Arts and Social Sciences, Irving K. Barber Faculty of (Okanagan)
Psychology, Department of (Okanagan)
DSpace
UBCO
Lisi, Diana Michelle
Doctor of Philosophy - PhD
University of British Columbia
Introduction: The field of emotion regulation (ER) has grown exponentially over the past two decades. Yet, the literature to date has largely focused on the independent efforts of an individual in the ER process, and social processes – specifically, interpersonal emotion regulation (IER) – are comparatively understudied. Moreover, given the ubiquity of digital technologies and the ease and convenience with which they afford opportunities for social connection, how these modes of communication may be used for IER is worthy of exploration. The current research utilized an intensive longitudinal design to better understand the extent to which in-person and digitally-mediated methods of communication may be associated with different emotional and social outcomes, as well as the role of IER in this relationship. Methods: A sample of 237 emerging adults completed brief surveys twice daily for seven days. Surveys assessed modes of communication used for IER, specific IER strategies used in the interaction, positive and negative emotions, and satisfaction with responses. Multilevel modeling assessed the association of mode of communication with support satisfaction and emotions. Multilevel mediation assessed whether these relationships could be explained by differences in IER strategies. Results: Compared to in-person communication, those who used an overall greater proportion of digital communication across the study period reported lower satisfaction and higher negative emotions. Positive IER strategies significantly mediated the relationship between mode of communication and socioemotional outcomes, both within time points and on average across the study period. That is, digital (versus in-person) communication was associated with reports of lower satisfaction and positive emotions, and this was explained, in part, by lower levels of positive IER strategies (and specifically, less laughter) in digital interactions. Conclusions: This is the first known study to examine IER over digital and in-person methods of communication, as well as provide context into the reasons why negative outcomes have previously been linked to digital communication. Future research that considers additional within- and between-person factors associated with mode of communication is required. This research additionally highlights the need to examine the burgeoning field of IER as it occurs across diverse methods of communication.
IT’S NOT (REALLY) THE MEDIUM, IT’S THE MESSAGE: INTERPERSONAL EMOTION REGULATION ACROSS IN-PERSON AND DIGITALLY-MEDIATED CONTEXTS by Diana Michelle Lisi M.A., University of British Columbia, 2016 B.A. (Spec. Hons.), York University, 2007 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Psychology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) August 2021 © Diana Michelle Lisi, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a dissertation entitled: IT’S NOT (REALLY) THE MEDIUM, IT’S THE MESSAGE: INTERPERSONAL EMOTION REGULATION ACROSS IN-PERSON AND DIGITALLY-MEDIATED CONTEXTS submitted by Diana Michelle Lisi in partial fulfillment of the requirements of the degree of Doctor of Philosophy. Dr. Susan Holtzman, Irving K. Barber Faculty of Arts and Social Sciences Supervisor Dr. Brian O’Connor, Irving K. Barber Faculty of Arts and Social Sciences Supervisory Committee Member Dr. Derrick Wirtz, Irving K. Barber Faculty of Arts and Social Sciences Supervisory Committee Member Dr. John-Tyler Binfet, Okanagan School of Education University Examiner Dr. Tom Hollenstein, Queen’s University External Examiner iii Abstract Introduction: The field of emotion regulation (ER) has grown exponentially over the past two decades. Yet, the literature to date has largely focused on the independent efforts of an individual in the ER process, and social processes – specifically, interpersonal emotion regulation (IER) – are comparatively understudied. Moreover, given the ubiquity of digital technologies and the ease and convenience with which they afford opportunities for social connection, how these modes of communication may be used for IER is worthy of exploration. The current research utilized an intensive longitudinal design to better understand the extent to which in-person and digitally-mediated methods of communication may be associated with different emotional and social outcomes, as well as the role of IER in this relationship. Methods: A sample of 237 emerging adults completed brief surveys twice daily for seven days. Surveys assessed modes of communication used for IER, specific IER strategies used in the interaction, positive and negative emotions, and satisfaction with responses. Multilevel modeling assessed the association of mode of communication with support satisfaction and emotions. Multilevel mediation assessed whether these relationships could be explained by differences in IER strategies. Results: Compared to in-person communication, those who used an overall greater proportion of digital communication across the study period reported lower satisfaction and higher negative emotions. Positive IER strategies significantly mediated the relationship between mode of communication and socioemotional outcomes, both within time points and on average across the study period. That is, digital (versus in-person) communication was associated with reports of lower satisfaction and positive emotions, and this was explained, in iv part, by lower levels of positive IER strategies (and specifically, less laughter) in digital interactions. Conclusions: This is the first known study to examine IER over digital and in-person methods of communication, as well as provide context into the reasons why negative outcomes have previously been linked to digital communication. Future research that considers additional within- and between-person factors associated with mode of communication is required. This research additionally highlights the need to examine the burgeoning field of IER as it occurs across diverse methods of communication. v Lay Summary Interpersonal emotion regulation (IER) refers to the involvement of others in one’s emotion management attempts. Given the widespread popularity of digital communication technologies, the current study examined how the use of digital versus in-person communication may impact the process and outcomes of IER. A sample of young adults filled out twice daily surveys over a one-week period, which asked about who they turned to for support, what modes of communication they used, which supportive strategies occurred, and their subsequent satisfaction with support and emotions. Results indicated that those who used a greater proportion of digital (versus in-person) communication across the study period were less satisfied with support attempts and experienced more negative emotions. The link between digital communication and worse social and emotional outcomes was explained, in part, by lower levels of positive IER strategies (especially less laughter) occurring in digital interactions. Theoretical and practical implications are discussed. vi Preface This study was approved by the Behavioural Research Ethics Board at the University of British Columbia’s Okanagan campus (H17-02243). Diana Lisi was responsible for the conceptualization and design of the study, completing and overseeing data collection, data analysis, and writing of the final dissertation. Preliminary analyses were presented at the 2019 Annual Society for Personality and Social Psychology (SPSP) Convention: Holtzman, S., Lisi, D., & Andrews, E. (2019, February). What happens when only digital communication is used to share emotions? Poster presented at the 20th Annual Society for Personality and Social Psychology (SPSP) Convention, Portland, OR. vii Table of Contents Abstract .................................................................................................................................. iii Lay Summary ......................................................................................................................... v Preface .................................................................................................................................... vi Table of Contents ................................................................................................................. vii List of Tables ......................................................................................................................... ix List of Figures ........................................................................................................................ xi Acknowledgements............................................................................................................... xii Dedication ............................................................................................................................ xiv Introduction ............................................................................................................................ 1 Emotion Regulation ............................................................................................................. 2 Social Support as a Strategy to Cope with Distress ............................................................. 5 Interpersonal Emotion Regulation as a Unique Construct ................................................... 6 Effectiveness of Interpersonal Emotion Regulation Strategies............................................ 9 Emotion Regulation in a Digital World ............................................................................. 12 Theories of Computer-Mediated Communication ............................................................. 13 Empirical Findings for the Role of Digital Communication.............................................. 18 Digitally Supportive Behaviours ........................................................................................ 21 Limitations of Existing Research ....................................................................................... 24 The Current Research ......................................................................................................... 26 Research Aims and Hypotheses ......................................................................................... 27 Methods ................................................................................................................................. 34 Participants and Sample Size ............................................................................................. 34 Procedure ........................................................................................................................... 35 Materials............................................................................................................................. 38 Measures ............................................................................................................................ 39 Statistical Analyses ............................................................................................................ 41 viii Multilevel Modeling ....................................................................................................... 43 Multilevel Mediation ...................................................................................................... 46 Results ................................................................................................................................... 52 Data Screening ................................................................................................................... 52 Response Rates .................................................................................................................. 54 Sample Characteristics ....................................................................................................... 55 Research Aim 1: Descriptive Characteristics of Daily IER ............................................... 55 IER Strategies ................................................................................................................ 55 Sources of Support ......................................................................................................... 57 Modes of Communication .............................................................................................. 58 Research Aim 2: Association Between Mode of Communication and Satisfaction with Support and Emotions ..................................................................................................... 60 Research Aim 3: The Mediating Role of IER Strategies ................................................... 63 Positive IER Strategies as a Mediator ........................................................................... 64 Negative IER Strategies as a Mediator .......................................................................... 69 Exploratory Analyses ......................................................................................................... 70 Discussion .............................................................................................................................. 76 Study Limitations ............................................................................................................... 89 Future Directions................................................................................................................ 91 Theoretical and Clinical Implications ................................................................................ 94 Conclusion ......................................................................................................................... 96 References ............................................................................................................................. 98 Appendices .......................................................................................................................... 136 Appendix A – Consent Form ........................................................................................... 136 Appendix B – Daily Assessments .................................................................................... 139 Appendix C – Parameter Estimates for Exploratory Analyses ........................................ 142 ix List of Tables Table 1 Sample Characteristics for n = 237 ............................................................................56 Table 2 Descriptive Statistics for Positive and Negative IER Strategies ................................57 Table 3 Parameter Estimates for Research Aim 2: Mode of Communication and Satisfaction, Positive Emotions, and Negative Emotions ......................................................................63 Table 4 Parameter Estimates for Model 4: Mode of Communication (X), Total Positive IER Strategies (M), and Satisfaction with Support (Y)............................................................67 Table 5 Parameter Estimates for Model 5: Mode of Communication (X), Total Positive IER Strategies (M), and Total Positive Emotions (Y)..............................................................67 Table 6 Parameter Estimates for Model 6: Mode of Communication (X), Total Positive IER Strategies (M), and Total Negative Emotions (Y) ............................................................68 Table 7 Parameter Estimates for Model 7: Mode of Communication (X), Total Negative IER Strategies (M), and Satisfaction with Support (Y)............................................................71 Table 8 Parameter Estimates for Model 8: Mode of Communication (X), Total Negative IER Strategies (M), and Total Positive Emotions (Y)..............................................................71 Table 9 Parameter Estimates for Model 9: Mode of Communication (X), Total Negative IER Strategies (M), and Total Negative Emotions (Y) ............................................................72 Table C1 Parameter Estimates: Mode of Communication (X), “Listened to My Problems” (M), and Satisfaction with Support (Y) ..........................................................................142 Table C2 Parameter Estimates: Mode of Communication (X), “Listened to My Problems” (M), and Total Positive Emotions (Y) ............................................................................142 Table C3 Parameter Estimates: Mode of Communication (X), “Listened to My Problems” (M), and Total Negative Emotions (Y) ...........................................................................143 x Table C4 Parameter Estimates: Mode of Communication (X), “Pointed Out My Positive Characteristics” (M), and Satisfaction with Support (Y) ................................................143 Table C5 Parameter Estimates: Mode of Communication (X), “Pointed Out My Positive Characteristics” (M), and Total Positive Emotions (Y) ..................................................144 Table C6 Parameter Estimates: Mode of Communication (X), “Pointed Out My Positive Characteristics” (M), and Total Negative Emotions (Y) ................................................144 Table C7 Parameter Estimates: Mode of Communication (X), “Gave Me Advice” (M), and Satisfaction with Support (Y) .........................................................................................145 Table C8 Parameter Estimates: Mode of Communication (X), “Gave Me Advice” (M), and Total Positive Emotions (Y) ...........................................................................................145 Table C9 Parameter Estimates: Mode of Communication (X), “Gave Me Advice” (M), and Total Negative Emotions (Y) ..........................................................................................146 Table C10 Parameter Estimates: Mode of Communication (X), “Made Me Laugh” (M), and Satisfaction with Support (Y) .........................................................................................146 Table C11 Parameter Estimates: Mode of Communication (X), “Made Me Laugh” (M), and Total Positive Emotions (Y) ...........................................................................................147 Table C12 Parameter Estimates: Mode of Communication (X), “Made Me Laugh” (M), and Total Negative Emotions (Y) ..........................................................................................147 xi List of Figures Figure 1 Summary of Hypotheses for the Mediating Role of IER Strategies in the Association between Mode of Communication and Study Outcomes .................................................32 Figure 2 Sample 1-1-1 Deconflated Multilevel Mediation Model for the Current Study ......48 Figure 3 Main Support Source Reported Across n = 929 Interactions ...................................58 Figure 4 Modes of Communication Used with Main Support Source ....................................59 xii Acknowledgements First and foremost, I must acknowledge the unwavering support and dedication from my supervisor, Dr. Susan Holtzman. It has been an absolute pleasure to grow under your mentorship and guidance. Thank you for always supporting my research ideas, matching my levels of excitement during moments of success, and supporting me through periods of uncertainty or exhaustion. I am so appreciative of your guidance, commitment, encouragement, and friendship. Special thanks are extended to my committee members, specifically, Drs. Brian O’Connor and Derrick Wirtz, who have supported me and this project over the past few years. Thanks are also extended to my external committee members, Drs. John-Tyler Binfet and Tom Hollenstein, for their helpful insights and feedback. I would also like to acknowledge the volunteers and research assistants in the Health Psychology Lab. Specifically, thanks are given to Maia Terran, Makenzie Houston, Yun-Lin Wang, Yun-Han Wang, Koby Anderson, Davis Wiggs, and Christina Holtjer for their help with recruitment and data collection. I am grateful for and appreciative of the financial support provided by the Social Sciences and Humanities Research Council, the University of British Columbia, and the Psychology of Technology Institute. No milestone can be accomplished without the social and emotional support of those around them. I have been so fortunate to have a fantastic cohort of colleagues and friends with me along this journey. To Marina Le and Carmela White – you have been standout features of this incredible part of my life. I will fondly look back at my time in graduate school in large part because of your friendship. Every ounce of kindness, encouragement, and xiii laughter you have provided will forever be imprinted on my heart. Thank you both for so many happy memories and for getting me through this process relatively unscathed. The never-ending support and cheerleading from my family and friends have not gone unnoticed. I am so grateful for each and every reminder of how far I have come and how much I have to be proud of. I cannot imagine being here without you all in my corner. Attempting to properly acknowledge and thank my parents in a restrictive few lines is perhaps the most challenging part of this milestone. There simply is too much to say that can even remotely express the amount of appreciation I feel towards everything they have given me to date. In a shocking twist of events, I am finally speechless. Mommy and Daddy – thank you for every word of encouragement, vote of confidence, care package, cup of coffee, and glass of wine. I will carry gratitude towards all that you have done for me for the rest of my life. You have both played a large part in my success, and for that, you deserve all the recognition in the world. xiv Dedication This one’s for me. “One day, in retrospect, the years of struggle will strike you as the most beautiful.” -Sigmund Freud 1 Introduction Social and emotional development begins in infancy and continues across the lifespan. Core competencies of social and emotional learning include the ability to be self-aware of one’s emotional experience and the adaptive regulation of one’s emotions (Collaborative for Academic, Social, and Emotional Learning [CASEL], 2021). Research has shown that the ability to effectively manage one’s emotions is an important indicator of adaptive psychological and social functioning (Aldao et al., 2010; English et al., 2012). As emotions arise, individuals may engage in intra- or interpersonal processes to regulate these (often distressing) emotional states. Just as individuals strengthen core competencies of their own abilities for self-awareness and self-management of their emotional experiences, they also develop and hone skills aimed at social awareness and relational maintenance (CASEL, 2021). As interpersonal relationships are central determinants of emotional health and overall well-being, interactions that involve social efforts at coping or support may help to facilitate adaptive management of emotions. As society increasingly continues to evolve in a digital world, digital or computer-mediated modes of communication are ubiquitous methods for maintaining connection with others. However, research on the role of social regulation processes across these “newer” ways of interacting with others is lagging, especially as technology continues to evolve at a rapid pace. Within the broader context of understanding these methods of communication further, more insight regarding its prevalence, utility, and effectiveness in facilitating social and emotional functioning is required. The overall objective of the present study was to unite the otherwise distinct and broader fields of emotion regulation, social support, and computer-mediated communication in an attempt to fill a gap in our current understanding of how 2 relationships can help individuals regulate their feelings of distress, and how digital methods of communication measure up to in-person methods in doing so. An overview into these broader literatures is first provided and is followed by a more in-depth analysis of the role of mode of communication in the interpersonal regulation of emotions. Emotion Regulation Emotion regulation (ER) refers to the management of an individual’s emotions in terms of which emotion is being experienced, when the emotion is experienced, and how the emotion is experienced or expressed (Gross, 1998). ER is a process that is utilized in order to attenuate, enhance, or maintain the magnitude, intensity, or duration of an emotion, either via implicit or explicit processes (Gross, 2015; Webb et al., 2015). It is considered successful when an individual manages their emotions so that they are in line with their goals of changing their emotional trajectory (Gross et al., 2011). One of the most widely referenced frameworks for understanding ER is Gross’ (1998; 2015) process (and extended process) model. The process models stipulate that individuals engage in two valuation systems, one responsible for emotion generation, and the other for regulation. Four stages of ER have been proposed: identification of the need for ER, selection of strategies to assist with ER, implementation of said strategies, and ongoing monitoring of their effectiveness (Gross, 2015). Within this framework, Gross purports that an individual can modify their emotional experience via one of five families of ER strategies (Gross, 1998; 2015). Antecedent-focused strategies, or those that occur before an emotional response has been triggered, include strategies aimed at situation selection, situation modification, attentional deployment (e.g., 3 distraction), and cognitive change (e.g., reappraisal). Response-focused strategies, on the other hand, are aimed at changing a physiological, experiential, or behavioural response after an emotion has already been triggered (Gross, 1998). This family of strategies is more broadly known as response modulation, and includes emotional suppression and behaviours such as using drugs and alcohol, exercise, or social support to help cope with an unpleasant emotional state. Theoretically, regulation that occurs earlier in the ER process (i.e., before an emotional response is triggered) is more efficacious, and much research has supported the role of antecedent-focused strategies (e.g., Sheppes & Gross, 2012). However, response-focused processes, and response modulation strategies, in particular, warrant closer attention, especially as emotion responses may be modulated in a number of ways (Gross, 1998). Interest in regulatory processes has received much attention to date (Gross, 1999). Since contemporary approaches to ER emerged in the 1990’s, the field has grown exponentially (Gross, 1999; 2015). A plethora of research has now consistently shown that ER has important implications for a wide variety of outcomes across the lifespan (Gross, 2015). For instance, there is strong evidence to suggest that children who are better able to regulate their emotions achieve greater academic success (Eisenberg et al., 2010; Graziano et al., 2007) and report better relationships with their peers (Calkins et al., 1999; Mischel et al., 2011). In adolescence and young adulthood, individuals with better ER skills have a lower risk of developing a wide variety of mental health problems (e.g., Campbell-Sills & Barlow, 2007; Dvorak et al., 2014; Harrison et al., 2010). In fact, research has shown that emotion dysregulation is related to a variety of clinical conditions, including mood and anxiety (Aldao et al., 2010; Campbell-Sills et al., 2014; Joormann & Siemer, 2014), trauma-related (Ehring & Quack, 2010), eating (Svaldi et al., 2012), and alcohol- and substance-use (Kober, 2014) 4 disorders. Researchers have started to utilize the process model to identify the key points along the ER trajectory where clinical difficulties may arise. For instance, individuals may over-interpret their emotional experiences (e.g., anxiety disorders), utilize maladaptive strategies (e.g., self-harm), or fail to monitor the utility of a strategy (e.g., continued rumination in depression; Sheppes et al., 2015). Clearly, ER represents a critical construct in understanding how individuals respond to and manage their emotional experiences. However, the ER literature has largely conceptualized ER as an intrapersonal, independent process. Extant ER research has focused – and perhaps over-focused – on the role of two strategies in particular: cognitive reappraisal and emotional suppression, with hundreds of experimental studies comparing their effectiveness (Webb et al., 2012). Yet research also suggests that people tend to use a variety of strategies to manage their emotions (Aldao & Nolen-Hoeksema, 2013; Grommisch et al., 2019), and those that have been most studied (i.e., cognitive reappraisal and emotional suppression) are not necessarily the most commonly used in people’s daily lives. For example, in a daily diary study assessing the use of 40 different ER strategies over a ten-day period, participants used an average of 15 regulatory strategies in response to their negative emotions (Heiy & Cheavens, 2014). What’s more, other strategies, including social support, were more frequently used and more impactful on negative emotions compared to reappraisal and suppression. This research not only highlights the need for consideration of a wider range of ER strategies than has previously been examined, it also underscores the importance of examining strategies that are social in nature. Indeed, one criticism of Gross’ process model is that it fails to consider the social influences that may be involved in the ER process (Webb 5 et al., 2015). This is especially surprising given that humans have a basic motivation to seek social contact during periods of stress (Schachter, 1959), and that many emotions, such as guilt, pride, and envy, are inherently social in and of themselves (Buck, 2014; Parkinson, 1996; Tangney & Salovey, 1999; van Kleef et al., 2016). While Gross alludes to the fact that social ER processes exist (Gross, 2015), a more intricate understanding of the interpersonal ER process remains to be seen (Parkinson, 2015; Webb et al., 2015). Social Support as a Strategy to Cope with Distress The study of social relationships as a resource for managing difficult emotions and situations has a long history in the stress and coping literature (Cohen & Wills, 1985; Cutrona & Russell, 1987). Seeking support during times of stress has been conceptualized as a coping strategy that can elicit various types of assistance, including emotional, informational, appraisal, and instrumental support (House & Kahn, 1985). Within these categories are specific supportive behaviours. For example, emotional support can involve expressing sympathy, offering words of comfort, and listening to someone’s problems. A related construct that lies at the intersection of the social support and ER literature is the social sharing of emotions. Social sharing of emotions involves discussing emotional experiences or episodes with others and is thought to occur repeatedly across emotion types and valence (Rimé, 2009). In fact, social sharing has been considered an integral part of the emotional experience altogether (Rimé, 2017), with it occurring soon after an emotion has been experienced (Rimé, 2009; Rimé et al., 1991). Motivations to share one’s experiences can differ depending on emotional valence, with positive emotions shared as a way to capitalize on and inform others of one’s experience, and negative emotions shared as a way 6 to vent, enhance bonds, and receive understanding and social support from others (Delfosse et al., 2004). Evidence of support seeking and social sharing following distressing events has been well-documented (e.g., Luminet, Bouts et al., 2000; Rimé et al., 1992), and a wealth of research supports the idea that social sharing and social support from others can be highly beneficial in managing one’s emotions and for overall health and well-being (Gable et al., 2004; Rimé, 2009; Ryan et al., 2005). When social relationships are considered within the formal study of ER, researchers may assess social support seeking as a strategy to manage emotions. However, as highlighted by Lennarz and colleagues (2019), studies of social support as an ER strategy tend to operationalize support in diverse and inconsistent ways, including asking for advice, spending time together, and co-ruminating; all which appear to vary in effectiveness. Indeed, even well-intentioned support is not always effective, and can sometimes yield adverse effects (Feng, 2014; Lehman et al., 1986; MacGeorge et al., 2008; Wortman & Lehman, 1985). Thus, without examining the nature of specific supportive behaviours within ER attempts, there remain lost opportunities for a more comprehensive understanding of how interpersonal interactions may be uniquely influencing the ER process. Interpersonal Emotion Regulation as a Unique Construct Compared to decades of research on ER, the field of interpersonal emotion regulation (IER) is still in its nascent stage of development. IER is broadly conceptualized as a regulatory process in which another person is involved in an individual’s ER attempt. IER has only begun to garner more attention, and this may be because it is conceptually similar to, and overlaps with, well-known constructs, such as social support and coping; two bodies 7 of research in their own right. Because a clear and specific definition of IER is lacking, it remains difficult to distinguish IER from these related constructs (Compas et al., 2014, 2017; Dixon-Gordon, Bernecker, & Christensen, 2015; Turliuc & Jitaru, 2019). For instance, coping and social support are similar to IER in that they are both regulatory responses and are both enacted to change an emotional response. Both processes also typically occur in response to stressful circumstances. However, there are some nuances to differentiate these constructs. First, whereas processes like coping occur exclusively in response to stress, IER can occur across a much wider range of situations and emotions. That is, rather than solely focusing on ways to decrease or down-regulate stress and negative emotions, IER can be examined with respect to the regulation of both negative and positive emotions (Sheppes & Gross, 2012; Tamir, 2009). Second, processes like coping tend to involve controlled and purposeful efforts. While IER can involve similar efforts, it can additionally include implicit, automatic, or unconscious processes (Compas et al., 2014). Third, constructs such as coping and social sharing are generally understood from the perspective of the individual experiencing the distress or emotion. Conversely, social support tends to focus on the regulation of others’ emotions (Dixon-Gordon, Bernecker, & Christensen, 2015) via emotional, instrumental, or informational support. Compared to coping and social support, IER, on the other hand, is conceptualized as an interpersonal process that can be aimed at regulating one’s own emotions (i.e., intrinsic processes) as well as others’ emotions (i.e., extrinsic processes). For that reason, in addition to examining intrinsic and extrinsic processes independently, IER can also be examined as a dyadic or cyclical process, where both members of a dyad hold their own regulatory goals that may interact with or influence one another. Coping, social support, 8 and by extension, social sharing, can therefore be thought of as overlapping constructs within the broader process of IER (Compas et al., 2014; Dixon-Gordon, Bernecker, & Christensen, 2015), rather than parallel or synonymous processes. While the literature has called for a more comprehensive understanding and examination of IER (e.g., Barthel et al., 2018; Compas et al., 2017; Dixon-Gordon, Bernecker, & Christensen, 2015; Turliuc & Jitaru, 2019), definitions and terminology remain inconsistent across studies and most coping, social support, and IER research continue to be conducted as distinct bodies of literature (Compas et al., 2017; Dixon-Gordon, Bernecker, & Christensen, 2015). In an attempt to synthesize these “pieces” of various social regulation processes into a unifying construct of IER, seminal work by Zaki and Williams (2013) has begun to lay a foundation for future examination of the IER process. Using a dyadic perspective, Zaki and Williams’ (2013) framework attempts to organize the complex process of IER along two orthogonal factors. The first factor considers which member of the dyad’s goals for ER are being considered. Intrinsic regulation involves situations where one’s own emotions (Person A) are the focus of IER, whereas extrinsic regulation centres around Person B’s goals of regulating either their own or Person A’s emotions. The second factor considers processes that support IER, or the extent to which ER is dependent on the response of others. For example, social sharing would be deemed response-independent if the mere act of sharing (i.e., venting) helps to manage a person’s emotions, whereas seeking emotional support might be deemed response-dependent if an individual is counting on their partner’s efforts to manage their emotions. Further, inherent to the study of intrinsic response-dependent IER is how individuals appraise the effectiveness of the responses of others in managing their own emotions, as it is the perception of support that has been continually associated with positive 9 outcomes (e.g., Barrera, 1986; Cohen & Wills, 1985). Zaki and Williams’ (2013) framework of IER therefore deems that IER processes can occur – and be assessed – in one of four ways, dependent on these two orthogonal factors. Intrinsic response-dependent processes, which closely resemble the social support process (and the importance of perceived support), may be particularly worthy of exploration. Not only would this allow researchers to closely examine the specific IER strategies that occur within interpersonal interactions, it would also provide opportunities for assessment of the IER strategies’ role in predicting social and emotional outcomes. Effectiveness of Interpersonal Emotion Regulation Strategies As highlighted above, the focus of the IER literature has been on developing a theoretical basis and attempting to distinguish it from similar social regulatory constructs (e.g., Compas et al., 2014; Dixon-Gordon, Bernecker, & Christensen, 2015; Niven, 2017; Turliuc & Jitaru, 2019; Zaki & Williams, 2013). However, a small but growing body of research has attempted to move beyond the theoretical underpinnings to begin examining the specific strategies that comprise IER interactions, people’s motivations behind using these strategies, and the influence of IER on social and emotional outcomes. Given that the theoretical and psychological understanding of IER is still evolving, attempts at creating a classification of IER strategies (and associated measurement tools) has yielded a diverse assortment of possible strategies. Whereas some approaches have attempted to map strategies onto the (intrapersonal) ER process model (Little et al., 2012), others have developed new classifications based on comparable social regulation processes (Niven et al., 2009, 2011). 10 Though a review of these studies is beyond the scope of the current research, two measures of IER have received the most attention to date. In a preliminary attempt at understanding concrete interpersonal strategies, Niven and colleagues (2009) created a classification of 378 strategies reported across various areas of literature (e.g., social support and caregiving). Their conceptualization yielded two main categories: those that centred around the motives of the support provider (i.e., strategies aimed at either improving or worsening their partner’s affect) and those that represented the means with which regulation is achieved (i.e., strategies that involve engagement with the situation or emotion versus those that are more relationship-focused). This classification was eventually developed into the first known measure of IER, the Emotion Regulation of Others and Self (EROS; Niven et al., 2011). A second self-report questionnaire was later developed to assess the ways in which people regulate their own emotions through others as well as their reasons for doing so (Hofmann et al., 2016). The authors similarly identified over 400 reasons for engaging in IER. Factor analyses ultimately revealed four main categories of strategies: enhancing positive affect (i.e., seeking out others to increase positive affect), perspective taking (i.e., using others to remind you that others have it worse), soothing (i.e., the provision of comfort and support), and social modeling (i.e., modeling your behaviours based on others’ input). Though psychometric properties in both measures were strong, little empirical research has evaluated the prevalence or effectiveness of these strategies. Recent evidence suggests that IER is linked with favourable social and emotional outcomes. Broadly speaking, Niven and colleagues have found that affect-improving strategies positively influenced ratings of relationship quality (Niven et al., 2019) and promoted increases in popularity over time (Niven et al., 2015). High levels of social 11 connection were also found among those who, not only had higher tendencies to engage in IER, but also among those who believed that doing so would be efficacious in managing their emotions (Williams et al., 2018). More specifically, IER interactions that provide emotional comfort, make someone laugh, or capitalize on positive experiences have been associated with positive emotions and well-being (Chan & Rawana, 2021; Gable et al., 2004; Lisi, 2016), as well as lower levels of depression (Ray-Yol et al., 2020). Interactions where others facilitated a targets’ perspective taking or enhancement of positive emotions have also been associated with greater well-being, fewer internalizing symptoms (Chan & Rawana, 2021), and greater relationship quality (Chan & Rawana, 2021; Gable et al., 2004). In contrast, research has identified some IER strategies that are associated with unfavourable outcomes. For instance, social modeling (i.e., altering one’s behaviours after interpersonal input), a proposed category of IER strategies (Hofmann et al., 2016), was associated with lower well-being and more internalizing symptoms (Chan & Rawana, 2021). When interaction partners engage in brooding, rumination, or expressive suppression, more symptoms of psychopathology have been reported (Christensen et al., 2020; Horn & Maercker, 2016). Similarly, engaging in venting and reassurance seeking to manage emotions was associated with more negative emotions, greater symptoms of anxiety, and generally more interpersonal problems (Dixon-Gordon et al., 2018). Thus, there is a greater need to assess and measure the prevalence, use, and effectiveness of IER strategies in order to better understand the role they play on social and emotional outcomes. Specifically, it is not known how prevalent IER is on a daily basis, the specific IER strategies that occur within interpersonal interactions, or the role of these IER 12 strategies in terms of emotions or satisfaction with the supportive response. From an intrinsic response-dependent framework of IER, identifying the occurrence of various IER strategies and the extent to which they predict emotional and satisfaction outcomes will assist in furthering our understanding of the IER process, more generally. Emotion Regulation in a Digital World The current working framework for IER based on Zaki and Williams’ (2013) conceptualization has provided a foundation for researchers to more concretely assess IER and its strategies. Yet, there is an underlying assumption in both the ER and IER literatures that these regulatory process attempts are made in-person. In contrast, the extent of its use across computer-mediated communication (CMC) has received limited attention. CMC is a broad term that encompasses any communication occurring between two digital devices, such as laptops, tablets, and smartphones. This can include a wide variety of avenues for interacting with others, such as via text messaging, email, chat forums, and social networking sites. As of 2019, 96% of American adults owned a cellphone and over three-quarters of those owned a smartphone, specifically (Pew Research Center, 2019b). Further, nearly half of Americans have stated that they could not live without their smartphone (Pew Research Center, 2017). Individuals are thus perennially accessible to others via their mobile devices (Chayko, 2014; Kushlev et al., 2019; Ling, 2012). The ease, accessibility, and convenience with which people can now connect with their social networks makes it an attractive option for seeking and providing social support (Small & Sukhu, 2016). As the use of CMC has risen, various theories have emerged to understand whether this impacts the nature of one’s 13 interactions and how it may do so. An overview of CMC theories that may specifically inform the study of IER is provided below. Theories of Computer-Mediated Communication Even before the internet was introduced to the general public, theorists from diverse disciplines have sought to understand how computers and other technologies could be used for interpersonal interaction. Most relevant to the current research are a number of theoretical frameworks that have aimed to understand how the characteristics of CMC (in comparison to in-person contexts) may influence how individuals express themselves, relate to others, and adapt to communication limitations (see Walther, 2011). CMC research has been a largely polarized field characterized by two prominent streams of thought: one that focuses on the deficits inherent in digital communication, and the other which looks past these deficits to highlight a human need for connection, individuals’ own abilities for adaptability, and how CMC can help serve to maintain relationships over time. The first stream of thought involves the focus on CMC being largely devoid of nonverbal cues. Because CMC, and particularly text-based communication, is limited in this way, it is arguably less capable (compared to in-person interactions) of transmitting important information between users. This view of CMC is subsumed by a set of theories referred to as the cues-filtered-out (CFO; Culnan & Markus, 1987) theories of communication. CFO theories argue that text-based CMC is inferior to in-person communication because it lacks cues such as eye contact, tone of voice, facial expressions, gestures, and body language; all of which provide important information about one’s 14 experiences and emotional state. Two specific CFO theories warrant more detailed discussed here: social presence theory and media richness theory. Social presence theory (SPT; Short et al., 1976) holds that different types of media are able to communicate a sense of interpersonal presence via the transmission of various nonverbal cues. The number of cues transmitted over a given technology is thought to impact the perceived presence of an individual in an interaction as well as the salience of the interpersonal relationship. Modes of communication with greater bandwidth (i.e., those with a greater ability to transmit multiple cues), such as in-person communication, are better able to convey signals of interpersonal warmth and involvement through nonverbal behaviours. In contrast, communication with limited bandwidth, such as text messaging, lacks the ability to transmit these signals, and may contribute to perceptions that one’s communication partner is less involved or engaged in the interaction, as less socioemotional information is exchanged (Rice & Love, 1987). A number of studies have examined the level of social presence in various mediated contexts and indeed concluded that higher levels of social presence are found within in-person interactions compared to computer-mediated contexts (see C. Oh et al., 2018). Since it was first proposed, however, SPT has evolved from being overly focused on the properties of the medium and instead has begun placing a greater emphasis on the psychological and behavioural factors that influence how social presence can be construed or fostered across media (Biocca et al., 2003; Fester & Cowley, 2018). This includes consideration of user characteristics (e.g., traits, communication skills, skill level with the medium), the context of the interaction (e.g., topics discussed, nature of the relationship, privacy), and the ways in which the medium is used (e.g., synchronicity, interactivity, perceiving presence cues; Kehrwald, 2008; Petrocchi et al., 2020; Tu, 2000). 15 Another prominent CFO perspective is media richness theory (MRT; Daft & Lengel, 1986). MRT holds that certain media are inherently more desirable because more abundant cues can be transmitted over increased bandwidth. Media richness is thought to exist on a continuum, with in-person communication viewed as a gold standard of “rich” communication, and posters, bulk mail, or advertisements at the leanest end of the spectrum (Daft et al., 1987). According to MRT, richer forms of communication are most efficient and effective for relational development and maintenance for a number of reasons. First, rich forms of communication are typically synchronous in nature and allow for verbal and nonverbal information to be transmitted simultaneously, facilitating opportunities for rapid and immediate feedback between users. Compared to leaner methods, richer forms of communication also allow language to be more natural or conversational. Central to MRT is the notion that there should be an ideal match between the richness of a medium and the equivocality of the message. That is, the greater the number of possible interpretations for a message (i.e., the greater the ambiguity), the richer the method required for the sake of efficiency and effective communication. Communication of emotionally complex or arousing matters, for instance, is therefore theoretically more appropriate for richer compared to leaner media (Daft & Lengel, 1986). As with other CFO theories, MRT has been criticized for not acknowledging that there may be instances when individuals prefer or only have access to leaner media. Those who are primarily concerned about their self-presentation, for example, may prefer and select leaner channels to increase equivocality and ambiguity of their messages (O’Sullivan, 2000). In long distance relationships, digital or text-based communication may be the only option to connect with others. In fact, electronic propinquity theory (Korzenny, 1978) states that when 16 no other options for connection are available, individuals still feel a sense of closeness with others when using CMC, and this has been supported empirically (Walther & Bazarova, 2008). While CFO theories have largely painted an unfavourable view of digitally-mediated technologies, a contrasting approach has taken a more optimistic perspective. Rather than viewing CMC as inherently flawed because of its limited nonverbal cues, other theories have contended that technologically-mediated discourse can be just as effective, personal, meaningful, and rich as in-person communication despite its lack of nonverbal cues. Social information processing (SIP) theory (Walther, 1992) is among the most prominent. SIP theory holds that people’s motivations to develop (or maintain) favourable interpersonal interactions exist regardless of the medium they are using. In situations where nonverbal cues cannot be exchanged, individuals use the cues that are available to them and adapt their communication style to fit that medium. For instance, users of text messaging can still exchange social information through the content and style of their messages (e.g., punctuation marks, capitalization), as well as through the timing with which messages are exchanged. They can also infuse emotions into text through emotion icons (i.e., ‘emoticons’), video or audio messages, or graphic interchanges (i.e., gifs). That information helps to develop a cognitive model for one’s partner as well as their relationship. Though digital communication may take longer (i.e., to read and compose messages), SIP theory argues that, with time, digital communicators are just as able to form interpersonal impressions of each other and work towards relational development, often doing so in ways that emulate in-person contexts (Walther & Parks, 2002). In fact, over time, as individuals become more familiar with and gain more experience using a medium, their perception of its richness and 17 ability to foster closeness with another person increases (i.e., channel expansion