011), and has been shown to promote adjustment and positive coping among first-year college students (Davidson et al., 2012), injured athletes

(Lu & Hsu, 2013), and individuals with multiple sclerosis (Madan & Pakenham, 2014) and diabetes (Makarem, Smith, Mudambi, & Hunt, 2014). Hope has been shown to mediate the relationship between perfectionism and depressive symptoms (Mathew,

Dunning, Coats, & Whelan, 2014), as well as to exert both direct and indirect effects on depressive symptoms cross-sectionally (Chang & DeSimone, 2001) and longitudinally

(Arnau, Rosen, Finch, Rhudy, & Fortunato, 2007).

With Hope Theory, Snyder and colleagues (1991) sought to examine individuals’ personal goal pursuits beyond the relation between hope and general positive outcomes.

Indeed, there is a consistently documented association between hope and academic achievement (Davidson, Feldman, & Margalit, 2012; Levi, Einav, Aiv, Raskind, &

Margalit, 2014; Marques, Pais-Ribeiro, & Lopez, 2011; Rand, Martin, & Shea, 2011), athletic achievement (Curry, Snyder, Cook, Ruby, & Rehm, 1997), and work performance (Reichard et al., 2013). In two related studies tracking undergraduate

4 students’ personal goal progress over the course of an academic semester, baseline levels of hope significantly predicted goal progress at one-month (Guter & Cheavens, 2016) and three-month (Feldman et al., 2009) follow-up.

Importantly, according to Snyder, these positive outcomes result from the relation between individuals’ levels of hope and their personal goal pursuit processes (Snyder et al., 1991; Snyder, 2002). In other words, at its core, the theory posits that optimal goal setting behaviors act as the mechanisms through which hope and goal attainment are related: higher hope individuals engage in effective goal setting behaviors, and, in turn, effective goal setting behaviors facilitate goal attainment.

Hope and Goal Setting Theory: Goal setting behaviors

Like Snyder (1991; 2002), researchers of Goal Setting Theory (GST) are similarly interested in the relationship between goal setting behaviors and goal outcomes.

However, where Hope Theory researchers begin with an emphasis on individuals and how their self-reported hope relates to their goal setting behaviors, GST researchers focus on the association between goal setting behaviors and outcomes. Despite these differences, integration of the two theories is valuable to understanding successful goal pursuit. Such integration is facilitated by investigation of similar aspects of goal setting and striving including goal establishment, planning, and motivation (e.g., Austin &

Vancouver, 1996; Gollwitzer, 2015; Snyder, 2002). Thus, we propose testing a model that combines the empirical findings from Hope and GST to provide empirical support for the underlying tenets of the Hope Model (Figure 2).

Establishment of goals. Within Hope Theory, dispositional hope can be used to predict the properties of goals that individuals are likely to set (e.g., number of goals, diversity of goals, personal importance, difficulty, specificity, prosocial focus; Snyder,

2002). GST has established relationships between the same properties and goal outcomes

(e.g., Locke & Latham, 2002). For example, hope is theorized (Snyder, 2002) to be related to the number and diversity of goals set and evidence supports this contention with findings that higher levels of hope are associated with more goals across a wider variety of life domains (Snyder et al., 1991). GST research has shown that setting a greater number and diversity of goals is associated with increased likelihood of attainment (Locke & Latham, 2013), perhaps because multiple goals often build upon one another.

Hope Theory posits that individuals with higher hope, as compared to their lower hope peers, set goals that are more important and to which they are more committed

(Snyder, 2002; Snyder et al., 1997). However, in an explicit test of this association,

Feldman and colleagues (2009) failed to find a significant relation between hope and personal goal importance ratings. On the other hand, in a cross-sectional study, Cheavens and colleagues (under review) asked undergraduate participants to set personal goals for the semester and trained a team of research assistants to code goals on a number of descriptors. They found that hope scores accounted for significant variance in coder-rated

(i.e., objective) importance, such that participants higher in hope generated goals that were rated as more objectively important. Relatedly, GST research has demonstrated that individuals’ personal appraisals of goal importance is one of the strongest predictors of

6 goal outcome (Locke & Latham, 2002; 2013). For example, in a longitudinal study tracking personal goal progress over a 3-month period, Beattie, Hardy, and Woodman

(2015) found that personal goal importance significantly predicted goal progress and completion beyond both early progress and self-efficacy. Furthermore, for goals that individuals deemed relatively unimportant, there was no significant goal progress over the 3-month study period. Similarly, in a longitudinal study of first-time adolescent offenders’ completion of a goal-focused intervention, goal commitment had a significant effect on program completion (Belciug et al., 2016). The relation between importance and outcome may be related to the integral role of motivation, or agency, in the goal pursuit process; it is likely easier to generate and sustain motivation for goals which are personally important (Snyder, 2002; Tory & Scholer, 2015).

According to Hope Theory, higher hope is also related to setting more challenging and specific goals (Snyder, 2002; Snyder et al., 1997), a relationship that has been supported empirically (Cheavens et al., under review; Snyder et al., 1991). GST research consistently finds a strong, positive relationship between goal difficulty and performance, with effect sizes (Cohen’s d) ranging from .52 to .82 (Locke & Latham,

2002). Goal difficulty and specificity have been found to co-vary (Austin & Vancouver,

1996); thus, researchers often examine these two dimensions in tandem, comparing specific and difficult goals to vague goals and easy goals. Meta-analytic findings support that, when compared to goals to “do your best,” specific, difficult goals are consistently related to higher performance (d = .42 to .80; Locke & Latham, 2002). GST researchers explain the influence of difficulty and specificity on outcome by returning to the function

7 of goals. First, goals serve a directive function, guiding the partitioning of physical and mental resources; thus, more challenging goals that require sustained commitment are more likely to remain at the forefront of awareness, in turn taking precedence over competing easier goals (Tory & Scholer, 2015). Second, goal specificity provides clear metrics by which to monitor progress and attainment (Cheavens & Ritschel, 2014;

Snyder, 2002), helping individuals to decide whether to continue or modify goal striving efforts. Indeed, Rudd, Aaker, and Norton (2014) reported that a significantly greater increase in happiness of participants assigned to complete a specific, prosocial goal (e.g., make someone smile) when compared to participants assigned to complete an abstract, prosocial goal (e.g., make someone happy) was mediated by the discrepancy between participants’ expectations and reality. Furthermore, Locke, Chah, Harrison, and

Lustgarten (1989) used an idea generation task to differentiate the effects of goal specificity and difficulty, and found that difficulty affected overall performance outcome

(perhaps reflective of difficulty’s influence on motivation), while specificity affected variability in performance (perhaps reflective of the clear objectives and pathways inherently associated with goal specificity).

Goals can be further classified by valence—whether they are framed to achieve a desired state or outcome (approach goals) or framed to avoid failure or undesirable outcomes (avoidance goals; Baranik et al., 2010). When compared to avoidance goal pursuits, pursuing approach goals is generally considered more adaptive (Elliot &

Friedman, 2007; Locke & Latham, 2013), and is associated with more positive emotions, favorable self-evaluations, greater psychological well-being, and optimal goal outcomes

(Coats, Janoff-Bulman, & Alpert, 1996). For example, Elliot and colleagues (1997) reported a significant negative correlation between avoidance goals and retrospective well-being (r = -.35, p < .005), and found that pursuing more avoidance goals was associated with greater declines in subjective well-being over time. Hope Theory posits that higher hope should be associated with setting approach goals (Snyder, 1994; 2002), bolstering the documented relationships between hope and positive outcomes including goal attainment. An empirical test of Hope Theory by Cheavens and colleagues (under review) did not find a significant relationship between hope and goal valence. However, the authors suggested that their sample was relatively hopeful, with a restricted range of hope scores and a particularly high proportion of approach-oriented goals (70.37% of all goals), perhaps precluding the ability to detect significant findings. Thus, we believe that continued investigation is warranted given the well-documented relationships between approach goals, outcome, and wellbeing, as well as between hope and these same variables.

Goals that focus on benefiting others are significantly related to hope (Cheavens et al., under review) and to goal outcome (Rudd et al., 2014), perhaps through influence on persistence and task engagement (Grant et al., 2007). In one study, Castanheira,

Chambel, Lopes, and Oliveira-Cruz (2016) found that among members of the military, the degree to which individuals evaluate their actions as valued and as having a positive impact were associated with higher levels of work engagement. The relationship between prosocial tasks and increased motivation is also theorized to be a function of the potential for social dimensions of work to act as sources of meaning and value (Grant & Parker,

2009), which are themselves associated with intrinsic motivation and work engagement

(Oldham & Hackman, 2010). These findings map onto goals’ theorized functional role of providing meaning and purpose (Elliot et al., 2007) and help to explain the documented association between hope, prosocial personal goals, and goal attainment (e.g., Cheavens et al., under review).

Finding agency/motivation. Motivation is an integral part of goal pursuit, and a common area of self-regulatory failure both during initiation of goal striving and when encountering challenges (Gollwitzer, 2015; Gollwitzer & Oettingen, 2011; Snyder,

2002). Within Hope Theory, motivational processes are conceptualized as agency, and higher-hope individuals are theorized to be more effective at harnessing and using mental energy when compared to their lower-hope peers (Snyder, 2002; Snyder et al., 1997). The relationship between hope and motivated goal pursuit might be related to the previously described properties of the goals which higher hope individuals typically set (e.g., goals of greater personal importance and difficulty; approach orientation; prosocial focus;

Snyder, 2002), which GST has shown are properties simultaneously associated with increased energy and engagement (Grant et al., 2007; Tory & Scholer, 2015).

Additionally, as previously described, hopeful individuals’ prior successes likely strengthen agency thinking about current and future goal pursuits (Snyder, 2002).

Research examining the validity of the hypothesized relationship between hope and agency thinking has taken several forms. First, there are investigations establishing concurrent associations between hope/agency scores and construct-consistent outcomes

(e.g., information seeking, proactive coping, confidence in goal pursuit). For example,

Hope Scale agency scores are significantly related to proactive rehabilitative behaviors in sports injury recovery (Lu & Hsu, 2013), engagement in health behaviors (Berg, Ritschel,

Swan, An, & Ahluwalia, 2011), and proactive career behaviors (Hirschi, 2013), suggesting that higher hope individuals are more likely to take a proactive approach to goal pursuit. Alexander and Onwuebuzie (2007) reported that hope was significantly inversely related to self-reported procrastination and fear of failure among college students, potentially tapping into the self-determination and confidence reflected in agency.

Hope is also believed to be associated with generalized expectations of success, greater confidence in problem-solving skills, and higher self-esteem (Snyder et al., 1991), indicative of the mental energy and confidence reflected in agency. Researchers have used task-based methodology to probe the conceptualization of agency beyond simple outcome associations. In a two-part study, Snyder, LaPointe, Crowson, and Early (1998) provided high-hope and low-hope participants with the option of listening to audiotape messages that varied in depressive content (Study 1; e.g., “My life keeps getting better” versus “Why can’t I ever succeed”), and statements about successful or unsuccessful goal-attainment (Study 2; e.g., “Yes, I can do this,” versus “I don’t seem to have options”). They found that high-hope participants were significantly more likely to choose tapes with positive messages (Study 1) and those describing successful goal attainment (Study 2) when compared to low-hope participants, bolstering previous findings that hope is significantly related to focusing on the likelihood of success and feelings of confidence (Snyder et al., 1991).

