1 Positive Emotion Regulation

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Positive Emotion Regulation: Patterns and Associations with Psychological Health

Thesis

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

Graduate School of The Ohio State University

David Robert Cregg, B.S.

Graduate Program in Psychology

The Ohio State University

2017

Thesis Committee:

Dr. Jennifer S. Cheavens, Advisor

Dr. Daniel R. Strunk

Dr. Baldwin M. Way

Copyrighted by

David Robert Cregg

2017

Abstract

Evidence to date suggests that a higher level of positive emotion is generally associated with improved psychological health (e.g., Tugade, Fredrickson, & Barrett, 2004).

However, the specific features characterizing the ways in which upregulation of positive emotion is associated with good psychological functioning are less understood. I investigated how three factors may link regulation of positive emotions to greater psychological health: 1) the presence of both a large repertoire and persistent use of regulation strategies; 2) a “match” between the features of a situation and the strategy used; and 3) the use of specific categories of regulatory strategies, such as expression

(capitalization), and less use of suppression. One-hundred and thirty-four undergraduates

(mean age = 19.22; 73% female; 78% Caucasian) indicated the strategies they would use to maintain or improve their mood in response to eleven hypothetical positive situations.

After their initial response, participants were prompted four more times to report how they would respond if their initial strategy was not working. Participants then completed a battery of self-report measures assessing psychological health variables, including measures of positive emotion and psychopathology. Coders rated the quality

(effectiveness) of each strategy and assigned them to categories. Coders also rated each situation for its degree of ambiguity (how ambivalent the situation was), and whether it represented a more hedonic (i.e., short-term pleasure) or eudaimonic (i.e., long-term

ii meaning) form of well-being. Data were analyzed with a series of correlations and regression models using the three factors above as predictors and the psychological health

(PH) measures as criterion variables. Repertoire was associated with several indices of positive emotion, but was unrelated to measures of psychopathology. In contrast, persistence was unrelated to PH, except for an inverse association with intensity of positive emotion across situations. The use of suppression was related to greater borderline personality disorder features and lower ratings of happiness, whereas the use of planning, past focus, or expression was related to higher reports of positive emotion.

Finally, individuals using expression more frequently in situations of a more eudaimonic nature reported a higher intensity of positive emotion across situations (β = .21, p = .02); likewise, individuals using cognitive awareness (savoring) more frequently in situations of a more hedonic nature also demonstrated a trend toward more intense positive emotion

(β = .16, p = .07). Collectively, these findings preliminarily suggest the importance of

“regulatory diversity” (Quoidbach, Berry, Hansenne, & Mikolajczak, 2010), i.e., varying one’s use of strategies, and hint at the role of situation-strategy matches in upregulating positive emotion. However, several limitations of this study warrant cautious interpretation of the results, such as the correlational nature of the data, the large number of tests conducted, and the disproportionality among the frequencies of strategy categories. Suggestions for future research are discussed, such as the use of real-world data and experimental work to clarify the connection between repertoire and positive affect.

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Dedication

To Mom, Dad, and Shannon, for your unconditional love and support.

Acknowledgments

I would first like to express my sincere gratitude to my advisor, Dr. Jennifer S.

Cheavens. I could not have finished this project without your enthusiasm, creativity, guidance, and support. Thank you for all the time you have invested in developing me as a student, and for encouraging me to put my best foot forward. It made this project better.

I would also like to offer my thanks to my committee members, Dr. Daniel R. Strunk and

Dr. Baldwin M. Way. Your insightful comments and good humor strengthened this project, and helped bring it to successful completion. This study would not have been possible without my colleagues in the Mood and Personality Studies Lab: Matthew

Southward and Erin Altenburger, who designed and implemented the original data collection; Sara Moss, who co-trained the coding team with me; Kristen Howard, who also assisted with coding the data; and Anne Wilson and Cinthia Benitez for your helpful comments and encouragement in lab meetings. Finally, I am indebted to my research assistants who spent countless hours coding the data: Katya Bubeleva, Julia Wiedemann, and Allison Wittenberg. Thank you for all your hard work and sacrifice week after week throughout this process.

Vita

2010…………………………………………James E. Taylor High School

2014…………………………………………B.S. Psychology, The University of Texas at

Austin

2014 – 2015……………………………...... Research Coordinator, The Institute for

Spirituality and Health at the Texas Medical

Center

2015 to present………………………………Graduate Teaching Associate, Department

of Psychology, The Ohio State University

Fields of Study

Major Field: Psychology

Minor Field: Quantitative Psychology

Table of Contents

Abstract ...... ii Dedication ...... iv Acknowledgments...... v Vita ...... vi List of Tables ...... ix List of Figures ...... x Chapter 1: Introduction ...... 1 Positive and Negative Emotion Regulation ...... 4 Strategies to Upregulate Positive Emotion ...... 7 Does Context of the Situation Matter?...... 10 Flexibility in Emotion Regulation: Repertoire and Persistence ...... 15 Current Study ...... 17 Hypotheses ...... 17 Chapter 2: Methods ...... 20 Participants ...... 20 Measures ...... 20 Procedure ...... 22 Data Preparation...... 22 Data Analytic Strategy ...... 27 Chapter 3: Results ...... 30 Descriptives...... 30 Test of Hypothesis 1 – Associations of Repertoire, Persistence, and Suppression with Psychological Health ...... 34 Test of Hypothesis 2 - Situational Context ...... 35 Test of Hypothesis 3 - Specific Associations of Strategy Types ...... 36

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Chapter 4: Discussion and Future Directions ...... 38 Support for Hypotheses...... 41 Part One of the Model: Repertoire, Persistence, and Quality ...... 41 Part Two of the Model: Situational Context ...... 48 Specific Associations of Strategy Types...... 50 Limitations ...... 51 Conclusion ...... 57 References ...... 59 Appendix A: Tables ...... 71 Appendix B: Figures ...... 83

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List of Tables

Table 1. Text Descriptions of the Positive Scenarios and Characteristics of Each ...... 72 Table 2. Descriptions and Frequencies of Emotion Regulation Strategies ...... 76 Table 3. Means, Standard Deviations, and Correlations ...... 77 Table 4. Multiple Regression with Repertoire, Persistence, Suppression and Symptoms of Psychopathology, Positive Affect ...... 78 Table 5. Associations Between Specific Strategies and Symptoms of Psychopathology, Positive Affect ...... 79 Table 6. Association between Situational Interactions and Symptoms of Psychopathology, Positive Affect ...... 81 Table 7. Mean Emotion Ratings for each Situation ...... 82

List of Figures

Figure 1. Overall mean ratings for positive emotions, averaged across all situations. Error bars are standard deviations ...... 84 Figure 2. Histogram of the distribution of ambiguity ratings across situations ...... 85 Figure 3. Histogram of the distribution of hedonic ratings across situations ...... 86 Figure 4. Histogram of the distribution of eudaimonic ratings across situations ...... 87 Figure 5. Histogram of the interaction term for cognitive change (reappraisal) frequency times the ambiguity rating ...... 88 Figure 6. Histogram of the interaction term for cognitive awareness (savoring) frequency times the hedonic rating ...... 88 Figure 7. Histogram of the interaction term for expression (capitalization) frequency times the eudaimonic rating ...... 88

Chapter 1: Introduction

In his seminal paper, Gross (1998) argued that emotions are behavioral response tendencies that facilitate effective agency in the world. For example, anger may prep the body for a quick motor response in the face of threat (Finucane, 2011), whereas gratitude may prompt reciprocation of a kind action, which consequently may promote social bonding between individuals (Algoe & Way, 2014). In both cases, theoretically, emotions are part of a system that increases the likelihood of engaging in actions that have been evolutionarily selected for the situation – that is, the actions promote survival or the building of social capital (which in turn facilitates reproductive success; Kanazawa &

Savage, 2009). However, individuals may experience a variety of emotions in a situation, and, accordingly, may experience competing response tendencies. For instance, one might feel fear prior to a stage performance, and thus be motivated to withdraw from the crowd’s attention, while simultaneously feeling excited at the possibility of a career- boosting opportunity, and thus be motivated to engage with the crowd. Given these competing response tendencies, how does one behave? This question illustrates the importance of emotion regulation, which has been defined as the heterogeneous set of processes that alter the onset, intensity, or duration of an emotion (Gross & Thompson,

2007). In the case of the stage performance, assuming that performing is in line with one’s goals, an adaptive response might be to increase (upregulate) the experience of

1 excitement to boost one’s confidence, and decrease (downregulate) the experience of fear that may hinder one’s ability to perform. Such an example also highlights the importance of flexibly regulating emotions according to the dynamics of the situation, a point I will return to later in this paper.

Before one can begin to study emotion regulation, however, it is necessary to have a theoretical framework, as there are a seemingly endless variety of strategies to regulate emotions. As Gross (1998) points out, these strategies may be automatic or controlled, conscious or unconscious, and may occur at different stages in the experience of emotion. The study of emotion regulation, therefore, can be an overwhelming endeavor. However, as a scheme for organizing regulation strategies, Gross (1998) developed a “process model” of emotion regulation that groups strategies into those that occur early and late in the emotion generation process. Those that occur early in the generation process, he argues, influence which particular emotion is experienced. These strategies include situation selection, situation modification, attention deployment, and cognitive change, each of which I will briefly discuss.

First, situation selection involves approaching or avoiding certain situations to feel (or not feel) a particular emotion. For example, one might seek out a party to feel excitement, or avoid the 5:00 traffic to feel less frustration. Next, there is situation modification. Once a situation has been selected, individuals may actively modify it to attain a desired emotional state. For example, when one has decided to work on a budget proposal, he or she may turn on pleasant music to feel less boredom. Third, there is attention deployment. Even when one is in a situation that cannot be modified, he or she

2 may deploy attention toward particular features of the situation to influence their mood.

For example, individuals could distract themselves with pleasant thoughts when watching a frightening movie, or concentrate on the positive feedback one receives on a paper.

Finally, there is cognitive change; once a situation has been selected and its emotionally salient features attended to, an individual must then imbue the situation with meaning.

Cognitive change involves strategies that modify the meaning and interpretations attached to events. A paradigmatic example of cognitive change is reappraisal, which involves reinterpreting the meaning of an event in an alternative, often positive, manner.

For instance, imagine an employee says a greeting to her coworker in the hallway, who subsequently does not respond. The employee may initially assume that her coworker is rude and intentionally ignoring her, which may lead to feeling angry; alternatively, she may reappraise this initial assumption and consider the possibility that her coworker was simply distracted, an interpretation that is likely to dispel any negative emotion. The final set of strategies Gross outlines are those involving “response modulation”, which occurs late in the emotion generation process. These are attempts to alter the physiological or experiential aspect of an emotion after it has already been initiated. Examples include relaxation to dampen negative emotions like anxiety and sadness, and using suppression to prevent overt expressions of anger. Several other emotion regulation strategies have been identified based on the categories of Gross’ model, and meta-analytic evidence suggests that differences in effectiveness exist between categories, with cognitive change being among the most effective for influencing emotions, and attentional deployment being the least effective (Webb, Miles, & Sheeran, 2012).

Just as there are a variety of strategies to regulate emotions, there are also a variety of motivations for doing so. That is, individuals seek to change their emotions in accordance with different goals. These goals may include regulating for instrumental reasons, i.e., altering emotions to achieve some desired objective (English, Lee, John, &

Gross, 2017), such as when a football player listens to angry music to “amp” himself up for a game. These goals may also include hedonic reasons, i.e., changing emotion to decrease pain and increase pleasure, as when an individual engages in reappraisal to feel less sadness, or reflects on fond memories to feel more gratitude. It is important to note that hedonic and instrumental motivations are not mutually exclusive, and individuals frequently report a desire to improve their mood (i.e., increase positive emotion and decrease negative emotion) while simultaneously regulating their emotions to achieve some goal (English et al., 2017).