theory; Carlson & Zmud, 1999). Therefore, the absence of nonverbal information does not necessitate unfavourable outcomes; instead, the same degree of intimacy can be reached, albeit over a longer duration. The hyperpersonal model of CMC (Walther, 1996), an extension of SIP theory, holds that CMC can actually result in interactions that are more desirable, intimate, and that include more self-disclosure compared to in-person interactions. In the absence of nonverbal cues, receivers “fill in the gaps” to form impressions of their interaction partner based on any available cues (e.g., paralinguistic cues) or past social relations with that or other similar individuals (Lea & Spears, 1992; Walther, 2011). To their advantage, users of text-based CMC can also focus their cognitive resources on constructing or refining messages in order to ensure that they are thoughtful and in line with their intended goals for that conversation. This contrasts with in-person interactions, where individuals are tasked with splitting their cognitive abilities between managing verbal responses and nonverbal behaviours, while also maintaining appropriate conversational flow. Indeed, experimental research has suggested that, compared to in-person contexts, when there are reduced social cues (i.e., in CMC), people may be better able to recognize the degree of support in a message (i.e., their cognitive resources are less burdened), and in turn, evaluate it more positively (Rains et al., 2016). For these reasons, CMC can be a preferential and effective way of communicating with others, especially for those who are more introverted (Dolev-Cohen & Barak, 2013) or prone to anxiety (Reid & Reid, 2007). In sum, numerous theories have attempted to postulate the ways in which CMC differs from in-person communication. While some theories hold that CMC will always be 18 inferior to digital methods, competing perspectives suggest that individuals are able to utilize digital technologies in adaptive ways to make it just as (or more) effective than in-person contexts for sharing information and fostering social connection. Empirical Findings for the Role of Digital Communication The long history of conflicting CMC theories is mirrored in the empirical literature. Across cross-sectional, experimental, and longitudinal studies, findings regarding the relative benefits and costs of CMC have also been mixed, with some studies suggestive that CMC, such as texting or social media, are associated with positive affect and relational satisfaction, and others demonstrating that, especially in comparison to its in-person counterpart, it yields poorer socioemotional outcomes. Benefits of CMC include its ease and convenience of use and its ability to provide increased, expanded, and private access to others (see Rains & Wright, 2016). Research has demonstrated that people go online to recruit support from their social networks when they need assistance (Boase et al., 2006) and that this can serve to provide emotional relief (Dolev-Cohen & Barak, 2013) and bolster emotional outcomes. For example, users of Facebook receive more emotional support, companionship, and instrumental help from their social ties compared to those who do not use Facebook (Hampton et al., 2011). Support provided over social media has also been also associated with benefits, including fewer depressive thoughts and feelings (Cole et al., 2017), and a host of positive mental and physical health outcomes (Gilmour et al., 2020). With respect to text messaging, correlational research has linked a greater number of text messages exchanged with lower levels of loneliness (Park et al., 2016). Experimental studies have also shown that receiving support 19 via text is associated with positive emotional outcomes compared to receiving no support at all (e.g., Holtzman et al., 2017; Sherman et al., 2013). One study even demonstrated that texting had similar benefits to in-person communication in terms of negative affect (Holtzman et al., 2017). Furthermore, evidence supports the notion that the use of digital communication can benefit relationships. Adolescents and young adults who engage in more frequent texting report higher levels of intimacy and satisfaction in their friendships (Dolev-Cohen & Barak, 2013) and across relationships, more generally (Park et al., 2016). Young adults also reported that relationships with their parents and family members improved because of their cell phone use (Chen & Katz, 2009; Crosswhite et al., 2014; Kennedy et al., 2008; Miller-Ott et al., 2014). When contrasted directly with in-person interactions, some research has even demonstrated that CMC can foster a greater degree of disclosure and relational development (Jiang et al., 2011; Ranney & Troop-Gordon, 2015; Tidwell & Walther, 2002). Despite emerging research showing positive outcomes for support and well-being, CMC has also been frequently linked to unfavourable outcomes. Compared to in-person contexts, interacting with others online has been associated with less positive and more negative emotions (Duvenage et al., 2020; Kafetsios et al., 2017; Ranney & Troop-Gordon, 2015) and increases in loneliness and depression over time (Kraut et al., 1998). Despite findings to support a positive impact of social media on well-being, a host of studies have also linked Facebook use with greater odds of depression (Shensa et al., 2020), declines in subjective well-being over time (Kross et al., 2013; Shakya & Christaskis, 2017), as well as negative relationship outcomes (Clayton et al., 2013). Similar negative effects have also been observed in studies of text and instant messaging. Across experimental studies, use of text 20 messaging was associated with significantly higher cortisol levels (Seltzer et al., 2012) and lower levels of positive affect (Holtzman et al., 2017; Sacco & Ismail, 2014) compared to in-person interactions. In fact, one study even reported text messaging to be statistically equivalent to receiving no support at all (Seltzer et al., 2012). Furthermore, even when used in conjunction with in-person communication, individuals who used texting for social interaction reported feeling worse and less connected to their partners compared to when they solely used in-person communication (Kushlev & Heintzelman, 2018). Further evidence has linked CMC with poorer social and relational outcomes. Compared to in-person communication, text or instant messaging has been associated with reports of less fulfilling conversations with friends (Angster et al., 2010), lower levels of oxytocin (Seltzer et al., 2012), and generally lower levels of satisfaction with the interaction (Goodman-Deane et al., 2016; Holtzman et al., 2017; Kafetsios et al., 2017; Mallen et al., 2003; Sacco & Ismail, 2014). Despite these worse outcomes, however, it is important to note that levels of emotional understanding (Mallen et al., 2003), satisfaction (Holtzman et al., 2017), and bonding (Sherman et al., 2013) were still high across both conditions, suggesting that texting can yield at least some positive social outcomes. Using a longitudinal design, Trepte and colleagues (2015) assessed the provision of online and offline (in-person) social support on satisfaction in four waves over a two-year period. Their results demonstrated that social support provided in both online and offline contexts did not differ in terms of satisfaction with support over the course of the study. However, notable differences were found when assessing life satisfaction: whereas offline support was predictive of greater life satisfaction, online support was not. In contrast, in the first attempt at examining digital versus in-person ER in daily life, Colasante and colleagues 21 (2020) asked digital “natives” (i.e., those who have grown up using digital technologies) to report on the intensity of their negative emotions, the extent to which digital (specifically, texting and video chat) or in-person support was received for their emotions, and their perceived success in managing the emotions thrice daily for 14 days. Multilevel indirect effects models assessed whether mode of communication mediated the relationship between emotional intensity and ER success. Contrasts of the indirect effects were statistically equivalent, suggesting that digital and in-person communication played similar roles in effectively mediating the regulation of negative emotions. These findings held even after accounting for trait levels of social avoidance and social situation (i.e., whether participants were alone or around others at the time of the negative emotion), with the exception that those higher in social avoidance receiving less support overall. Study findings generally suggest that digital communication may be as effective for ER as in-person communication. Likewise, Verduyn and colleagues (2021) assessed the use of digital (smartphone) and non-digital (face-to-face) communication across a 15-day period. On days when individuals engaged in a higher than average amount of face-to-face communication, they rated higher levels of positive and lower levels of negative affect. The inverse relationship was found for digital communication. However, when their data was aggregated over the course of the study period (i.e., at the between-person level), levels of positive and negative affect did not differ based on how often participants used digital communication across the 15 days. Digitally Supportive Behaviours A closer consideration of the specific nature of digital and non-digital interactions may help to explain the mixed findings. Much of the CMC literature has used cross-sectional 22 or experimental research to test a variety of factors to explain the social and emotional outcomes associated with use of digital technologies, and how these factors are operationalized vary greatly (e.g., length of time or excessiveness of its use, the number of messages exchanged, or the number of people in one’s online network). This approach may also be too simplistic to unveil or explain the multitude of ways in which people’s social and emotional lives may be influenced. Instead, it is likely that there are other contextual factors to help explain these outcomes, which may differ based on the reasons for using CMC in the first place. For example, if an individual is using CMC to discuss sensitive or emotionally-relevant material (like in the IER process), the quality of the interaction and, specifically, the ways in which an interaction partner responds will be of great importance. Within the context of interpersonal support or IER, there is reason to believe that in-person communication may be better able to convey emotional content, empathic concern, and emotional support behaviours, and therefore be deemed more satisfactory to those who use them (e.g., Trepte et al., 2015). Communication of emotional experiences typically involves a combination of facial, vocal, body, and to a lesser extent, verbal cues (Planalp, 1998; Planalp et al., 1996), inviting more salience and emphasis on indirect (or nonverbal) cues of emotion. Indeed, a great deal of emotional content is displayed in the features and expressions of the face (Ekman & Oster, 1987; Keltner et al., 2003). Especially in text-based discourse, reliance on written word without these nonverbal cues can make it challenging to comprehend the true intent, emotion, or level of concern behind a message. Lack of nonverbal cues may also increase opportunities for misperception or miscommunication (Kelly & Miller-Ott, 2018). In the absence of nonverbal indicators, individuals try to infer meaning, level of concern, or sincerity using other available linguistic cues, such as 23 punctuation (Gunraj et al., 2016; Hancock et al., 2007; Reynolds et al., 2017) or emoticon use (Miller et al., 2016; Walther & D’Addario, 2001), but these inferences may be inaccurate and tend to be negatively biased (Gunraj et al., 2016; Miller et al., 2016; Walther & D’Addario, 2001). In a similar vein, there is empirical evidence that, compared to methods with greater bandwidth and richness, those who use instant messaging report significantly fewer affiliation cues (e.g., smiling, laughing, nodding) from their partners and feel less bonded to them, even when digital affiliation variants (e.g., emoticons, typed laughter) are used (Sherman et al., 2013). Attempts at infusing emotion into texts may also be ineffective because they may not promote the same emotional mimicry or contagion in the recipient, precluding the perception of a shared emotional experience. Indeed, the emotion contagion and mimicry literatures purport that facial expressions, vocalizations, or other motor movements are automatically mimicked by others, and produce matched expressions or converged emotional states (e.g., Bavelas et al., 1986; Hatfield et al., 1993). Evidence suggests that this mimicry of facial expressions can produce congruent emotional experiences between interactants (see Hess & Fischer, 2013), which can then foster empathic concern and connection. Thus, when using CMC, it may be more difficult to convey emotional content and foster a shared emotional experience between two people. Text or instant messaging is also inherently asynchronous. Individuals who share their emotional experiences with others may not receive a response until a period of time has passed. Especially after sharing negative emotional experiences, individuals may feel particularly vulnerable and appraise their partner as being unavailable, inattentive, or disinterested. Indeed, research suggests that those who believe their messages or phone calls have been ignored experience a wide range of negative emotional states (Agarwal & Lu, 24 2020). Thus, the relative absence of nonverbal behaviours in text-based contexts, as well as its asynchronous features, may make it more challenging to share emotions with others, express empathic concern, or provide and receive emotional support. On the other hand, there are reasons to suggest that CMC might actually be better suited to convey empathic concern and emotional support compared to in-person settings. As highlighted by the hyperpersonal model (Walther, 1996), CMC affords users the ability to construct messages in desirable ways, and in ways that invite interaction partners to engage in self-disclosure. In fact, research has demonstrated that disclosures made over CMC can be more intimate than their in-person counterparts (Tidwell & Walther, 2002) for these reasons. CMC allows its users to compose well thought-out messages that may help to foster more intimate discussions with their interaction partner (e.g., Tidwell & Walther, 2002). In essence, users can focus their cognitive abilities on constructing, editing, and tailoring their messages in thoughtful ways without the need to also manage their nonverbal behaviours or feel pressured to provide an immediate response for the sake of conversational flow. Additionally, from the perspective of the individual engaging in self-disclosure, privately authoring a written account of one’s emotional experiences can be therapeutic in and of itself, by helping to articulate, process, and reflect on emotions, and generally assist with self-organization of their experiences (Pennebaker, 1995). Limitations of Existing Research In sum, a small but emerging body of work is attempting to integrate the fields of ER and CMC to examine the nature and impact of supportive social interactions within online formats. However, there are limitations in each independent body of work, as well as the 25 emerging integrative field, that may be limiting progress. Within the ER literature, research has overly focused on intrapersonal strategies without sufficiently exploring the IER process or strategies that occur within an interaction. As a result, fundamental questions remain. Research, for example, has yet to identify how prominent IER is in daily life, what IER strategies are being used on a daily basis, and how effective these strategies are in regulating emotions. Furthermore, the majority of past work on social regulation processes, like IER, has operated under the assumption that individuals are connecting with others in person. Yet, given the ubiquity of digital technologies, an important contextual factor to evaluating IER may be missed. In fact, only one known study has attempted to compare ER across digital and in-person contexts (Colasante et al., 2020). From a CMC framework, the literature is populated with conflicting theoretical and empirical work. While the differences across this research may be explained by the evolution of technology through the years, it may also be because studies have often operationalized CMC as the frequency or amount of use, for example, without closely considering the content and nature of the interactions. Furthermore, CMC research tends to be cross-sectional or experimental in nature, so a more naturalistic examination of the role of CMC is warranted in order to examine nuances in how findings relate and generalize to daily life. 26 The Current Research The current study drew upon IER and CMC theory to examine the roles of mode of communication (specifically, digital versus in-person) and IER processes in regulating negative emotions. The focus was on intrinsic response-dependent IER; that is, where individuals seek to regulate their own emotions and where ER is dependent on the response of the support person. This study addresses limitations of past work by using an intensive longitudinal design to assess the nature of IER as it naturally unfolds over a one-week period. This type of design has advantages over cross-sectional and experimental research in that it allows for more nuanced examinations of the naturalistic nature in which the variables of interest unfold (Bolger & Laurenceau, 2013). That is, this design allows for an in-depth assessment of how digital communication may differ compared to in-person interactions in the extent to which IER strategies are used across both mediums. These designs also provide more ecologically-valid approaches to generalize findings to individuals’ daily lives. This study is also one of the first of its kind to characterize and compare the nature of IER as it occurs across in-person and digital contexts. Specifically, this study attempted to increase our understanding of why digital communication has been associated with less favourable outcomes, and proposes that differences in IER strategies across in-person and digital communication may help to explain these outcomes. The current research was conducted on a sample of emerging adults (18-29 years; Arnett, 2015). Emerging adults face an inordinate amount of uncertainty across a wide variety of life domains (Arnett, 2000) and the literature has suggested that many young people struggle with regulating their emotions during this phase of life (Orgeta, 2009). Individuals within this age group also experience the highest levels of stress (APA, 2018), 27 and in Canada, they report the greatest number of mental health problems compared to any other age group across the lifespan (Smetanin et al., 2011). From a social perspective, it is also during this developmental period that individuals engage in the highest levels of social support seeking behaviours (Zimmerman & Iwanksi, 2014). Furthermore, given that technology has become a critical method of communicating and connecting with others in this age group (Forgays et al., 2014; Pew Research Center, 2019a), it is especially pertinent to examine whether the IER process can be effectively pursued across digitally-mediated forms of communication as compared to in-person methods. Research Aims and Hypotheses The current study had three research aims that are described in detail below. Capitalizing on the advantages of intensive longitudinal methodology, the current study examined both within-person and between-person effects. Research Aim 1 The first research aim was to specifically describe the extent to which emerging adults use IER strategies to regulate their negative emotions, as well as the extent to which they use various modes of communication to do so on a daily basis. Other dynamic characteristics of the IER process, such as the intensity of specific emotions, sources of social support, and immediacy of responses when using digital communication for IER, were also explored. As this aim was to provide an in-depth, descriptive picture of the IER landscape, no hypotheses were put forth. 28 Research Aim 2 The second research aim was to examine whether the mode of communication used for IER (digital only versus in-person only) was associated with satisfaction with support, positive emotions, and negative emotions. CMC theories make conflicting predictions regarding the relative importance that mode of communication plays on these outcomes. SIP theory (and others; Walther, 1992; 2011) suggests that differences in socioemotional outcomes should not exist or that CMC may even outperform in-person methods (Walther, 1996). However, CFO theories (Culnan & Markus, 1987; Daft & Lengel, 1986; Short et al., 1976) predict that individuals will be less satisfied with their interactions and experience poorer emotional outcomes. As reviewed above, the majority of experimental research directly comparing in-person to digital methods has demonstrated advantages for in-person communication, specifically in terms of satisfaction with support (e.g., Goodman-Deane et al., 2016; Holtzman et al., 2017; Kafetsios et al., 2017; Mallen et al., 2003; Sherman et al., 2013; Trepte et al., 2015) and positive and negative affect (e.g., Cole et al., 2017; Holtzman et al., 2017, Kafetsios et al., 2017; Kushlev & Heintzelman, 2018; Ranney & Troop-Gordon, 2015; Sacco & Ismail, 2014; Shensa et al., 2020). Nonetheless, other work has demonstrated that there are advantages associated with digitally-mediated methods, including greater positive affect and well-being (Dolev-Cohen & Barak, 2013; Holtzman et al., 2017; Park et al., 2016; Sherman et al., 2013) and relationship outcomes (Chen & Katz, 2009; Dolev-Cohen & Barak, 2013; Jiang et al., 2011; Miller-Ott et al., 2014; Park et al., 2016; Tidwell & Walther, 2002), even when compared to in-person communication (Colasante et al., 2020). Given that in-person communication has most reliably resulted in favourable socioemotional 29 outcomes when directly compared to its digital counterpart, the following hypotheses were put forth: Hypothesis 1a. Compared to in-person interactions, the use of digital communication will be associated with lower levels of satisfaction with support and positive emotions; and Hypothesis 1b. Compared to in-person interactions, the use of digital communication will be associated with higher levels of negative emotions. These associations were expected at both the within- and between-person levels of analysis. In other words, at the within-person level, it was expected that participants would report lower levels of satisfaction and positive emotions and higher levels of negative emotions at time points when they used digital (versus in-person) communication. At the between-person level, it was expected that these patterns of associations would be observed among participants who, on average, used a greater proportion of digital communication across the study period. Research Aim 3 The third aim was to assess whether the association between mode of communication and study outcomes could be explained, at least in part, by the IER strategies used in the interaction; that is, whether the more unfavourable outcomes often associated with digital communication could be partially attributed to the differential use of positive and negative IER strategies. As reviewed above, CFO theories predict that CMC will be inferior to in-person communication given its lack of nonverbal cues, such as facial expressions, eye contact, tone 30 of voice, and body language, which are known to be important in establishing empathy and communicating support and understanding (e.g., Haase & Tepper, 1972). In their absence, individuals may not feel listened to or may believe that their messages are being ignored or deemed unimportant. Similarly, affiliation cues, like smiling or laughing, may be more difficult to convey and mimic in CMC compared to in-person contexts (Sherman et al., 2013). Asynchronicity may also foster the perceptions of disinterest or disengagement that are often associated with lowered sense of social presence or interactivity (e.g., Hertlein & Chan, 2020; Tu, 2000). Finally, the increase in miscommunication noted in text-based discourse (e.g., Kelly & Miller-Ott, 2018) may lead individuals to perceive that digital exchanges provide fewer instances of positive support. For these reasons, it was expected that, at both levels of analysis: Hypothesis 2a. Digital (versus in-person) communication will be associated with lower levels of positive IER strategies and higher levels of negative IER strategies. The benefits of interpersonal support for relational and emotional outcomes have been well-documented (e.g., Chan & Rawana, 2021; Gable et al., 2004; Lisi, 2016; Niven et al., 2012; H. Oh et al., 2014). Similarly, negative social interactions (or failed support attempts), such as those involving criticism, rejection, or that facilitate rumination or avoidance, are also linked with adverse effects on well-being (Christensen et al., 2020; Dixon-Gordon et al., 2018; Wortman & Lehman, 1985). Given this, at both levels of analysis, it was expected that: Hypothesis 2b. Higher levels of positive IER strategies will be associated with higher levels of satisfaction with support and positive emotions, and lower levels of negative emotions; and 31 Hypothesis 2c. Higher levels of negative IER strategies will be associated with lower levels of satisfaction and positive emotions, and higher levels of negative emotions. There are likely multiple independent mediators to explain this relationship, but this study focused on two specific mediators, positive and negative IER strategies, and the individual roles they play in this relationship. Thus, the following specific mediation effects were expected at both levels of analysis: Hypothesis 2d. Positive IER strategies will mediate the relationship between mode of communication and satisfaction, positive emotions, and negative emotions. Specifically, digital (versus in-person) communication will be associated with lower satisfaction and positive emotions, and higher negative emotions via lower levels of positive IER strategies; and Hypothesis 2e. Negative IER strategies will mediate the relationship between mode of communication and satisfaction, positive emotions, and negative emotions. Specifically, digital (versus in-person) communication will be associated with lower satisfaction and positive emotions, and higher negative emotions via higher levels of negative IER strategies. A summary of hypothesized associations under Research Aim 3 is outlined in Figure 1, where Panel A depicts the expected associations between mode of communication and IER strategies (paths a1 and a2), as well as between IER strategies and satisfaction and positive emotions (paths b1 and b2). Similarly, Panel B depicts the hypothesized inverse associations between all predictor variables and negative emotions. Direct associations (paths c’1 and c’2) are depicted for illustrative purposes but are not being formally predicted in Research Aim 3. 