Generating pathways/planning. Planning represents another integral part of goal pursuit, especially in light of the complex hierarchy of goals and sub-goals individuals constantly pursue (Austin & Vancouver, 1996). Having an ability to identify multiple goal-relevant pathways and compare the costs and benefits of each promotes prudent allocation of resources, prioritization, and ultimately greater likelihood of goal attainment

(Gollwitzer, 2015; Latham & Arshoff, 2015; Snyder, 1994). Furthermore, research indicates that planning exerts both mediating and moderating influences in the relationships between goal properties and outcome. First, plans appear to serve as a mechanism linking goal properties and subsequent performance (Latham & Arshoff,

2015); for example, in a longitudinal study of first-time juvenile offenders’ completion of a goal-focused intervention, solution building (i.e., planning) mediated the relation between goal commitment and program completion (Belciug et al., 2016). Additionally, the presence or absence of plans may impact the relative magnitude of a goal property’s effect on outcome (Knight, Durham, & Locke, 2001); for example, specific, difficult goals combined with high quality plans lead to better outcomes than either variable alone

(Wood et al., 2013).

Planning is embedded within Hope Theory (Snyder, 1994; 2002) as pathways thinking, or “a sense of being able to generate successful plans to meet goals” (Snyder et al., 1991, p. 570). Higher hope individuals are theorized to generate more pathways

(Snyder et al., 1997) of higher quality (i.e., more specific, realistic, and goal-relevant;

Snyder, 1994) than those generated by their lower-hope peers, and are theorized to use them with greater decisiveness (Synder, 2002). When Snyder and colleagues (1991)

12 asked participants to generate routes to achieve desired grades, they found that the number of pathways generated was a linear function of hope: high-hope participants listed more pathways than medium-hope participants, who listed more pathways than low-hope participants. Further, when compared to low-hope participants, high-hope participants self-reported significantly higher likelihood of using each pathway and higher confidence that the pathways would lead to goal attainment. Cheavens and colleagues (under review) replicated this research by asking undergraduate participants to generate pathways to a set of standardized goals. They found that the number of pathways individuals generated varied as a function of hope such that higher levels of hope were associated with significantly more pathways, though they did not find a significant difference in the quality ratings of the generated pathways. The authors posit the lack of significant findings may be related to the lack of variability in the sample’s hope and pathways scores, a methodological consideration in the present study.

Other investigations have probed the relationship between hope and pathways- generating behaviors in more applied settings. In one study, a sample of 76 management executives were presented with a realistic, novel scenario and were given two weeks to generate solutions (Peterson & Byron, 2008). More hopeful employees generated more solutions (β = .71, p < .001) of higher quality (β = .54, p < .001) than their less hopeful counterparts. Furthermore, hope contributed significant incremental variance beyond that contributed by self-efficacy in the number of solutions (∆R2 = .32, p < .001) and quality of solutions (∆R2 = .18, p < .001) that employees generated. Similarly, Irving, Snyder, and Crowson (1998) asked a sample of college women to generate up to seven ways they

13 could respond to imagined cancer-related scenarios (i.e., reducing cancer risk; detecting illness early; exerting control over course or outcome; controlling cancer’s impact on daily life). Higher hope women generated significantly more coping pathways in response to the hypothetical scenarios when compared to their lower hope peers. Taken together, there is ample evidence that hope can be used to predict planning ability.

The present study

Hope Theory provides an umbrella framework through which to understand goal pursuit outcomes. According to this theory, more hopeful people are successful in their goal striving because they set higher-quality goals and are better able to identify pathways to reach them. They pursue goals with confidence, even in the face of challenges or adversity. Hope researchers have documented the link between hope and goal outcomes (e.g., Feldman et al., 2009; Guter & Cheavens, 2016), as well as the link between hope and goal setting behaviors (e.g., Snyder et al., 1991; Cheavens et al., under review); GST researchers have supported the association between goal setting behaviors and goal outcomes (e.g., Locke & Latham, 2002). However, no published study has tested the complete model by assessing individuals’ levels of hope, goal setting behaviors, and goal outcomes. In this study, we tested the complete Hope Model which posits that the relationship between hope and goal outcome is indirectly accounted for by goal setting behaviors. We also tested the convergence between Hope Scale scores

(Snyder et al., 1991) and this study’s behavioral goal mapping activity. To reduce the number of statistical tests required to evaluate our main hypotheses, we first conducted a

Principal Component Analysis of coded goal data using a pilot sample. This enabled us to create composite scores of coder-rated goal variables.

Study Hypotheses

1. Hope will be associated with goal-striving perceptions (i.e., goal commitment,

confidence, perceived difficulty; pathway perceived effectiveness, predicted use)

and goal properties (i.e., quality, direction, prosocial focus).

2. Goal-striving perceptions (i.e., goal commitment, confidence, perceived

difficulty; pathway perceived effectiveness, predicted use) and goal properties

(i.e., quality, direction, prosocial focus) will predict goal outcomes.

3. Hope scores at Time 1 will significantly predict use of planned pathways,

occurrence of predicted obstacles, and goal completion at Time 2 and Time 3.

4. The relation between hope and goal outcomes will be mediated by goal-striving

perceptions (i.e., goal commitment, confidence, perceived difficulty; pathway

perceived effectiveness, predicted use) and goal properties (i.e., quality, direction,

prosocial focus).

Chapter 2: Study 1 Method

Participants

Participants (N = 121) were undergraduate students enrolled in introductory psychology courses at The Ohio State University. More than half of the sample was female (58.7%) and the average age was 18.98 years (SD = 1.73). The majority of participants identified as Caucasian (72.7%), with the remaining participants identifying as Asian (17.4%), Black (4.1%), and Hispanic/Latino (2.5%). Participants (n = 10) who provided fewer than two 2-month goals were excluded from Principal Component

Analysis (PCA). We chose PCA over other data reduction techniques (i.e., Exploratory

Factor Analysis) because it analyzes both unique and error variance and is therefore considered more appropriate for data reduction to create composite scores (Tabachnick &

Fidell, 2007). In keeping with recommendations (e.g., Bryant & Yarnold, 1995), variables were standardized before conducting analyses to ease interpretation.

Measures

Goal-reporting activities.

Goal Mapping Activity (see Appendix A). Participants completed a goal mapping activity, designed for this study based on Snyder’s (1994; 2002) Hope Theory.

Respondents were asked to provide a list of up to ten short-term (2-week) and ten long- term (2-month) goals. Participants provided an average of 4.39 (SD = 2.21) 2-week goals

and 3.54 (SD = 1.95) 2-month goals. Additional elements of the goal mapping activity were not used for Study 1 and are, therefore, described in the Study 2 procedures.

Procedure

Participants were recruited through the Research Experience Program (REP) at

The Ohio State University during the Fall of 2016. All aspects of recruitment and data collection took place online. A short recruitment message was posted on the REP website. After signing up through the REP portal, interested participants received an email with a Qualtrics (2016) survey link that opened to an electronic informed consent form. After providing consent, they were directed to provide demographic information and complete the Goal Mapping Activity.

Data Analytic Plan

Coding of qualitative data. Six undergraduate research assistants coded participants’ goals on seven descriptors (see Table 1 for a brief operationalization of each descriptor): Specificity, Importance, Difficulty, Controllability, Change/Maintain focus,

Orientation, and Prosocial focus. Coders also categorized goals into one of ten domains

(e.g., academic, physical health, social). Prior to coding study data, coders were responsible for independently coding between 150 to 250 example goals per week. The principal investigator calculated intraclass correlation coefficients (ICCs) for each goal descriptor category and held hour-long group training meetings to discuss goals on which coders’ ratings were most discrepant. Training continued until ICCs greater than .70 on all descriptors were sustained for two consecutive weeks. All raters coded each of the 959 goals. Four descriptors (Specificity, Importance, Difficulty, Controllability) were coded

by all six raters and the remaining three (Change/Maintain Focus, Orientation, and

Altruism) were coded by three of the six coders. ICCs for each descriptor were good to excellent (Cohen, 1988) and are reported in Table 1.

Data Reduction Technique. We conducted a principal component analysis

(PCA) to reduce the number of descriptors to be used in analyses. We used recommendations outlined by Kline (1994), Gorsuch (1983), and Tabachnick and Fiddell

(2007), to guide decision making regarding combining descriptor variables.

Chapter 3: Study 1 Results

We began by examining descriptive information and the simple correlations between goal descriptors (Table 2). Specificity was strongly correlated with Difficulty (r

= .57, p < .01) and moderately correlated with Importance (r = .29, p < .01), and

Importance was also significantly associated with Difficulty (r = .54, p < .01) and

Controllability (r = .26, p < .01). Controllability also demonstrated a moderate association with Difficulty (r = .57, p < .01). The positive associations between these variables reflects that goals that are more specific also tended to be more difficult, important, and within the goal-setter’s control. Additionally, there were moderate negative associations between Difficulty and Prosocial Focus (r = -.24, p < .05) and

Orientation (r = -.24, p < .05), reflecting that goals that were less approach-oriented (i.e., more avoidance-oriented) and less prosocial tended to be more difficult.

When we examined distributions of goal descriptors, we found that average ratings of 2-month goal Specificity, Importance, Difficulty, Controllability, and Change yielded normal distributions. However, Prosocial focus (i.e., the degree to which a goal focuses on others) and Orientation (i.e., approach or avoidance) were both skewed. More significantly, 95% of Prosocial focus ratings fell beneath a score of 1.5 on a 0 to 6 scale, where 0 represented goals solely focused on the self and 6 represented goals solely focused on benefiting others, while 90% of Orientation ratings were greater than 5 on a 0

to 6 scale, where 0 represented completely avoidance-oriented goals and 6 represented completely approach-oriented goals. Because Tabachnick and Fidell (2007) state that distributions of variables need not meet assumptions of normality for inclusion in a PCA, we chose to first conduct a PCA using an oblique rotation (i.e., Direct Oblimin) with

Kaiser Normalization including all seven goal descriptors. However, Prosocial focus was excluded from subsequent models because the KMO measure of sampling adequacy was less than .50 when it was included, reflecting minimal proportion of variance in variables that might be caused by underlying factors (Kaiser, 1974). A PCA including Specificity,

Importance, Difficulty, Controllability, Change/Maintain, and Orientation yielded a

Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy of .64, greater than the .60 standard for interpretability (Tabachnick & Fidell, 2007). Additionally, Bartlett’s test of sphericity was significant at p < .01, allowing us to reject the null hypothesis that the correlation matrix is an identity matrix. The rotation converged in four iterations and revealed a two-component solution, accounting for 55.01% of the variance (see Table 3).

The two components were weakly correlated (r = .20).

The following variables were computed for analyses in Study 2: Component 1 –

Goal Quality (average of Specificity, Importance, Difficulty, Controllability); Component

2 – Direction (average of Change/Maintain and Orientation). A Prosocial score was computed by first counting the number of goals that benefited others to any degree

(score > 0) and then computing the proportion of goals (out of five goals generated) that benefited others.