Positive and Negative Emotion Regulation

While the study of emotion regulation is important for understanding general human behavior, researchers have also identified specific links between emotion regulation and psychological health (for a review, see Nyklíček, Vingerhoets, &

Zeelenberg, 2011). For example, a number of clinical disorders are associated with excessive use of suppression and avoidance as regulation strategies to eliminate negative emotion (see Aldao, Nolen-Hoeksema, & Schweizer, 2010; Carl, Soskin, Kerns, &

Barlow, 2013 for reviews). In contrast, the regulation patterns of “flourishing” (Keyes,

2002) individuals have informed which regulation strategies are best suited for optimizing positive emotions, such as savoring (Smith, Harrison, Kurtz, & Bryant, 2014)

4 and mindfulness (Gratz & Roemer, 2004). Thus, careful study of emotion regulation patterns is instrumental in conceptualizing models of psychological health.

Most research to date has focused on regulation of negative emotions (Heiy &

Cheavens, 2014), perhaps due to the intense and frequent experience of negative emotion and accompanying specific action-tendencies (e.g., avoidance) characteristic of many clinical disorders. Notably, a failure to adequately downregulate negative emotions like fear, disgust, and sadness has been associated with feeling overwhelmed and out of control for those with anxiety and mood disorders such as depression and agoraphobia

(Carl et al., 2013). Difficulty downregulating negative emotions has also been associated with features of Borderline Personality Disorder (Cheavens & Heiy, 2011) and posttraumatic stress disorder (Xiong et al., 2013). Given these patterns, many researchers have focused on identifying techniques to downregulate negative emotions, such as cognitive reappraisal (Moyal, Henik, & Anholt, 2013).

However, in recent years, researchers have turned their attention toward regulation of positive emotions as well. While many of the same principles that apply to regulation of negative emotions may also be applicable to positive emotions, there is evidence that positive emotions involve distinct psychological, biological, and behavioral processes, and thus deserve independent attention (Carl et al., 2013). For example, Diener and Emmons (1984) found that, averaged across time, an individual’s levels of positive and negative emotion are independent of one another and are associated with different personality constructs. For example, sociability is positively correlated with positive emotion but has no relationship with negative emotion, whereas interpersonal warmth is

5 inversely related to negative but not positive emotion. Psychologically, positive and negative emotions relate to fundamentally different appraisals of a stimulus, with positive emotions signaling a perceived opportunity and negative emotions signaling a perceived threat in the environment (Garland et al., 2010). Positive and negative emotions also activate different biological systems. Kim and Hamann (2007) found that regulating positive emotion has a more pronounced effect on altering amygdala activity than that of negative emotion, suggesting a greater neural malleability of positive emotion. Likewise, researchers have found evidence of hemispheric lateralization of emotions, with relatively more activation in the left hemisphere for positive emotions, and relatively more activation in the right hemisphere for negative emotions (Ahern & Schwartz, 1985).

Physiologically, positive emotions seem to counteract the “downward spirals” of negative emotions’ effects on the body, e.g., stabilizing the cardiovascular stress induced by negative emotions (Garland et al., 2010). Finally, positive and negative emotions prompt contrasting behaviors. According to Fredrickson (1998), positive emotions such as joy, curiosity, and love lead to novel approach behaviors. These approach behaviors engender further novel actions that “broaden and build” one’s physical, intellectual, and social resources. She gives the example of joy motivating a child to play. Such play may involve curious romping around (physical skills), manipulating objects such as Legos

(visual-cognitive skills), and playing games with other children (social skills). In contrast, negative emotions narrow one’s attention and behavioral repertoire in favor of immediate, survival-focused goals. Fear, for example, constricts one’s attention to a threat in the environment, prompting avoidance of and escape from the feared object.

While this avoidant response promotes safety in the short-term, it does not expand one’s resources for later use in the manner that responses to positive emotions do.

Just as positive and negative emotions operate with distinct mechanisms, they also have contrasting relationships with health. Generally, a higher level of positive emotion is salutary, especially in proportion to one’s level of negative emotion (Tugade,

Fredrickson, & Barrett, 2004). Higher levels of positive emotion have been linked to reduced risk of disease and hypertension (Richman et al., 2005), greater longevity

(Danner, Snowdon, & Friesen, 2001), and greater mental health (Zakiee, Rostami, &

Kamasi, 2014). Conversely, higher levels of negative emotion have been linked to increased risk of disease (Glaser & Kiecolt-Glaser, 2005) and hypertension (Symonides et al., 2014), greater risk of mortality (van den Borek et al., 2013), and increased rates of mental illness (Charles, Piazza, Mogle, Sliwinski, & Almeida, 2014). Perhaps unsurprisingly, then, greater upregulation and less downregulation of positive emotions are associated with improved subjective well-being and resilience to stress (Tugade &

Fredrickson, 2007).

Strategies to Upregulate Positive Emotion

Given the links between positive emotion and improved health, it may be important to cultivate strategies that effectively upregulate positive emotions.

Researchers have identified a number of such strategies. For example, as mentioned earlier, positive reappraisal is a strategy in which stressful or ambiguous stimuli are re- construed as benign, valuable, or beneficial (Garland, Gaylord, & Park, 2009).

Researchers have demonstrated that positive reappraisal promotes a number of adaptive

7 outcomes, such as reductions in anxiety (Mathews, Ridgeway, Cook, & Yiend, 2007), and it has also been linked to reduced depressive symptoms, improved self-acceptance, and improved social relations (Nowlan, Wuthrich, & Rapee, 2015). A second strategy to upregulate positive emotion is savoring, which has been linked to greater happiness (Jose,

Lim, & Bryant, 2012) and satisfaction with life (Smith & Bryant, 2016). Savoring has been described as a process by which individuals fully attend to and appreciate the pleasant aspects of a given moment, thereby increasing the duration and intensity of enjoyment of the experience (Bryant, 2003). A third well-studied strategy is capitalizing

(or capitalization), which, as the name implies, involves capitalizing upon the natural boost from a positive event through expressive displays of emotion to others (e.g., celebrating and sharing good news with family and friends). In a seminal longitudinal work on capitalization, Langston (1994) found that while positive life events predicted positive affect in all subjects, individuals who engaged in more capitalization experienced heightened positive affect; that is, expressive displays had an additive effect on the positive life event. Finally, a fourth major upregulation strategy is behavioral activation, which involves actively engaging in rewarding behavior, such as hiking or going dancing with friends. Behavioral activation is perhaps best known as an efficacious treatment for depression (Spates, Pagoto, & Kalata, 2006). However, meta-analytic evidence indicates that behavioral activation is effective for increasing well-being in non-clinical samples as well (Mazzucchelli, Kane, & Rees, 2010).

While researchers have shown these upregulation strategies are independently beneficial, experimental work has also demonstrated that using these strategies

8 collectively results in a significant improvement in health. Weytens, Luminet,

Verhofstadt, and Mikolajczak (2014) developed a 6-week positive emotion regulation intervention based on Gross’ (1998) process model that combined elements of behavioral activation, savoring, capitalization, and positive reappraisal. They found that, compared to a wait-list condition, this intervention significantly increased participants’ subjective happiness and life satisfaction, as well as decreased depressive symptoms, physical symptoms, and perceived stress. However, because the strategies were combined, the individual contribution of any one strategy to the health outcomes cannot be discerned.

Thus, one remaining question is whether certain strategies are comparatively more effective than others. In relation to this question, Quoidbach, Berry, Hansenne, and

Mikolajczak (2010) found correlational evidence that suggests different strategies may have specific effects on well-being. They asked individuals to select either capitalization or savoring as the strategy they would use in response to a hypothetical positive event.

They then tested the associations between these strategies and psychological health variables, and found that savoring was associated with higher positive affect but not life satisfaction, whereas capitalization was associated with higher life satisfaction but not positive affect. Though correlational, Quiodbach and colleagues’ (2010) results suggest the possibility that strategies may affect psychological functioning in unique ways, with savoring impacting psychological health on a more short-term, hedonic level, and capitalizing impacting health in a more long-term manner. However, a limitation of this study is that participants did not spontaneously generate strategies they would use on their own, but rather they selected among a pre-determined set of options. Thus,

9 participants may have selected strategies that they do not typically use, and information about strategies in addition to savoring and capitalizing is not available. Therefore, research is needed on positive emotion regulation strategies that 1) does not constrain the number of strategies included and 2) reflects the types of strategies participants are likely to use in their daily lives. One purpose of my study, then, was to examine the comparative associations of strategies with psychological health, but in doing so strengthen the ecological validity of the research by having participants generate their own strategies. This method removes the constraint on the number of strategies that can be studied, and may produce responses that are more indicative of the strategies individuals use in their daily routines.

Does Context of the Situation Matter?

In addition to knowing which strategies generally promote psychological health, it is important to know in what context those strategies are most beneficial. That is, there may be situations in which strategy A is most effective, while being less effective in a situation with different characteristics. For example, savoring may effectively enhance pleasure while enjoying a delicious meal, but may be relatively less effective in an ambiguous situation, such as a job opportunity that offers less pay but is more intrinsically rewarding. For this latter situation, positive reappraisal may be more effective in increasing and maintaining positive emotion (e.g., stating, “Though it offers less pay, it will benefit my career more down the road”). In support of this consideration of situational factors, Kashdan, Young, and Mitchell (2015) recently called for a

“systematic and concerted attention to context” when researching regulation of positive

10 emotion (p. 1). They cautioned against blankly categorizing strategies as healthy or unhealthy, arguing instead that situational factors must be considered in determining the adaptiveness of a given behavior. Likewise, Tugade and Fredrickson (2007) stated that an area for further inquiry is the measurement of situational factors influencing emotion regulation. But perhaps the most elaborate expression of the need to examine context is found in the work of Aldao (2013), who argues that emotion regulation may be conceptualized as “appropriate responses to the ever-changing demands posed by the environment [emphasis added]” (p. 1). Aldao noted that despite the stress placed upon context in the theoretical realm, empirical work demonstrating the importance of context to strategy use has been slow to develop. She offered several suggestions to close this gap between theory and empirical research. One of the primary suggestions was to include four core considerations in the study of emotion regulation: 1) the organism implementing emotion regulation, 2) the emotion-eliciting stimuli of the environment, 3) the selection and implementation of strategies, and 4) the types of outcomes considered.

She stated that in assessing these factors, researchers should examine how variations within each element interact to produce different effects. For example, a researcher might assess the effect of reappraisal on both depressive symptoms and positive affect (different outcomes), and in both an anger inducing and a sadness inducing situation (different emotion-eliciting stimuli) to see if differential effects emerge. Additionally, Aldao critiqued the over-reliance to date on standardized film clips to elicit emotions, which may involve passive observation of others’ feelings, rather than actively imagining oneself in the situation. Third, she stressed the need to investigate strategies that

11 individuals implement spontaneously, rather than forcing participants to choose from a limited subset of strategies, a problem I noted in the work of Quoidbach and colleagues

(2010). Thus, to summarize the suggestions of Aldao, there is a need for research on situational context that 1) actively induces participants to imagine or otherwise experience the situation, and 2) directs participants to spontaneously generate their own strategies. In the current study, these suggestions were incorporated through a vignettes method in which participants freely responded to a prompt after imagining themselves in a situation.

Though several scholars have argued for consideration of context, to date little empirical work exists to confirm how strategies might be matched to situations of differing characteristics. However, there is evidence that different situations elicit a preference for certain strategies over others. For example, Brans and colleagues (Brans,

Koval, Verduyn, Lim, & Kuppens, 2013) used experience sampling to investigate the use of emotion regulation strategies across time, and found that most of the variation in strategy use occurred across different situations, rather than between individuals, suggesting that contextual factors may play a larger role in strategy use than individual differences. More specifically, Sheppes et al. (2014) found that when individuals are in a situation in which they expect to encounter an unpleasant stimulus again in the future, they are likely to use reappraisal to change their evaluation of the object. However, if they do not expect to encounter the stimulus again, they tend to use distraction, presumably to provide immediate relief. Likewise, Scheibe, Sheppes, and Staudinger

(2015) found that individuals prefer to use distraction when viewing negatively valenced

12 pictures of high emotional intensity, but tend to choose cognitive reappraisal when viewing less emotionally intense images. Researchers have also found that, compared to their routine lives, individuals use more cognitive reappraisal and less expressive suppression at the Burning Man Festival, a pattern that is typically linked to more positive outcomes (McRae, Heller, John, & Gross, 2011). The researchers suggest that these different regulatory profiles emerged due to the emphasis placed at the festival upon free expression and a more removed, distanced reflection upon life. Complementing this finding, English et al. (2017) found that in their routine lives (in contrast to a festival), individuals are more likely to use suppression when in the presence of others, particularly non-close others, providing further evidence that social context shapes strategy use.