32 Figure 1 Summary of Hypotheses for the Mediating Role of IER Strategies in the Association between Mode of Communication and Study Outcomes Note. “Mode of Communication” was dichotomous and coded as 1 = digital only communication and 0 = in-person only communication. “+” signifies an expected positive association between variables and “-” signifies an expected negative association between variables. Exploratory Aims Research on the role of specific IER strategies in the ER process is theoretically and empirically scarce, and, when considering the extent to which they have been examined in a Mode of CommunicationUse of Positive IER Strategies-Satisfaction;Positive Emotions++Use of Negative IER Strategies-Mode of CommunicationUse of Positive IER Strategies-Negative Emotions+-Use of Negative IER Strategies+Panel APanel Bpath a1path a1path a2path a2path b1path b2path b3path b4path c’1path c’2 33 digital context, virtually nonexistent. This study attempts to fill this gap in the literature by exploring the mediating role of each individual IER strategy on satisfaction with support and positive and negative emotions. As this aim was exploratory, no formal hypotheses were put forth. However, following prior research (e.g., Trepte et al., 2015), it was generally anticipated that positive strategies which are more emotionally supportive or more emotion-focused in nature (e.g., active listening, use of humour) would be more difficult to convey over digital communication and therefore participants were expected to report lower levels of these strategies when using digital communication, both within-person and when aggregated across the study period. In contrast, mode of communication may matter less when communicating informational support, such as providing or receiving advice, suggestive that the influences of digital and in-person communication may equivalently influence social and emotional outcomes. 34 Methods Participants and Sample Size Recruitment was conducted using the University of British Columbia Okanagan (UBCO) campus’ undergraduate psychology participant pool through flyers distributed around campus, and by classroom visits. To be eligible for inclusion in the study, participants were required to be between the ages of 18 and 25, be enrolled in an undergraduate program at UBCO, have regular access to a smartphone, and be fluent in English. Participants were excluded if they had ever received a diagnosis of borderline personality disorder, bipolar disorder, or schizophrenia. Data collection spanned from December 2017 to October 2018. Individuals enrolled in eligible psychology courses were given a choice of either receiving partial course credit (up to 2.5 SONA credits) or being entered into a draw for a $100 Amazon (Canada) gift card. Participants who completed at least 75% of the daily assessments received the full 2.5 credits. Those who completed less than 75% were given credits on a prorated basis. Participants not enrolled in an eligible psychology course were automatically entered into the gift card draw. The current study contained data at two levels of analysis, as each participant provided multiple assessments over the study period. Level 1 consisted of each time point in which data was collected and Level 2 consisted of each person being assessed. Recommendations for an adequate sample size in hierarchical designs is a complex issue dependent on a variety of factors, such as whether researchers are focused on fixed or random effects or if cross-level interactions are modeled. Because of this, the literature is devoid of any specific guidelines that are appropriate across all hierarchical data structures; 35 rather, a variety of standards have been proposed. For instance, if modeling fixed effects, sample size recommendations state that 30 units at each level of analysis are recommended (Kreft, 1996), and if modeling cross-level interactions, current rules of thumb state that 50 groups with 20 individuals per group are preferred (Hox, 2010; Maas & Hox, 2005). More generally, the consensus across the literature states that it is the sample size of the highest grouping variable (e.g., Level 2 in a two-level design) that has a greater impact on power compared to the Level 1 sample size (e.g., Maas & Hox, 2005; Scherbaum & Ferreter, 2009; Snijders & Bosker, 1993, 2012), and most studies examining sample size have indeed focused on assessing the impact of sample size at Level 2. Research that has examined the impact of sample size at both levels has suggested there is a trade-off between grouping sizes. That is, studies with adequate sample sizes at Level 2 but with sparse within-cluster (i.e., Level 1) data may still be victim to biased or overestimated effects (Clarke & Wheaton, 2007). However, the evidence still suggests that any inaccuracy in these parameters may be less of an issue when the number of groups increases in size (e.g., Bell et al., 2008, 2010; Clarke, 2008). Thus, the number of participants (rather than the number of time points they are measured at) warranted closer consideration in this study’s design in order to account for possible sample (and cluster) size limitations. The current study thus aimed to recruit a sample of at least 200 people, well above traditional guidelines (Hox, 2010; Kreft, 1996; Maas & Hox, 2005), with up to 14 time points per person. Procedure This study consisted of two parts. The first part of the study was completed in a laboratory setting, with participants filling out baseline questionnaires and testing software 36 required for the second part of the study on their smartphones. The second part of the study comprised the intensive longitudinal design, which involved collecting assessments of participant’s emotions and social sharing of their experiences twice daily over the course of a one-week period. Further details of the study procedure are outlined below. Part One. Participants arrived at the lab and were greeted by a trained research assistant. The research assistant reviewed informed consent with eligible participants, including a review of the study’s purpose, potential risks and benefits, and withdrawal procedures. Participants were seated at a lab computer and oriented to the baseline survey, which included questions to verify eligibility. A battery of baseline questionnaires was completed, which assessed demographic information and relevant contextual variables, such as personality, social network information (i.e., structure, quality, and accessibility), general psychological distress, and details about their typical smartphone use. Only those questions included in the current study are described in the measures section below. On average, baseline questionnaires were answered in approximately 32 minutes (SD = 11:18). Once baseline questionnaires were completed, participants downloaded a free smartphone application to their iOS or Android device, which was necessary for completion of Part Two. Research assistants ensured the smartphone application was tested on the participant’s device and that participants were comfortable using the application prior to leaving the lab. The study coordinator followed up with any participants who had difficulty downloading or testing the application on their device until any issues were resolved. Questions regarding the smartphone application, its notifications, or the study requirements were addressed before the participant left the lab, and participants were provided with the study coordinator’s contact 37 information if any technological issues arose over the study period. Participation credits for Part One were provided following completion of this first portion of the study. Part Two. In the second part of the study, participants used the downloaded smartphone application to complete brief assessments of their current emotions, IER strategy use, modes of communication used, and specific support sources turned to over the course of the day. To ensure assessment of multiple daily experiences and be mindful of participant burden, assessments were collected twice daily. A fixed interval data collection schedule (at 1 p.m. and 8 p.m.) was utilized in order to ensure participants had ample opportunity for interpersonal interactions. Data was collected for seven consecutive days (beginning the day after the onsite component) to capture both weekday and weekend interactions. Participants received a notification on their smartphones and were instructed to complete the questionnaire immediately, or as soon as possible in situations where they were unable to do so (e.g., in class, driving) in order to get an accurate assessment of their current emotional state. Participants were provided a two-hour window to complete the questionnaires before the survey became inactive. On average, participants responded to the daily questionnaires 30 minutes after receiving the notification (M = 29:47, SD = 34:44). The mean time to complete the daily assessments was under 3 minutes (M = 2:49, SD = 7:23), with the majority of assessments (82.7%) completed in three minutes or less. Each daily assessment began with questions about participants’ current levels of positive and negative emotions. Participants who endorsed experiencing at least some degree of negative emotionality (i.e., any negative emotion value greater than 0 or “not at all”) received several follow-up questions, including identifying the main support source they turned to for support with their negative emotions, the modes of communication used with 38 this individual, the extent to which several IER strategies occurred during the interaction, and their satisfaction with the interaction. Both daily assessments were identical, with two exceptions. First, the afternoon assessment asked about participant’s experiences so far that day, whereas the evening assessment asked about the period since the previous (afternoon) assessment they completed. Second, the evening assessment additionally inquired about the extent of participants’ independent ER efforts over the course of the entire day. Given the focus on the IER (versus ER) process, only the use of IER strategies were explored in the current study. This study was approved by the Behavioural Research Ethics Board at the UBCO campus (H17-02243) and all participants provided informed consent. A copy of the consent form is provided in Appendix A – Consent Form. Materials Baseline measures were collected using SurveyMonkey. Daily assessments were collected using the smartphone application, MetricWire (www.metricwire.com), which was free for participants to download to their iOS or Android devices. The study was designed for use on a smartphone (versus a laptop or desktop), specifically, to facilitate ease of use in collecting daily diary assessments. MetricWire facilitates experience-sampling research by allowing users the opportunity to participate using their smartphone. Participants received notifications on their compatible smartphone devices via the MetricWire application independent of an internet connection. All responses were time-stamped upon submission. 39 Measures Baseline Measures Demographic variables (age, ethnicity, gender, relationship status, place of birth, living arrangements) collected in the baseline survey were included in the current study for the purpose of characterizing the sample. Daily Assessment Measures The main research questions in the current study involved only Level 1, within-person variables. Only those within-person variables included in the study analyses are described. See Appendix B – Daily Assessments for a full list of items included in the daily assessment. Emotions. Current emotional intensity was assessed for seven positive and eight negative emotions at each assessment (“How _____ are you feeling right now?”). Adapted from a previous daily diary study (Heiy & Cheavens, 2014), positive emotions assessed were “amused,” “calm,” “excited,” “interested,” “joyful,” “loved,” and “proud,” and negative emotions assessed were “angry,” “anxious,” “embarrassed,” “guilty,” “irritable,” “lonely,” “sad,” and “stressed.” Emotions were presented alphabetically, and alternating in positive and negative valence. Each emotion was rated on a four-point Likert scale assessing the degree to which the emotion was being experienced at the current moment (“not at all” [0], “a little bit” [1], “somewhat” [2], “a lot” [3]). A total emotion intensity score was calculated separately for positive and negative emotions, with possible total scores ranging from 0 to 21 and 0 to 24, respectively. Internal consistency for the emotion subscales was good, with respective 40 Cronbach’s alpha values of .89 and .84 for overall positive and negative emotions. Internal consistency did not differ between afternoon and evening time points for positive ( = .88 and .89, respectively) or negative ( = .83 and .84, respectively) emotions. Satisfaction with Responses. After indicating who they considered to be their main support source, participants provided their level of satisfaction with the way their main support source responded in each interaction. This was assessed with a single item (“To what extent were you satisfied with the way this person responded to you?”). Satisfaction was rated on a 4-point Likert scale, with response options ranging from “not at all” (0) to “a lot” (3). IER Strategies. IER strategies were assessed using a modified version of the Emotion Regulation of Others and Self (EROS)’s extrinsic scale (Niven et al., 2011). This scale is typically used to assess the extent to which individuals use a variety of IER strategies to manage other people’s (versus their own) feelings. The EROS conceptualizes IER strategies as being either affect-improving or affect-worsening based on the user’s motivations. However, as the current study did not assess the motives of strategy use, items were re-worded to assess the participants’ perception of the extent to which the IER strategies occurred in the interaction. Additionally, the scale’s classifications of “affect-improving” and “affect-worsening” strategies are referred to as “positive” and “negative” strategies in this study to reflect their theoretical effect on emotions, as alluded to by Niven and colleagues (2011). In total, four positive and five negative IER strategies were assessed in the current study. Participants were asked to indicate the extent to which each of the nine strategies occurred in the interactions with their main support source since the previous assessment or, 41 for the first time point, the beginning of the day (“When you turned to this person, to what extent did he/she ______?”). Positive strategies were: “listen to your problems,” “point out your positive characteristics,” “give you advice,” “make you laugh,” and negative strategies were: “criticize you,” “seem uninterested,” “talk down to you,” “ignore you,” and “seem annoyed with you.” Strategies alternated in valence when presented. Response options ranged from “not at all” (0) to “a lot” (3). The overall extent to which strategies occurred in an interaction were totalled separately for positive and negative strategies with possible total scores ranging from 0 to 12 or 0 to 15 at each time point, respectively. Mode of Communication. Participants were asked to specify all modes of communication used when sharing with their main support source. Response options were: in-person conversation, talking on the phone, video chat (e.g., FaceTime, Skype), texting, Snapchat, Facebook, Instagram, Twitter, or other. In the current study, texting and use of social media applications (i.e., Snapchat, Facebook, Instagram, or Twitter) were considered digital modes of communication. Although video chat is digitally-mediated, it was not included with the other digital modes of communication in the current study due to the availability of audio and visual cues. For all modes of communication (aside from in-person conversation), participants were asked to estimate the time it took for their main support source to respond to them. Statistical Analyses Prior to analyses, data screening and preliminary descriptive statistics were done in order to characterize the sample. These steps were all performed in IBM SPSS version 23.0. 42 Research Aim 1 sought to provide descriptive information regarding the nuances of daily IER among emerging adults. These analyses were based on any time point in which participants reported turning to others for assistance in managing their negative emotions. Frequencies and descriptive statistics were conducted on emotions, IER strategies, support sources, modes of communication used, and estimated response times, all computed using IBM SPSS. Statistical comparisons between levels of positive and negative emotions and between positive and negative IER strategies were conducted using Wilcoxon signed-rank tests. Research Aims 2 and 3 utilized multilevel modeling and multilevel mediation analyses, respectively, to assess the impact of mode of communication and IER strategies on study outcomes (satisfaction with responses, positive and negative emotions). Details on these statistical approaches are provided below. The analyses for Aims 2 and 3 were based on a subset of time points in which individuals endorsed (a) turning to others (as opposed to keeping to themselves) and (b) that the interaction involved only digital communication or only in-person communication in order to facilitate a more straightforward comparison. Time points where participants used a combination of both in-person and digitally-mediated methods of communication (or other modes, such as video or telephone calls) were not included. Given the potential for this to greatly reduce the number of time points (and therefore the cluster sizes at Level 1), statistical approaches appropriate for small sample (and cluster) sizes were followed. For example, multilevel mediated regression (rather than multilevel structural equation modeling) was used to specifically assess Research Aim 3 (McNeish, 2017). All multilevel models were run using Mplus version 8.4. 43 Multilevel Modeling Multilevel modeling (MLM) is appropriate when data are nested in a hierarchical fashion (in this case, daily assessments at Level 1 were nested within participant at Level 2; e.g., Hox, 2010; Nezlek, 2008; Raudenbush & Bryk, 2002; Snijders & Bosker, 2012). Level 1 data estimates within-person variability and Level 2 data between-person variability; one inherent benefit of MLM is that it assesses both sources of variability simultaneously. That is, MLM assesses variability that exists across an individual’s responses (time point level), as well as the variability that exists between individuals in the sample (person level). To do so, MLM uses equations to account for this variability at both levels of analysis. For instance, it is able to assess whether, for each person, a score at one time point is significantly different from the average score of all of that person’s other time points (within) as well as whether one person’s overall scores are significantly different compared to the overall average score across the entirety of the sample (between). Prior to carrying out MLM analyses, sufficient variability in the dependent variable must be identified in the absence of any predictors. This “null” model, which is devoid of any predictors, produces an intraclass correlation (ICC), an indicator of whether there is enough of an influence of the nesting variable (i.e., the participant) to warrant MLM to begin with. The null model is also used as a baseline for which successive models are built and assessed for improvement to model fit. Level 1, within-person predictors are then entered into the model as fixed; that is, treating the influence of the predictor as consistent for all individuals. If the presence of the fixed predictor improves model fit over the null model, the fixed effect is retained. Next, the slopes are permitted to vary randomly, which allows the influence of the predictor to vary across participants. Random slopes can elicit a more representative 44 depiction of the variation that exists within a population; however, they can also introduce complications in models, increase rates of nonconvergence, and be difficult to detect in small sample (and cluster) sizes (e.g., Hox & McNeish, 2020). The next step is to compare the fit of the random slopes model to the fit of the fixed effects model. If the model with random parameters is not a significant improvement to overall model fit compared to the fixed effects model, the model is retained as having fixed slopes. Level 2, between-person predictors are then added to the model, and similarly assessed for improvement to model fit (i.e., over and above the models with the Level 1 fixed or random predictors). Models examining within-person variables, however, can vary either within- or between-person. That is, effects can exist at one level of analysis or both. To assess whether effects exist at both levels of analysis, Level 1 variables can be aggregated (or person-mean centred) and entered into multilevel models at Level 2 to assess how an individual’s scores compare to the overall sample, and therefore, assess the differences that may exist between people. Thus, MLM is run at both levels to identify whether effects exist within-people, between-people, or both. Centring procedures were guided by Raudenbush and Bryk (2002). The predictor variable (mode of communication) was entered into all models uncentred as it was a dichotomous variable with a meaningful zero value (Nezlek, 2012; Raudenbush & Bryk, 2002). Robust maximum likelihood was used as the estimation method for all models. This method uses full information maximum likelihood estimation, which has the benefit of incorporating all available data in analyses. Also, there is growing consensus that it may be more advantageous than conventional estimation approaches as it provides reasonable standard error estimates that are robust to less-than-optimal conditions, such as unbalanced 45 cluster sizes, small samples, or departures from normality (Heck & Thomas, 2015; Muthén & Muthén, 1998-2017). Confidence intervals (95%) were used to assess the significance of all effects. Intervals that do not include zero suggest that the null hypothesis can be rejected and that the measured effect is indeed statistically significant. Research Aim 2 specifically utilized MLM to assess whether mode of communication was associated with satisfaction with support and positive and negative emotions. The Level 1 predictor was the binary mode of communication variable (1 = digital communication only and 0 = in-person communication only). The Level 1 outcome variables (satisfaction with support and positive and negative emotions) were continuous. Three distinct models examined the predictive role of mode of communication on each of the three outcome variables: satisfaction with responses (Model 1), positive emotions (Model 2), and negative emotions (Model 3). For illustrative purposes, a sample model predicting satisfaction with support (with fixed slopes; Model 1) is described in detail below: Level 1: Satisfactionij = β0j + β1j(Mode of Communicationij) + rij , Level 2: β0j = γ00 + u0j , β1j = γ10 , where satisfaction rating (outcome variable; Satisfactionij) at any given time point, i, for a given participant, j, is equal to the person’s mean level of satisfaction across all time points (intercept; β0j) plus their mode of communication (β1j) as well as any deviation from this average (residual; rij). In other words, Level 1 equations assess the role that each time point has on satisfaction relative to a person’s average satisfaction level across time points. This is known as the within-person model. Level 2, between-person models are also calculated for each participant to assess whether significant differences exist from one person to the next. 46 Specifically, Level 2 models first compare whether a participant’s own average level of satisfaction is statistically different from the average satisfaction level across all participants in the sample (γ00) plus any other unexplained variation (u0j). The Level 2 model also assesses the average relationship between mode of communication and satisfaction across participants (i.e., the mean slope; γ10). Multilevel Mediation Multilevel mediation was used to test whether the association between mode of communication and study outcomes could be explained, at least in part, by the types of IER strategies used in the interaction. Contemporary approaches to mediation negate the need to test for mediation through a series of causal steps (e.g., A. Hayes, 2018). Instead, there has been growing consensus on testing the indirect effect, or the product of the a and b paths, specifically, for an indication of whether mediation exists, as it is the ab product that is central to mediation, not the individual paths themselves (A. Hayes, 2018). Additionally, researchers are encouraged to test the indirect effect specifically, given that the estimation of the direct effect often includes a considerable amount of error that can obfuscate the indirect effect (A. Hayes, 2018). Finally, the assumption that each of the paths must be significant overlooks models with “inconsistent” mediation, where opposite signed indirect and direct effects