Chapter 4: Study 2 Method

Participants

Participants (N = 139) were undergraduate students enrolled in introductory psychology courses at The Ohio State University. The sample was predominantly female

(61.2%) and reported an average age of 19.30 years (SD = 1.61). The majority of participants identified as Caucasian (77.7%), with the remaining participants identifying as Asian (15.1%), Black (2.9%), and Hispanic/Latino (1.4%). Five participants were excluded from analyses for inadequate survey completion (i.e., providing no information about personal goals). An additional six participants were excluded from analyses for not completing either follow-up survey. Excluded participants did not differ from included participants on demographic variables including age, gender, and ethnicity (ps > .19) or key study variables including goal properties and Hope Scale scores (ps > .54). Of the remaining 128 participants, 114 completed both 2-week and 2-month follow-up surveys.

Twelve participants completed only 2-week follow-up, and two completed only 2-month follow-up. Thus, there were 126 participants in analyses of 2-week goals and 116 participants in analyses with 2-month goals.

Measures

Self-report scales.

The Hope Scale (HS; Snyder et al., 1991). The HS is a self-report questionnaire designed to assess individuals’ perceptions of the two primary facets of dispositional 21

hope: perceived ability to generate routes to achieve one’s goals (pathways thinking; sample item: “I can think of many ways in life to get the things that are most important to me”) and perceived ability to harness mental energy to achieve one’s goals (agency thinking; sample item: “I energetically pursue my goals”). Each of 12 items is rated on an

8-point Likert-type scale, ranging from 1 (definitely false) to 8 (definitely true), and items are summed to create a total hope score, as well as pathways and agency subscales. Past researchers have reported acceptable to good internal consistency in validation samples

(total hope score: α = .74 - .84; Pathways: α = .63 - .80; Agency: α = .71 - .76; Snyder et al., 1991). In the present sample, internal consistency for the total score (α = .85),

Pathways subscale (α = .72), and Agency subscale (α = .84) were acceptable to good

(Cohen, 1988).

Goal-reporting measures.

Goal Mapping Activity (see Appendix C). Participants completed the same goal mapping activity from Study 1 in which they were asked to provide a list of personal goals for the next 2-weeks and for the next 2-months. Study 1 participants were not given guidelines on the number of goals to describe; however, participants enrolled in Study 2 were asked to provide a list of at least five current short-term (2-week) goals and at least five long-term (2-month) goals. For each goal provided, participants then rated their commitment to the goal, their confidence in achieving the goal, and their perception of goal difficulty on 0 to 6 Likert-type scales. Self-reported scores of commitment, confidence, and perceived difficulty were averaged across the first five 2-week goals provided (yielding scores of 2-week goal commitment, confidence, and perceived

difficulty), as well as across the first five 2-month goals provided (yielding scores of 2- month goal commitment, confidence, and perceived difficulty).

Participants then selected their most important 2-week and 2-month goal and were asked to list all possible ways they could achieve that goal (i.e., pathways). They then rated their anticipated likelihood of using each route, as well as their perception of its effectiveness in reaching that goal. Self-reported scores of anticipated pathways use and perceived effectiveness were averaged for 2-week pathways, as well as for the 2-month pathways they provided. Finally, participants listed potential obstacles to their most important goals and rated the likelihood of each obstacles’ occurrence. All variables were standardized prior to conducting primary analyses to ease interpretation.

Goal Progress and Completion Survey (see Appendix C). This self-report questionnaire, adapted for this study from previous longitudinal goal research (Feldman et al., 2009), asks participants to rate their goal progress on both 2-week and 2-month goals, their use of previously planned pathways, and whether they faced anticipated obstacles on a 0 to 6 Likert-type scale. During administration of this survey at 2-week follow-up, participants were asked to rate their commitment, confidence, and perceived difficulty of any of the 2-month goals that they had not yet achieved.

Procedure

Participants were recruited through the Research Experience Program (REP) at

The Ohio State University during the Spring of 2017. All aspects of recruitment and data collection took place online. A short recruitment message was posted on the REP website. Because the conclusions drawn from past hope studies using collegiate samples

have been limited by the relatively high levels of hope common among college students

(e.g., Cheavens et al., under review), we oversampled students with HS (Snyder et al.,

1991) scores one standard deviation below the mean based on an optional pre-screening survey. Other than an initial personalized notification regarding the availability of the

REP posting, prescreened participants followed the same procedures as all other participants.

As outlined in Study 1 procedures, after signing up through the REP portal, interested participants received an email with a Qualtrics (2016) survey link that opened to an electronic informed consent form. Once participants provided their consent, they provided demographic information and completed the Hope Scale (Snyder, et al., 1991) and Goal Mapping Activity. Two weeks after baseline assessment, participants received the Goal Progress and Completion Survey to assess completion of 2-week goals, as well as progress toward 2-month goals. Two months after baseline assessment, participants received a final electronic survey asking them to rate the achievement of their 2-month goals.

Coding of qualitative data

Six coders rated all 1553 goals1. ICCs were all acceptable to excellent (Cohen,

1988; see Table 1). Two additional undergraduate coders were provided with lists of pathways that participants generated for their goals and were asked to count the number of unique pathways provided. For example, given the goal “Get all A’s this semester” and the pathways (1) Study more, (2) Study longer, (3) Get a tutor, and (4) Complete all

1 Note: Coding of all goal data (i.e., the 959 goals from Study 1 and the 1553 goals from Study 2) occurred simultaneously. 24

assignments, coders would rate three unique pathways because the first two pathways

(Study more and Study longer) would be considered substantially overlapping. Both coders rated all pathways lists (Krippendorff’s alpha = .80). Any discrepancies in count data were resolved by the principal investigator.

Chapter 5: Study 2 Results

Descriptive Statistics

We began by checking the appropriate statistical assumptions and identifying potential outliers and covariates. Despite efforts to recruit a sample that adequately represented individuals lower in hope, the final sample was relatively hopeful

(M = 49.08, SD = 7.16), with average Hope Scores comparable to those reported in other studies with collegiate samples (e.g., Cheavens et al., under review). Hope Scale scores were normally distributed, with only one participant scoring > 3.2 SDs below the mean.

Although the score is still within the range we would expect and there is no evidence that this individual's responses were inconsistent or invalid, we conducted all analyses with and without this participant to determine if this outlier was influential. None of the results of the major mediation analyses changed when this participant was excluded. However, there were three specific tests for which the influence of HS was no longer significant when the outlier was removed. Each of these instances is footnoted.

On average, participants provided a total of 5.92 2-week goals (SD = 1.48, range:

5 to 10) and 4.54 distinct pathways for their primary 2-week goals (SD = 1.24, range: 1 to

6), and 5.28 2-month goals (SD = .81, range: 5 to 10) and 4.55 distinct pathways for their primary 2-month goals (SD = 1.22, range 1 to 6). These count data were not significantly related to Hope Scores (ps > .15). In terms of goal domains, the majority of goals focused upon academics and/or career (41.5% of 2-week goals; 40.0% of 2-month goals), 26

physical fitness or health (21.8% of 2-week goals; 18.6% of 2-month goals), and productivity (13.2% of 2-week goals; 14.3% of 2-month goals). These three domains accounted for 76.5% of 2-week goals and 72.9% of 2-month goals. Other goal domain frequencies and example goals are displayed in Table 4.

Next, we examined the distributions of relevant variables to test assumptions of normality. As was the case with the Study 1 data, the distribution of prosocial goals was significantly skewed. Even when using a loose definition of “prosocial” (i.e., the goal includes even minimal focus on benefiting others), only 14.96% of 2-month goals were rated as prosocially-focused. Additionally, Goal direction (average of Orientation and

Change-focus) was skewed, with the vast majority of goals being positively valenced and future-oriented (M = 5.36, SD = .47; Median = 5.4; Mode = 5.33).

Finally, we examined the relations between demographic variables and goal setting variables to determine whether they should be included as covariates in subsequent models. Neither gender nor ethnicity were significantly related to goal properties (ps > .31), goal striving perceptions (ps > .31), or goal completion (ps > .46).

Therefore, neither demographic variable was entered as a covariate in subsequent models.

Hypothesis 1: Hope will be associated with goal-setting behaviors.

Hypothesis 1a. Hope scores at Time 1 will be positively associated with goal- striving perceptions (i.e., self-reported goal commitment, confidence, perceived difficulty; pathway perceived effectiveness, predicted use), and objective goal properties (i.e., coder-rated quality, direction, prosocial focus).

To test this hypothesis, we examined the concurrent associations between HS scores and goal setting variables (Tables 5 and 6). There were no significant relations between HS scores and scores of goal quality, direction, perceived difficulty, or anticipated pathway use for either 2-week or 2-month goals. However, for both 2-week and 2-month goals, HS scores demonstrated positive small-to-medium sized associations

(Cohen, 1988) with self-reported goal commitment, confidence, and perceived effectiveness of planned pathways. There was also a small, positive, significant association between HS scores and prosocial ratings of 2-month goals but the association was non-significant for 2-week goals.

Hypothesis 1b. Hope Scores at Time 1 will predict greater progress towards 2- month goals. HS scores at Time 1 will also predict stronger 2-month goal commitment and confidence and lower perceived difficulty at Time 2.

At 2-week follow-up, participants were asked to report on progress towards 2- month goals and rate their confidence, commitment, and perceptions of difficulty for any not-yet-attained goals (Table 7). Higher HS scores were associated with significantly greater 2-month goal progress at 2-week follow-up, β = .23, adj. R2=.05, F(1, 123) =

5.87, p = .022. A series of three linear regression models revealed that, even when controlling for Time 1 ratings, HS significantly predicted ratings of 2-month goal

2Note: When the outlier was removed, HS scores no longer significantly predicted 2-month goal progress at Time 2, β = .20, adj. R2=.02, F(1, 122) = 3.47, p = .07.

commitment, β = .20, p < .01, and confidence, β = .19, p < .01, but not perceptions of difficulty, β = -.10, p = .16, at Time 23.

Hypothesis 2: Better goal setting will be associated with better outcomes.

Hypothesis 2a. Goal-striving perceptions (i.e., goal commitment, confidence, perceived difficulty; pathway perceived effectiveness, predicted use) and goal properties

(i.e., quality, direction, prosocial focus) will predict 2-week goal outcomes at Time 2.

We ran a series of linear regression models entering goal-setting variables as predictors of 2-week goal completion (Table 8). The overall model of self-reported commitment, confidence, and perceived difficulty predicting 2-week goal outcomes was significant, adj. R2=.25, F(3, 122) = 14.54, p < .01, as were the independent effects of each of the three predictor variables. Because perceived effectiveness of planned pathways and anticipated pathway use were highly correlated (r = .52, p < .01), we could not enter them into the same regression model. We chose to use perceived pathway effectiveness as the predictor in subsequent regression analyses because Hope Theory specifies that hope should be associated with generating more pathways and with greater confidence in pathway usefulness, but not necessarily with greater intention to use them

(Snyder, 2002). A model entering perceived effectiveness of planned pathways to predict

2-week goal outcomes was significant, adj. R2 = .06, F(1,123) = 9.23, p < .014. The model including coder-rated goal quality, direction, and prosocial focus explained a

3 Note: When the outlier was removed and when controlling for Time 1 ratings, the independent effect of Hope on time 2 goal commitment (β = .13, p = .09) and confidence (β = .15, p = .06) was not significant. 4Note: The results of this regression analysis were the same when anticipated pathway use was entered into the model, adj. R2=.08, F(1,123) = 9.17, p < .01. 29

significant proportion of variance in 2-week goal outcomes, adj. R2 = .07, F(3, 122) =

4.24, p < .01. Of the three goal properties included in the model, goal quality was the only significant independent predictor of 2-week goal outcomes, β = .30, p < .01. Finally, we entered all of the significant predictors (i.e., commitment, confidence, difficulty, pathway effectiveness, goal quality) into a combined model predicting 2-week goal outcomes (Table 8). The overall model was significant F(5,119) = 9.90, p < .01, accounting for 26% of the variance. Of the five predictors, confidence (β = .26, p = .02) and difficulty (β = -.22, p = .01) were the only significant independent predictors. More specifically, higher self-reported confidence and lower perceptions of goal difficulty were associated with better 2-week goal outcomes.