Although these studies form initial foundations for investigating context, the question of a “match” between situations and strategies remains. That is, none of these studies investigated whether there is an interaction between strategies and situations in predicting measures of psychological health (e.g., depression, anxiety, positive affect, etc.). Therefore, they inform us as to how individuals are regulating their emotions in different situations, but do not inform us as to whether there is a stronger “fit” for particular strategies used in a situation. This question is particularly relevant for regulation of positive emotions, as it is uncertain whether contextual nuances matter, or if all strategies that upregulate positive emotion are equally effective across contexts (i.e., all good things are equally good regardless of the situation). The results I mentioned from

Quoidbach et al. (2010) may provide an initial hint at a contextual match for strategies.

Recall they found that savoring (focusing attention on the present moment) is primarily associated with positive affect, whereas capitalizing (sharing good news with others) is primarily associated with life satisfaction. The short-term nature of positive affect versus the more long-term, broader nature of life satisfaction suggests that these two strategies may map onto different forms of well-being; namely, hedonic well-being for savoring and eudaimonic well-being for capitalizing. Hedonic well-being involves short-term, pleasurable subjective experiences, whereas eudaimonic well-being is characterized by experiences that promote long-term personal growth, meaning, and connections with others (Tugade & Fredrickson, 2007). Accordingly, in situations that present a greater opportunity for hedonic well-being (such as watching a funny video), individuals may benefit most from use of a short-term focused strategy like savoring to immediately boost positive affect. Conversely, in situations that present more of an opportunity for long- term growth and meaning (such as being nominated for a departmental award), individuals may benefit more from sharing the news with others (capitalizing). Similarly, given the function of positive reappraisal in re-construing ambiguous stimuli in a more positive manner (Mathews et al., 2007), individuals may benefit most from using this strategy in more ambiguous situations, such as receiving an offer for a prestigious fellowship that pays poorly. Therefore, a second purpose of my study was to examine the links between psychological health and use of savoring in more hedonically-oriented situations, capitalizing in more eudaimonically-oriented situations, and reappraisal in more ambiguous situations. In so doing, I used empirical data to provide an initial step toward answering whether situational contexts are best matched with particular strategies.

Flexibility in Emotion Regulation: Repertoire and Persistence

Related to the idea of situational context is the flexibility with which one switches between strategies. If situations are defined by different features, with an optimal strategy suited to each situation, then an individual must possess 1) a number of strategies that they can implement across changing situations, as well as 2) the wisdom to know when to persist with a strategy versus switching to another when it is ill suited to the task. This first idea has been conceptualized as one’s strategy “repertoire”, or the number of unique strategies an individual implements across various situations (Lougheed & Hollenstein,

2012). The second concept may be conceptualized as “persistence”, or the tendency to continue using an initially selected strategy without switching to another (Southward,

Altenburger, Moss, Cregg, & Cheavens, 2017). One’s repertoire may be analogous to the number of tools at one’s disposal, and thus a large repertoire is more likely to contain the right tool for the job. Indeed, prior work has suggested the importance of a large repertoire for optimal regulation of both negative emotion (Blalock, DeVellis, Holt, &

Hahn, 1993; Lam & McBride-Chang, 2007; Southward et al., 2017) and positive emotion

(Quoidbach et al., 2010). Currently, evidence on the role of persistence in emotion regulation is more mixed than the evidence regarding repertoire, as recent investigations suggest that persistence may be linked to greater psychological health only in some contexts. For example, Birk and Bonanno (2016) demonstrated that less persistence (i.e., more switching between strategies) was associated with greater life satisfaction among those who were particularly sensitive to interoceptive cues, such as heart rate.

Conversely, switching was related to lower life satisfaction among those who were not

15 sensitive to such cues. These results suggest that attunement to internal feedback is an important indicator of when it is adaptive to switch strategies. Likewise, our previous work with negative emotions indicates that when individuals receive feedback that a strategy is not initially effective, more persistence (less switching) is associated with greater psychological health (Southward et al., 2017). One interpretation of our results is that a “flailing effect” may occur when switching, that is, aimlessly shifting between strategies without giving any one of them time to be effective. Combining the results from Southward et al. (2017) and Birk and Bonanno (2016), the upshot may be that indiscriminate switching is inversely associated with markers of psychological health.

That is, switching either before it is indicated by internal cues, or before allowing sufficient time for the strategy to work. However, both studies were focused on regulation of negative emotions. To my knowledge, no studies to date have directly examined the role of persistence in regulating positive emotions. Additionally, it is uncertain how persistence would relate to psychological health when placed together in a model with repertoire. Thus, a third aim of the present study was to determine how strategy flexibility, operationalized as repertoire and persistence, is associated with psychological health in the context of positive emotions. Moreover, an additional consideration is that strategies may differ in quality, with some strategies judged to be adaptive in a situation (high quality), and others maladaptive (low quality). Accordingly, persisting with a large repertoire of poor quality strategies may not be adaptive. Thus, we also included a measure of strategy quality to obtain a more accurate estimate of the associations of repertoire and persistence with psychological health.

Current Study

In summary, the purpose of this study was to test a novel model of optimal positive emotion regulation. This model tested the role of two primary components. First, a large repertoire of high quality strategies used persistently, and second, a match between the strategy and features of the situation. Another aim of the current study was to investigate the comparative associations of different strategies with psychological health.

To test these questions, we provided undergraduates with a series of hypothetical positive events and asked them to generate strategies they would use to maintain or upregulate positive emotion. We then grouped these strategies into different categories to test how various patterns of emotion regulation relate to psychological health.

This study builds on past research in three important ways. First, we did not limit participants in the strategies they could report, thus allowing more freedom and ecological validity in the responses they provided. Second, this study assessed how situations and strategies may interact to form an association with psychological health, thus providing a test that goes beyond a description of which strategies are used most frequently in different situations. And third, we applied a previously tested model of flexibility in negative emotion regulation to positive emotions (Southward et al., 2017).

Hypotheses

1. Based on our previous work examining repertoire, persistence, and quality as

they pertain to negative emotion regulation (Southward et al., 2017), I

predicted that a similar pattern would emerge for the current study. That is, I

expected that a larger repertoire, the use of high quality strategies, and greater

persistence (less switching) would be positively associated with psychological

health (PH; i.e., lower depression, anxiety, and BPD features, and greater

positive affect).

2. I predicted the following matches between situations and strategies (see Table

1 for a description of all situations, and Table 2 for descriptions and frequency

of use of all strategy categories).

In situations that involve more opportunity for long-term life

consequences, meaning, and connection with others (eudaimonic well-being),

a greater frequency of capitalizing (“expression” in the current study) will be

associated with higher PH. In situations that involve more opportunity for

short-term hedonic pleasures, a greater frequency of savoring (“cognitive

awareness” in the current study) will be associated with higher PH.

In situations that are more ambiguous in nature (they elicit conflicting

positive and negative emotions), a greater frequency of reappraisal (“cognitive

change” in the current study) will be associated with higher PH.

3. Based on previous research demonstrating their links with health, I predicted

that a greater frequency of behavioral activation, savoring (cognitive

awareness), reappraisal (cognitive change), and capitalizing (expression)

strategies would be significantly positively associated with PH variables. In

contrast, I predicted that a greater frequency of emotional suppression

strategies would be negatively associated with PH (for the generally harmful

effects of suppression, see Srivastava, Tamir, McGonigal, John, & Gross,

2009). Because comparing the associations of each strategy to one another is exploratory, I did not have a priori hypotheses about which strategies would have the strongest relationships with PH.

Chapter 2: Methods

Participants

Undergraduate students at The Ohio State University were recruited through an online posting via the Research Experience Program. Participants (N = 134; Mage = 19.22,

SD = 1.84, range: 18 – 34; 73% female, 78% Caucasian) were given partial course credit for participation. Participants were only excluded if they were younger than 18 years old.

All study procedures were IRB approved.

Measures

Positive scenarios (2016; unpublished). Eleven hypothetical vignettes were created to represent typical experiences college students may have that would elicit positive emotions. Example scenarios include receiving an act of kindness from a roommate, expressions of romantic love, and achieving fitness goals (see Table 1 for all scenarios). Scenarios were used to prompt participants to generate emotion regulation strategies (see procedure below).

Positive emotion ratings. Eight positive emotions were rated for each vignette: happiness, excitement, gratitude, interest, hope, pride, amusement, and love. These emotions were selected and supplemented from prior research that reported the most frequently experienced positive emotions among undergraduates (Heiy & Cheavens,

2014). Each emotion was rated for how strongly it would be felt in the situation from 0

(not at all strongly) to 100 (very strongly).

Symptoms of psychopathology.

Center for Epidemiologic Studies - Depression Scale (CES-D; Radloff, 1977).

The CES-D is a 20-item scale designed to assess symptoms of depression (including feelings and behaviors) occurring in the past week. Respondents indicate the frequency of each feeling or behavior in the past week, ranging from 0 (rarely or none of the time) to 3

(most or all of the time). Items are summed to create a total score, with a recommended cutoff of 16 for a clinically relevant level of depressive symptoms. Internal consistency in the current sample was good (Cronbach’s alpha = .87).

State-Trait Anxiety Inventory, Trait Version, Form Y (STAI-T; Spielberger,

Gorsuch, Luchene, Vagg, & Jacobs, 1983). The STAI-T is a 20-item scale designed to assess a relatively stable disposition to experience anxiety. For each item, respondents report how they generally feel on a four-point scale. After reverse scoring, items are summed to create a total score ranging from 20 – 80, with higher scores indicating a greater level of trait anxiety. Internal consistency in the current sample was excellent

(Cronbach’s alpha = .91).

Inventory of Interpersonal Problems – Borderline Personality Disorder subscale (IIP-BPD; Lejuez, Daughters, Nowak, Lynch, Rosenthal, & Kosson, 2003).

The IIP-BPD is an 18-item self-report subscale designed to assess features of Borderline

Personality Disorder in unselected samples. It is calculated by taking the average of the

Aggression and Interpersonal Sensitivity subscales from the IIP (Horowitz, Rosenberg,

Baer, Ureno, & Villasenor, 1988). Participants use a five-point scale to rate how well each statement applies to them. Internal consistency for each subscale in the current sample was good (Cronbach’s alpha = .82 for Sensitivity and .89 for Aggression), as well as for the combined scale (Cronbach’s alpha = .89).

Procedure

Participants completed all measures online using the survey tool Qualtrics (2013).

Participants were presented with the 11 positive vignettes in a random order. After each vignette, they rated the extent to which they would feel each of the eight positive emotions in that situation. Next, they were asked to provide a free-response to the question “What would you think or do first to maintain or improve your mood?” After providing initial responses to all scenarios, the vignettes were presented a second time, along with the participant’s initial free-response. In order to assess switching of strategies, the participants were shown the prompt “If that strategy is not working, what would you think or do to maintain or improve your mood? (You may continue thinking or doing the same thing)” and asked to provide a second free response. This prompt was repeated an additional three times for each scenario before moving on to the next vignette. In total, participants generated five free responses for each vignette (i.e., 55 responses total per participant). After responding to the vignettes, participants completed the psychopathology measures in a random order (the CES-D, STAI-T, and IIP-BPD).