can cancel each other out (MacKinnon et al., 2000). The current study tested a 1-1-1 mediation model, where the predictor, mediator, and outcome variable were measured at Level 1. Similar to MLM, variables in the mediation model had the potential to vary within and between participants. For instance, negative affect 47 was measured twice daily for one week, and may have varied across the study period as a result of situation-specific factors, as well as factors specific to the individuals themselves, and any other unexplained variation. Therefore, even though variables were assessed at Level 1, each variable and their effects can have variability that exists at the within level, between level, or both. Traditional multilevel mediation analyses conflate this variation into a single slope value. However, to more accurately assess whether indirect effects exist within- or between-people, “deconflation” of the within- and between- effects has been recommended (Krull & MacKinnon, 2001; Preacher et al., 2010, 2011; Zhang et al., 2009), especially in small samples (Zigler & Ye, 2019). Deconflation essentially models effects separately at both levels of analysis. In a 1-1-1 model, where all variables are measured within-person, an aggregate measure of the observed mediator variable (i.e., IER strategies) is incorporated at the between level of the model to account for the between-person variability that may inherently exist. The deconflated model then yields two sets of effects that are estimated: one for the within part of the model (Level 1) and the other for the between (Level 2). In a sample 1-1-1 deconflated model of positive IER strategies mediating the association between mode of communication and satisfaction with responses, Figure 2 shows that each of the observed variables in the study accounted for and assessed variability both within and between people. Coefficients corresponding to paths a, b, c’ (direct effect), and c (total effect), were estimated at each level, and two subsequent indirect effects, INDW and INDB, were calculated. Centring procedures and estimation methods followed those described for MLM. Level 1 mediators were person-mean centred prior to entry into the mediation models. In 48 Figure 2 Sample 1-1-1 Deconflated Multilevel Mediation Model for the Current Study Note. “Mode of communication” was dichotomous and coded as 1 = digital only communication and 0 = in-person only communication. other words, a person’s score at each time point was compared to their own overall average. To account for between-person variability, aggregate (mean) scores for the mediators were entered as Level 2 predictors to compare an individual’s scores to the overall sample in order to assess the differences between people. Observed VariablesMode of CommunicationjSatisfactionjUse of Positive IER StrategiesjWithinMode of CommunicationijSatisfactionijUse of Positive IER StrategiesijBetweenXij Mij Yijawithinabetween bbetweenc'betweenc’withinbwithinINDbetween = aINDwithin = a 49 A total of six mediation models were built to address the third research aim. That is, six (simple) multilevel mediation models were built to assess the mediating role of positive IER strategies (Models 4, 5, 6) and negative IER strategies (Models 7, 8, 9) on each of the three outcome variables. All mediation models were built following syntax for a 1-1-1 deconflated model provided by Preacher and colleagues’ (2010; 2011). A sample mediation model with fixed slopes (again predicting satisfaction with support and the mediating role of positive IER strategies) is defined by the following equations: Level 1: Positive IER Strategiesij = β0j(1) + βaj (Mode of Communicationij) + rij(1) , (1) Satisfactionij = β0j(2) + βc’j (Mode of Communicationij) + βbj (Positive IER Strategiesij) + rij(2) . (2) Level 2: β0j(1) = γ00(1) + u0j(1) , β0j(2) = γ00(2) + u0j(2) , aj = a , bj = b , c’j = c’. In multilevel mediation, two equations are specified at Level 1: one that estimates an average level of IER strategies used across time points for each participant (Equation 1) and another that estimates the average level of satisfaction across time points for each participant (Equation 2). Specifically, Equation 1 assesses whether mode of communication used in a given time point predicts positive IER strategies (βaj) and Equation 2 assesses whether mode 50 of communication at a given time point has an effect on satisfaction ratings (βbj) via the positive IER strategies used in that time point (i.e., the joint effect of mode of communication and positive IER strategies on satisfaction; βc’j). Like in traditional MLM, each term in the Level 1 equations also specifies a corresponding equation at Level 2 to simultaneously assess person-level differences and quantify overall average effects across individuals. That is, an overall average IER strategy rating (β0j(1)) is estimated based on an individual’s average IER strategy ratings (γ00(1)) plus any other unexplained deviation from this average (u0j(1)). Additionally, a Level 2 equation is computed for each of the mediation paths to estimate the effects for each person. Random effects models would theoretically also factor in a term to account for the random slopes, or variability across participants. The indirect effect of mode of communication on satisfaction through IER strategies is then computed via the product of the a and b paths, IND = ab, and in deconflated mediation models, this product is computed at both levels. Fixed slopes assume no covariance between regression coefficients, a and b, however if variation across participants (i.e., random slopes) was modeled, the indirect effect would be equal to the sum of the product, ab, plus a covariance term. Confidence intervals (95%) tested the significance of the effects in all models. This was done using the delta or Sobel test method, the default in Mplus. One criticism of this method in testing the product of two coefficients (i.e., the indirect effect) is that it assumes indirect effects have a normal and symmetric distribution and since this is not often the case (e.g., MacKinnon et al., 2004), its confidence intervals can be inaccurate. As bootstrapping or resampling methods are not yet available for hierarchical data, the distribution-of-product (DOP) approach was used. The DOP method produces more accurate results compared to the traditional Sobel test (MacKinnon et al., 2004; Tofighi & Thoemmes, 2014) and was 51 therefore used to test the indirect effects. Beta estimates and standard errors of the a and b paths were entered into the RMediation package (Tofighi & MacKinnon, 2011) in R version 4.0.3, where confidence intervals for indirect effects can be computed using a variety of methods, such as the DOP method. Exploratory analyses also assessed the mediating role of each individual IER strategy in the relationship between mode of communication and the outcome variables again using 1-1-1 multilevel mediation described above. 52 Results Data Screening In preparation for analyses, data was screened following recommendations by Tabachnick & Fidell (2013). Missing data, the presence of univariate and multivariate outliers, and normality were examined. The prevalence of missing data was examined in each of the daily diary study variables (mode of communication, positive and negative emotions, satisfaction with support, and positive and negative IER strategies). Less than 1% of missing data was present in each variable, which is within appropriate guidelines (e.g., Heck & Thomas; Tabachnick & Fidell, 2013). A total of 548 time points (95.8%) had complete data. Robust estimators in MLM allow data to be at least missing at random (versus missing completely at random). To assess for this, patterns of missing data were inspected. Aside from two time points in which only negative IER strategies were missing, no notable patterns of missingness were observed. Data was thus assumed to be missing at random and therefore all available data was used in analyses. In three instances (two participants), careless responding was identified and these time points were removed. Inspection of univariate and multivariate outliers was performed on the positive and negative emotion subscales and the positive and negative IER subscales. Outlier analysis was not conducted on the satisfaction with support or the individual IER strategy variables given the narrow range of possible responses (i.e., 0 to 3) and the fact that values at either end of this range are valid. Inspection of boxplots as well as calculation of standardized z-scores revealed five and 16 extreme cases (z > ± 3.29) on negative emotions and negative IER strategies, respectively. In all 21 cases, these scores were changed to be one unit smaller (or 53 larger) than the next extreme value in order to reduce their deviance from normality (Tabachnick & Fidell, 2013). Score-altered variables were included in the assessment of multivariate outliers; nine were identified through Mahalanobis distance with p < .001. Normality for the daily diary study variables were inspected in terms of skewness and kurtosis values and a visual inspection of histograms (Tabachnick & Fidell, 2013). All variables were within acceptable limits for skew and kurtosis (z < ± 1.96) except for total negative IER strategies, which was excessively positively skewed and leptokurtic (i.e., very low endorsement). Logarithmic transformation of this variable (plus a constant of 1) assisted with bringing skew and kurtosis values closer to zero (i.e., within z < ± 1.0). An examination of Mahalanobis distance scores no longer revealed the presence of any multivariate outliers. The frequency distribution of the mode of communication variable indicated that 28.8% of valid time points involved digital only communication, which was considered acceptable (Rummel, 1970, as cited in Tabachnick & Fidell, 2013). Main analyses were run both with and without transformed and altered variables. Since the results were largely unchanged, the results are presented using the original, unaltered variables to aid interpretation. Individual IER strategies were also examined in preparation for exploratory analyses. Positive IER strategies were within acceptable limits for skew and kurtosis. However, all negative IER strategies were extremely positively skewed and leptokurtic, with values of skewness and kurtosis well-above even extreme cut-offs (e.g., values were between ± 5 < z < 14). The majority of negative IER strategies were reported as not occurring at all during an interaction. In fact, only 4.5% to 8.0% and 7.2% to 14.7% of time points reported a negative IER strategy was present at least to some degree in digital and in-person contexts, 54 respectively. In consideration of these factors, individual negative IER strategies were omitted from further examination and exploratory analyses. Finally, collinearity diagnostics were run to assess for multicollinearity and singularity. Although two root variables had variance proportions above .70, multicollinearity between pairs of variables was not evident. Bivariate correlations revealed that all pairs of variables were correlated between r = ± .04 and r = ± .62. Response Rates Two hundred and seventy-one eligible participants completed Part One of the study and 254 of those (93.7%) completed Part Two. Participants who completed only Part One of the study did not differ significantly from those who also completed Part Two in terms of their gender, age, or ethnicity, p’s > .05. There was a total of 3,556 possible participation opportunities for the 254 participants who completed the daily diary portion of the study (14 time points per person), of which 3,091 (86.9%) responses were received. Response rates were similar for afternoon and evening assessments (82.8% and 80.8%, respectively). The majority of participants (n = 214; 84.3%) completed at least 75% of all possible time points. Of all time points received, participants reported experiencing at least some degree of negative emotions in approximately 72% of cases. Participants indicated that they had turned to social supports for assistance in dealing with these negative emotions in 41.8% of these time points (n = 929) and therefore analyses addressing Research Aims 1 to 3 utilized only these 929 time points, which came from 237 participants. Compared to participants who did not turn to anyone for support in dealing with negative emotions, those who engaged in IER 55 were more likely to be female, t(17.73) = -2.81, p < .05. There were no differences in terms of age and ethnicity (p’s > .05). Sample Characteristics Sample characteristics for the 237 participants are presented in Table 1. The mean age of participants was 19.81 years (SD = 1.64), of which the majority (76.8%) identified as female. The sample was largely Caucasian (71.7%) and approximately three-quarters of participants were born in Canada (75.9%). Most were single at the time of data collection (57.0%). Research Aim 1: Descriptive Characteristics of Daily IER The current section describes the nature of daily IER among those 237 participants who reported turning to others for support in dealing with their negative emotions. Of note, participants reported higher levels of positive emotions (M = 8.54, SD = 4.55, range = 0-21) compared to negative emotions (M = 5.49, SD = 4.20, range = 1-24) over the course of the study period and this difference was significant (Z = -12.22, p < .001). IER Strategies Overall, participants reported that their support sources responded to them with a significantly greater degree of positive IER strategies (M = 7.68, SD = 2.83, range = 0-12) compared to negative strategies (M = 1.29, SD = 2.16, range = 0-13; Z = -24.95, p < .001). At least one positive IER strategy was reported (i.e., occurred at least “a little bit” on at least one 56 Table 1 Sample Characteristics for n = 237 n % Gender Identity Male 53 22.3 Female 182 76.8 Non-binary 1 0.4 Relationship Status Single/never married 135 57.0 In a relationship 100 42.2 Other 2 0.8 Race/Ethnicity White/Caucasian 170 71.7 Asian 41 17.3 Black/African American 4 1.7 Spanish/Hispanic/Latino 3 1.3 Aboriginal/Indigenous 2 0.8 Other 16 6.8 Born in Canada 180 75.9 Living Arrangements Alone 28 11.8 With parent(s) 56 23.6 With romantic partner 16 6.8 With close friends 39 16.5 With roommates/peers 64 27.0 Other 27 11.4 Note. Percentages not totalling 100% reflect missing data. IER strategy) in almost all time points (98.8%), and at least one negative IER strategy in less than half (46.5%) of time points. The mean number of positive strategies used at each time point was 3.56 (SD = 0.83, median = 4), whereas the mean number of negative strategies used was 0.91 (SD = 1.31, median = 0); a difference that was statistically significant (Z = -24.80, p < .001). See Table 2 for a breakdown of the means and standard deviations for each strategy. 57 Table 2 Descriptive Statistics for Positive and Negative IER Strategies n Mean SD Time points (%)b Total extent of positive strategiesa 925 7.68 2.83 918 (99.2) Listen to your problems 923 2.46 .78 905 (98.0) Give you advice 924 1.79 .98 823 (89.1) Make you laugh 919 1.73 1.01 797 (86.7) Point out your positive characteristics 918 1.73 1.01 785 (85.5) Total extent of negative strategiesa 925 1.29 2.16 432 (46.7) Criticize you 924 .31 .63 212 (22.9) Seem uninterested 921 .29 .65 192 (20.8) Talk down to you 920 .28 .67 168 (18.2) Seem annoyed with you 916 .25 .60 161 (17.6) Ignore you 921 .16 .48 110 (11.9) Note. Individual IER strategies were measured on a four-point Likert scale (0 = “not at all,” 1 = “a little bit,” 2 = “somewhat,” and 3 = “a lot”). aPossible range is 0 to 12 for total positive IER and 0 to 15 for total negative IER strategy use. bTime points = number of available cases that the strategy was endorsed at least “a little bit.” Multiple strategies may have been used at one time point. Sources of Support Participants reported turning to an average of 1.31 people (SD = 0.61, range 1-5) during their interactions. In the majority of time points (76.2%), participants turned to a single support source. Participants were asked to identify who they considered to be their main support source. A close friend was most often identified as the main person turned to for support (39.7%), followed by a romantic partner (29.3%), and their mother (16.4%). See Figure 3 for a breakdown of main support sources. 58 Figure 3 Main Support Source Reported Across n = 929 Interactions Modes of Communication During each time point when participants engaged in IER, they reported using an average of 1.33 different modes of communication (SD = 0.80, range 1-6) with their main support source. In-person interactions were the most frequently used method for obtaining support (n = 534; 57.5%) followed by texting (n = 267; 28.7%) and telephone conversations (n = 249; 26.8%). Other digital forms of communication were used less frequently, as follows: Snapchat (8.8%), video chat (6.0%), Facebook (2.0%), Instagram (2.0%), and Close Friend; 39.72%Mother16.36%Father4.95%Romantic Partner29.28%Sibling3.98%Acquaintance1.18%Classmate2.80%Other1.72% 59 Twitter (0.1%). See Figure 4 for all modes of communication used across the 929 time points. Figure 4 Modes of Communication Used with Main Support Source Note. Figure includes instances in which multiple modes of communication were used. Of the 929 instances in which IER occurred, the majority of time points (61.7%) reported using only in-person (n = 408; 43.9%) or only digital (n = 165; 17.8%) communication. The 165 digital only time points were comprised of texting alone (n = 123; 74.5%), social media alone (n = 24; 14.5%), and both texting and social media (n = 18; In-person43.38%Phone20.23%Video chat4.55%Texting21.69%Snapchat6.66%Facebook1.54%Instagram1.54%Twitter0.08% Other0.32% 60 10.9%). In the remaining 37.8% of IER time points, telephone only communication was reported most (n = 147; 41.9%), followed by digital and in-person (n = 59; 16.8%), and video chat only (n = 35; 10.0%). All other permutations of communication methods were reported in less than 10% of time points. When any mode other than in-person communication was used, participants were asked to estimate the time it took to receive a response from their main support source. Of those 513 time points, participants reported receiving a response from their support sources immediately (41.9%) or within a few minutes (38.2%), followed by responses within an hour (13.8%), within a few hours (4.1%), or those which they had not yet received (1.9%). Within those that involved only digital communication, the majority of responses were received within a few minutes (44.8%) followed by responses received immediately (23.9%), within an hour (22.1%), within a few hours (6.1%), or those which they had not yet received (3.1%). Research Aim 2: Association Between Mode of Communication and Satisfaction with Support and Emotions Three separate models (Models 1 to 3) were built to assess the relationship between mode of communication and each of the study outcome variables (i.e., satisfaction with support, positive emotions, and negative emotions). To ensure a more precise comparison between digital and in-person communication, these analyses only utilized time points in which individuals used either only digital or only in-person communication methods. This resulted in a sample of 573 time points from 202 participants (range = 1 to 9 time points).1 1 Participants in this subsample did not statistically differ from the larger sample based on gender, age, or ethnicity, p’s > .05. 61 Model 1: Mode of Communication and Satisfaction A null model was constructed to estimate the between- and within-person variability in satisfaction ratings. The ICC value was .33, indicating that 77% of the variability was within-people. When the mode of communication variable was assessed as a fixed parameter, the model fit improved significantly compared to the null model, χ2(1) = 5.80, p < .05. When the effects were permitted to vary randomly, model fit was not significantly improved over and above the fixed effects model. The predictor was thus retained as being fixed, suggesting that the Level 1 association between mode of communication and satisfaction was consistent across individuals in the sample. Results from the final fixed effects model indicate that at times when participants used digital communication, they reported significantly lower levels of satisfaction with how their support person responded to them (γ10 = -0.19, SE = 0.08, p < .05, 95% CI [-0.34, -0.04]; M = 2.18, SD = 0.80), compared to when they used in-person communication (M = 2.44, SD = 0.77). Deconflation of the model into its within- and between-level effects revealed that the significant main effect was at the between, but not within, level (between: γ10B = -0.61, SE = 0.04, p < .001, 95% CI [-1.13, -0.09]; within: γ10W = -0.10, SE = 0.10, p = .30, 95% CI [-0.29, 0.09]), only partially supporting hypotheses. In other words, the aggregated effect at the between-person level signifies that individuals who used an overall greater proportion of digital only communication across the study period reported significantly lower overall levels of satisfaction. However, at time points when individuals reported using only digital communication, their satisfaction with support did not differ significantly compared to time points when they used in-person communication. 62 Model 2: Mode of Communication and Positive Emotions Following the same procedure outlined above, the null model was constructed and it revealed a similar amount of between-person variability in positive emotions (ICC = .35). When mode of communication was assessed as a fixed predictor, it did not significantly improve model fit over the null model, χ2(1) = 3.51, p = .06. The mean level of positive emotions at time points when digital only communication was used was 7.64 (SD = 4.68) and was 8.57 (SD = 4.44) when in-person communication was used. Models failed to reveal significant effects of mode of communication at either the between- or within-person level. These results were unexpected and counter to hypotheses. Model 3: Mode of Communication and Negative Emotions Finally, a model was created to assess the role of mode of communication and negative emotions. The null model revealed that over one-third of the variability in negative emotions was between people (ICC = .37). Mode of communication improved the model fit as a fixed parameter over the null model, χ2(1) = 7.74, p < .01, but when subsequently assessing the parameter as a random predictor, model fit did not improve further. Again, this suggests that the effect of mode of communication on negative emotions was consistent across the sample. The final fixed effects model indicated that, compared to using only in-person communication (M = 4.79, SD = 3.74), using only digital was associated with significantly higher levels of negative emotions (γ10 = 1.28, SE = 0.41, p < .01, 95% CI [0.48, 2.09]; M = 6.47, SD = 4.46). Effects were again found at the between-, but not within-person, level of analysis (between: γ10B = 3.69, SE = 1.35, p < .01, 95% CI [1.05, 6.33]; within: γ10W = 0.81, SE = 0.53, p = .14, 95% CI [-0.23, 1.86]), suggesting that participants who used a 63 greater proportion of digital only communication across the study period also reported significantly higher levels of negative emotions, partially supporting hypotheses. Parameter estimates for all models are displayed in Table 3. Table 3 Parameter Estimates for Research Aim 2: Mode of Communication and Satisfaction, Positive Emotions, and Negative Emotions Parameter Estimate Std. Error Sig. (2-tailed) 95% CI Lower Upper Model 1: Satisfaction Intercept (γ00) 2.424 .049 .000 2.327 2.520 Mode of communication (γ10) -.189 .078 .015 -.342 -.036 Within (γ10W) -.099 .096 .304 -.287 .089 Between (γ10B) -.607 .040 .000 -1.126 -.088 Model 2: Positive emotions Intercept (γ00) 8.618 .282 .000 8.066 9.170 Mode of communication (γ10) -.863 .460 .061 -1.765 .038 Within (γ10W) -.856 .596 .151 -2.024 .312 Between (γ10B) -.927 1.508 .539 -3.883 2.029 Model 3: Negative emotions Intercept (γ00) 4.811 .238 .000 4.344 5.278 Mode of communication (γ10) 1.289 .414 .002 .478 2.100 Within (γ10W) .813 .532 .127 -.231 1.856 Between (γ10B) 3.686 1.346 .006 1.047 6.325 Note. Mode of communication was coded as 1 = digital only and 0 = in-person only. Research Aim 3: The Mediating Role of IER Strategies The next aim was to determine whether differences in IER strategies could help explain the association between mode of communication and study outcomes. Six multilevel mediation models were built (i.e., simple multilevel mediation models were run for both 64 positive and negative IER strategies, for each of the three outcomes). Deconflated models again allowed for a closer consideration of the separate within- and between-person effects specific to mediation. That is, deconflated mediation models allow for examination of whether indirect effects exist solely at the within-level, between-level, or both. Positive IER Strategies as a Mediator Model 4: Positive IER Strategies Mediating the Mode of Communication-Satisfaction Relationship (see Table 4). A deconflated 1-1-1 mediation model yielded within- and between-person effects. At the within-person level, analyses revealed that the level of positive IER strategies mediated the relationship between mode of communication and satisfaction with support (INDW = -0.08, SE = 0.04, p < .05, 95% CI = [-0.15, -0.02]). Examination of the paths that comprise the indirect effect indicated that at times when participants turned to support sources using digital (versus in-person) communication, they reported lower levels of positive IER strategies (aW path; ß = -0.49, SE = 0.21, p < .05, 95% CI [-0.90, -0.09]). These lower levels of positive IER strategies were associated with lower levels of satisfaction (bW path; ß = 0.17, SE = 0.02, p < .001, 95% CI [0.14, 0.20]). The direct effect was not statistically significant. At the between-person level, the indirect effect of mode of communication on satisfaction with support via positive IER strategies was also statistically significant (INDB = -0.30, SE = 0.14, p < .05, 95% CI = [-0.610, -0.002]). Participants who reported using a greater proportion of digital communication also reported a lower than overall average