Hypothesis 2b(i). Goal-striving perceptions (i.e., goal commitment, confidence, perceived difficulty; pathway perceived effectiveness, predicted use) and goal properties

(i.e., quality, direction, prosocial focus) will predict 2-month goal outcomes at Time 3.

We repeated the same set of regression analyses entering goal setting variables as predictors of 2-month goal outcomes at Time 3 (Table 9). The model with self-reported goal commitment, confidence, and perceived difficulty at baseline as predictors of 2- month goal outcomes was significant, adj. R2=.28, F(3, 112) = 15.62, p < .01, as were the independent effects of each predictor variable. The model testing the impact of perceived effectiveness of planned pathways on 2-month goal outcomes was not significant, p

= .075. The model entering coder-rated goal quality, direction, and prosocial focus as predictors of 2-month goal outcomes was also not significant, p = .11, although the

5 Note: A model entering anticipated pathway use was also not significant, p = .33. 30

independent effect of goal quality on outcome was marginally significant, β = .18, p

= .05. Finally, we ran a regression entering the significant or marginally significant predictors (i.e., commitment, confidence, difficulty, goal quality) into a combined model predicting 2-month goal outcomes (Table 9). The overall model was significant adj.

R2=.27, F(4,111) = 11.69, p < .01, as were the independent effects of commitment, β

= .33, p < .01, confidence, β = .25, p = .02, and difficulty, β = -.24, p = .01.

Hypothesis 2b(ii). Early progress towards 2-month goals (i.e., at Time 2) will predict goal outcomes at Time 3. Time 2 ratings of commitment, confidence, and perceived difficulty, controlling for Time 1 ratings, will predict goal outcomes at Time 3.

As expected, greater progress towards 2-month goals at Time 2 (i.e., two-weeks post-baseline) predicted better goal outcomes at Time 3 (i.e., two-months post-baseline), adj. R2=.32, F(1, 112) = 54.02, p < .01 (Table 10). A series of three linear regression models revealed that, even when controlling for Time 1 ratings, Time 2 ratings of goal commitment, β = .41, p < .01, and confidence, β = .47, p < .01, but not perceptions of difficulty, β = -.20, p = .08, predicted 2-month goal outcomes (Table 10). Finally, we ran a regression entering Time 2 ratings of commitment, confidence, and perceived difficulty

(controlling for Time 1 ratings), and progress predicting 2-month goal outcomes. The overall model was significant adj. R2=.47, F(4,111) = 15.30, p < .01, as were the independent effects of commitment at Time 1, β = .21, p = .04, and progress at Time 2, β

= .34, p < .01.

Hypothesis 3: Hope and goal outcomes.

Hypothesis 3a. Hope scores at Time 1 will significantly predict use of 2-week planned pathways, occurrence of predicted obstacles, and goal completion at Time 2.

HS did not predict 2-week pathway use, p = .07, or occurrence of predicted obstacles, p = .18, at Time 2 (Table 11). However, hope scores did significantly predict 2- week goal outcomes, adj. R2=.09, F(1, 122) = 13.01, p < .01. Examination of the standardized coefficients revealed that a 1-unit increase in HS was associated with a .30- unit increase in goal outcomes.

Hypothesis 3b. Hope scores at Time 1 will significantly predict use of planned pathways, occurrence of predicted obstacles, and 2-month goal completion.

As was the case for the models examining HS and 2-week goal follow-up, HS did not predict use of planned pathways (p = .12) or occurrence of predicted obstacles (p

= .37; Table 10). However, HS did significantly predict 2-month goal outcomes, adj.

R2=.08, F(1, 112) = 10.75, p < .01. Examination of the standardized coefficients revealed that a 1-unit increase in HS resulted in a .28-unit increase in goal outcomes.

Hypothesis 4. The relation between hope and goal outcomes will be mediated by goal-striving perceptions (i.e., goal commitment, confidence, perceived difficulty; pathway perceived effectiveness, predicted use) and goal properties (i.e., quality, direction, prosocial focus).

Hypothesis 4a. The relation between hope and 2-week goal outcomes will be accounted for by goal setting variables.

The hypothesis that commitment, confidence, and perceived difficulty would mediate the relation between hope and 2-week goal outcomes was partially supported

(Figure 3). The total effect of HS on 2-week goal outcomes indicated that participants who differed by 1-unit on the HS were estimated to differ by .30 units on the 2-week goal completion scale, c = .30, p < .01, 95% CI [.13, .46]. Of the three hypothesized mediators, the indirect effect through confidence accounted for significant variance in 2- week goal outcomes (a2b2 = .06, 95% CI [.003, .14]) over and above the non-significant parallel mediators (i.e., commitment, a1b1 = .05, 95% CI [-.001, .12]; difficulty, a3b3 =

-.003, 95% CI [-.05, .04]) and the significant direct effect of hope, c’ = .19, p = .02, 95%

CI [.04, .34]. Participants 1-unit higher in hope rated themselves, on average, as .29 units higher in confidence, p < .01, 95% CI [.13, .46], and participants 1-unit higher in confidence were estimated to score .21 units higher on 2-week goal completion, p = .04,

95% CI [.0002, .42].

The hypothesis that pathway properties would indirectly account for the relation between hope and 2-week goal outcomes was not supported (Figure 4). The indirect effects through pathways perceived effectiveness (a1b1 = .02, 95% CI [-.05, .10]) and

6 predicted use (a2b2 = .02, 95% CI [-.01, .08]) were not significantly different from zero .

Similarly, the hypothesis that goal properties would indirectly account for the relation between hope and 2-week goal outcomes was not supported (Figure 5). The indirect effects through goal quality (a1b1 = .03, 95% CI [-.03, .09]), direction (a2b2= -.003, 95%

6 Note: When entered into separate mediation models, neither the indirect effect of perceived pathway effectiveness (ab = .06, 95% CI [-.01, .13]) nor predicted use (ab = .02, 95% CI [-.01, .08]) were significantly different from zero. 33

CI [-.03, .01]), and prosocial focus (a3b3= .002, 95% CI [-.02, .02]) were not significantly different from zero.

Hypothesis 4b. The relation between hope and 2-month goal outcomes will be accounted for by goal setting variables.

The hypothesis that goal commitment, confidence, and perceived difficulty at

Time 2 (two weeks post-baseline assessment) would mediate the relation between hope and 2-month goal outcomes was supported (Figure 6). The total effect of hope on 2- month goal outcomes reflected that participants who differed by 1-unit on the HS were estimated to differ by .29 units on the 2-month goal completion scale, p < .01, 95% CI

[.12, .47]. Of the three hypothesized mediators, both commitment (a1b1= .07, 95% CI

[.11, .39]) and confidence (a2b2 = .15, 95% CI [.06, .26]) accounted for unique variance in goal outcomes with difficulty (a3b3 = .03, 95% CI [-.01, .07]) and the direct effect of hope in the model. Furthermore, after accounting for these indirect effects, there was no evidence of a direct effect of hope on outcomes, c’= .04, p = .61, 95% CI [-.12, .20].

The hypothesis that pathway properties would indirectly account for the relation between hope and 2-month goal outcomes was not supported (Figure 7). The indirect effects through perceived effectiveness (a1b1 = .02, 95% CI [-.03, .10]) and predicted use

7 (a2b2 = .001, 95% CI [-.03, .03]) were not significantly different from zero .

The hypothesis that goal properties would indirectly account for the relation between hope and 2-month goal outcomes was also not supported (Figure 8). The indirect

7 Note: When entered into separate mediation models, neither the indirect effect of perceived pathway effectiveness (ab = .02, 95% CI [-.02, .10]) nor predicted use (ab = .001, 95% CI [-.02, .03]) were significantly different from zero. 34

effects through quality (a1b1 = .01, 95% CI [-.03, .04]), direction (a2b2 < .01, 95% CI

[-.04, .01]), and prosocial focus (a3b3 = .01, 95% CI [-.02, .06]) were not significantly different from zero.

Exploratory Analysis: Hope as a Moderator.

Given that the lack of support for the primary mediation hypothesis (i.e., goal quality mediating the relation between hope and outcome) contradicted a broad empirical base, we considered whether the relation between 2-month goal quality and 2-month goal outcomes differed as a function of hope (Figure 9). When entering goal quality, Time 1 hope, and their product as predictors of Time 3 goal outcomes, the product of goal quality and hope significantly predicted 2-month outcomes, β = -.21, SE = .08, p = .01. We then probed this interaction by examining the effect of goal quality on 2-month goal outcomes at 1 SD of hope (Figure 10). Among participants relatively lower in hope at Time 1 (-1

SD), higher goal quality predicted greater goal completion, X→Y|M = 41.67 = .35, p < .01.

Among participants relatively higher in hope (+1 SD), Time 1 goal quality was unrelated to 2-month goal outcomes, X→Y|M = 56.42 = -.08, p = .52. Using the Johnson-Neyman technique, goal quality only predicted 2-month goal completion among those with relatively lower HS scores (zs < -.16), a region of significance that captured 42.98% of the sample. When hope was explored as a moderator of 2-week goal quality and 2-week goal outcomes, the interaction was not significant, β = .001, SE = .15, p = .70.

Chapter 6: Discussion

The main purpose of this study was to test whether the well-documented relation between hope and goal outcome is indirectly accounted for by goal setting behaviors. As predicted, higher hope significantly predicted better goal outcome. However, the central hypothesis regarding the mediating role of goal setting behaviors was only partially supported. More specifically, while there were positive, small-to-medium sized associations between hope and self-reported (i.e., “subjective”) goal commitment, confidence, and perceived effectiveness of planned pathways, hope was not significantly related to coder-rated (i.e., “objective”) variables, including goal quality and direction.

Furthermore, only self-reported commitment and confidence, and not objective variables, significantly mediated of the relation between hope and goal outcome. Using exploratory analyses, we found that hope moderated the relation between goal quality and 2-month goal outcomes such that at lower levels of hope, individuals who set higher quality goals achieved their goals at rates indistinguishable from higher-hope individuals, while at higher levels of hope, goal achievement was unrelated to goal quality. In addition to providing information about the relations between hope, goal setting, and goal outcome, the results illuminate methodological considerations for future research.