Data Preparation

Categorization of strategies. A team of three graduate students created an initial list of strategies based upon previous research (Heiy & Cheavens, 2014; Southward et al.,

2017), which they then grouped together by common themes to reduce redundancy in coding. This grouping resulted in a final list of nine strategy categories (see Table 2 for descriptions). Three undergraduate coders then categorized each free response into one of the nine categories. Coders rated each participant’s block of five consecutive responses within a vignette in order; however, blocks were randomized by participant number and vignette number. The raters demonstrated good reliability (Krippendorff’s alpha = .79 with 5,000 bootstrap samples). I used the modal rater response as the final category for any given response (i.e., the category for which at least two out of three raters agreed). I resolved responses with no agreement among raters, which accounted for only 3% of total responses. I conducted all analyses with and without these discrepant responses, and no differences were found (i.e., the same significant and non-significant associations were observed across analyses). Thus, the discrepant responses were included in the final dataset and will not be discussed further. Additionally, for the final analyses, I removed

“non-specific” responses from the dataset, as these involved irrelevant statements that conveyed little information about emotion regulation (e.g., responding with “nothing” or

“I don’t know”), leaving a total possibility of eight categories. Had these non-specific responses been included, they would have accounted for approximately 27% of the dataset (n = 2,043). Removing the non-specific responses did not substantially alter the reliability (Krippendorff’s alpha = .77 with 5,000 bootstrap samples), and responses with no agreement among raters still accounted for only 3% of total responses.

In addition to categorizing responses, raters classified each strategy as either high quality (likely to be effective or helpful in that situation) or low quality (unlikely to be

23 effective or helpful in that situation). This measure was intended to examine individual differences in the adaptivity of participants’ strategies, as well as to control for strategy quality when assessing the unique associations of repertoire and persistence with psychological health. Quality was rated dichotomously for individual strategies, with high quality responses scored as 1 and low quality responses scored as 0. Coders agreed on the quality of a strategy for 95% of responses. The quality scores for each response in the dataset were averaged across raters. Next, these scores were averaged again across each participant’s full set of responses, creating a quasi-continuous variable ranging from

0 – 1, where a score closer to 1 indicated a tendency to use high quality strategies.

Therefore, each participant had a single quality score that assessed their tendency to use high quality responses across all situations.

Situational context. A team of three graduate students trained by me rated each scenario on three dimensions: hedonic well-being, eudaimonic well-being, and ambiguity. All three dimensions were rated on a 5-point scale, with higher scores indicating a higher level of that construct. For hedonic well-being, a 5 indicated a situation was highly hedonic (primarily offering an opportunity for short-term pleasure), and a 1 indicated a situation was not at all hedonic in nature. For eudaimonic well-being, a 5 indicated a situation was highly eudaimonic in nature (primarily offering an opportunity for deeper meaning, expression of values, or long-term personal growth), and a 1 indicated the situation was not at all eudaimonic. Finally, for ambiguity, higher scores indicated a more ambiguous situation (conflicting positive and negative features), and lower scores indicated a more “purely” positive situation. Reliabilities were calculated

24 using a two-way random, consistency, average model of intraclass correlation coefficient

(ICC) in SPSS. Reliabilities across the dimensions ranged from good to excellent, with a value of .85 for hedonic well-being, .92 for eudaimonic well-being, and .91 for ambiguity. For the final analyses, values were averaged across the three raters to create an average rating for each dimension, for each situation. Thus, there were 33 average dimensional ratings in total (3 dimensional ratings for each of the 11 vignettes).

Operationalizing repertoire and persistence. I calculated each participant’s repertoire by counting the number of unique strategies used across all 11 vignettes (i.e., how many of the eight1 possible strategies were used in total). Second, in order to assess one’s tendency to persist with strategies, I averaged the number of times each participant changed strategies per vignette. That is, the more changes made within a particular vignette, on average, the less persistent the strategy use. A strategy change was defined as any time a strategy category differed from the response immediately preceding it, within each block of five consecutive responses. The number of changes could range from zero

(did not change at all for that vignette) to four (changed every time). I then multiplied the average change score by -1 to interpret it as persistence, rather than the number of strategy changes.

Positive affect. I calculated a single mean positive emotion score for each participant by averaging his or her ratings for all 8 positive emotions across all 11 situations. Additionally, prior research suggests that in addition to the overall mean level of positive emotion, experiencing a relative variety and abundance of several discrete

1 After removing all non-specific responses from the dataset. 25 emotions is important to well-being (Quiodbach et al., 2014). Therefore, I included a measure that indexes the diversity of an individual’s emotional experience. This

“emodiversity” measure (Quiodbach et al., 2014) takes into account both the number of discrete emotions experienced as well as the relative intensity of experiencing a particular emotion compared to others. In the current study, those with higher emodiversity scores experienced a larger number of different emotions in relatively balanced proportion to one another. I generated this emodiversity score for each participant using a modification of Quoidbach et al.’s (2014) emodiversity calculator and SPSS syntax

(www.emodiversity.com), in which I changed frequency of emotion to reflect the intensity of emotion used in the current study. The following formula was used to produce the emodiversity score:

푠 Emodiversity = ∑푖=1(푝푖 × ln 푝푖 )

In which s is the total number of emotions (out of eight) experienced by the individual, summed across all scenarios, and pi is the relative abundance (proportion) of experiencing a particular emotion compared to others (defined in this case as how intensely that emotion tended to be felt compared to others). More specifically, the formula:

1. Divides the average score for how strongly an individual experienced emotion

#1 across all situations by the total average for how strongly he or she

experienced all positive emotions across situations.

2. Multiplies the proportion in step 1 by its natural log (p1 × ln p1).

3. Repeats steps 1 and 2 for each specific emotion, and

4. Sums all the (pi × ln pi) products and multiplies the total by -1.

Thus, higher values represent greater emodiversity, i.e., a greater variety and more even intensity of experiencing positive emotions.

A third measure of positive affect that may be important is the peak emotional experience across situations. For example, an individual may experience one emotion, such as love, particularly strongly in a situation involving a romantic relationship, but not feel such an emotion while waiting for a doctor’s visit. If we simply average this individual’s emotion ratings across all situations, their overall mean positive emotion score may be artificially low, even if he or she tends to rate one or two emotions highly in each situation. Thus, to capture individual differences in peak emotional experiences, I summed the highest emotion rated by each participant for every vignette. This created a total score for each participant that assessed their maximum emotional experience across situations, a construct I will refer to as “peak positive emotion” (peak PE).

Data Analytic Strategy

All variables in the study were standardized prior to statistical analyses to ease interpretation.

My first test was an examination of how repertoire, persistence, and quality relate to PH using a series of correlations and multiple regression models. However, the variance in quality was found to be highly restricted (M = .96, SD = .05). Given this potential ceiling effect, I elected to replace quality with frequency of suppression use in all analyses (see “Replacing Quality with Suppression” below). Thus, for the multiple regression, I entered repertoire, persistence, and frequency of suppression as

27 simultaneous predictors and each of the PH variables as criterion variables (the three measures of positive affect and the three measures of psychopathology - CESD, STAI-T, and IIP-BPD). I ran a separate regression model for each PH variable.

To test the fit between a strategy and the features of a situation, I calculated the frequency that each individual used a particular strategy, such as reappraisal, in each of the 11 situations. I then multiplied these frequencies by the corresponding dimensional rating for each situation, creating 11 interaction terms for each type of rating (reappraisal x ambiguity, savoring x hedonic, and capitalizing x eudaimonic). These interaction terms measured an individual’s tendency to use a strategy more frequently in situations involving a higher dimension of interest. For example, an individual who used reappraisal

3 times in a situation with an ambiguity rating of 5 would have a value of 15 for the interaction term for that situation (3 times 5), whereas an individual who used reappraisal only 2 times in the same situation would only have a value of 10 (2 times 5). After creating these interaction terms for each situation, I summed them to form an aggregate index across all situations. Each participant had three aggregate indices total: one for reappraisal x ambiguity, one for savoring x hedonic, and one for capitalizing x eudaimonic. Thus, for example, a higher aggregate score on the reappraisal x ambiguity index indicates that the individual tended to use reappraisal more frequently in more ambiguous situations. After creating these aggregate indices, I then ran several independent simple linear regression models with each aggregate index as the predictor and each PH variable as the criterion variables. Again, I conducted a separate regression for each criterion variable.

Lastly, to test the associations of each specific strategy type with PH, I again ran a series of independent simple regression models with the frequency that participants used each strategy across all situations as the predictors and each of the PH variables as the criterion variables.

Chapter 3: Results

Descriptives

Positive affect. I first examined descriptive statistics for the positive emotion ratings. The overall mean positive emotion rating (averaged across all 11 vignettes and all

8 emotions) was 60, with a standard deviation of 15. On average, happiness was the most strongly rated emotion across situations (M = 76, SD = 12), followed by excitement (M =

71, SD = 14) and interest (M = 67, SD = 17). The lowest rated emotions were love (M =

43, SD = 22), amusement (M = 49, SD = 23), and pride (M = 54, SD = 17). See Figure 1 for a graph of the average emotion ratings across situations. Next, I examined which emotion was rated the highest within each vignette. Seven of the eight emotions were rated as the highest emotion felt for at least one situation, with pride being the only exception.2 Notably, even love and amusement, the lowest rated emotions across situations on average, were rated as the strongest emotions in situations 1 (romantic evening with one’s partner) and 8 (watching YouTube videos of dogs), respectively. See

Table 1 for the strongest emotion experienced in each situation, and Table 7 for all emotion ratings across situations. Finally, the mean emodiversity score was 2.03 (SD =

.05) out of a maximum value of 2.99, and the mean peak positive emotion score (the sum

2 However, pride fell short by only two points or less from being the highest emotion experienced in three situations (situations 6, 10, and 11). 30 of the highest emotion rating from each situation) was 944.13 (SD = 163.94) out of a maximum value of 1,100. To determine the extent of overlap among the three positive affect indices, I calculated the correlations between mean positive emotion (mean PE), emodiversity, and peak positive emotion (peak PE). All three variables were significantly positively correlated (all ps < .01), with emodiversity and mean PE demonstrating the strongest relationship (r = .67), followed by peak PE and mean PE (r = .43), and emodiversity and peak PE demonstrating the weakest relationship (r = .28).

Psychopathology. Next, I examined descriptive statistics for each of the three measures of psychopathology. Participants had a mean CES-D score of 19.01 (SD =

9.60), which is above the mean of 9.25 and recommended cutoff of 16 for clinically relevant depressive symptoms reported in the original validation sample (Radloff, 1977).

However, our sample’s mean is consistent with our previous work that reported a CES-D mean of 19.71 (SD = 10.31; Southward et al., 2017), and is within one standard deviation of the mean of 14.58 (SD = 9.37) reported by other researchers using an undergraduate sample (Herman et al., 2011). The average IIP-BPD score was 1.21 (SD = .70), a value consistent with the mean of 1.15 and standard deviation of .76 reported in prior research with a college sample (Lejuez et al., 2003). Finally, our sample had an average STAI-T score of 43.03 (SD = 10.14), which is again consistent with the mean of 41.02 and standard deviation of 9.20 reported in prior research with a college sample (Rawson,

Bloomer, & Kendall, 1994).

Repertoire, Persistence, Quality, and Strategy Types. Finally, I examined descriptive statistics for repertoire, persistence, and quality. Participants had an average

31 repertoire of approximately five of the eight possible strategies (M = 5.18, SD = 1.42).

Participants changed strategies an average of 1.14 times per vignette (SD = .69), out of the maximum possible score of 4. Furthermore, 75% of the sample fell below 1.64 changes, indicating that individuals tended to persist with a chosen strategy, on average.

Participants also tended to use high quality strategies overall, with an average quality rating of .96 (SD = .05) out of a maximum score of 1. Lastly, I examined the frequencies for how often each strategy category was used across situations (Table 2). The three most used categories were planning (46% of responses), expression (21% of responses), and behavioral activation (12% of responses). Together, these three strategies accounted for

79% of total responses. Additionally, I examined the most used strategies for each vignette, and found that either planning or expression were the most used strategies within each vignette. Non-specific responses that convey little information (involving statements such as “nothing” and “I don’t know”) were removed from the dataset. These non-specific statements totaled 2,043 responses, which would have comprised nearly

27% of the dataset had they been included.