level of positive IER strategies across the study period (aB path; ß = -1.84, SE = 0.94, p = .049, 95% CI [-3.676, -0.011]). This lower than average level of positive IER strategies also 65 predicted a lower satisfaction with support (bB path; ß = 0.16, SE = 0.02, p < .001, 95% CI [0.13, 0.19]). The direct effect was not statistically significant. Overall, results supported hypotheses for the indirect role of mode of communication on satisfaction via positive IER strategies, and at both levels of analysis. Model 5: Positive IER Strategies Mediating the Mode of Communication-Positive Emotions Relationship (see Table 5). Despite the fact that a nonsignificant effect was found between mode of communication and positive emotions in Research Aim 2, mediation analyses were still tested. Drawing from contemporary approaches to mediation reviewed above (e.g., A. Hayes, 2018; MacKinnon et al., 2000), a true indication of a mediated relationship may be missed by solely relying on the overall association between two variables. Furthermore, as the current study represents one of the first to assess this research question (and provide further information about deconflated effects), mediation analyses were still conducted. A similar analytic approach as above was used to model mediation with positive emotions. When positive IER strategies were included in the model, the relationship between mode of communication and positive emotions was found to indeed be mediated by the level of positive IER strategies used in the interaction (INDW = -0.19, SE = 0.10, p < .05, 95% CI = [-0.40, -0.03]). Specifically, the use of digital only (versus in-person only) communication predicted a lower level of positive IER strategies being used (aW path; ß = -0.50, SE = 0.21, p < .05, 95% CI [-0.90, -0.09]), which then predicted a lower level of positive emotions being reported (bW path; ß = 0.39, SE = 0.09, p < .001, 95% CI [0.21, 0.57]). The direct effect was not statistically significant. A statistically significant indirect effect was not found at the between-person level (INDB = -1.24, SE = 0.64, p = .05, 95% CI [-2.663, 0.004]). Using a greater proportion of 66 digital only communication across the study period did not predict positive IER strategies to a statistically significant degree (aB path; ß = -1.81, SE = 0.92, p = .051, 95% CI [-3.62, 0.01]), however the use of a greater overall level of positive IER strategies did significantly predict a greater degree of positive emotions (bB path; ß = 0.70, SE = 0.11, p < .001, 95% CI [0.47, 0.91]). The direct effect was not statistically significant. Overall, there was partial support of an indirect relationship for the role of positive IER strategies on positive emotions, with a statistically significant mediated effect found within-people. Model 6: Positive IER Strategies Mediating the Mode of Communication-Negative Emotions Relationship (see Table 6). At the within-person level, analyses revealed that positive IER strategies mediated the relationship between mode of communication and negative emotions on the basis of the confidence interval, but not the test of statistical significance (INDW = 0.10, SE = 0.07, p = .12, 95% CI [0.002, 0.255]). Examination of the paths that comprise the indirect effect indicated that at times when digital communication was used, a significantly lower level of positive IER strategies was reported (aW path; ß = -0.49, SE = 0.21, p < .05, 95% CI [-0.89, -0.09]), and in turn, higher levels of negative emotions were experienced (bW path; ß = -0.21, SE = 0.09, p < .05, 95% CI [-0.39, -0.02]). The direct effect was not statistically significant. At the between-level of analysis, a significant indirect effect was not found (INDB = 0.56, SE = 0.33, p = .09, 95% CI [-0.006, 1.384]). Examination of the individual paths that comprise the effect, however, were significant. Using a greater proportion of digital communication across the study period predicted a lower than overall average level of 67 Table 4 Parameter Estimates for Model 4: Mode of Communication (X), Total Positive IER Strategies (M), and Satisfaction with Support (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.493 .205 .016 -.896 -.090 -1.844 .935 .049 -3.676 -.011 b path .167 .015 .000 .136 .197 .162 .015 .000 .132 .191 c’ path .024 .080 .762 -.132 .181 -.402 .210 .056 -.814 .011 Indirect effect (ab) -.082 .036 .023 -.153 -.015 -.298 .142 .036 -.610 -.002 Total effect (c path) -.058 .088 .510 -.230 .114 -.700 .255 .006 -1.200 -.200 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab Table 5 Parameter Estimates for Model 5: Mode of Communication (X), Total Positive IER Strategies (M), and Total Positive Emotions (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.495 .206 .016 -.899 -.091 -1.805 .924 .051 -3.616 .006 b path .390 .094 .000 .205 .574 .689 .114 .000 .465 .912 c’ path -.591 .581 .309 -1.730 .548 .141 1.346 .917 -2.498 2.780 Indirect effect (ab) -.193 .096 .045 -.401 -.031 -1.243 .636 .050 -2.663 .004 Total effect (c path) -.784 .586 .181 -1.933 .365 -1.102 1.479 .456 -4.000 1.796 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab 68 Table 6 Parameter Estimates for Model 6: Mode of Communication (X), Total Positive IER Strategies (M), and Total Negative Emotions (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.492 .205 .016 -.894 -.090 -1.864 .950 .050 -3.727 -.002 b path -.209 .094 .027 -.394 -.024 -.302 .104 .004 -.507 -.097 c’ path .659 .534 .217 -.388 1.707 3.242 1.297 .012 .699 5.785 Indirect effect (ab) .103 .066 .117 .002 .255 .563 .329 .087 -.006 1.384 Total effect (c path) .762 .530 .150 -.276 1.801 3.805 1.332 .004 1.193 6.416 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab 69 positive IER strategies (aB path; ß = -1.86, SE = 0.95, p = .05, 95% CI [-3.727, -0.002]). In turn, this lower than average use of positive IER strategies was related to negative emotions (bB path; ß = -0.30, SE = 0.10, p < .01, 95% CI [-0.51, -0.10]). The direct effect was statistically significant (c’B path; ß = 3.24, SE = 1.30, p < .05, 95% CI [0.70, 5.79]). Again, results partially supported the indirect effect of positive IER strategies on negative emotions, with a significant mediation effect found within-people. Negative IER Strategies as a Mediator Model 7: Negative IER Strategies Mediating the Mode of Communication-Satisfaction Relationship (see Table 7). The indirect effect of mode of communication on satisfaction with support via negative IER strategies was non-significant at both the within- and between-person level, contrary to expectations. At both levels, mode of communication also failed to predict the degree of negative IER strategies reported. However, level of negative IER strategies was a significant predictor of satisfaction at both the within (bW path; ß = -0.12, SE = 0.04, p < .01, 95% CI [-0.19, -0.05]) and between-person (bB path; ß = -0.18, p < .001, SE = 0.03, 95% CI [-0.25, -0.12]) levels. Direct effects at both levels were not statistically significant. Model 8: Negative IER Strategies Mediating the Mode of Communication-Positive Emotions Relationship (see Table 8). Indirect effects of mode of communication on positive emotions via negative IER strategies were not significant at either the within- or between-person level (INDW : ß = -0.32, SE = 0.03, p = .33, 95% CI [-0.12, 0.03]); INDB : ß = -0.04, SE = 0.12, p = .71, 95% CI [-0.46, 0.28]), against expectations. At both levels of analysis, mode of communication did not predict negative IER strategies and negative IER 70 strategies were not significantly related to positive emotions. Direct effects at both levels of analysis were not statistically significant. Model 9: Negative IER Strategies Mediating the Mode of Communication-Negative Emotions Relationship (see Table 9). There were no significant within- or between-person indirect effects found in the relationship between mode of communication and negative emotions via negative IER strategies (INDW : ß = 0.01, SE = 0.02, p = .53, 95% CI [-0.04, 0.09]); INDB : ß = 0.31, SE = 0.36, p = .38, 95% CI [-0.41, 1.21]), contrary to expectations. A closer examination of the mediation paths revealed that, between people, the use of a greater overall level of negative IER strategies across the study period was related to higher levels of negative emotions (bB path; ß = 0.47, SE = 0.17, p < .01, 95% CI [0.13, 0.80]). The direct effect was statistically significant only at the between level (c’B path; ß = 3.18, SE = 1.32, p < .05, 95% CI [0.59, 5.78]). Exploratory Analyses Exploratory mediation models were built using a similar 1-1-1 design to examine the mediating role of each of the individual positive IER strategies (listening to one’s problems, pointing out one’s positive characteristics, providing advice, and making someone laugh). As negative IER strategies were very rarely reported, and as their overall total failed to detect mediation, individual strategies were not included in exploratory analyses. “Listened to My Problems.” Exploratory analyses failed to detect any significant indirect effects at either level of analysis. Closer examination of the mediation paths also revealed that mode of communication did not significantly predict the use of this strategy, however when participants reported that their support person listened to their problems to a 71 Table 7 Parameter Estimates for Model 7: Mode of Communication (X), Total Negative IER Strategies (M), and Satisfaction with Support (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path .155 .145 .287 -.130 .439 .662 .768 .389 -.843 2.166 b path -.116 .036 .001 -.187 -.046 -.182 .033 .000 -.247 -.117 c’ path -.094 .099 .341 -.288 .100 -.419 .228 .066 -.867 .028 Indirect effect (ab) -.018 .016 .249 -.059 .015 -.121 .129 .348 -.416 .154 Total effect (c path) -.112 .095 .235 -.297 .073 -.540 .261 .038 -1.051 -.029 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab Table 8 Parameter Estimates for Model 8: Mode of Communication (X), Total Negative IER Strategies (M), and Total Positive Emotions (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path .155 .145 .286 -.130 .440 .613 .759 .419 -.873 2.100 b path -.206 .109 .059 -.420 .008 -.069 .170 .686 -.402 .265 c’ path -.803 .602 .182 -1.983 .377 -.951 1.580 .547 -4.049 2.147 Indirect effect (ab) -.032 .033 .334 -.122 .028 -.042 .115 .713 -.463 .283 Total effect (c path) -.835 .599 .162 -2.010 .339 -.993 1.554 .523 -4.039 2.053 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab 72 Table 9 Parameter Estimates for Model 9: Mode of Communication (X), Total Negative IER Strategies (M), and Total Negative Emotions (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path .154 .145 .286 -.129 .438 .667 .773 .389 -.849 2.183 b path .090 .138 .514 -.180 .360 .468 .172 .006 .131 .804 c’ path .843 .521 .105 -.177 1.864 3.182 1.323 .016 .589 5.776 Indirect effect (ab) .014 .022 .532 -.041 .093 .312 .355 .381 -.410 1.212 Total effect (c path) .857 .523 .101 -.169 1.883 3.494 1.353 .010 .842 6.146 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab 73 greater than average degree, they also reported higher satisfaction with the support (bW path; ß = 0.37, SE = 0.06, p < .001, 95% CI [0.26, 0.48]; bB path; ß = 0.76, SE = 0.06, p < .001, 95% CI [0.63, 0.88]). Findings for positive and negative emotions were similar in that greater overall endorsement of the strategy “listened to my problems” was associated with a higher level of positive emotions (bB path; ß = 1.65, SE = 0.46, p < .001, 95% CI [0.75, 2.56]) and lower level of negative emotions (bB path; ß = -1.33, SE = 0.42, p < .01, 95% CI [-2.16, -0.50]) at the between level only. “Pointed Out My Positive Characteristics.” Indirect effects of mode of communication on all three outcome variables via “pointed out my positive characteristics” were not found at either the within- or between-person level. Examination of the mediation paths revealed that mode of communication (within and between people) was not associated with participants’ report of “pointed out my positive characteristics.” However, a positive association was found between the use of this strategy and satisfaction with support (bW path; ß = 0.30, SE = 0.05, p < .001, 95% CI [0.20, 0.39]; bB path; ß = 0.36, SE = 0.05, p < .001, 95% CI [0.26, 0.46]) and positive emotions (bW path; ß = 0.60, SE = 0.25, p < .05, 95% CI [0.11, 1.08]; bB path; ß = 1.40, SE = 0.34, p < .001, 95% CI [0.73, 2.07]), and at both levels of analysis. There was no significant association between this strategy and negative emotions. “Gave Me Advice.” Analyses failed to detect a significant indirect effect of mode of communication on each of the outcome variables via the strategy “gave me advice.” Mediation paths again revealed that mode of communication did not predict levels of the strategy “gave me advice” at either level of analysis, however the use of this strategy to a greater than average degree was significantly associated with a higher level of satisfaction with support (bW path; ß = 0.32, SE = 0.04, p < .001, 95% CI [0.23, 0.40]; bB path; ß = 0.35, 74 SE = 0.06, p < .001, 95% CI [0.24, 0.46]). Findings in the positive emotions model revealed similar results, but only at the between-person level (bB path; ß = 1.63, SE = 0.36, p < .001, 95% CI [0.93, 2.34]). No significant paths were found between “gave me advice” and negative emotions. “Made Me Laugh.” The exploratory analyses revealed significant indirect effects of mode of communication on all three outcome variables via the strategy “made me laugh.” The indirect effect for satisfaction with support was found at both levels of analysis (INDW = -0.04, SE = 0.02, p < .05, 95% CI = [-0.09, -0.01]; INDB = -0.30, SE = 0.09, p < .01, 95% CI = [-0.54, -0.09]). For the emotion outcome variables, indirect effects were again found at both the within- (positive emotions: INDW = -0.26, SE = 0.12, p < .05, 95% CI = [-0.52, -0.05]; negative emotions: INDW = 0.19, SE = 0.09, p < .05, 95% CI = [0.04, 0.38]) and between-person (positive emotions: INDB = -1.93, SE = 0.68, p < .01, 95% CI = [-3.49, -0.63]; negative emotions: INDB = 0.82, SE = 0.37, p < .05, 95% CI = [0.14, 1.78]) level. Examination of the paths that comprised the mediated effect revealed that across time points, when participants reported using digital communication in their IER interactions, they were also less likely to report that their support person made them laugh (aW path; ß = -0.17, SE = 0.07, p < .05, 95% CI [-0.31, -0.03]), and this in turn predicted lower satisfaction ratings (bW path; ß = 0.25, SE = 0.04, p < .001, 95% CI [0.17, 0.34]), lower positive emotions (bW path; ß = 1.51, SE = 0.27, p < .001, 95% CI [0.98, 2.05]), and higher negative emotions (bW path; ß = -1.09, SE = 0.23, p < .001, 95% CI [-1.54, -0.64]). Likewise, between people, participants who reported a greater proportion of digital communication across the study period were less likely to report that their support provider made them laugh (aB path; ß = -0.88, SE = 0.30, p < .01, 95% CI [-1.47, -0.29]). This in turn predicted lower levels of 75 satisfaction with support (bB path; ß = 0.34, SE = 0.06, p < .001, 95% CI [0.23, 0.45]), lower levels of positive emotions (bB path; ß = 2.18, SE = 0.37, p < .001, 95% CI [1.46, 2.91]), and higher negative emotions (bB path; ß = -0.92, SE = 0.34, p < .01, 95% CI [-1.58, -0.26]). Parameter estimates for all exploratory analyses are reported in Appendix C – Parameter Estimates for Exploratory Analyses. 76 Discussion It has long been recognized that early on in development, individuals learn to seek social contact when confronted with distressing experiences. Technological advances have made it possible to connect with and seek support from others in diverse ways, and research has yet to fully understand the nuances in how technology can be best used for social support and ER. The field of IER also remains in its infancy and few studies have investigated how IER unfolds in a digital world. Combined with the increased prevalence and ubiquity of technology, the scarcity of research examining IER outside of in-person interactions introduces a significant knowledge gap that was addressed in the current study. As extant findings on the role and effectiveness of CMC have been mixed, this study provides a first glimpse into one factor – IER – that helps to explain why this may be the case. Findings from this intensive longitudinal study revealed that digital (compared to in-person) communication was associated with lower levels of positive IER strategies both within time points, and when looking across the study period. The use of IER strategies helped to explain the negative association between digital communication and both satisfaction with responses and positive emotions. Exploratory results suggested that these findings may have been driven by lower levels of laughter in digital (versus in-person) communication. Specifically, in digital communication, reports that support providers made participants laugh occurred to a lesser degree during digital (compared to in-person) communication, and this helped to explain the relationship between mode of communication and satisfaction with responses, as well as positive and negative emotions, at both levels of analysis. 77 Descriptive Characteristics of IER Across Diverse Media The first aim of the current study was to offer a detailed glimpse into the ways in which IER is used within in-person and digital contexts, and to explore the more general characteristics of IER in emerging adults as it unfolds on a daily basis. Most notably, in-person communication prevailed as the predominant way emerging adults interacted with others during stressful or upsetting circumstances. In-person communication was used on its own (or with other modes of communication) in almost 60% of time points that involved IER, which was more than double the rates with which texting (28.7%) and telephone (26.8%) conversations were employed, consistent with past work (Choi & Toma, 2014). As a stand-alone method, in-person only communication was used for IER during 43.9% of time points, which was again more than twice as common as using only digital communication for IER (i.e., less than 20% of time points). When digital communication was used for IER, emerging adults often appeared to use it in a synchronous manner, with the majority of responses from support sources being received within a few minutes (44.2%) or immediately (23.6%), in line with prevailing social norms (e.g., Forgays et al., 2014; Hall & Baym, 2012; Horstmanshof & Power, 2005; Laursen, 2005). Thus, when digital communication is being used for IER, it tends to closely mimic the synchronicity of in-person interactions. Given the high rates with which smartphone use has been reported in this age group (Pew Research Center, 2019a), as well as the concern among the general public that youth are overly reliant on digital devices (Pew Research Center, 2018), these findings are particularly noteworthy. This study also gathered more general information regarding the prevalence of IER and the source of support for IER among emerging adults. Participants in the current study reported sharing their negative emotional experiences most commonly with close friends 78 (39.7%), followed by romantic partners (29.3%), and mothers (16.4%), consistent with past literature highlighting the growing importance of relationships outside of the family of origin during this phase of development (e.g., Arnett, 2007; Feeney, 2004; Furman & Buhrmester, 1992). Overall, at least some degree of negative emotionality was experienced in over 70% of time points, and these negative emotions were shared in approximately 40% of those time points. Though no formal hypotheses were put forth, rates of sharing personal and emotional experiences have indeed ranged from 30 to 40 percent in observational studies (Dunbar et al., 1997). Past research suggests that individuals are more inclined and apt to seek support from others in response to high emotional intensity (Colasante et al., 2020; Dixon-Gordon, Aldao, & De Los Reyes, 2015; Luminet, Bouts et al., 2000; Sheppes & Gross, 2012). Yet, the present sample was generally well-adjusted, with significantly higher levels of positive (versus negative) emotions and overall low levels of negative emotions reported over the study period. Higher levels of positive affect, well-being, and happiness are known to act as protective factors against stress (Fredrickson, 2001; Fredrickson & Joiner, 2002) and have been associated with improved psychological and physical health outcomes (e.g., Deiner et al., 2017; Lyubomirsky et al., 2005). Thus, participants may not have reached out to others for IER support in a majority of time points simply because they did not perceive a need to do so. Furthermore, there may have been other factors or barriers that influenced social sharing of negative emotions, including sharing preferences (e.g., Choi & Toma, 2014), difficulties verbalizing emotions (Luminet, Zech et al., 2000), or perceived responsiveness of one’s interaction partner (Laurenceau et al., 1998). 79 Differential Associations Between Mode of Communication on Satisfaction and Emotions Another key aim of the current study was to better understand whether the mode of communication used for sharing negative emotions would impact emerging adults’ positive and negative emotions as well as their overall satisfaction with responses. Consistent with study hypotheses, using only digital forms of communication for IER was associated with lower levels of satisfaction and higher levels of negative emotions compared to in-person only communication, but these findings only emerged at the between-person level. That is, participants who generally reported greater reliance on digital communication (as measured by the extent to which they only used digital for IER across the study period) reported overall lower satisfaction and higher levels of negative emotions across the study period. These findings are in line with past experimental and longitudinal research that has found digital communication to be associated with worse satisfaction (Goodman-Deane et al., 2016; Holtzman et al., 2017; Mallen et al., 2003; Sacco & Ismail, 2014) and negative affect (Kushlev & Heintzelman, 2018; Verduyn et al., 2021) when directly compared to in-person interactions. From a theoretical standpoint, these results provide some support for the CFO theories of communication (e.g., Culnan & Markus, 1987), which state that digital methods are inferior to in-person communication because they lack nonverbal cues. Indeed, long before the rise of digitally-mediated communication, reports estimated that approximately 60 to 65 percent of communication was nonverbal (e.g., Philpot, 1983, as cited in Burgoon et al., 2010). As such, a great deal of important information may be lost when communicating solely by text or written word, and this may help to explain the significant associations between in-person communication and socioemotional outcomes. 