Hope and goal outcomes: Goal-setting perceptions as mediators

As predicted, baseline hope was associated with more completion of 2-week and

2-month goal outcomes, replicating the link between hope and successful personal goal pursuits (e.g., Feldman et al., 2009; Guter & Cheavens, 2016; Snyder et al., 1991). Also 36

as expected, baseline commitment to 2-week goals indirectly accounted for a significant portion of the positive association between hope and 2-week goal outcomes. Moreover, the association between baseline hope scores and 2-month goal outcome was mediated by confidence at 2-week follow-up. In other words, at 2-week follow-up, higher hope individuals reported greater confidence about the achievement of their longer-term goal prospects, which accounted for higher goal completion ratings at two months. These findings provide an empirical bridge between Hope Theory (Snyder, 1994; 2002) and

Goal Setting Theory (e.g., Beattie et al., 2012; Hollenbeck & Williams, 1987; Locke &

Latham, 2002) and support the assertion that higher hope individuals are more successful at goal pursuit, in part, because they have more commitment to and confidence in their ability to pursue goals.

To further elucidate these findings and more clearly understand causal relations between variables, in future studies, researchers should attempt to directly manipulate hope to evaluate the impact on confidence, commitment, and goal outcome. Although trait hope is theorized to be dispositional (Cheavens & Ritschel, 2014), and therefore difficult to manipulate, it is possible that state hope (i.e., individuals’ momentary perceptions of ability to achieve their goals) would be more easily modified in laboratory-based settings. For example, researchers could randomly assign participants to a condition to increase state hope (e.g., administer a task that is designed to appear challenging, but is impossible to lose; ask participants to recount a time that they were able to achieve a difficult personal goal) or to a condition to decrease state hope (e.g., administer a task that is impossible to win; ask participants to recount a time that they

were unable to achieve a personal goal despite their best efforts). After administering the

State Hope Scale (Snyder et al., 1996) as a manipulation check, researchers could observe participants as they complete a series of laboratory tasks. This would enable researchers to evaluate whether the manipulation impacts behaviorally-observable measures of commitment or confidence (e.g., time spent on task, creativity in problem solving, observer ratings of behaviors) and actual task performance. Additionally, researchers could add a pre-screening assessment of trait hope (i.e., the Hope Scale; Snyder et al.,

1991) to explore whether dispositional hope moderates the impact of laboratory-based state hope manipulations on goal striving variables (e.g., time spent on tasks, persistence, motivation, outcomes). Indeed, Snyder and colleagues (1991) found that lower hope individuals reported a significant decrease in state agency when asked to imagine getting a poor grade in an important class, while higher hope individuals’ agency scores remained unchanged in spite of imagined negative feedback. Thus, it is possible that state hope is more easily manipulated at lower levels of dispositional hope than at higher levels of hope, reflecting overall greater resiliency and tenacity in goal striving among higher hope individuals.

Hope and goal outcomes: Goal properties as mediators

Contrary to expectations, the associations between hope and 2-month goal quality and direction were not significant, and there was only evidence of a weak association between hope and prosocial focus of 2-month goals. The associations between hope and

2-week goal quality, direction, or prosocial-focus also were not significant. None of these variables exerted an indirect effect on goal outcome. In other words, in this study, only

self-reported (not coder-rated) goal setting variables mediated the relation between hope and goal outcome. Furthermore, other than a small, positive association between hope and 2-month prosocial goals, there were no significant relations between hope and coder- rated goal variables. The lack of a significant relation between hope and objective goal properties failed to replicate Cheavens and colleagues’ (under review) recent findings that higher hope individuals set higher quality goals. We were, therefore, unable to empirically document that higher quality goals acted as a mechanism linking hope and goal outcomes. In fact, across all analyses (including those examining goal properties and outcomes without consideration of hope), self-reported goal variables demonstrated stronger associations with goal outcomes than did coder-rated variables. For example, for both 2-week and 2-month goals, self-reported ratings of commitment, confidence, and perceived difficulty predicted goal outcomes, while the only coder-rated goal property that significantly predicted outcomes was 2-week goal quality. This pattern is likely partially a measurement artifact; it is unsurprising that self-report measures correlate most strongly with other self-report measures. It may also reflect the personal nature of goal striving: it makes sense that individuals’ personal evaluations of goal importance would be a stronger motivator than a goal’s objective importance. Similarly, objective goal difficulty is likely less important than an individual’s sense of confidence in ability or perceptions of pathway effectiveness.

Exploratory results: Hope level and differential goal striving

Although we did not find evidence of a mediational effect of goal quality on outcome, an exploratory analysis revealed that for lower-hope individuals, but not higher-

hope individuals, setting higher-quality goals was indeed predictive of better outcomes.

There are two important implications of this pattern of results. First, setting high quality goals appears to be an effective route to goal achievement for lower-hope participants.

Given study design, we cannot conclude whether the lower-hope participants who set high quality goals in this study would have done so without the structure of the goal mapping activity; however, it is possible that for some lower-hope participants, the process of writing down goals, generating routes to achieve them, reflecting upon confidence and commitment, and predicting obstacles amounted to a brief goal setting intervention that, when completed successfully, led to significantly higher quality goals and better outcomes. Past studies have found significant effects of low-dose goal setting interventions (Davidson, Feldman, & Margalit, 2012; Feldman & Draher, 2011) and of basic goal monitoring interventions (Harkin et al., 2016). Future investigations using a randomized design are needed to test whether the goal mapping activity in this study exerted a direct influence on goal setting and outcomes for lower-hope participants, as well as explore why only some, but not all, lower-hope participants may have benefitted from setting higher-quality goals in this study.

Higher-hope participants in this study, on the other hand, successfully achieved their goals regardless of goal quality. Thus, the second major implication of these findings is that, for higher-hope individuals, there were other mechanisms beyond the scope of this study driving their goal pursuit successes. Although the purpose of this study was to examine hope and conscious, deliberate goal setting, decades of research have shed light on other behaviors and processes associated with differential goal

outcomes. This research includes study of goal-related learning history (Bandura, 1977;

Spieker & Hinsz, 2004), affect (Johnson et al., 2010; Maglio, Gollwitzer, & Oettingen,

2014; Moberly & Watkins, 2010), goal-value congruence (Guter & Cheavens, 2016), construal (Mann, de Ridder, & Fujita, 2013), multiple goal pursuit and inter-goal facilitation (Riediger, Freund, & Baltes, 2005; Unsworth, Yeo, & Beck, 2014), response to obstacles (Jones, Papadakis, Orr, & Strauman, 2013), and non-conscious goal pursuit

(Custers & Aarts, 2010; Papies, 2016; Shah, 2005). Snyder (1994; 2002) and proponents of Hope Theory have long argued that, in addition to the role of conscious, deliberate goal setting, these other behaviors and processes are integral to understanding how hope and goal outcomes are related. If, as the results of this study suggest, the primary mechanism linking hope and successful goal pursuit is not goal quality, future researchers should explore other aspects of goal pursuit that may link hope and goal outcomes.

Study limitations and future directions

Given the myriad studies demonstrating a strong, replicable relation between goal quality and outcome (Locke & Latham, 2013) and between hope and goal setting (e.g.,

Cheavens et al., under review; Feldman et al., 2009; Nelissen, 2017), it seems unlikely that objective goal properties are unrelated, in general, to hope or goal outcomes.

Therefore, other explanations of these results should be considered.

Sample. First, despite effort to recruit a sample with adequate representation of lower-hope individuals, the sample reported relatively high hope scores, which may have limited the ability to detect an association between hope and goal setting. Hope scores in this study were comparable to those reported in other undergraduate samples (e.g.,

Cheavens et al., under review; Curry et al., 1997); however, the authors of these studies also cited a relatively restricted range of hope scores as a limitation potentially reducing the ability to detect an effect. In the past, researchers have responded to the low variability in hope scores among college student samples by calling for greater recruitment of lower hope participants. However, the fact that we were unable to recruit a sample with diverse hope scores in this study, despite our explicit attempts to do so, suggests that college students who complete a longitudinal study of goal setting may, by definition, be higher hope individuals; thus, future researchers should consider conducting hope studies in more diverse samples (e.g., clinical populations, older adults, adults in the work-force, etc.).

Relatedly, psychological research is frequently criticized for over-dependence upon college samples, as critics question whether college students can be used to study broad psychological and social processes (Henry, 2008; Sears, 1986). Evidence suggests that college students differ from other segments of the general population in ways relevant to the current research question, including in the structure and content of their goals. For example, as was the case within this study, college students tend to have a particularly high concentration of achievement-oriented goals (Darnon, Dompnier, &

Marijn Poortvliet, 2012) and goals focused on identity (Arnett, 2000). In this study, similar to results reported in other recent investigations of college student goal setting

(e.g., Cheavens et al., under review), most goals were approach-oriented (Median on 0 - 6 scale = 5.40) and so few (14.96%) were prosocially-focused that the coder-rated properties of direction and prosocial-focus potentially lacked practical significance.

Future research including more diverse samples would likely increase variability in hope scores and goal content, in turn increasing power of statistical tests. Studying more diverse samples would also allow for investigation of whether hope is associated with adaptive goal striving through different developmental periods. There is evidence that goal setting and pursuit change with age (e.g., Lapp & Spaniol, 2017; Riediger et al.,

2007; Saajanaho et al., 2016), and other research suggests that hope acts as a buffer for individuals undergoing difficult life transitions. For example, in one six-year longitudinal study of emotional well-being of adolescents, Ciarrochi and colleagues (2015) found that hope exerted the strongest influence on positive outcomes during periods when adolescents faced the greatest number of challenging developmental tasks. Similar studies have documented beneficial effects of hope during adjustment to college (Hansen,

Trujillo, Boland, & MaCkinnon, 2014; Snyder et al., 2002), for individuals undergoing physical rehabilitation after injury (Lu & Hsu, 2013), and for individuals recently diagnosed with Multiple Sclerosis (Madan & Pakenham, 2014). Given clear associations between hope and adaptive responses to challenges including transitions, studying hope and goal striving across the lifespan might help reveal why some individuals are better able to adapt to the unique challenges associated with goal striving in older age (i.e., the need for flexible goal striving; Bailly, Gana, Hervé, Joulain, & Alaphilippe, 2014).

Coding. In this study, the use of a college sample was not the only reason for limited variability in goal data. Although there was some variation in goal difficulty (e.g.,

Get an A on my test vs. Get at least a C on my test) and specificity (e.g., Go to the gym 3x this week vs. Get in shape), in hindsight, the sensitivity of the coding manual used in this

study did not align with ratings of goal difficulty or objective importance that can be reasonably be expected of college students, particularly in a 2-week or 2-month period.

More specifically, in an attempt to increase external validity and generalizability, the upper end of the 7-point Likert-type scale used to train coders on goal importance was labeled as “This goal has significant implications for the individual or society” and included examples like Get into medical school, and Start my own non-profit organization. Similarly, the upper end of the goal Difficulty scale was labeled as, “For most college students, this goal would require very significant hard work/time/effort” and included the example Graduate from medical school at the top of my class. Given that participants were asked to provide goals they could complete in the next two months, these particular anchors far exceed the difficulty or importance of any goals that participants could be expected to provide. Thus, the use of these anchors in the context of the 2-month study period severely restricted the range of coded scores, limiting variability and potentially limiting ability to detect an effect. In the future, coding manuals used to rate goals should reflect the sample (e.g., college students versus college graduates or other adults), as well as constraints imposed by study periods (e.g., what is reasonable to assume can be accomplished in a 2-week, 2-month, or 2-year period).