Replacing Quality with Suppression. The restriction of variance in quality, such that most individuals had close to the maximum average quality score (M = .96, SD =

.05), creates a potential ceiling effect, in which accurate correlations between quality and health may not be obtained due to the restriction along the continuum of strategy quality.

Furthermore, as Austin and Brunner (2003) demonstrated, including such a restricted variable in a multiple regression model can inflate the Type I error rate for the other predictors. Accordingly, I elected to remove average quality from subsequent analyses.

However, given my prediction that the use of suppression would be negatively associated with psychological health, it would also be expected to have a lower quality score than other strategies. Thus, as an exploratory test, I inspected the difference in quality between suppression and all other strategies in the dataset. To do so, I dummy coded every strategy in the dataset as either suppression (N = 208) or non-suppression (N = 5,390), with non-suppression serving as the reference group. I then entered these dummy variables as predictors into a regression model with quality as the criterion variable.

Suppression strategies were significantly related to quality, such that suppression was rated to be of significantly lower quality (M = .23) than non-suppression strategies (M =

.99, b = −.76, p < .01). Thus, to avoid confounding quality and suppression in the regression models, I replaced quality with frequency of suppression use in all analyses. In this way, the unique associations of repertoire and persistence with PH could be determined while controlling for the use of suppression, a low quality strategy.

Furthermore, I applied the same predictions to suppression as with quality, with less use of suppression expected to relate to higher PH.

Test of Hypothesis 1 – Associations of Repertoire, Persistence, and Suppression with Psychological Health

My first question concerned how to build a model of optimal positive emotion regulation with repertoire, persistence, and suppression frequency (in lieu of quality). My prediction was that larger repertoires, greater persistence, and less use of suppression would all be positively associated with PH (i.e., lower CESD, STAI-T, and IIP-BPD, and greater mean PE, emodiversity, and peak PE). I assessed the association of each of these constructs with PH in two ways. First, I examined their relationships with PH at a bivariate level, e.g., looking at independent correlations between one’s repertoire and PH variables (Table 3). Second, I examined their unique associations with PH with a series of multiple regression models in which repertoire, persistence, and suppression were entered as simultaneous predictors and each PH variable entered as separate criterion variables

(Table 4). Before placing the three predictors together into a regression model, I examined correlations among them to determine the degree of collinearity present. Larger repertoires were associated with less persistence (r = −.54, p < .01), otherwise no associations among the predictors were found. Field (2009) recommends that correlations of magnitude r > |.80| are cause for concern of multicollinearity, a value for which the association between repertoire and persistence is well below. Additionally, I computed tolerance and VIF statistics for repertoire and persistence in all analyses. All tolerance statistics were greater than .70, and all VIF statistics were less than 1.42. Again, Field

(2009) recommends that tolerance values below .20, and VIF values above 10 are cause for concern. Accordingly, there does not appear to be strong evidence for

34 multicollinearity between repertoire and persistence, making them suitable to be placed simultaneously into a regression model.

Repertoire was positively associated with mean PE and peak PE in both the bivariate (both rs > .17 and ps < .05) and multiple regression models (both βs > .20 and ps < .05). Additionally, repertoire was positively related to emodiversity at the bivariate level (r = .20, p <.05), and marginally related to emodiversity in the multiple regression

(β = .17, p < .10). Persistence was inversely associated with peak PE at the bivariate level

(r = −.19, p = .03), but not in the multiple regression model (β = .01, p = .93). The use of suppression strategies was related to higher IIP-BPD scores at the bivariate level (r = .18, p < .05), and marginally related to higher BPD scores in the multiple regression model (β

= .17, p = .06). No significant associations with CES-D scores were found.

Test of Hypothesis 2 - Situational Context

My second question concerned the match between a strategy and the features of a situation. I predicted that greater PH would be associated with the following: a higher use of reappraisal (cognitive change) in more ambiguous situations, a higher use of capitalizing (expression) in more eudaimonic situations, and a higher use of savoring

(cognitive awareness) in more hedonic situations.

Before testing the situational interactions, I examined descriptive statistics among each of the three context ratings (hedonic well-being, eudaimonic well-being, and ambiguity). On a 1 – 5 scale, the mean ambiguity rating across situations was 1.94 (SD =

1.07), the mean hedonic rating was 3.24 (SD = 1.07), and the mean eudaimonic rating was 3.39 (SD = 1.33). See Table 1 for the average context ratings for each vignette, and

Figures 2 – 4 for distributions of each rating across situations. Next, I looked at the correlations between the ratings to assess the amount of overlap among these constructs.

Situations rated to be more ambiguous were lower in hedonic well-being (r = −.68, p =

.02). Likewise, situations rated to be of higher eudaimonic well-being were also lower in hedonic well-being (r = −.79, p < .01). No relationship was found between ambiguity and eudaimonic well-being (r = .22, p = .52).

To assess the match of a strategy to a situation, I examined associations between each of the three situation x strategy interaction terms (e.g., capitalization x eudaimonic) and each PH variable. Individuals who used a greater frequency of capitalization in more eudaimonic situations reported higher peak PE (β = .21, p = .02). Additionally, there was a trend for individuals using a greater frequency of savoring in more hedonic situations to report higher peak PE (β = .16, p = .07). No other significant associations with PH were found. See Table 6 for regression weights and Figures 5 – 7 for distributions of each situation-strategy interaction term.

Test of Hypothesis 3 - Specific Associations of Strategy Types

My final question was an investigation into the relationship between specific strategies and PH. I predicted that more frequent use of behavioral activation, savoring

(cognitive awareness), reappraisal (cognitive change), and capitalization (expression) would be associated with greater PH.

Individuals who reported more frequent use of planning strategies had higher emodiversity scores (β = .20, p = .02), higher mean levels of positive emotion (β = .20, p

= .02), and higher peak PE (β = .29, p < .01). A greater frequency of expression use was

36 also associated with higher peak PE (β = .18, p = .03). Additionally, there was a trend for a positive association between peak PE and cognitive awareness (β = .16, p = .06). Past focused strategies were also associated with greater peak PE (β = .21, p = .02), as well as marginally associated with higher mean PE (β = .16, p = .07). A greater frequency of behavioral activation was marginally associated with lower mean PE (β = −.15, p = .08).

No other significant associations were found (Table 5).

Specificity with Each Emotion. Given the positive association between repertoire and emodiversity reported earlier, I wanted to explore whether different strategies relate to distinct emotions. Therefore, I computed correlations between the strategy frequencies and the overall mean for each of the eight positive emotions.

Planning had significant or marginally significant positive associations with all emotions except interest and pride (all r > .16, all p < .08). Past focus was positively associated with happiness, excitement, and gratitude (all r > .20, all p < .02). Finally, suppression was negatively associated with happiness (r = − .25, p < .01), and behavioral activation was negatively associated with happiness, interest, and hope (all r < − .17, all p < .05), as well as marginally negatively associated with pride (r = −.15, p = .08).

Chapter 4: Discussion and Future Directions

The central aim of this study was to investigate how different patterns of regulating positive emotions relate to psychological health. Before evaluating the hypotheses, several features of the data should be noted. First, participants reported they would expect to experience a range of emotional experiences across the situations. All eight emotions were endorsed to some extent across situations (Figure 1), and the highest emotion reported within each vignette tended to match the features of the situation. For example, participants endorsed love as the strongest emotion in an interaction with a romantic partner, amusement when watching funny videos, and gratitude when receiving a kind gesture from a friend. These results suggest that 1) participants considered the situational dynamics when rating each emotion, as opposed to responding uniformly across all eight emotions, and 2) the vignettes adequately captured variance in emotional experiences, which in turn suggests that positive events may not all influence emotions in the same way. That is, beyond valence, there are particular features of a situation that engender different emotional experiences, such as with whom one is interacting and the task one is doing. Finally, the relationships among the three positive affect variables suggest that while these constructs are related, they are not redundant (all r ≤ .67). That is, indices such as emodiversity and peak positive emotion may reveal information about the experience of positive affect beyond what is provided by mean emotion levels.

Indeed, had I not included emodiversity and peak PE, I would have missed potentially important relationships with positive affect, such as the associations with repertoire and the “fit” of the strategy to the situation.

Second, like the emotion ratings, the apparent match between the context ratings

(e.g., ambiguity) and the situations suggests that 1) these dimensions were present and varied among our vignettes, and 2) the coders accurately took the nuances of the vignettes into account. For example, situations in which one would not expect a conflicting emotional response, such as being complimented on one’s figure by friends, were rated as relatively low in ambiguity (M = 1.33), whereas more ambivalent situations were rated as relatively high in ambiguity, such as taking an exam in a class one has struggled in (M = 3.33). Furthermore, the context ratings related to one another in coherent ways. For example, hedonic well-being is characterized by short-term, pleasurable experiences, whereas eudaimonic well-being is characterized by meaningful experiences with longer-term implications. Given the contrast between the short-term nature of hedonic well-being and the long-term nature of eudaimonic well-being, one might expect a negative correlation between these constructs. Accordingly, situations of greater eudaimonic value were judged to be lower in hedonic value (r = −.79). Similarly, more ambiguous situations were also lower in hedonic value (r = −.68). Again, this finding aligns with what would be expected, given that hedonic well-being is characterized by immediate pleasure, and ambiguous situations involve some unpleasant emotions in the moment. Lastly, there was no relationship between ambiguity and eudaimonic well-being. Although this was not expected, it may be that the long-term

39 growth and connections characteristic of eudaimonic well-being are possible regardless of the ambiguity of a situation. For example, one may be just as likely to experience deepened meaning and connection in a highly ambiguous situation – such as winning a fellowship that pays poorly – as a more “purely” positive situation – such as being nominated for a writing award (both of which received high eudaimonic ratings of 4.67, but received contrasting ambiguity ratings of 4.33 and 1.00, respectively).

Finally, it is important to note the large number of non-specific responses in the dataset. Non-specific statements, which included variants of phrases like “nothing” and “I don’t know,” totaled 2,043 responses, which would have made up nearly 27% of the entire dataset had they been included. This large number contrasts with our previous work with negative emotions (Southward et al., 2017). With a similarly sized sample (N

= 144), there were only 521 non-specific responses in this previous study, or approximately 7% of the dataset, a difference of 20% from the present study. Although this large number of non-specific responses was unexpected, it is consistent with recent work by English and colleagues (English et al., 2017) that demonstrates individuals are less likely to exert effort in regulating emotions during “high points” of their lives (i.e., positive events). Thus, one potential explanation for the large number of non-specific responses in this study is that individuals are not as deliberate in thinking about how to regulate positive emotions. When individuals experience negative emotions, they are often motivated to downregulate them in some fashion to feel better or to solve a problem

(English et al., 2017). Accordingly, individuals may consciously consider which strategies they use to cope with negative emotions. In contrast, individuals may not feel

40 the same need to regulate positive emotions, as there is no pain to eliminate, and thus, less of an impetus to devote conscious attention to strategy choices. If that is the case, then asking individuals to repeatedly generate strategies to upregulate positive emotion may constitute a difficult and foreign task, hence the large number of statements such as

“I don’t know” in this study. This is just one post-hoc hypothesis, however, and a follow- up study is needed to confirm this explanation. Researchers could test this explanation by having participants generate as many strategies as possible to improve their mood in response to both negatively and positively valenced vignettes. If participants produce significantly more strategies in response to negative situations, it would suggest that individuals possess greater awareness of how to regulate negative emotions, and have comparatively less insight into strategies for regulating positive emotions.

Support for Hypotheses

I proposed that an optimal model of positive emotion regulation would be composed of two primary components: 1) a large repertoire of high quality strategies used consistently, and 2) a “match” between the features of the situation and the strategy.

These hypotheses were largely unsupported, save for some associations with positive affect. Additionally, I explored the connections between psychological health and specific strategies, and found few significant associations, again with the exception of some relationships with positive affect.