80 Based on the within-person results of the simple multilevel models, mode of communication was not related to satisfaction with support or emotions within a given time point, which was contrary to study expectations and past research (e.g., Goodman-Deane et al., 2016; Kafestsios et al., 2017; Mallen et al., 2003; Ranney & Troop-Gordon, 2015; Sacco & Ismail, 2014). From a statistical standpoint, these findings were perplexing, especially as the majority of the variability in all outcome variables assessed was present within-people. One interpretation of this finding is that, within-people on a daily basis, there are a myriad of independent and interacting factors that are predictive of satisfaction with support and emotions and this precluded the ability to identify a simple main effect of mode of communication. The more powerful aggregate effect of digital communication on study outcomes suggests that while it may be sufficient to share negative emotions via digital communication every now and again, there may be cause for concern if digital communication is excessive or displaces in-person methods (Kushlev et al., 2019; Kushlev & Leitao, 2020; L. Lin et al., 2016; X. Lin & Lachman, 2021; Twenge et al., 2019). Indeed, heavy users of digital media have been shown to be up to 171% more likely to report lower levels of well-being and higher levels of unhappiness as well as being more likely to experience depression or suicidal ideation compared to those who used digital media less (Twenge & Campbell, 2019). IER Strategies Across In-Person and Digital Contexts While the use of digital communication for IER seemed relatively inconsequential for emotions and satisfaction with support within a given time point, a somewhat different picture emerged once the use of IER strategies was considered. Broadly speaking, the use of 81 digital communication seemed to have a more direct impact on the ways in which support sources responded to participants during their attempts to manage their emotions, and this in turn had significant associations with participants’ emotions and satisfaction with responses. Positive IER Strategies Significant differences in socioemotional outcomes between digital and in-person communication have been documented in the literature, but the third aim of this study was to examine why this may be the case, and specifically, whether the strategies used in each method of communication help to elucidate extant findings. It was hypothesized that positive IER strategies would occur to a lesser extent (and negative IER strategies to a greater extent) in digital contexts, and that this would help to explain differences in satisfaction and emotional outcomes, both at the within- and between-person level. These hypotheses were partially confirmed. Significant within-person mediation effects for satisfaction and positive emotions were found, such that at times when participants reported digital only communication, they subsequently reported lower levels of positive IER strategies, which in turn predicted significantly lower levels of satisfaction and positive emotions. A significant between-person mediation effect was also found for satisfaction. That is, when a greater proportion of digital only communication was used across the study period, lower overall satisfaction levels were reported, and this was due in part to lower reports of positive IER strategies occurring across interactions, on average. A parallel between-person indirect effect was not statistically significant for positive IER strategies mediating mode of communication and positive emotions (p = .05, 95% CI [-2.663, 0.004]), but the effect was in the expected direction. Lastly, in the negative emotions model, indirect effects were significant at the 82 within-person level, and though indirect effects were not found at the between-level, the individual mediation paths between mode of communication and IER strategies, as well as between IER strategies and negative emotions were statistically significant and in the expected direction. As a direct relationship with negative emotions was found, it may then be that other factors aside from positive IER strategies can better explain the association between mode of communication and negative emotions. Overall, these results are both encouraging and disheartening. On one hand, they reveal that digital communication itself is not inherently flawed; there are times when using it for IER provides similar degrees of support compared to in-person methods, and there is emerging research to suggest that any negative effects that do occur are balanced by other protective factors (Hancock et al., 2019). On the other hand, however, results do suggest that individuals may not be experiencing the same level of support for their negative emotions when using digital communication as they do in in-person contexts. Positive IER strategies seem to occur less during digital (rather than in-person) contexts. This is a novel finding that warrants further discussion, especially as the positive IER strategies assessed in the current study have been previously shown and theorized to improve affect (Niven et al., 2011). One interpretation is that communication of empathic concern may be more difficult to convey via digital methods of communication. Grondin and colleagues’ (2019) developed a framework to understand empathy in computer-mediated technologies. In a given interaction, they highlight four steps that must occur in order to establish empathy: (1) an individual (the “target”) emits socioemotional cues to a support provider, (2) the support provider feels empathy towards the target, (3) the support provider conveys empathy to the target, and (4) the target perceives empathy from the support provider. Each step in this 83 process is filtered through the mode of communication used and is influenced by a variety of contextual factors, such as the media’s richness and a person’s response time, for example. There are, then, a number of points in a digital interaction where one might expect IER to be less effective. First, the participant (“target”) must have been able to effectively communicate the degree and nature of his or her emotions and emotional distress. As a great deal of emotional content is communicated through nonverbal signals (e.g., facial expressions, tone of voice, or posture; Planalp, 1998), in a traditional text-based method of communicating, it would be difficult to translate that information solely using words (especially at the height of an emotional experience; Sheppes et al., 2011; Torre & Lieberman, 2018) or find an appropriate emoticon or gif to capture the extent of one’s feelings. Further, direct verbal expression of an emotion is predicated on the fact that the target is fully aware of the discrete emotions being experienced, which is not always easy to discern (Erbas et al., 2018; Harmon-Jones et al., 2017; Izard, 1992). Thus, if the target’s emotional experiences are being filtered or “diluted” through digital communication, the support provider may not have as much information to fully understand his or her partners’ emotional state and experience the same degree of empathic concern that he or she otherwise might in in-person contexts. The same limitations also exist when IER strategies are then used as a means of providing support to the target. That is, support providers’ attempts to use various IER strategies (e.g., to improve affect) may also be limited by the medium being used because any attempts to convey understanding or empathic concern are largely restricted to linguistic content. Indeed, empathic behaviours consist of both verbal (e.g., affirmations, elaborations) and nonverbal (e.g., nodding, tone of voice/intonation/pitch, eye contact) signals (Haase & Tepper, 1972; Jones & Guerrero, 2001; Riess & Kraft-Todd, 2014). Targets 84 therefore may be left feeling unsupported by the people they are closest to, even if those support people did feel and attempt to provide empathic concern or support in an effort to improve their partner’s affect. Findings across positive IER mediation models demonstrate that it is critical for researchers to go beyond simple comparisons of in-person versus digitally-mediated communication and instead look at the specific nature of the interactions that occur across different modes of communication. In doing so, we uncovered novel results that helped to articulate and clarify (rather than repeat) extant mixed findings between mode of communication and socioemotional outcomes. Important interpretations were also gleaned from examining the noted effects at each level of analysis. Direct effects were found between variables on the aggregate level, but a closer and more nuanced examination of the indirect relationships revealed that when interactions were examined to a more fine-grained degree, the behaviours that occur within interactions were important in predicting social and emotional outcomes. In essence, mediation models provided more clarity to understanding the reasons why mode of communication has been associated to satisfaction and emotional outcomes. Furthermore, there was at least some indication across the mediation models’ deconflated effects that a consistent, albeit not always statistically significant, pattern was present at both levels: individual mediation paths between mode of communication and positive IER strategies and between positive IER strategies and outcomes were all in the expected directions, and all but one were statistically significant. Thus, despite weak (or nonsignificant) indirect effects, digital communication still predicted fewer positive IER strategies, and these strategies predicted lower levels of satisfaction and positive emotions and higher levels of negative emotions. Differential findings at each level of analysis 85 emphasize the need and importance, from a statistical perspective, to take advantage of hierarchical data by looking at both levels of analysis, and utilizing appropriate techniques (e.g., deconflation of effects) to assist in doing so. Specific Positive IER Strategies The current study explored whether specific strategies could be driving the effect of positive IER strategies. When each strategy was examined separately, “made me laugh” was the only one to emerge as an independent and significant mediator. Digital communication was associated with lower reports that support sources made participants laugh and this was associated with worse satisfaction, lower positive emotions, and higher negative emotions. This mediation effect was identified at both levels of analysis for all three outcome variables, meaning that at time points when participants used only digital for IER, they were less likely to report that their support person made them laugh and that participants who generally reported more frequent use of only digital communication tended to report lower levels of laughter due to responses from support providers across the study period. This result is consistent with recent research in a controlled lab setting that found text messaging to elicit significantly fewer reports of laughter following stressful experiences, when compared to other forms of communication (Holtzman & Lisi, 2021). Digital communication may yield less laughter in an interaction for several reasons. Laughter has been purported to be an inherently social behaviour, with it most often occurring during social encounters (Scott et al., 2014). Indeed, there is evidence that individuals are more likely to laugh in the presence of others compared to when they are alone (Osborne & Chapman, 1977; Provine, 1992; Provine & Fischer, 1989). Laughter is also 86 contagious; individuals respond to laughter through laughter, even without a precipitating joke or humourous remark (Provine, 1992; Smoski & Bachorowski, 2003). In fact, laugh tracks or “canned” laughter are used to effectively increase laugh production in audiences (Chapman, 1973; Martin & Gray, 1996; Smyth & Fuller, 1972). Through an emotional mimicry lens, it has been suggested that humans have evolved with internal neural circuitry and auditory-motor mechanisms to detect the features of laughter, such as its unique vocalization and behavioural patterns, and respond to laughter with automated, mirror-like processes, similar to contagious yawning (Warren et al., 2006). However, while the specific mechanisms of contagious laughter are unclear, laughter’s unique acoustic properties (i.e., vowel-like, harmonic structure with repetitive, short syllables) likely contribute to its contagious and positive affect-inducing effects (Bachorowski & Owren, 2001). These acoustic elements of laughter are absent in text-based discourse and, so, their potential for mimicry is greatly reduced if not eliminated altogether. Researchers have also demonstrated that humans have higher-order linguistic processing, such that, in competition for the vocal tract, language is prioritized over laughter (Provine & Emmorey, 2006). In fact, laughter is thought to “punctuate” speech (Provine, 1993), occurring at places where punctuation appears, as opposed to interrupting a sentence’s phrase structure (Provine et al., 2007). This “punctuation effect” has been demonstrated in spoken, signed, and written mediums alike (Provine, 1993; Provine & Emmorey, 2006) and even applies to the use of emoticons (e.g., ‘😂’ to signify laughter) in text messaging (Provine et al., 2007). Examination of other individual IER strategies (e.g., “listened to my problems,” “pointed out my positive characteristics,” “gave me advice”) did not significantly explain the relationship between mode of communication and socioemotional outcomes. Parameter 87 estimates for the indirect effects of these other strategies, however, were in the expected directions, with a trend observed for the strategy “listened to my problems” (ß = -0.05, SE = 0.03, p = .09, 95% CI [-0.12, 0.01]) on satisfaction with support. Examination of the a paths indeed revealed that aside from “made me laugh,” the positive IER strategies examined in this study appear to be just as likely to occur in digital and in-person interactions. Indeed, attention and interest (key components of active listening) can be communicated via paraphrasing, validation, and asking clarifying questions, which may play a role in explaining the effectiveness of text-based or online psychotherapy and the strong alliances that are formed in these contexts (Barak et al., 2008; Hanley & Reynolds, 2009). The provision of informational support, such as giving advice, also tends to be more reliant on the content of the information itself (e.g., feasibility, relevance; Feng & MacGeorge, 2010; MacGeorge et al., 2008, 2016), as opposed to the way it is communicated, indicating that this strategy, too, can be effectively used via CMC. Negative IER Strategies Contrary to expectations, negative IER strategies (i.e., “criticized me,” “seemed uninterested,” “talked down to me,” “ignored me,” and “seemed annoyed with me”) did not mediate the digital-satisfaction or digital-emotion relationships, suggesting that the use of negative IER strategies do not seem to explain the poorer outcomes associated with digital communication. In fact, examination of the a paths revealed that across all models, using digital communication did not significantly predict negative IER strategies. In other words, negative IER strategies were no more likely to occur in digital communication than they were in in-person contexts. While this is discrepant from previous studies suggesting that 88 individuals may be more harsh, impolite, or disinhibited in digital contexts (Hobman et al., 2002; Suler, 2004), there may be several reasons for this finding. First, negative IER strategies were infrequently reported and when they occurred, it was to a very minimal extent, so there may not have been sufficient variability to detect an effect. It is also possible that individual difference factors, such as rejection sensitivity (Downey & Feldman, 1996), social anxiety (Torgrud et al., 2004), or mindfulness (DeClerck & Holtzman, 2018), may be more important predictors of the incidence of, or at least the perception of, negative interpersonal interactions. Finally, the research suggesting increased deindividuation and disinhibition in online contexts has predominantly focused on communication with weak ties or unfamiliar individuals, not close ties such as those most often reported in the current study. From a SIP theory perspective, with more time and use, users of CMC come to establish a framework and familiarity with how their chosen medium is used for communication. Thus, individuals in the current study have likely already established such a framework with their main support sources and therefore may be less likely to perceive their interactions as unduly harsh or critical. In examining the effects on socioemotional outcomes when negative IER strategies were used, however, findings (b paths) generally revealed that a greater degree of negative IER strategies was associated with lower satisfaction and higher negative emotions. This is consistent with past research showing that negative social interactions and interpersonal conflict are indeed associated with worse mood, well-being, and worse relationship or social outcomes (Baumeister et al., 2001; Lakey et al., 1994). 89 Study Limitations The intensive longitudinal design employed in the current study offered a number of advantages, including attaining a naturalistic snapshot of emerging adults’ use of various modes of communication for the purposes of intrinsic response-dependent IER, the association between modes of communication and socioemotional outcomes, and a first look at examining whether IER strategies might mediate this relationship. Nonetheless, limitations exist. First and foremost, the assessment of IER strategies relied on self-report and thus the results must be interpreted in terms of the participant’s own perception of the extent to which strategies occurred in an interaction. Second, the study focused on IER processes at times when participants reported experiencing at least “a little bit” of any of the negative emotions in the daily surveys. Findings therefore reflect the nature of daily IER across a variety of negative emotional states but do not differentiate between discrete negative emotions. Although some research has shown that patterns of social sharing are similar across discrete emotions (Rimé et al., 1991), it is possible that the IER process may look different for certain types of emotions. For instance, ER of anger and sadness differ in terms of which strategies are used and how effective they are in regulating those emotions (Dixon-Gordon, Aldao, & De Los Reyes, 2015; Rivers et al., 2007). The current study also assumed that individuals would prefer to down-regulate their negative emotions, consistent with mood management theory (Zillmann, 1988), however, this was not directly assessed. Finally, when indicating who their main support source was, participants were able to endorse that they had turned to “no one” for support. While it was assumed that these participants used intrapersonal strategies for managing their emotions (or no strategies at all), it is possible that this item 90 may be reflective of a variety of factors, including a person’s lack of interpersonal supports more generally. The current study utilized one of the few measures available to assess IER strategies. The theoretically-driven list of strategies was predicated on the implicit assumption that IER interactions occurred in-person. In fact, some of the strategies were not assessed in the current research because their applicability to CMC contexts was ambiguous and somewhat biased towards in-person interactions (e.g., “spent time with me”). The study was therefore not able to fully capture or assess all the ways in which support may be delivered, especially in digital contexts. This limitation highlights a broader need for more established measures of IER, inclusive of strategies that apply to other modes of communication. In order to conduct a clear-cut comparison between in-person and digital support, only those time points that involved a single mode of communication (digital only or in-person only) were used. This created an artificial dichotomy in an otherwise naturalistic dataset. Further, because digital only communication was infrequently used for IER, this greatly reduced the number of time points (and power) in within-person analyses. Confidence intervals of the indirect effects were also wide (and close to zero), which may be reflective of low levels of power. Restricting time points to those where only a single mode of communication was used resulted in 60 instances (out of 929 timepoints collected) where only one observation per person was obtained. Therefore, because there was no within-person variability in these instances, analyses did not always assess deviation from one’s average. Finally, restricting time points ultimately limited generalizability to situations where multiple methods of communication are used to interact with others. Indeed, media multiplexity theory contends that as the closeness of ties increases, so does the number of 91 communication channels they use to stay connected with one another (Haythornthwaite, 2005). In the current study, multiple methods were used in about 40% of time points. Therefore, the current study may have missed opportunities to examine IER interactions across multiple modes of communication. As highlighted by researchers, there may be important insights to be gained by examining communication from an interdependence perspective; that is, how multiple modes of communication are used simultaneously for interpersonal connection (Caughlin & Sharabi, 2013). Finally, the study is also limited in its generalizability to other populations. The sample was predominantly female, Caucasian, emerging adult, university students. Cultural differences have been documented in IER processes (De Leersnyder et al., 2013; Liddell & Williams, 2019; Matsumoto et al., 2008), the relative importance or function of social groups (Burleson & Mortenson, 2003), as well as choices of communication medium (Kim et al., 2011). Gender and age differences in social support seeking behaviours and the use of various modes of communication have also been reported (Barrie et al., 2019; Zimmerman & Iwanksi, 2014). Future Directions The current study highlights several important directions for future exploration at the intersection of technology and IER. First, further research is needed to explore additional factors that help to explain the differing effects of digital and in-person communication on satisfaction with support and emotions and whether certain variables may moderate the relationship between mode of communication and emotional outcomes. For instance, between-person or individual difference factors, such as social anxiety and personality, may 92 predispose individuals towards using or experiencing greater benefits from certain modes of communication compared to others. Those who experience symptoms of social anxiety, for example, may be more comfortable using digitally-mediated methods of communication (High & Caplan, 2009; Laghi et al., 2013; Pierce, 2009; Reid & Reid, 2007) and report greater benefits from doing so (Yen et al., 2012). Individuals may also vary in their beliefs about the utility and appropriateness of CMC for IER (R. Hayes et al., 2016). Given that the majority of variability in the study outcomes was within-person, further research is also needed to explore other within-level predictors of daily fluctuations in satisfaction with support and emotions. For instance, a distressing or triggering event precipitating the daily assessments may have heightened levels of negative emotions (Heiy & Cheavens, 2014; Luminet, Bouts et al., 2000), rendered certain strategies more or less effective (Sheppes & Gross, 2012), or may have influenced motives behind choice of communication method. Relationship type may also contribute to the selection and effectiveness of certain modes of communication. For instance, communication between two digital natives may be deemed more preferable, satisfactory, or effective compared to interactions between a digital native and their parent due to generational differences in the use of technology (Barrie et al., 2019). More focus on negative interpersonal interactions is also required. The current sample reported an overall low frequency of negative interactions, and even when negative strategies were reported, they occurred to an extremely low degree (with mean intensity ratings for individual strategies ranging from 0.16 to 0.31 on a 0 to 4 scale). This precluded any conclusions about the unique contributions of negative IER strategies on satisfaction or emotions. Strong evidence suggests that negative interactions do have important influences on one’s emotional experiences (Baumeister et al., 2001), warranting closer attention to the 93 role of negative IER strategies. Research designs with a longer study period or that use an event-contingent diary (in which individuals report on negative events as they occur) may be better-suited to capture negative interpersonal interactions in naturalistic settings. Future research may also benefit from assessing a sample of individuals experiencing greater degrees of distress or interpersonal difficulties. Additionally, development of more comprehensive IER measures may benefit from examining a wide array of negative IER strategies, including “milder” variants, such as mild rejection, slights, unfavourable evaluations or comments, or even ambiguity in messaging. The current study demonstrated that existing negative IER strategies do not seem to occur to a significant degree between interactions with close ties. A more inclusive set of affect-worsening strategies may help to capture instances where IER interactions between close ties may not necessarily be favourable. Finally, there is a need to better establish causality between the use of various modes of communication and socioemotional outcomes, which is best suited for experimental designs. Variables in the current study were ordered temporally to assist in predictive associations; that is, during a given time point, individuals chose a mode of communication to connect with their main support source, engaged in various IER strategies, and then reported their levels of satisfaction with support and positive and negative emotions at the time of diary completion. Therefore, although findings are suggestive of predictive associations between communication mode, IER strategies and satisfaction and emotions, conclusions about causality are not possible without future experimental research. Prospective studies may also be of benefit by examining cumulative effects of IER strategies over extended periods of time, and across various modes of communication. 