Study timeframe. The relatively short duration of the study and the structure of the goal mapping activity likely limited the data in other ways. Asking participants to set goals they hoped to accomplish in the next 2-weeks yielded a set of goals that were, predictably, easy to accomplish, limited in focus (i.e., tests and assignments, programmatic demands, sleep/exercise), and, therefore, limited in objective importance

and difficulty. What was not anticipated was the similarly limited focus of participants’

2-month goals. However, given the robust literature on the effects of priming on the difficulty and content of goals (Gendolla, 2015; Shah, 2003; Papies, 2016), it is possible that asking participants to generate 2-week and 2-month goals in immediate succession may have primed them to set 2-month goals that were mere extensions of their shorter- term goals. For example, the goal, “Get an A on my Chemistry test in the next 2-weeks,” is logically followed by, “Get an A in my Chemistry class in the next 2-months.” Indeed, in this study, the frequencies of goal domains across 2-week and 2-month goals were strikingly similar (e.g., Academic or Career goals made up 41.5% of 2-week goals and

40.0% of 2-month goals; Physical Health goals made up 21.8% of 2-week goals and

18.6% of 2-month goals; Productivity goals made up 13.2% of 2-week goals and 14.3% of 2-month goals).

Eliminating the 2-week goal outcome portion of the present study may enhance the diversity of 2-month goals provided, as would extending the overall study period beyond 2-months. Moreover, studying personal goal pursuit over longer study periods

(i.e., over multiple months or years) would enable exploration of how hope relates to significantly longer-term goal pursuits. Research suggests that pursuit of longer-term goals requires a complex set of self-regulatory processes including drawing motivation from multiple sources, creating and attending to multiple sources of feedback, balancing persistence and flexible goal pursuit, and creating positive short-term experiences to maintain interest (Bateman & Barry, 2012). A significant body of longitudinal research suggests that hope is associated with general adaptive behaviors (e.g., healthy coping,

effective problem solving) and beneficial outcomes (e.g., high GPA, better treatment outcomes, psychosocial adjustment) over time (e.g., Ciarrochi et al., 2015; Madan &

Pakenham, 2014; Oles, Fukui, Rand, & Salyers, 2015; Snyder et al., 2002); however, there has been less research examining individuals’ personal goal pursuits over extended study periods. It is conceivable that higher hope individuals are better equipped to handle the challenges of longer-term goal pursuits, a hypothesis which could be tested with naturalistic goal setting studies of longer duration.

Content and structure of goal reporting activities. The wording of specific items in this study may have also impacted select findings. More specifically, although we hypothesized that higher hope would be positively associated with accurate obstacle prediction, the item assessing obstacles at Time 2 and 3 (i.e., Rate the extent to which each anticipated obstacle interfered with your goal pursuit) conflated reporting of whether an obstacle occurred and whether it interfered with goal pursuit. Depending on how participants interpreted the question, high scores may unintentionally reflect a lack of skillful response to obstacles rather than accurate obstacle prediction, while low scores may reflect that the obstacle never occurred (i.e., reflecting inaccurate obstacle prediction) or that it occurred without interfering with goal pursuit (i.e., reflecting skillful response to obstacles). In the future, separate questions should be used to evaluate whether a predicted obstacle occurred and whether that obstacle interfered with goal striving. Furthermore, it is also possible that asking participants to consider and plan for obstacles that might occur actually reduces the likelihood that obstacles will interfere with goal striving. One potential solution to this dilemma is to observe individuals’

responses to obstacles in a laboratory-based setting or to ask individuals to retrospectively recount obstacles that blocked goal striving and describe their styles of responding.

Other structural elements of the goal mapping activity likely impacted the ability to evaluate aspects of the research question. For example, during Study 1, participants were instructed to list up to 10 goals they were striving to achieve. With the lack of a required minimum, most participants (55.4%) provided three or fewer goals. Because we were interested in the diversity of goals individuals set and, therefore, wanted to collect information on more than two or three goals, we chose to instruct Study 2 participants to describe at least five goals and five pathways (for both 2-week and 2-month goals). In response, the vast majority (82.7%) of participants provided exactly five goals and pathways. Given the widely-accepted assumption that most, if not all, of human behavior is goal-directed (Ajzen, 1985; Austin & Vancouver, 1996), regardless of hope, participants were undoubtedly pursuing far more than the ten goals (i.e., five 2-week goals; five 2-month goals) they described in their survey responses. Thus, although

Snyder (1994, 2002) asserted that more hopeful individuals should set more goals, generate more pathways, and anticipate more obstacles with greater accuracy, given the instructions in this study, it is perhaps unsurprising that there were no significant relations between hope and any of these variables.

The unintended consequences of the decision to instruct participants to provide a specific number of goals/pathways reflects a methodological dilemma faced at each stage of project design: If participants were provided less structure, they would likely provide

inconsistent amounts of data, making it hard to evaluate certain study hypotheses and complicating analyses (e.g., how to compute averages if some participants provide two goals while others provide eight goals); however, providing participants with specific directions potentially diluted any effect of hope by artificially changing or restricting participants’ goal setting processes. Hope Theory does not assert that only more hopeful people can set high quality goals and skillfully map routes to achieve them, but rather that more hopeful people engage in energetic, skillful goal pursuit more frequently and proficiently than their less hopeful peers (Snyder et al., 2002). Indeed, the fact that confidence and commitment mediated the relation between hope and goal outcomes may partially reflect the difference in effort required for goal striving experienced by higher and lower hope individuals.

Conclusions

For more than two decades, hope researchers have postulated that higher hope is associated with superior goal achievement because more hopeful individuals confidently and skillfully pursue higher-quality goals to which they are more committed. To my knowledge, this is the first study that used a longitudinal design and objective measures of goal quality to explore this association. The results of this study add to the wealth of literature showing that higher hope is associated with more successful goal pursuit. We found that while self-reported confidence and commitment did mediate the relation between hope and goal outcomes, coder-rated goal quality did not. However, an exploratory moderation analysis revealed that higher hope individuals achieved their goals regardless of goal quality, while lower-hope participants’ goal quality did

significantly affect outcomes. In other words, although setting high quality goals may be an effective mechanism for goal outcomes among lower-hope individuals, there are likely other mechanisms, in addition to confidence and commitment, driving the relation between higher hope and better outcomes. If replicated, these findings suggest that goal setting interventions focused on setting high quality goals and pathways may be of particular benefit to lower-hope, and not higher-hope, individuals. Future research is needed to explore other mechanisms as well as examine how confident, energetic goal pursuit translates into better outcomes.

The implications of successful goal pursuit are vast: obtaining an education, finding and succeeding in a fulfilling job, building relationships, managing conflict and stress, promoting health, and adapting to challenges depend on the types of goals individuals set and the way that they pursue them. Even with an abundance of information on goal properties and pursuit processes that are associated with positive outcomes, there remains no clear consensus on the specific mechanisms by which certain individuals are more or less successful in personal goal pursuit. Although there is likely no single explanation of successful goal pursuit, these results support that Hope Theory provides a useful framework to study individual goal striving. Continued investigations of the mechanisms of hope in laboratory-based and naturalistic settings would have important implications for our ability to help struggling individuals be more successful in personal goal pursuit.

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Appendix A: Tables

Table 1 Operationalization and Descriptive Statistics of Coder-Rated Goal Descriptors Goals Data: Study 1 Goals Data: Study 2 Goal Brief Operationalization (N = 959) (N = 1553) Descriptor M (SD) ICC M (SD) ICC Specificity Ease of tracking goal progress or 4.15 (.08) .90 4.40 (.03) .90 attainment Difficulty Mental/physical energy required to 1.48 (.15) .89 1.51 (.03) .88 achieve this goal Importance Magnitude of implications for the 1.23 (.07) .77 1.35 (.02) .81 individual or society Controllability Attainment dependent on goal setter 5.37 (.02) .90 5.15 (.02) .90 vs. external factors Change Focused on changing versus 4.65 (.11) .77 5.01(.20) .86 maintaining status quo 0.36 Prosocial Focused solely on the goal-setter vs. 0.69 (.003) .86 .89 on benefiting others (.003) Oriented towards approaching Orientation desired outcomes versus avoiding 5.68 (.04) .78 5.76 (.01) .88 negative outcomes Note: ICCs corrected for number of raters.

Table 2

Study 1: Average 2-Month Goal Descriptor Ratings (N = 111) †

Variable 1. 2. 3. 4. 5. 6. 7. 1. Specificity -- 2. Difficulty .57✧ -- ✧ ✧ 6 3. Importance .29 .54 --

4. Controllability .15✧ .32✧ .26✧ -- 5. Change .12✧ .03✧ .17✧ .04 -- 6. Prosocial -.01✧ -.24* .09✧ .06 .15 -- 7. Orientation .11✧ -.24* .23* .10 .15 .09 -- M 4.21 1.67 1.36✧ .81 4.59 .21 5.64 SD 1.17 0.53✧ 0.37✧ .54 0.82 .56 0.61 Range 1.83 - 6.00 .33 - 3.42 .17 - 2.75 .00 - 2.25 2.50 - 6.00 .00 - 2.67 2.50 - 6.00

† Note: Average computed using first three 2-month goals.

* p < .05 ✧ p < .01

Table 3 Component Loadings and Communalities for Principal Component Analysis With Oblimin Rotation of Goal Descriptors

Goal Component Component Communality Descriptor 1: Quality 2: Direction

Specificity .71 -.001 .51

Difficulty .89 -.003 .78

Importance .66 .24 .56

Controllability .57 -.10 .31

Change -.14 .85 .70

Orientation .15 .62 .44

Eigenvalues 2.16 1.29

% of variance 37.43 17.57

Note. Component loadings retained in composite score appear in boldface.