Part One of the Model: Repertoire, Persistence, and Quality

Repertoire. I predicted that a larger repertoire of strategies would be positively related to psychological health. In support of this prediction, a larger repertoire was

41 related to all three indices of positive affect in both the multiple regression and bivariate models. This finding lends some further evidence to the potential benefits of “regulatory diversity,” or varying one’s use of strategies across situations (Quoidbach et al., 2010). If individuals have an array of strategies at their disposal, they may be able to maximize their experience of positive emotion across different situations; for example, using savoring when enjoying a meal and capitalizing when receiving good news. Likewise, if strategies promote different emotional experiences, then a larger repertoire may promote a richer and more diversified emotional life, or greater emodiversity, which in turn has been found to relate to greater psychological and physical health (Quoidbach et al.,

2014). Thus, a larger repertoire may be one pathway by which greater emodiversity is achieved. However, it is important to remember that the relationships between repertoire and positive affect in this study are correlational, and, therefore, third variable explanations cannot be excluded. For example, Isen, Daubman, and Nowicki (1987) found that positive emotions increase creative thinking. Therefore, it is possible that individuals who experience a high level and variety of positive emotions are prone to be more creative in generating new strategies, which would account for the association between positive affect and a larger repertoire. Likewise, the experience of different emotions may influence the selection of different strategies. For example, individuals who feel gratitude may be inclined to use past-focused strategies as they reflect on things for which they are thankful. If they also tend to experience hope, they may engage in planning to chart out paths to their goals. In such a case, these individuals would have a repertoire of two strategies (past focus and planning). In contrast, an individual who

42 primarily experiences gratitude, and not hope, may be inclined only toward the use of past focus. In this case, the person would have a smaller repertoire of only one strategy

(past focus). Although I found some evidence of specificity between strategies and emotions (e.g., past focused strategies with happiness, excitement, and gratitude), the lack of variability in the overall distribution of categories casts some doubt upon the legitimacy of these associations (see limitations section). Thus, the specific pathway by which repertoire and emodiversity may be related is unknown. Furthermore, to my knowledge, this study is the first to find a correlation between repertoire and emodiversity; therefore, replication is needed across different samples and ways of categorizing strategies to see if this association is robust. If there is indeed a relationship, researchers could determine whether repertoire causes greater emodiversity with a longitudinal design in which repertoire is experimentally manipulated. For example, repertoire could be manipulated by randomly assigning individuals to groups that receive training in varying numbers of strategies to implement exclusively for a brief period of their lives. The experience of positive emotion could then be assessed across time points.

Such a design would provide some insight into whether there is a linear escalation in emodiversity as the repertoire increases. If that were the case, it would suggest that a larger repertoire does indeed lead to greater emodiversity.

Finally, it should be noted that I did not find any associations between repertoire and measures of psychopathology (i.e., symptoms of depression and anxiety and BPD features). While this was unexpected and inconsistent with our previous work

(demonstrating a small inverse association between repertoire and depression; Southward

43 et al., 2017), it may be that a repertoire of strategies to upregulate positive emotion has a weaker association with negative emotion constructs, such as anxiety and depression, than with positive emotion. That is, given the independence of positive and negative emotion levels over time (Diener & Emmons, 1984), successful upregulation of positive emotion may relate strongly to measures of positive affect, but not to proxies for negative affect. Indeed, prior work suggests interventions that increase positive affect, such as gratitude exercises, may not strongly influence experiences of anxiety and depression

(Celano et al., 2017; Cregg & Cheavens, 2017). That said, the current study used a non- clinical undergraduate sample, thus relationships between repertoire and psychopathology may have been obscured. Future researchers may wish to include clinical individuals in their sample to ensure the most robust associations can be found.

Additionally, researchers should include several measures of positive functioning, such as life satisfaction and meaning in life, in addition to measures of psychopathology.

Including such measures would allow for a more thorough investigation into the connections between regulatory diversity and psychological health.

Persistence. I found an inverse association between persistence and peak PE, such that greater persistence was associated with lower peak PE. One might infer from this finding that persistence diminishes positive emotion. For example, it is possible that when trying to maintain positive emotion, doggedly sticking to the same strategy leads to

“hedonic adaptation” of emotion (Lyubomirsky, 2010), whereas varying strategies may keep the emotional experience fresh. That said, it is important to introduce several caveats here. First, as with repertoire, it may again be the case that individuals who

44 experience higher peaks of positive emotion are more creative in introducing changes within a situation (Isen et al., 1987), thus explaining why individuals who experience higher peak PE change more (i.e., persist less). Second, the inverse relationship between persistence and peak PE was only observed at the bivariate level. When controlling for repertoire and suppression, the relationship between persistence and peak PE was non- significant. Furthermore, persistence was not related to either emodiversity or mean PE at any level, meaning that persistence had no relationship with the majority of positive affect measures. For these reasons, caution is warranted in interpreting the relationship between persistence and peak PE. Moreover, I failed to find any associations between persistence and measures of psychopathology. Thus, the overall pattern of results suggests that, in the context of positive emotions, persistence is not related to psychological health. This finding contrasts with previous work that suggests the benefits of persistence when regulating negative emotions (Birk & Bonanno, 2016; Southward et al., 2017). One possible explanation for this discrepancy is that our sample tended to persist with their initially chosen responses, on average, and thus there may have been an insufficient range to find a significant relationship. In our previous study (Southward et al., 2017), the average number of changes was 2.6 per vignette, whereas the current sample changed strategies an average of only 1.14 times per vignette, with 75% of the distribution falling below 1.64 changes. This diminished number of changes in the current study is likely due to the coding scheme, with only 9 categories in contrast to the

30 of our previous work. Although there were strong reasons to limit the number of categories (see limitations below), the limited number of options of the coding scheme

45 likely constrained the number of changes participants made. Nevertheless, if the lack of association between persistence and psychological health is not an artifact of the coding scheme, a second possibility is that persistence simply does not serve a key role in regulation of positive emotions. When individuals experience negative emotions, such as fear or anger, they may be motivated to eliminate the emotion as quickly as possible.

Thus, changing strategies may be akin to a “flailing effect” (Conklin, Bradley, & Westen,

2006; Southward et al., 2017), in which one is desperately swapping out strategies in search of one that will eliminate the negative emotion. Such swapping may not allow time for any one strategy to be effective, consequently undermining attempts to reduce negative emotion. Therefore, persistence may have adaptive value when regulating negative emotions. In contrast, individuals are generally motivated to maintain and increase positive emotions. Accordingly, switching strategies to upregulate positive emotions may not have the same flailing effect, and thus may have little impact one way or the other on psychological health. In fact, the high quality ratings for the majority of the strategies in this study suggest that, with the exception of suppression, all strategies may be helpful for upregulating positive emotion to some extent. Thus, whether one persists with a strategy or switches to another strategy that is also effective may have little impact on psychological health. This is a post-hoc explanation, however, and again the correlational nature of the data prevents me from making strong conclusions. Future researchers could experimentally test the effect of persistence by providing participants with a positive emotion induction, then randomly assign them to either persist with the

46 same strategy or swap between a set of strategies. The effect of persistence on upregulating positive affect could then be assessed.

Quality and Suppression. I initially predicted the use of higher quality strategies would be associated with greater psychological health. However, given the highly restrained average quality rating, and the confounding between quality and suppression use, I replaced quality with suppression frequency in the regression models. In line with my prediction, frequency of suppression use was found to positively relate to BPD features, and to negatively relate to happiness. This pattern is consistent with the view that suppression is a generally maladaptive strategy (John & Gross, 2004), though some exceptions may exist (such as using suppression when displays of emotion may be perceived as inappropriate, as when in the presence of a superior). Furthermore, this result falls in accord with a well-established finding that individuals with BPD features tend to engage in emotional suppression, vacillating between emotional outbursts and emotional inhibition in an attempt to constrain inappropriate displays of emotion (Beblo et al., 2013). The link between BPD and suppression in this study provides some validation that participants likely responded to the vignettes in a manner indicative of real life.

Though I did not include strategy quality in the final analyses, the lack of variability in our quality measure may be meaningful in itself, for it suggests that, with rare exception, most strategies to upregulate positive emotion are judged as helpful in most situations. That is, our coders only rarely judged strategies to be of low quality, and these largely fell into the category of suppression. Accordingly, our results suggest that,

47 in contrast to coping with negative emotions (Southward et al., 2017), individuals generally report effective strategies to increase positive emotion, even if they do not devote as much conscious attention to their strategy choices (as the large number of non- specific responses would suggest). Thus, in regard to positive emotion regulation, the question may not be whether a strategy is effective or not, but how effective. We used a simple binary coding scheme to judge strategies as high or low quality. While this scheme demonstrated high reliability, it may not be sufficient to capture subtle differences in strategy effectiveness, particularly if the majority of upregulation strategies are helpful to some degree. Future researchers should include a continuous measure of strategy quality to more thoroughly assess the connection between quality and psychological health, thereby avoiding the ceiling effect observed in this study.

Part Two of the Model: Situational Context

I predicted the use of strategies in a particular context would be associated with greater psychological health. Specifically, I predicted that those who respond to ambiguous situations with reappraisal, those who used savoring in situations of a higher hedonic nature, and those who used capitalizing in situations of a more eudaimonic nature would exhibit greater psychological health. These predictions were largely unsupported, with two exceptions. Individuals who used a greater frequency of capitalization in more eudaimonically-oriented situations reported higher peak PE; additionally, individuals who used savoring in more hedonically-oriented situations exhibited a trend toward higher peak PE. These findings provide a glimpse into the possibility of a match between the features of a situation and the strategy. Nevertheless,

48 these interactions are only a starting point, and replication is needed before much can be made of the apparent strategy-situation association with peak PE.

Indeed, there was no significant association between the three context interaction terms and any other psychological health variable. A couple of factors should be considered when drawing conclusions from these null findings. First, for the interaction terms involving savoring and reappraisal (but not capitalizing), the relative lack of variability may have diminished the power to detect significant associations. The distributions for both of these interactions were highly positively skewed, with most cases clustered around the minimum values (see Figures 5 – 7). Second, the cross- sectional nature of the psychopathology measures may have obscured any situational interactions. That is, the measures of psychopathology were not specific to each situation, but rather were collected at a single time-point. Therefore, the associations I tested were between situation-specific interaction terms and measures of psychopathology that are independent of the situation. To assess strategy-situation fit with the most power, measures of health specific to the situations in question may be necessary. For example, researchers could collect daily measures of psychopathology and assess how they fluctuate with patterns of strategy use in different situations.

It is also important to note that the null findings of this study do not disconfirm the importance of context to strategy use more generally. Indeed, such a conclusion would be inconsistent with past experimental work that suggests the adaptiveness of strategies can be context dependent, e.g., the use of suppression when it would be inappropriate to display one’s emotions to another (Bonanno, Papa, Lalande, Westphal,

& Coifman, 2004). Again, an experimental design would provide more conclusive evidence of whether the adaptive value of strategies changes with context. For example, situations that vary along the dimension of ambiguity could be recreated in a lab, and participants could be randomly assigned to these situations. The amount of reappraisal used in each situation could then be assessed to see if differential effects on health and emotion measures emerge. Finally, I examined only a small set of possible situational interactions. Future researchers would do well to examine other potential matches between situations and strategies. For example, some evidence suggests that in certain contexts, downregulation of positive emotion and upregulation of negative emotion can actually be adaptive, as when happiness impairs performance on a competitive task but anger boosts it (Tamir, Bigman, Rhodes, Salerno, & Schreier, 2015). Exploring these interactions further may reveal important nuances in the adaptiveness of strategy choices.

Specific Associations of Strategy Types

My final prediction was that a greater frequency of behavioral activation, savoring, reappraisal, and capitalizing would be associated with greater psychological health. Though some significant associations were found, in general, the data do not support these predictions.