94 Theoretical and Clinical Implications For many years, researchers have reported mixed findings on the role and effectiveness of CMC across a variety of psychological and emotional outcomes. Two broad (yet polarized) perspectives on CMC have been proposed to help advance knowledge of the benefits and consequences of various modes of communication. However, research has continued to report mixed findings. The current study (specifically, Aim 2) is no exception, as it lends support to the CFO theories of communication as well as to SIP theory by demonstrating that despite poorer outcomes observed when an overall greater use of digital communication was used across the study period, within a given time point, it can be just as satisfying and influential on emotions as in-person methods. Importantly, however, when the qualitative components of the interactions were examined more closely, IER strategies helped to explain this association. Rather than solely focus on the inherent qualities (or lack thereof) of various modes of communication, this research makes clear that there are factors above and beyond the inherent characteristics of media that help to explain socioemotional outcomes. In order to understand why certain media are influential on one’s social and emotional lives, this study highlighted a need to draw attention to the ways in which the mediums are used, and specifically, what the interactions entail. As this study shows for the first time, when CMC is used for providing social support for ER, its influence on socioemotional outcomes is partly dependent on the types of IER strategies that occur. SIP theory (and others) has demonstrated the potential for individuals to adapt to CMC (and its limitations) in ways that help to preserve and maintain the benefits of interpersonal interaction and communication. However, as unveiled by the current study, one area worthy of additional attention is how users can best transmit or convey laughter (or humour, more 95 broadly) via digital communication. Despite the numerous ways users can attempt to infuse or communicate laughter in their messages (e.g., emoticons; typed laughter), results suggest that there are features specific to laughter that are not as easily (or effectively) transmissible via digital technologies. There is also a need for further development and refinement of IER theory. For example, clear consensus on the overarching construct of IER, compared to affect regulation, coping, and social support, has still not been reached. This undoubtedly influences the ways in which research can empirically examine the construct of IER, and clearly distinguish it from similar social regulation processes. Additionally, given the ubiquity of digital technology in our social lives, the current research highlights pressing need for IER theory to integrate the role that the digital context may have on adaptive emotional functioning that is facilitated through social interactions. A number of clinical implications can also be gleaned from this research. Given the upsurge of digital interactions, especially due to the COVID-19 pandemic, now more than ever, individuals are turning to these forms of communication for interpersonal connection (Pew Research Center, 2020). Technology provides numerous opportunities to connect with and receive support from others, and while there are advantages of seeking emotional support digitally (e.g., privacy, convenience), this research draws attention to some potential disadvantages. The fact that digital methods of communication resulted in interactions with significantly lower levels of positive IER strategies is an important finding that may help to explain why the use of digital communication has been associated with mental health difficulties and well-being. In order to mitigate the negative effects that digital communication can have on satisfaction and emotions, it is crucial that users of digital 96 technologies become aware of the ways in which text-based discourse may have unintended consequences on the quality of their interactions. Promoting awareness of the ways in which communication can be effectively (and ineffectively) used to foster support and connection is required, as well as improving digital literacy. Drawing from work on social and emotional learning (e.g., Brackett et al., 2019; CASEL, 2021), individuals are encouraged to develop effective ways of expressing, evaluating, and processing information regarding social interactions and emotional well-being specific to online contexts (Eshet, 2012) in an effort to circumvent poor socioemotional outcomes. Beyond bringing increased awareness to how one’s message may be construed by others, strategies may include providing context to one’s feelings or reducing ambiguity, asking more questions to demonstrate interest, providing validating statements, using clear and concise language, and finding more effective ways to convey humour and elicit laughter. The prevalence of digital communication continues to grow, and an important implication of this research is in increasing awareness of the implications digital interactions may have and in developing and fostering adaptive uses of one’s device to connect and share their emotions with loved ones. Conclusion Humans are innately social beings who turn to others during times of stress (Beckes & Coan, 2011). For years, ER research has examined the independent efforts of individuals in managing their emotions, but only recently has the field of ER begun to better understand the ways that emotions are regulated interpersonally. As technology continues to introduce novel ways of communicating with others, the complexity in understanding interpersonal communication and IER grows. Despite the level of concern among the general public that 97 youth are overly reliant on digital technologies, results from the current study revealed that emerging adults tend to seek support for negative emotions through predominantly in-person (not digital) contact. While numerous studies have assessed the relationship between mode of communication and socioemotional outcomes, this is the only known study to explore the mediating role of IER in explaining this relationship. Using an intensive longitudinal design, the current study revealed that lower levels of positive IER strategies, and particularly the strategy “made me laugh,” helped to explain the negative relationship between digital communication and satisfaction and emotions. Given the ubiquity and prevalence of technology, especially during the recent COVID-19 pandemic, these findings highlight a need for improved digital literacy, with specific focus on strengthening social and emotional skills in online contexts. 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Emotion regulation from early adolescence to emerging adulthood and middle adulthood: Age differences, gender differences, and emotion-specific developmental variations. International Journal of Behavioral Development, 38(2), 182-194. https://doi.org/10.1177/0165025413515405 136 Appendices Appendix A – Consent Form CONSENT FORM Study Title: Daily Social Support Study Principal Investigator: Dr. Susan Holtzman Associate Professor, Dep’t of Psychology, UBC Okanagan Contact: (250) 215-3817; susan.holtzman@ubc.ca Co-Investigator: Diana Lisi Ph.D. Student, Dep’t of Psychology, UBC Okanagan Contact: diana.lisi@ubc.ca You are being asked to participate in a study examining the factors associated with the social support seeking decisions emerging adults make. Please read this form in its entirety to understand the purpose of the study and its risks and benefits before you decide to participate. You are encouraged to take as much time as you need to make your decision. You are encouraged to ask the principal investigator or co-investigator to explain anything that you do not understand and make sure that all of your questions have been answered before signing this consent form. Your participation is completely voluntary. This research is part of the co-investigator’s doctoral dissertation in Psychology at UBC Okanagan. Dissertations are considered public documents, and will be available on the Internet at cIRcle (https://circle.ubc.ca), a digital collection of documents from UBC members. What is the purpose of this study? The purpose of this study is to gain a better understanding of how emerging adults make decisions on which support sources they use to help regulate feelings of anxiety in their daily life. We are interested in exploring the different factors that predict why some people are sought for support more than others. We are also interested in examining the specific strategies used in an effort to regulate emotions. Results obtained from this study may be presented at scholarly conferences and/or published in a peer-reviewed journal. Who can participate in this study? Individuals who are undergraduate students from the University of British Columbia’s Okanagan campus, who are emerging adults (18-25 years), and who have daily access to a smartphone are eligible to participate. Individuals must also be fluent in English to ensure that all questionnaires are understood. Individuals who have been diagnosed with the schizophrenia, bipolar disorder, or borderline personality disorder are not eligible to participate. What am I being asked to do? This study has two parts. In the first part of the study, you will be asked to complete an initial questionnaire assessing basic demographics, personality, and details about the structure and quality of people in your social support network. This initial questionnaire will take approximately 30-45 minutes to complete. Following the initial questionnaire, you will be asked to download a free mobile application, MetricWire, on your Android or iOS device, which will be used in the second part of the study. Part two of the study involves completing a series of brief questionnaires on your mobile phone for seven consecutive days. You will be asked to complete these questionnaires two times per day (at 137 1 p.m. and 8 p.m.). Each daily questionnaire will be brief (approximately 5 minutes) and will be done entirely on your mobile phone, using the MetricWire app downloaded in Part One. You may receive an email from the study team over the course of the study if technological issues arise, or if we are not receiving your responses. The time to complete the entire study (Part One and Part Two) will be approximately 2 hours. What are the benefits of participating? If you are registered in an eligible psychology course: You have the option of receiving up to 2.5 Psychology Department SONA research credits (1 on-site and 1.5 online) OR being entered into a draw for a $100 Amazon gift card. In order to receive full course credit (2.5) or be entered into the draw, you must complete at least 75% of the daily questionnaires, as well as the initial questionnaire. If you complete less than 75% of the daily assessments, SONA credits will be prorated (in increments of 0.5) to reflect the portion of the study you completed, with the minimum number of credits received being 0.5. If you are a UBC Okanagan student not registered in an eligible psychology course: You will be entered into a draw for a $100 Amazon gift card. If you withdraw from the study (by emailing the study co-investigator), you will still be entered into the gift card draw. The gift card draw will take place at the end of the study’s recruitment period (approximately April 2019; subject to change). It is unlikely that there will be any direct benefits gained from participating in this study. However, the information you provide may help to increase the scientific understanding of interpersonal emotion regulation in young adults. What are the risks of participating? You may experience a mild level of distress as a result of participating in this study and completing multiple daily assessments. Please be aware that the multiple daily assessments used in this study are common in scientific research. Keep in mind that you have the right not to answer any questions you feel uncomfortable answering, and that you may withdraw from this study at any time. Should you feel any distress or discomfort as a result of taking part in this study, feel free to contact Dr. Susan Holtzman, the Principal Investigator of this study at (250) 215-3817, and she will be happy to address these concerns with you. Will my information be kept confidential? All information obtained during this study will be strictly confidential. You will be asked to enter your email address at the end of the initial survey, and again after downloading MetricWire, in order to link your data across the two platforms. Your linked data will be stored on a password-protected document on the secure UBC Okanagan server (on Dr. Holtzman’s password-protected computer) for the duration of data collection. In MetricWire, you are automatically assigned a unique identifier number, which will be connected with your responses. However, your email address is only linked to the number of responses you submit (i.e., in order to track technical difficulties and completion rates), but to the specific content of your responses. No names or identifying information will appear on any study records or be used in any publications or presentations of the study results. Once the data collection phase is complete, all survey data will be transferred to Dr. Holtzman’s password-protected computer, and the master list containing your linked information will be deleted. The data will only be available to the study investigators and trained research assistants for the purposes of analyses. At the completion of the study, the survey data will be stored on Dr. Holtzman’s password-protected computer in a secure UBC Okanagan office, and on a password-protected shared research drive on the UBC server for a period of five years following publication. Initial questionnaire data will be collected through SurveyMonkey. This company houses its servers in the United States, and as such, is subject to US laws, specifically the US Patriot Act, which allows authorities access to the records of internet service providers. This questionnaire does not ask for personal identifiers or any other information that may be used to identify you (other than your email address). SurveyMonkey will not sell or distribute your email address in accordance with their privacy policy. If you choose to participate in the survey, you understand that your responses to the survey 138 questions will be stored and accessed in the USA. More information on the security and privacy policy for SurveyMonkey can be found at: https://www.surveymonkey.com/mp/policy/privacy-policy/ Daily assessments will be collected using the MetricWire mobile phone app. Servers are hosted by Amazon Web Services, which may be housed in the United States, and therefore subject to US laws, as well. MetricWire’s privacy policy complies with the Canadian Personal Information Protection and Electronic Documents Act (PIPEDA). More information about MetricWire’s privacy policy can be found at: https://metricwire.com/privacy-policy/ Can I say “no”? Your participation in this study is completely voluntary. If there are any questions that you do not feel comfortable answering throughout the study, you may refrain from doing so. You may choose not to participate and withdraw from the study at any time, provided you email the study co-investigator with your participant ID number (provided in Part One). If you are enrolled in an eligible Psychology course and you choose to withdraw from the study, you will still receive SONA credits for your time. If you participated in less than 75% of the study at the time of your withdrawal, your SONA credits will be prorated (in increments of 0.5) to reflect how much of the study you completed, with the minimum number of credits received being 0.5. If you are not enrolled in an eligible Psychology course and you choose to withdraw from the study, you will still be entered into the gift card draw. Please note that if you choose to withdraw from the study after submitting any responses, it is not possible to remove your data, as it is not linked with your information. Contact Information If you have any questions or concerns about the study, please contact the Principal Investigator, Dr. Susan Holtzman at (250) 215-3817. You may also contact the study co-investigator, Diana Lisi, at diana.lisi@ubc.ca. If you have any concerns or complaints about your rights as a research participant and/or your experiences while participating in this study, contact the Research Participant Complaint Line in the UBC Office of Research Services at 1-877-822-8598 or the UBC Okanagan Research Services Office at (250) 807-8832. Please reference the study number H17-02243 when calling so the Complaint Line Staff can better assist you. It is also possible to contact the Research Participant Complaint Line by email at RSIL@ors.ubc.ca. CONSENT By clicking Next, you are agreeing that you: 1. Meet eligibility criteria 2. Understand the potential risks and benefits associated with the study 3. Understand that your participation is completely voluntary 4. Agree that you may be contacted by email over the course of the study 5. Have received a copy of this consent form for your records 6. Have had the chance to have any questions about the study answered by the study coordinator. Optional Consent If you would like to receive a summary of the results of this study by email, please enter your email address below. ________________________________________ Are you interested in being contacted in the future to participate in a similar study? Our research team may have similar opportunities for participation in the future. Please indicate whether or not you may be interested in receiving more information about future studies. If you are interested in future studies, please enter your email address below. ________________________________________ 139 Appendix B – Daily Assessments Welcome to Part Two of the Daily Social Support Study! In this second part of the study, you are being asked to answer questions about your current emotions, social interactions, and ways of coping. Participation involves answering brief questionnaires on your phone two times a day (1 p.m. and 8 p.m.) for 7 consecutive days. Questionnaires will take approximately 5 minutes to complete. Please answer the questions AS SOON as you receive the in-app notification, unless unable to do so (e.g., driving, in class). In these instances, please respond to the questionnaire as soon as you can. You will have a 2-hour window before that questionnaire becomes inactive. 1. How __________ are you feeling right now? (0 = Not at all, 1 = A little bit, 2 = Somewhat, 3 = A lot) *Same question repeated for each of the emotions below; presented in alphabetical order, alternating in valence If any degree of a negative emotion was reported, questions 2 to 9 were displayed. 2. You indicated feeling at least some degree of negative emotions so far today/since your last entry. Did anything in particular cause you to feel upset? (Check all that apply) • School • Work (paid or volunteer) • A relationship • Available resources o Time o Money o Friends o Family o Romantic • Other (please specify): ________ • No • I’m not sure 3. So far today/Since your last entry, who did you turn to when you were feeling upset (e.g., anxious, sad, angry)? (Check all that apply) • A close friend • Mom • Dad • Romantic partner • Sibling • Acquaintance • Classmate • Other (please specify): ________ • No one Positive Negative Amused Angry Calm Anxious Excited Embarrassed Interested Guilty Joyful Irritable Loved Lonely Proud Sad Stressed 140 4. [If multiple sources selected]: Who was the MAIN person you turned to? • A close friend • Mom • Dad • Romantic partner • Sibling • Acquaintance • Classmate • Other (please specify): ________ 5. How did you communicate with this person? (Check all that apply) • In-person conversation • Facebook • Talking on the phone • Video chat (e.g., FaceTime, Skype) • Instagram • Twitter • Texting (e.g., iMessage, WhatsApp) • Snapchat • Other (please specify): ________ 6. [If not in-person conversation]: Roughly how long did it take for you to get a response from this person over this mode of communication? • Immediately • Within a few minutes • Within an hour • Within a few hours • They haven’t responded yet 7. When you turned to this person, to what extent did he/she _________? (0 = Not at all, 1 = A little bit, 2 = Somewhat, 3 = A lot) *Repeated for each of the strategies below Positive Negative Listen to your problems Criticize you Point out your positive characteristics Seem uninterested Give you advice Talk down to you Make you laugh Ignore you Seem annoyed with you Note. Adapted from Niven et al., 2009 and Niven et al., 2011 8. To what extent were you satisfied with the way this person responded to you? (0 = Not at all, 1 = A little bit, 2 = Somewhat, 3 = A lot) 9. [For each additional support source]: You also shared with your _________. How satisfied were you with the way this person responded to you? (0 = Not at all, 1 = A little bit, 2 = Somewhat, 3 = A lot) 10. To what extent did you see your close friend(s) in-person so far today/since the last assessment? (0 = Not at all, 1 = A little bit, 2 = Somewhat, 3 = A lot) 141 11. To what extent did you see your parent(s) in person so far today/since the last assessment? (0 = Not at all, 1 = A little bit, 2 = Somewhat, 3 = A lot) The end of day assessment also examined the following intrapersonal ER attempts. 12. Over the course of the whole day, to what extent did you _________? (0 = Not at all, 1 = A little bit, 2 = Somewhat, 3 = A lot) *Repeated for each of the following intrapersonal strategies • Try to influence your negative emotions • Shift your attention away from what was making you upset • Try to think about a situation differently in order to change how you were feeling • Try to avoid showing others how you were feeling on the inside 142 Appendix C – Parameter Estimates for Exploratory Analyses Table C1 Parameter Estimates: Mode of Communication (X), “Listened to My Problems” (M), and Satisfaction with Support (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.138 .080 .085 -.295 .019 -.328 .244 .179 -.806 .151 b path .372 .057 .000 .260 .484 .755 .064 .000 .629 .882 c' path -.032 .091 .725 -.210 .146 -.380 .190 .046 -.753 -.007 Indirect effect (ab) -.051 .030 .089 -.116 .007 -.247 .180 .168 -.618 .113 Total effect (c path) -.083 .093 .369 -.265 .099 -.627 .250 .012 -1.118 -.137 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab Table C2 Parameter Estimates: Mode of Communication (X), “Listened to My Problems” (M), and Total Positive Emotions (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.136 .081 .092 -.294 .022 -.326 .243 .179 -.802 .149 b path .402 .323 .213 -.230 1.035 1.651 .461 .000 .747 2.555 c' path -.787 .594 .185 -1.951 .376 -.440 1.509 .771 -3.398 2.518 Indirect effect (ab) -.055 .050 .271 -.201 .036 -.539 .441 .190 -1.511 .242 Total effect (c path) -.842 .591 .154 -2.000 .316 -.979 1.537 .524 -3.990 2.033 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab 143 Table C3 Parameter Estimates: Mode of Communication (X), “Listened to My Problems” (M), and Total Negative Emotions (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.136 .080 .091 -.293 .022 -.343 .246 .164 -.826 .140 b path -.025 .300 .934 -.612 .563 -1.330 .423 .002 -2.160 -.500 c' path .832 .533 .119 -.213 1.877 3.147 1.310 .016 .579 5.715 Indirect effect (ab) .003 .041 .934 -.096 .106 .457 .352 .195 -.179 1.291 Total effect (c path) .836 .530 .115 -.203 1.874 3.604 1.344 .007 .970 6.238 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab Table C4 Parameter Estimates: Mode of Communication (X), “Pointed Out My Positive Characteristics” (M), and Satisfaction with Support (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.100 .071 .161 -.240 .040 -.435 .336 .195 -1.094 .223 b path .297 .048 .000 .203 .392 .359 .051 .000 .260 .458 c' path -.055 .087 .527 -.224 .115 -.479 .238 .044 -.946 -.012 Indirect effect (ab) -.030 .022 .179 -.075 .011 -.156 .117 .182 -.409 .080 Total effect (c path) -.085 .092 .359 -.265 .096 -.635 .259 .014 -1.144 -.127 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab 144 Table C5 Parameter Estimates: Mode of Communication (X), “Pointed Out My Positive Characteristics” (M), and Total Positive Emotions (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.102 .072 .153 -.243 .038 -.433 .332 .192 -1.084 .217 b path .595 .246 .015 .113 1.076 1.402 .343 .000 .729 2.074 c' path -.784 .576 .173 -1.913 .345 -.351 1.435 .807 -3.164 2.462 Indirect effect (ab) -.061 .051 .237 -.183 .023 -.608 .477 .203 -1.689 .300 Total effect (c path) -.845 .589 .151 -1.999 .309 -.958 1.485 .519 -3.870 1.953 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab Table C6 Parameter Estimates: Mode of Communication (X), “Pointed Out My Positive Characteristics” (M), and Total Negative Emotions (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.102 .071 .152 -.242 .037 -.447 .340 .190 -1.114 .221 b path -.331 .211 .117 -.746 .083 -.556 .304 .068 -1.153 .041 c' path .765 .523 .144 -.260 1.791 3.474 1.326 .009 .875 6.072 Indirect effect (ab) .034 .034 .313 -.018 .119 .248 .220 .258 -.134 .858 Total effect (c path) .799 .528 .130 -.236 1.835 3.722 1.339 .005 1.097 6.347 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab 145 Table C7 Parameter Estimates: Mode of Communication (X), “Gave Me Advice” (M), and Satisfaction with Support (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.063 .079 .425 -.219 .092 -.189 .288 .512 -.752 .375 b path .316 .043 .000 .232 .400 .346 .056 .000 .236 .457 c' path -.079 .086 .362 -.248 .090 -.515 .243 .034 -.992 -.038 Indirect effect (ab) -.020 .025 .423 -.071 .029 -.065 .098 .506 -.271 .131 Total effect (c path) -.099 .091 .277 -.277 .079 -.580 .268 .030 -1.105 -.056 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab Table C8 Parameter Estimates: Mode of Communication (X), “Gave Me Advice” (M), and Total Positive Emotions (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.063 .080 .425 -.220 .093 -.179 .283 .528 -.734 .376 b path .337 .241 .162 -.136 .811 1.633 .361 .000 .926 2.340 c' path -.874 .597 .144 -2.045 .297 -.461 1.349 .733 -3.104 2.183 Indirect effect (ab) -.021 .029 .457 -.110 .039 -.292 .465 .530 -1.283 .625 Total effect (c path) -.895 .594 .132 -2.059 .269 -.753 1.491 .614 -3.674 2.169 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab 146 Table C9 Parameter Estimates: Mode of Communication (X), “Gave Me Advice” (M), and Total Negative Emotions (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.063 .079 .425 -.218 .092 -.189 .291 .514 -.759 .380 b path -.136 .212 .521 -.551 .279 -.469 .302 .120 -1.060 .122 c' path .814 .536 .129 -.237 1.864 3.556 1.318 .007 .972 6.140 Indirect effect (ab) .009 .017 .615 -.033 .068 .089 .143 .534 -.212 .499 Total effect (c path) .822 .533 .123 -.223 1.867 3.645 1.356 .007 .987 6.302 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab Table C10 Parameter Estimates: Mode of Communication (X), “Made Me Laugh” (M), and Satisfaction with Support (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.173 .071 .015 -.313 -.033 -.881 .302 .003 -1.472 -.290 b path .253 .042 .000 .170 .336 .337 .055 .000 .229 .446 c' path -.023 .096 .808 -.211 .165 -.392 .234 .094 -.851 .067 Indirect effect (ab) -.044 .021 .037 -.085 -.008 -.297 .094 .002 -.538 -.092 Total effect (c path) -.067 .096 .484 -.255 .121 -.689 .244 .005 -1.167 -.211 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab 147 Table C11 Parameter Estimates: Mode of Communication (X), “Made Me Laugh” (M), and Total Positive Emotions (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.174 .072 .015 -.315 -.034 -.885 .295 .003 -1.463 -.307 b path 1.512 .272 .000 .978 2.046 2.183 .370 .000 1.457 2.908 c' path -.444 .531 .403 -1.485 .596 .518 1.432 .717 -2.289 3.326 Indirect effect (ab) -.264 .120 .029 -.519 -.047 -1.932 .684 .005 -3.486 -.625 Total effect (c path) -.708 .573 .216 -1.831 .415 -1.414 1.434 .324 -4.224 1.396 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab Table C12 Parameter Estimates: Mode of Communication (X), “Made Me Laugh” (M), and Total Negative Emotions (Y) Within Between 95% CI 95% CI Estimate Std. Error Sig. (2-tailed) Lower Upper Estimate Std. Error Sig. (2-tailed) Lower Upper a path -.174 .071 .014 -.313 -.035 -.891 .301 .003 -1.480 -.301 b path -1.093 .229 .000 -1.542 -.643 -.922 .338 .006 -1.584 -.259 c' path .511 .490 .296 -.448 1.471 3.108 1.332 .020 .498 5.718 Indirect effect (ab) .190 .090 .035 .035 .382 .821 .370 .026 .144 1.776 Total effect (c path) .701 .515 .173 -.307 1.710 3.929 1.296 .002 1.388 6.470 Note. a path = X on M; b path = M on Y; c’ path = X and M on Y; Indirect effect (ab) = a path*b path; Total effect (c path) = c’ + ab
Thesis/Dissertation
eng
Psychology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Holtzman, Susan
It’s not (really) the medium, it’s the message : interpersonal emotion regulation across in-person and digitally-mediated contexts
Text