Table 4 Goal Domains and Examples 2-Week Goals 2-Month Goals Domain Example Goal Frequency Valid % Frequency Valid % Maintain 90+ in all Academic or Career courses; Write my 340 41.50 284 40.00 resume Go to the gym at Physical Health least 5x a week; Eat 179 21.80 132 18.60 less junk food Save money to fix Productivity my car; Clean my 108 13.20 102 14.30 room Make stronger Friend/Social friendships with 68 8.30 66 9.30 people in class Win IM basketball Hobby/pleasure game; Practice 54 6.60 56 7.90 photography skills Call my Family grandparents every 33 4.00 17 2.40 week Manage my stress Mental health better; Keep a 15 1.80 21 3.00 gratitude journal Strengthen my Spiritual relationship with 10 1.20 9 1.30 God Volunteer more; Community/Service Complete service 7 0.90 10 1.40 hours Work on self- Identity discipline; Get a 6 0.70 14 2.00 boyfriend

Table 5 Hypothesis 1a: 2-Week Goal-Setting Variables at Baseline: Descriptive Statistics and Correlations Variable 1. 2. 3. 4. 5. 6. 7. 8. 9. Hope 1. Hope Scale Score -- 2. Quality .09✧ -- Goal 3. Direction .08✧ .22* -- Properties 4. Prosocial Focus -.03✧ .14✧ .03 --

4 ✧ ✧ 5. Commitment .25 .26 -.05 .07 -- ✧ ✧ Goal 6. Confidence .30 .19* -.07 .05 .65 -- Striving 7. Difficulty .03✧ -.18* -.15 -.15 .18* -.12✧ -- Perceptions 8. Pathway Effectiveness .33✧ .14✧ .02 -.06 .40✧ .39✧ .03 -- 9. Anticipated Pathway Use .10✧ .03✧ -.07 -.12 .34✧ .34✧ .12 .52✧ -- M 49.08✧ 1.96✧ 5.40 .13 4.63✧ 4.24✧ 3.21 4.91✧ 3.97 SD 7.16✧ 0.28✧ 0.42 .14 0.78✧ 0.81✧ 1.00 0.88✧ 1.09 Note. Goal properties = coder-rated; Goal striving perceptions = self-reported (at Time 1). * p < .05 ✧p < .01

Table 6 Hypothesis 1a: 2-Month Goal-Setting Variables at Baseline: Descriptive Statistics and Correlations Variable 1. 2. 3. 4. 5. 6. 7. 8. 9. Hope 1. Hope Scale Score -- 2. Quality .03✧ -- Goal 3. Direction -.01✧ .15✧ -- Properties 4. Prosocial Focus .19* .06✧ -.01 -- ✧ ✧ 6

5 5. Commitment .29 .28 -.02 .01 -- ✧ ✧ Goal 6. Confidence .32 .20* -.05 .04 .49 -- Striving 7. Difficulty -.04✧ .04✧ -.03 -.14 .33✧ -.21* -- Perceptions 8. Pathway Effectiveness .24✧ -.03✧ -.13 .06 .51✧ .34✧ .24✧ -- 9. Anticipated Pathway Use .05✧ .12✧ -.01 -.02 .24✧ .36✧ .15✧ .50✧ -- M 49.08✧ 1.99✧ 5.37 .15 4.74✧ 4.02✧ 3.76✧ 5.20✧ 4.22 SD 7.16✧ 0.33✧ 0.47 .16 0.84✧ 0.88✧ 1.08✧ 0.78✧ 1.04 Note. Goal properties = coder-rated; Goal striving perceptions = self-reported (at Time 1). * p < .05 ✧p < .01

Table 7 Hypothesis 1b: Hope Scale Scores Predicting 2-Month Goal Progress and Commitment, Confidence, and Perceived Difficulty at T2

Predictor β SE p 2-Month Goal Progress (T2) Intercept -.01 .09 .95 Hope Score .23 .09 .01 2 adj. R = .05 F(1,122) = 6.88, p = .01

Commitment (T2) Intercept .03 .07 .66 Commitment (T1) .44 .07 <.01 Hope Score .20 .07 .01 2 adj. R =.31 F(2,121) = 28.65 p < .01

Confidence (T2) Intercept -.01 .07 .85 Confidence (T1) .55 .08 <.01 Hope Score .19 .07 .01 2 adj. R = .39 F(2,121) = 40.55, p < .01

Perceived Difficulty (T2) Intercept .04 .07 .59 Difficulty (T1) .56 .08 <.01 Hope Score -.10 .07 .16 2 adj. R = .32 F(2,121) = 29.48, p < .01

Note. T1 = Baseline; T2 = 2-week follow-up. Hope Scores reported at Time 1.

Table 8 Hypothesis 2a: 2-Week Goal Setting Variables Predicting Goal Outcomes 2-Week Goal Completion a Goal Striving Perceptions β SE p Intercept <.01 .08 .97 Commitment .22 .10 .04 Goal Confidence .26 .11 .02 Difficulty -.27 .08 <.01 2 adj. R = .25 F(3,122) = 14.54 p < .01 Intercept .04 .08 .65 Perceived Pathway .26 .09 .09 Effectiveness 2 adj. R = .06

F(1,123) = 9.23, p < .01

b Goal Properties β SE p Intercept .04 .08 .63 Quality .30 .09 <.01 Direction -.04 .08 .65 Goal Prosocial -.07 .08 .38 Focus 2 adj. R = .07

F(3,122) = 4.24, p < .01

c Combined Model β SE p Intercept .01 .07 .89 a Commitment .15 .11 .17 Confidence a .26 .11 .02 a Difficulty -.22 .08 .01 a Pathway Effectiveness .08 .08 .34 Quality b .15 .08 .07 2 adj. R = .26 F(5,119) = 9.90, p < .01 Note. a Goal striving perceptions = self-reported (at Time 1); b Goal properties = coder-rated. c Combined model includes all significant predictors from previous models.

Table 9 Hypothesis 2b(i): 2-month goal setting variables predicting goal outcomes. T1 Variables 2-Month Goal Completion Goal Striving Perceptions a β SE p Intercept -.003 .08 .97 Commitment .34 .10 <.01 a Goal Confidence .26 .10 .01 Difficulty -.24 .10 .01 2 adj. R = .28 F(3,112) = 15.62 p < .01 Intercept -.01 .09 .94 Perceived Pathway a .17 .09 .07 Effectiveness 2 adj. R = .02

F(1,114) = 3.45, p = .07 Goal Properties b β SE p Intercept -.01 .09 .92 Quality .18 .09 .05 b Goal Direction -.03 .10 .78 Prosocial Focus .13 .09 .15 2 adj. R = .03

F(3,112) = 2.05 p = .11 c Combined Model : T1 β SE p Intercept <-.01 .08 .96 a Commitment (T1) .33 .10 <.01 a Confidence (T1) .25 .11 .02 a Difficulty (T1) -.24 .10 .01 Quality b .04 .08 .63 2 adj. R = .27

F(4,111) = 11.69, p < .01 Note. T1 = baseline. a Goal striving perceptions = self-reported; b Goal properties = coder- rated. c Combined model includes all significant and marginally significant predictors from previous models.

Table 10 Hypothesis 2b(ii): 2-month goal setting variables predicting goal outcomes. T2 Variables 2-Month Goal Completion a Goal Striving Perceptions β SE p Intercept -.02 .08 .83 a Commitment (T1) .18 .09 .06 a Commitment (T2) .41 .10 <.01 2 adj. R =.31 F(2,121) = 28.65 p < .01

Intercept -.03 .08 .66 Confidence (T1) a .19 .10 .07

a Confidence (T2) .47 .10 <.01 2 adj. R = .39 F(2,121) = 40.55, p < .01

Intercept -.01 .09 .94 a Difficulty (T1) -.05 .11 .64 a Difficulty (T2) -.20 .11 .08 2 adj. R = .03 F(2,111) = 3.02, p = .05 c Combined Model : T2 with covariates Intercept -.03 .07 .71 a Commitment (T2) .15 .11 .18 a Confidence (T2) .23 .12 .06 a Difficulty (T2) -.06 .09 .52 Progress (T2) .34 .08 <.01 Covariates a Commitment (T1) .21 .10 .04 a Confidence (T1) -.04 .12 .72 a Difficulty (T1) -.17 .10 .08 2 adj. R = .47 F(7,106) = 15.30 p < .01 Note. a Goal striving perceptions = self-reported; c Combined model includes all significant predictors from previous models.

Table 11 Hypothesis 3a, 3b: Hope Scale Scores Predicting Follow-Up Goal Setting Variables Predictor β SE p Predictor β SE p

2-Week Pathway Use 2-Month Pathway Use Intercept .02 .09 .80 Intercept -.03 .09 .73 Hope Hope .16 .09 .07 .15 .10 .12 Score Score 2 2 adj. R = .02 adj. R = .01 F(1,117) = 3.26, p = .07 F(1,108) = 2.46, p = .12 2-Week Obstacle Occurrence 2-Month Obstacle Occurrence Intercept .01 .09 .91 Intercept .01 .09 .95 Hope Hope -.12 .09 .18 -.08 .09 .37 Score Score 2 2 adj. R = .01 adj. R < -.01 F(1,122) = 1.84, p = .18 F(1,112) = .82, p = .37 2-Week Goal Completion 2-Month Goal Completion Intercept .02 .08 .80 Intercept -.01 .09 .93 Hope Hope .30 .08 <.01 .28 .09 <.01 Score Score 2 2 adj. R = .09 adj. R = .08 F(1,122) = 13.01, p < .01 F(1,112) = 10.75, p < .01 Note. 2-week goal variables as reported at Time 2; 2-month variables as reported at Time 3.

Appendix B: Figures

Goal Setting Behaviors

Goal Hope Outcome

Figure 1. The Hope Model (Snyder, 1994; 2002).

Goal Setting Behaviors

Empirical GST studies: Empirical Hope studies: e.g. Gollwitzer, 2015; e.g., Cheavens et al., under Locke & Latham, review ; Snyder et al., 1991 2002; Rudd et al., 2015

Goal Hope Outcome

Empirical Hope studies: e.g.,

Feldman et al., 2009; Guter & Cheavens, 2016

Figure 2. Empirical support for Hope Theory from Hope and GST research.

Time 1 2-Week Hope Score Goal Outcome c = .30, p < .01, [.13, .46]

a1b1 = .05, [-.001, .12]

a b = .06, [.003, .14] 2 2 a3b3 = -.003, [-.05, .04]

Time 1 1. Commitment 2. Confidence 3. Difficulty a1 = .24, p = .01, [.07, .42] b1 = .20, p = .06, [-.01, .40] a2 = .29, p < .01, [.13, .46] b2 = .21, p < .05, [< .01, .42] a3 = .01, p = .90, [-.17, .19] b3 = -.27, p < .01, [-.43, -.11]

Time 1 2-Week Hope Score Goal Outcome c’ = .19, p = .02, [.04, .34]

Figure 3. Hypothesis 4a: Mediating role of goal perceptions (self-reported) in explaining

the relation between baseline Hope Scores and 2-week goal outcomes.

Time 1 2-Week Hope Score c = .31, p < .01, [.14, .46] Goal Outcome

a b = .02, [-.05, .10] 1 1 a2b2 = .02, [-.01, .08]

1. Perceived effectiveness 2. Anticipated use

a1 = .32, p < .01, [.15, .49] b1 = .07, p = .50, [-.13, .27] a2 = .10, p = .26, [-.08, .28] b2 = .19, p < .05, [<.01, .38]

Time 1 2-Week Hope Score c’ = .26, p < .01, [.09, .43] Goal Outcome

Figure 4. Hypothesis 4a: Mediating role of pathway perceptions (self-reported) in explaining the relation between baseline Hope Scores and 2-week goal outcomes.

Time 1 2-Week Hope Score c = .30, p < .01, [.13, .46] Goal Outcome

a1b1 = .02, [-.03, .09]

a2b2 = -.003, [-.03, .01]

a b < .01, [-.02, .02] 3 3

1. Quality 2. Direction 3. Prosocial Focus a1 = .10, p = .28, [-.08, .27] b1 = .27, p < .01, [.11, .44] a2 = .07, p = .44, [-.11, .25] b2 = -.05, p = .56, [-.21, .11] a3 = -.04, p = .70, [-.21, .14] b3 = -.05, p = .55, [-.21, .11]

Time 1 2-Week Hope Score Goal Outcome c’ = .27, p < .01, [.11, .43]

Figure 5. Hypothesis 4a: Mediating role of goal properties (other-rated) in explaining the

relation between baseline Hope Scores and 2-week goal outcomes.