Peak PE was found to have positive associations with capitalization and savoring, as well as with planning and past-focused strategies. Although these findings seem to lend some support to my predictions, I advise caution in interpreting them due to the lack of variability across the strategy categories. Three categories – planning, expression, and behavioral activation – accounted for the majority of responses in the study (see

50 limitations below). This disproportionality between categories raises questions about the validity of the associations I found. For example, planning was significantly related to all three positive affect indices (emodiversity, β = .20; mean PE, β = .20; and peak PE, β =

.29), which may suggest that planning has a uniquely strong relationship with positive affect. Perhaps individuals who are inclined to ask questions about potential actions, weigh pros and cons, and consider all possibilities experience a wider range of positive emotions because of their broadened thinking, and vice-versa (Isen et al., 1987).

However, as Rosenthal and Rosnow (1984) have pointed out, the more observations there are for a variable of interest, the higher the likelihood of obtaining a statistically significant association. Thus, the associations between planning and positive affect may simply be a spuriously significant result of the high frequency of planning, which accounted for 46% of total responses. Likewise, the counterintuitive trend of a negative association between behavioral activation and mean PE (β = −.15) may also be a result of the large proportion of behavioral activation used, which accounted for 12% of all responses. Given the ambiguity of these associations, replication will be necessary before inferring too much from the present data, even for associations that make intuitive sense, such as that between capitalization and peak PE.

Limitations

The results of this study should be interpreted with consideration for a few limitations. First, as mentioned, a small group of strategy categories accounted for a disproportionately high number of responses. Three categories – planning, expression, and behavioral activation – accounted for 79% of total responses. A few possibilities may

51 explain this disproportionality. First, our sample may have simply preferred these strategies. Perhaps they believed them to be the most useful, or they are the most commonly adopted strategies by undergraduates. Indeed, Quoidbach and colleagues also found that capitalizing (or “expression” in the current study) was the third most used strategy out of eight among their university sample (Quoidbach et al., 2010). A second possibility is that some features of our vignettes pulled for the use of these strategies to the exclusion of others. For example, the many interpersonally-oriented situations may have led individuals to select expression strategies (21% of responses), whereas reflecting on the past (2% of responses) may be less likely to occur when in the presence of others.

A third possibility is that the lack of variability is an artifact of the coding scheme. In our previous work, we utilized a coding scheme with 30 categories (Southward et al., 2017).

In the current study, we modified this coding scheme to have only 9 categories to reduce redundancy and to improve reliability. However, it is possible that in narrowing to 9 categories, we overly simplified the classification scheme, which may have resulted in some categories capturing a wider set of behaviors than others. For example, some categories included broad definitions of behavior, such as information gathering for planning, or conveying emotion for expression, which may have inflated the number of responses identified among these categories. Conversely, categories with more specific, narrowly-defined behavior, such as reappraising thoughts for cognitive change, may have resulted in fewer responses being identified. Regardless of the reason for the disproportionality between categories, the lack of variability makes conclusions drawn from our data somewhat ambiguous. Some significant associations, such as those

52 between planning and positive affect, may be a result of the large number of instances of planning. For other tests, such as the interaction between ambiguity and reappraisal, the relatively few instances of reappraisal may have limited the power to detect an association with psychological health.

In light of these problems, future work may benefit from a coding scheme that balances specificity with breadth. That is, defining categories in a manner specific enough to make them distinct, but broadly enough to permit a sufficient number of responses to be identified within each category. A more granular coding scheme would also allow more variability in constructs such as repertoire and change, which are necessarily constrained by the number of categories available for raters to code. By using a different coding scheme, researchers could determine if the associations obtained in our study replicate, or if they are simply an artifact of the coding scheme we used. If the associations of the present study are obtained across coding schemes, that would corroborate the idea that there are important individual differences in regulation of positive emotions, as prior work has suggested (Quiodbach et al., 2010).

A second limitation of this study is the correlational nature of the data. Any observed associations, such as that between repertoire and emodiversity, do not necessarily imply causality. Likewise, the absence of association does not nullify the possibility that constructs are causally related. For example, though I did not find evidence of a match between ambiguous situations and reappraisal, one cannot therefore conclude that the use of reappraisal in ambiguous situations is of no consequence to

53 psychological health. Such a conclusion could only be drawn from an experimental design, which was beyond the scope of the present study.

A third limitation is the vignettes were hypothetical scenarios, rather than real situations drawn from participants’ lives. Accordingly, participants responded based on an estimation of what they might do or feel in these situations. That said, the ratings of positive emotion tended to match what would be expected in the situation (e.g., love with a romantic partner), and the use of suppression was associated with BPD features, both of which suggest that participants’ responses likely match real experience to some degree.

Moreover, previous work has established that vignettes-based assessments of emotion regulation can relate to a variety of health outcomes in meaningful ways (Nelis,

Quoidbach, Hansenne, & Mikolajczak, 2011; Quoidbach et al., 2010). Nevertheless, I cannot rule out the possibility that participants’ responses do not match their real-life behavior.

A related potential limitation is that we prompted participants to generate responses multiple times in each scenario. This may have encouraged participants to provide novel responses that they might not otherwise think to use. However, given that we explicitly informed participants that they could continue using the same response, and that participants tended to persist with their responses, on average, this explanation seems less likely. That said, a much-needed avenue for further research is to confirm that responses to vignettes match actual behavior. One possible route to answer this question is to assess participants’ strategy usage in real-time with ecological momentary assessment (e.g., Heiy & Cheavens, 2014). Real-time responses could then be categorized

54 and correlated with responses to the vignettes. If the responses were highly correlated, it would help to establish vignettes as valid and efficient assessments of emotion regulation skills, which may be especially useful when real-time observations are not available.

A fourth limitation is the large number of tests I conducted. This was necessary to some degree, given the number of health variables I examined and the exploratory nature of some analyses (e.g., testing associations between PH and specific strategies).

However, conducting multiple tests inflates the family-wise type I error rate, thus increasing the odds of obtaining a spuriously significant result. Although I found some significant associations that were predicted in advance and are of theoretical importance, such as that between repertoire and positive affect, the multiple tests conducted increases the uncertainty regarding the strength of these associations. This caution extends to seemingly counterintuitive findings as well, such as the negative associations between behavioral activation and positive affect. However, despite the ambiguity resulting from the number of tests, the findings may be used as a launch point to generate hypotheses for future investigations. For example, the connection between repertoire and emodiversity is novel and theoretically compelling, and fruitful work can be done to examine this relationship across other experimental and correlational settings.

A fifth limitation is that we only assessed emotion regulation strategies that are 1) consciously chosen and 2) implemented for hedonic reasons — that is, with the motivation to feel better emotionally. Regarding the first point, while many strategies may be conscious efforts, such as reappraisal, modulation of emotions can also occur unconsciously, such as instinctively looking at a friend to share in the excitement of a

55 moment (Gross & Thompson, 2007). The prompt we used asked individuals to deliberately consider what strategies they would implement. Such a method does not lend itself to assessing implicit or unconscious regulation of emotions, and consequently the findings of this study are limited to conscious emotion regulation only. Researchers wishing to assess unconscious emotion regulation are advised to use a different method, such as implicit priming of strategy choices (Mauss, Cook, & Gross, 2007). Regarding the second point, we asked participants what they would do to “maintain or improve their mood,” thus communicating a specific goal of increasing positive feelings. However, individuals often regulate emotions to accomplish instrumental goals, which may not involve feeling better in the moment. For example, one may suppress negative emotions in the presence of a superior to avoid conflict, even if such a strategy does not improve mood (English et al., 2017). Therefore, the present study only applies to hedonic motivations for regulating emotion, i.e., regulating emotions to improve one’s mood, and does not apply to regulation for instrumental reasons.

Finally, our sample was composed entirely of undergraduate students, whose mean age was 19. Prior work has indicated that older adults differ in the regulation strategies they use – for example, adults in their 60s use positive reappraisal more successfully and frequently than younger adults (Urry & Gross, 2010). Consequently, it is unclear how well the results of our study generalize to an older sample. Future researchers should assess positive emotion regulation patterns among a variety of ages.

Conclusion

While there is a large body of knowledge on negative emotion regulation, unanswered questions about positive emotion regulation abound. Such questions include which upregulatory strategies are the most effective, in what contexts, and how researchers can model optimal emotion regulation most effectively. This study represents an initial attempt at answering such questions, and, despite its limitations, it provides a starting point for future investigations. Specifically, the findings suggest that a larger repertoire of strategies is linked to greater positive affect, providing further validation of the potential benefits of regulatory diversity. Conversely, a greater use of suppression is linked to greater features of BPD, suggesting that not all strategies to regulate positive emotion are adaptive. Finally, I found limited evidence of a strategy-situation match, such that greater use of capitalization in more eudaimonically-oriented situations and greater use of savoring in more hedonically-oriented situations are associated with higher peak positive emotion. Collectively, these results imply there are some individual and situational differences in optimal regulation of positive emotion, just as for negative emotion. Nevertheless, the large number of non-specific responses suggests individuals are not used to thinking about how to regulate positive emotions most effectively.

Furthermore, much work remains to be done to replicate and extend the findings of this study. I conducted a large number of tests, a fact made even more salient by the large number of non-significant findings. Moreover, it is unclear how much the correlational findings from these vignettes will translate to an experimental or real-world setting.

Future investigations along these lines will clarify what constitutes optimal regulation of positive emotions, and move our knowledge base forward.

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Appendix A: Tables

Table 1. Text Descriptions of the Positive Scenarios and Characteristics of Each

Strongest Hedonic well- Eudaimonic well- Text Presented Ambiguity Emotion and being being Mean Intensity 1) You and your romantic 1.00 3.00 5.00 Love (90) partner just had a nice evening together at dinner and the movies. You’ve been together for a long time and really like each other. Your relationship is going pretty smoothly. At the end of the night, they turn to you and say, “You are a wonderful person. I am so glad I met you. I love you.”

2) You just got back your 3.33 2.00 3.67 Hope (73) midterm in an important class in your major. You were initially excited about this class because you’ve been dreaming of pursuing this major since high school. You did poorly on the first exam and contemplated dropping the class, but decided to stick with it. You worked a lot harder and are much more confident going into your second exam.

3) Since starting at Ohio 1.67 3.00 3.67 Happiness (82) State, you’ve been hanging out with a small group of friends you met from your dorm and classes. You feel like you’re getting along well, but you’re still unsure whether they consider you their friend. Yesterday, they invited you to an intimate gathering of their closest friends.

Continued

Table 1 Continued

Text Presented Ambiguity Hedonic well- Eudaimonic well- Strongest being being Emotion and Mean Intensity 4) You recently met someone 2.67 3.33 3.00 Excitement who you think is attractive. (81) You went out on a few dates together and had a lot of fun. Since then, you’ve been texting back and forth a lot but haven’t made future plans. They just suggested meeting up at a get-together this coming weekend. You’re not sure if you’re going as friends or more than that, but you definitely think there’s something there.

5) You’re living with your 1.33 3.67 3.67 Gratitude (91) best friend and it’s going great. They’re very considerate and notice you have been tired with finals coming up. You haven’t been feeling well so they make you a tasty snack and go to bed early so you can, too.

6) Several months ago, you 4.33 1.67 4.67 Happiness (79) applied for a competitive and prestigious summer fellowship. You just received notice that you are one of the few selected to receive the fellowship. The acceptance letter also notifies you that the fellowship requires a full-time commitment and pays less than your normal summer job. The fellowship will, however, support you over the summer.

Continued

Table 1 Continued

Text Presented Ambiguity Hedonic well- Eudaimonic well- Strongest being being Emotion and Mean Intensity 7) You arrive at the doctor’s 1.33 5.00 1.00 Interest (75) office 30 minutes early. In the waiting room, you notice a book written by your favorite author. You become totally absorbed in it. The time flies by and before you know it the receptionist is calling your name. You can’t stop thinking about the story and you want to continue reading it.

8) It’s been a long boring day 1.33 5.00 1.00 Amusement at work. During a break, you (78) browse Youtube and find a montage of dogs being found guilty in front of their owners. The dogs have eaten the cat’s food, chewed up the furniture, or eaten their owner’s homework. It’s clear the dogs are guilty. They keep averting eye contact or surrendering by rolling on their backs. You can’t stop laughing.

9) You’ve been doing poorly 2.00 2.33 3.67 Gratitude (83) on your homework for one of your classes. You went to your TA’s office hours but the material hasn’t clicked. You mention this to a friend who offers to walk you through the material. It begins to make sense and you’re really starting to enjoy it. Continued

Table 1 Continued

Text Presented Ambiguity Hedonic well- Eudaimonic well- Strongest being being Emotion and Mean Intensity 10) You’ve been desperately 1.33 3.33 3.33 Happiness (87) trying to get in shape for an upcoming Florida vacation you and your friends planned. You have been dieting and exercising meticulously. You reached your fitness goals and want to maintain this fitness level in the coming weeks. This past week has been a rough one with exams but you stuck to your fitness regimen. Your friends take notice and compliment you on your figure.

11) It’s a few days after your 1.00 3.33 4.67 Happiness (92) last final paper and you receive an e-mail from your professor. In it, he says that you have far exceeded his expectations for any student. He would like to nominate you for a writing award.

Table 2. Descriptions and Frequencies of Emotion Regulation Strategies Note. Percentages below are calculated after removal of non-specific strategies, as these were not included in the final analyses. Frequency of Use Strategy Description n (%) 1. Planning Gathering information or making preparations 2,584 (46%) for some activity.

2. Behavioral Activation Seeking out rewarding activities, hobbies, 686 (12%) socializing, exercising, etc.

3. Expression and Conveying emotions through expressive 1,152 (21%) Reciprocation (capitalizing) sharing, bodily movements, physical touch, verbal and facial expressions. Reciprocating a kind behavior toward an individual.

4. Past Focusing Reflecting on the past, replaying past events, 122 (2%) or thinking about the steps taken to arrive at the current situation.

5. Future Focusing Thinking about likely future events, the 224 (4%) consequences of the situation, or reflecting on optimism/confidence about the future.

6. Cognitive Awareness Strategies that focus on present-moment 471 (8%) (savoring) awareness (such as savoring) and/or non- judgmental acceptance of one’s thoughts or situation.

7. Cognitive Change Constructively re-construing the content of 150 (3%) (reappraisal) one’s thoughts. 8. Suppression Attempts to deny or avoid thinking about the 208 (4%) present situation, or otherwise suppress its experience in some sense (such as substance use).

9. Non-specific Any irrelevant responses that convey no 2,043 (N/A) information about emotion regulation (e.g., responding with “nothing” or “I don’t know”)

Table 3. Means, Standard Deviations, and Correlations Note. * p < .05. ** p < .01. Persistence = the number of changes multiplied by −1. IIP-BPD = Inventory of Interpersonal Problems – Borderline Personality Disorder subscale; CES-D = Center for Epidemiologic Studies – Depression scale; STAI-T = State-Trait Anxiety Inventory, Trait Version; Mean PE = mean positive emotion; Peak PE = peak emotional experience (the sum of the highest emotion ratings across vignettes).

Measure N M SD 1 2 3 4 5 6 7 8 1. Repertoire 134 5.18 1.42

77 2. Persistence 134 −1.14 .69 −.54** 3. Suppression 134 1.55 2.11 .13 −.10 4. Emodiversity 134 2.03 .05 .20* −.14 −.01 5. Mean PE 134 60.42 14.95 .17* −.07 −.11 .67** 6. Peak PE 134 944.13 163.94 .36** −.19* −.08 .28** .43** 7. IIP-BPD 127 1.21 .70 .05 −.03 .18* .11 .12 .07 8. CES-D 127 19.01 9.60 −.03 −.01 .07 .05 −.02 −.03 .55** 9. STAI-T 124 43.03 10.14 −.03 −.06 .14 .01 −.05 −.04 .63** .71**

Table 4. Multiple Regression with Repertoire, Persistence, Suppression and Symptoms of Psychopathology, Positive Affect

Note. Persistence = the number of changes multiplied by −1. Suppression = frequency of suppression. IIP-BPD = Inventory of Interpersonal Problems – Borderline Personality Disorder subscale; CES-D = Center for Epidemiologic Studies – Depression scale; STAI-T = State-Trait Anxiety Inventory, Trait Version; Mean PE = mean positive emotion; Peak PE = peak emotional experience (the sum of the highest emotion ratings across vignettes).

IIP-BPD CES-D STAI-T Predictor β SE p 95% CI Β SE p 95% CI β SE p 95% CI Intercept −.01 .09 .93 (−.19, .17) −.01 .09 .96 (−.19, .18) −.01 .09 .94 (−.19, .17)

Repertoire .04 .11 .74 (−.18, .26) −.05 .11 .67 (−.27, .17) −.11 .11 .34 (−.33, .11)

Persistence −.01 .11 .96 (−.22, .21) −.06 .11 .56 (−.28, .15) −.11 .11 .34 (−.32, .11)

78 Suppression .17 .09 .06 (−.01, .35) .07 .09 .45 (−.11, .25) .14 .09 .12 (−.04, .32)

Adjusted R2 = .01 Adjusted R2 = −.02 Adjusted R2 = .004 F(3,120) = 1.36, p = .26 F(3,120) = .31, p = .82 F(3,120) = 1.18, p = .32

Emodiversity Mean PE Peak PE Predictor β SE p 95% CI Β SE p 95% CI β SE p 95% CI

Intercept .00 .09 1.00 (−.17, .17) .00 .09 1.00 (−.17, .17) .00 .08 1.00 (−.16, .16) Repertoire .17 .10 .09 (−.03, .38) .20 .10 <.05 (.00, .41) .38 .10 <.01 (.19, .57) Persistence −.05 .10 .61 (−.25, .15) .03 .10 .79 (−.18, .23) .01 .10 .93 (−.18, .20) Suppression −.04 .09 .65 (−.21, .13) −.14 .09 .12 (−.31, .03) −.13 .08 .12 (−.29, .04) Adjusted R2 = .02 Adjusted R2 = .05 Adjusted R2 = .13 F(3,130) = 1.91, p = .13 F(3,130) = 2.22, p = .09 F(3,130) = 7.46, p = <.01

Table 5. Associations Between Specific Strategies and Symptoms of Psychopathology, Positive Affect Note. BA = behavioral activation; CA = cognitive awareness; CC = cognitive change; IIP-BPD = Inventory of Interpersonal Problems – Borderline Personality Disorder subscale; CES-D = Center for Epidemiologic Studies – Depression scale; STAI-T = State-Trait Anxiety Inventory, Trait Version; Mean PE = mean positive emotion; Peak PE = peak emotional experience (the sum of the highest emotion ratings across vignettes).

IIP-BPD CES-D STAI-T Predictor β SE p 95% CI β SE p 95% CI β SE p 95% CI Planning .02 .10 .87 (−.17, .20) .13 .10 .18 (−.06, .31) .06 .10 .52 (−.13, .25) BA .07 .09 .40 (−.10, .25) .01 .09 .89 (−.16, .19) .08 .09 .36 (−.09, .26) Expression −.08 .09 .41 (−.26, .11) −.07 .09 .47 (−.25, .11) −.01 .09 .94 (−.19, .17)

79 Past Focus −.11 .09 .20 (−.29, .06) −.11 .09 .20 (−.29, .06) −.12 .09 .17 (−.29, .05) Future Focus .04 .09 .66 (−.14, .21) .02 .09 .79 (−.15, .20) .11 .09 .21 (−.06, .28) CA −.03 .09 .71 (−.21, .14) −.09 .09 .30 (−.27, .08) −.12 .09 .19 (−.29, .06) CC .03 .09 .72 (−.14, .21) −.05 .09 .61 (−.22, .13) −.06 .09 .52 (−.23, .12) Continued

Table 5 Continued

Emodiversity Mean PE Peak PE Predictor β SE p 95% CI β SE p 95% CI β SE p 95% CI Planning .20 .09 .02 (.03, .36) .20 .09 .02 (.03, .37) .29 .08 <.01 (.13, .46) BA .00 .09 .97 (−.18, .17) −.15 .09 .08 (−.32, .02) −.06 .09 .50 (−.23, .11) Expression −.02 .09 .84 (−.19, .15) −.02 .09 .86 (−.19, .16) .18 .09 .03 (.02, .35) Past Focus .11 .09 .21 (−.06, .28) .16 .09 .07 (−.01, .33) .21 .09 .02 (.04, .38) Future Focus .09 .09 .28 (−.08, .27) .11 .09 .22 (−.06, .28) .13 .09 .13 (−.04, .30) CA .01 .09 .26 (−.07, .27) .09 .09 .31 (−.08, .26) .16 .09 .06 (−.01, .33) CC .07 .09 .42 (−.10, .24) .01 .09 .91 (−.16, .18) .09 .09 .31 (−.08, .26)

Table 6. Association between Situational Interactions and Symptoms of Psychopathology, Positive Affect Note. Reapp Ambig = reappraisal (cognitive change) x ambiguity. Sav Hed = savoring (cognitive awareness) x hedonic well-being. Cap Eud = capitalization (expression) x eudaimonic well-being. BA = behavioral activation; CA = cognitive awareness; CC = cognitive change; IIP-BPD = Inventory of Interpersonal Problems – Borderline Personality Disorder subscale; CES-D = Center for Epidemiologic Studies – Depression scale; STAI-T = State-Trait Anxiety Inventory, Trait Version; Mean PE = mean positive emotion; Peak PE = peak emotional experience (the sum of the highest emotion ratings across vignettes).

IIP-BPD CES-D STAI-T Predictor β SE p 95% CI β SE p 95% CI β SE p 95% CI Reapp Ambig .00 .09 .99 (−.18, .18) −.05 .09 .58 (−.22, .13) −.06 .09 .50 (−.24, .12) Sav Hed −.04 .09 .63 (−.22, .14) −.09 .09 .31 (−.27, .09) −.12 .09 .20 (−.29, .06)

81 Cap Eud −.08 .09 .35 (−.26, .09) −.08 .09 .36 (−.26, .10) −.02 .09 .83 (−.20, .16)

Emodiversity Mean PE Peak PE Predictor β SE p 95% CI β SE p 95% CI β SE p 95% CI Reapp Ambig .06 .09 .48 (−.11, .23) .01 .09 .90 (−.16, .18) .10 .09 .27 (−.08, .27) Sav Hed .09 .09 .30 (−.08, .26) .08 .09 .36 (−.09, .25) .16 .09 .07 (−.02, .33) Cap Eud −.04 .09 .63 (−.21, .13) −.01 .08 .92 (−.18, .16) .21 .09 .02 (.04, .37)

Table 7. Mean Emotion Ratings for each Situation

Situation Happiness Excitement Gratitude Interest

1 89 84 70 77 2 49 48 33 57 3 82 80 69 72 4 76 81 46 79 5 81 57 91 48 6 79 79 75 73 7 54 59 32 75 8 73 50 27 63 9 76 71 83 66 10 87 82 73 56

82 11 92 92 86 74

Situation Hope Pride Amusement Love

1 79 60 57 90 2 73 47 24 17 3 68 49 53 43 4 76 44 49 45 5 49 41 40 69 6 60 77 43 31 7 34 24 56 28 8 23 20 78 36 9 73 52 39 34 10 65 86 47 48 11 70 91 52 39

Appendix B: Figures

83 100

40 Intensity Rating

10 76 71 62 67 61 54 49 43 0 Happiness Excitement Gratitude Interest Hope Pride Amusement Love

Figure 1. Overall mean ratings for positive emotions, averaged across all situations. Error bars are standard deviations

Figure 2. Histogram of the distribution of ambiguity ratings across situations

Figure 3. Histogram of the distribution of hedonic ratings across situations

Figure 4. Histogram of the distribution of eudaimonic ratings across situations

Figure 5. Histogram of the interaction term for cognitive change (reappraisal) frequency times the ambiguity rating

Figure 6. Histogram of the interaction term for cognitive awareness (savoring) frequency times the hedonic rating

Figure 7. Histogram of the interaction term for expression (capitalization) frequency times the eudaimonic rating