Time 1 2-Month Hope Score c = .29, p < .01, [.12, .47] Goal Outcome

a1b1 = .07, [.01, .16]

a2b2 = .15, [.06, .26]

a b = .03, [-.01, .07] 3 3 Time 2 1. Commitment 2. Confidence 3. Difficulty

b1 = .22, p = .04, [.01, .44] a = .33, p < .01, [.17, .50] 1 b2 = .39, p < .01, [.18, .61] a2 = .38, p < .01, [.21, .54] b3 = -.16, p = .05, [-.33, -.001] a3 = -.17, p = .06, [-.35, .01]

Time 1 2-Month

Hope Score Goal Outcome c’ = .04, p = .61, [-.12, .20]

Figure 6. Hypothesis 4b: Mediating role of goal perceptions (self-reported) in explaining

the relation between baseline Hope Scores and 2-month goal outcomes.

Time 1 2-Month Hope Score c = .28, p < .01, [.11, .45] Goal Outcome

a b = .02, [-.03, .10] 1 1 a2b2 < .01, [-.03, .03]

1. Perceived effectiveness 2.Anticipated use

a1 = .22, p = .02, [.04, .40] b1 = .09, p = .41, [-.12, .29] a2 = .04, p = .69, [-.15, .22] b2 = .03, p = .75, [-.17, .23]

Time 1 2-month Goal Hope Score c’ = .26, p < .01, [.09, .44] Outcome

Figure 7. Hypothesis 4b: Mediating role of pathway perceptions (self-reported) in

explaining the relation between baseline Hope Scores and 2-month goal outcomes.

Time 1 2-Month Hope Score Goal Outcome c = .28, p < .01, [.11, .45]

a1b1 = .01, [-.03, .04]

a b < .01, [-.04, .01] 2 2 a3b3 = .01, [-.02, .06]

1. Quality 2. Direction 3. Prosocial Focus

a1 = .05, p = .74, [-.13, .24] b1 = .14, p = .13, [-.04, .31] a2 = -.01, p = .95, [-.18, .17] b2 = -.01, p = .90, [-.19, .17] a3 = .20, p = .03, [.02, .38] b3 = .07, p = .46, [-.11, .24]

Time 1 2-month Goal

Hope Score c’ = .26, p < .01, [.09, .43] Outcome

Figure 8. Hypothesis 4b: Mediating role of goal properties (other-rated) in explaining the

relation between baseline Hope Scores and 2-month goal outcomes.

Time 1 Hope Score

2-Month Goal Quality Goal Outcomes

Figure 9. Hope Scores moderating the relation between coder-rated goal quality and 2- month goal completion.

4.5

3.5

Goal Goal Completion Low Hope (-1 SD) 3 High Hope (+1 SD)

2.5 Low High Goal Quality

Figure 10. Exploratory analysis: 2-month goal completion ratings as a function of Hope

Scores and goal quality ratings.

Appendix C: Goal Reporting Activities

Goal Mapping Activity

Start of Block: GOAL MAP

Q87 In this next exercise, we will ask you to provide information about goals that you want to accomplish. A goal is a specific objective, something that you are purposefully working towards. Getting an A on an upcoming test, resolving a disagreement with a friend, and improving a skill are all examples of goals people might have.

Q89 Each of us is constantly working towards many goals in many different aspects of our lives (academics, relationships, leisure, health, etc). Take a moment to think about the goals you currently have across different areas of your life. Then, in the spaces below, please list at least five goals that you hope to accomplish at any point over the next TWO WEEKS (i.e., from today through two weeks from today). Please be specific and remember to think about goals across all aspects of your life.

Goals for the next TWO WEEKS

o Goal 1 (1) ______

o Goal 2 (2) ______

o Goal 3 (3) ______

o Goal 4 (4) ______

o Goal 5 (5) ______

o Goal 6 (6) ______

o Goal 7 (7) ______

o Goal 8 (8) ______

o Goal 9 (9) ______

o Goal 10 (10) ______

Q93 Now, please use the lines below to provide the following information about your goals: How COMMITTED you are to accomplishing this goal. How DIFFICULT this goal feels to you. How CONFIDENT you are that you can accomplish this goal.

Q95 ${Short goal/ChoiceTextEntryValue/1} How COMMITTED are you to this goal? (1) How DIFFICULT does this goal feel? (2)

How CONFIDENT are you that you can reach this goal? (3)

Display This Question: If If Goals for the next TWO WEEKS Goal 2 Is Not Empty

Q97 ${Short goal/ChoiceTextEntryValue/2} How COMMITTED are you to this goal? (1) How DIFFICULT does this goal feel? (2)

How CONFIDENT are you that you can reach this goal? (3)

…. FOR ALL GOALS PROVIDED.

Q125 Awesome! Now, in the spaces below, please at least 5 goals that you hope to accomplish in the next TWO MONTHS. Please be specific and think about goals across all areas of your life.

Q127 Goals for the next TWO MONTHS

o Goal 1 (1) ______

o Goal 2 (2) ______

o Goal 3 (3) ______

o Goal 4 (4) ______

o Goal 5 (5) ______

o Goal 6 (6) ______

o Goal 7 (7) ______

o Goal 8 (8) ______

o Goal 9 (9) ______

o Goal 10 (10) ______

Q129 As you did before, please use the lines below to provide the following information about your goals: How COMMITTED you are to accomplishing this goal. How DIFFICULT this goal seems to you. How CONFIDENT you are that you can accomplish this goal.

Q131 ${Q127/ChoiceTextEntryValue/1} How COMMITTED are you to this goal? (1) How DIFFICULT does this goal feel? (2)

How CONFIDENT are you that you can reach this goal? (3)

… For all goals provided.

Carry Forward Entered Choices - Entered Text from "Goals for the next TWO WEEKS" Q151 Of the goals that you listed for the next TWO WEEKS, please select the goal on which you will spend the most time.

o Goal 1 (1)

o Goal 2 (2)

o Goal 3 (3)

o Goal 4 (4)

o Goal 5 (5) … ALL GOALS LISTED.

Q153 There are many different ways you may work to achieve your goal. For example.. Goal: Help find my neighbor’s lost dog. Ways I could accomplish this goal: · Call the Humane Society and ask whether the dog is there. · Go knock on all the neighbors’ doors to ask if they have seen the dog. · Make fliers and put them up around the neighborhood. · Go to the places they usually go on walks. Q155 Please list ALL of the possible ways you can accomplish your goal. You do not need to fill in all the spaces. However, it’s important to list ALL of the ways you can think of, even if you would not be very likely to use it.

Q157 My goal: ${Q151/ChoiceGroup/SelectedChoices} All possible ways I can reach this goal...

o (1) Possible way to achieve this goal (1) ______

o (2) Possible way to achieve this goal (2) ______

o (3) Possible way to achieve this goal (3) ______

o (4) Possible way to achieve this goal (4) ______

o (5) Possible way to achieve this goal (5) ______

Q159 The ways we can work towards a goal will differ in how effective they are AND how likely we are to actually use them. Recall the example goal of helping to find my neighbor's lost dog. I had identified the following ways to achieve this goal: Knock on all my neighbors' doors: This way might be effective, but I am not likely to use it because it is very time intensive. Call the Humane Society: This way might be less effective, but I am likely to do it because it is very easy and quick. Put up fliers: This way would probably be effective, AND I am likely to do it because it is a relatively easy way to get information to the entire neighborhood.

Q96 Please rate: How effective you think each way would be to reach your goal. How likely you are to actually use that potential route to your goal.

Q161 ${Q157/ChoiceTextEntryValue/1} How EFFECTIVE this way would be. (1)

How LIKELY I am to use this way. (2)

Display This Question: If If My goal: ${q://QID118/ChoiceGroup/SelectedChoices} All possible ways I can reach this goal... (2) Possible way to achieve this goal Is Not Empty

Q163 ${Q157/ChoiceTextEntryValue/2} How EFFECTIVE this way would be (1)

How LIKELY I am to use this way (2)

Q177 Quite often, problems or challenges occur as we work to pursue a goal. For example, if your goal is to get in shape during the summer, one challenge might be travel plans that impact your workout routine. In the spaces below, please list several problems you might face as you work to reach your goal.

Q179 Problems I might face as I work to reach my goal:

${Q151/ChoiceGroup/SelectedChoices}

o Obstacle 1 (1) ______

o Obstacle 2 (2) ______

o Obstacle 3 (3) ______

o Obstacle 4 (4) ______

o Obstacle 5 (5) ______

Q185 For the final part of the goal activity, we ask you to provide the same information for ONE of the 2-month goals that you first listed. Your time and effort is what makes this research possible. Thank you for your continued participation!

SAME QUESTIONS REPEATED FOR PRIMARY 2-MONTH GOAL.

Goal Progress and Completion Survey

Start of Block: 2 Week Goal Attainment Q78 During Part 1 of this survey, we asked you about the goals you had for the next two weeks. Please rate how much progress you have made towards each goal.

Q79 ${e://Field/2W%20G1} Goal Progress (1)

Q80 ${e://Field/2W%20G2} Goal Progress (1)

Display This Question: If 2W G3 Is Not Empty Q81 ${e://Field/2W%20G3} Goal Progress (1)

… FOR ALL GOALS. End of Block: 2 Week Goal Attainment Start of Block: 2 Week Pathway Ratings Q89 We had asked you to provide more detailed information about one of your two-week goals.

The goal you described was: ${e://Field/2W%20GOAL}

Please rate the extent to which you used the different potential ways you described to reach your goal.

${e://Field/2W%20P1} (1)

${e://Field/2W%20P2} (2)

${e://Field/2W%20P3} (3)

${e://Field/2W%20P4} (4)

${e://Field/2W%20P5} (5)

End of Block: 2-Week Pathways Ratings

Start of Block: 2-Week Obstacles Q207 We asked you to predict obstacles that might get in the way of reaching your goal.

Please rate the extent to which each obstacle negatively impacted goal progress. ${e://Field/2W%20O1} (1)

${e://Field/2W%20O2} (2)

${e://Field/2W%20O3} (3)

${e://Field/2W%20O4} (4)

${e://Field/2W%20O5} (5)

End of Block: 2-Week Obstacles

Start of Block: Unanticipated Obstacles

Q36 If you have not yet fully completed your goal, briefly describe any additional obstacles you've faced.

o Obstacle (1) ______

o Obstacle (2) ______

o Obstacle (3) ______

o Obstacle (4) ______

o Obstacle (5) ______

End of Block: Unanticipated Obstacles

Start of Block: 2 Month Goal Progress

Q185 We had also asked you about the goals you had for the next two months. Please rate how much progress you have made towards each goal. Additionally, if you have not yet completed the goal, please rate: How committed you are to the goal. How difficult this goal seems. How confident you are that you can reach this goal.

Q186 ${e://Field/2M%20G1} Goal Progress (1)

Q26 If you have not yet completed the goal, please rate: How committed you are to the goal. How difficult this goal feels. How confident you are that you can reach this goal. How committed are you to this goal? (1)

How difficult does this goal feel? (2)

How confident are you that you can reach this goal? (3)

REPEATED FOR ALL 2-MONTH GOALS.