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Young adult dating couple interactions in daily life: links to family aggression and physiological processes

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Young adult dating couple interactions in daily life: links to family aggression and physiological processes

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Young Adult Dating Couple Interactions in Daily Life:
Links to Family Aggression and Physiological Processes
by
Sohyun C. Han

A Dissertation presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
PSYCHOLOGY

August 2020

Copyright [2020] Sohyun C. Han

ii
Acknowledgements
This data described in this dissertation was supported by NSF Grant BCS-162727
(Margolin, PI), NIH- NICHD Grant No. R21HD072170-A1 (Margolin, PI), SC CTSI
(NIH/NCATS) through Grant UL1TR000130 (Margolin, PI), NSF GRFP Grant No. DGE-
0937362 (Timmons, PI), and an NSF GRFP Grant No. DGE-0937362 (Han, PI).
I am very grateful to my dissertation committee members, Drs. Richard John, Gayla
Margolin, Beth Meyerowitz, Shrikanth Narayanan, and Darby Saxbe for their help and guidance
throughout my training. I also want to extend a heartfelt thank you to past and current Family
Studies Project lab members for always being so encouraging and supportive: Dr. Reout Arbel,
Sarah Barrett, Dr. Brian Baucom, Geoff Corner, Dr. Larissa Del Piero, Merai Estafanous, Dr.
Kelly Kazmierski, Yehsong Kim, Dr. Ilana Kellerman Moss, Laura Perrone, Corey Pettit, Dr.
Michelle Ramos, Hannah Rasmussen, Dr. Aubrey Rodriguez, Dr. Hannah Schacter, Dr. Lauren
Shapiro, Stassja Sichko, and Dr. Addie Timmons. I am also thankful for all the families and
couples who have so generously given their time to participate in the Family Studies Project.
Being a part of the USC Home Data team has been one of the most rewarding
experiences during my time in grad school. Addie, thank you for being a great mentor, colleague,
and friend through it all. Theodora and Shri, thank you for always being so willing to collaborate
and share your expertise with us. I am extremely thankful to our amazing lab managers: Laura,
Sarah, Stassja, Corey, and Merai, as well as Yehsong, a fellow Home Data grad student – each of
you went above and beyond to contribute to this project behind the scenes. To all the many
undergraduate and post-baccalaureate research assistants – thank you! This project would not
have been possible without the tremendous amount of time and energy you put into this data. It
has been a joy and a privilege to work with all of you.

iii
I am extremely grateful to my advisor, Dr. Gayla Margolin, who has met with me nearly
every single week over the past 7 years (!) Your support and guidance has truly shaped me into
the scientist, clinician, and psychologist I am today. Our conversations never fail to spark new
ideas and exciting new directions – I am thrilled to continue working with you in the near future.
To the amazing women in my cohort: Laura Garcia, Hannah Khoddam, Gabby Lewine,
and Luiza Mali. I’m so thankful that we had each other to lean on throughout grad school and
our many life changes. To my friends and family – I know it seems like I have been in grad
school for an eternity! Thank you for all the ways you have grounded me and supported me
throughout this journey.
To my husband Jeffrey: I truly could not have completed this dissertation without you.
Especially after having Clara, you cheerfully and selflessly made it possible for me to have
uninterrupted writing time each week (even during a global pandemic!). Your unending support
of me and my pursuits is remarkable. To my dearest Clara: you were my biggest distraction and
also my biggest source of joy. Words cannot express how much I adore you.

iv
Table of Contents
Acknowledgements..........................................................................................................................ii
List of Tables..................................................................................................................................vi
List of Figures................................................................................................................................vii
Abstract.........................................................................................................................................viii
General Introduction........................................................................................................................1
Paper 1........................................................................................................................................9
Abstract..............................................................................................................................10
Introduction........................................................................................................................11
Method...............................................................................................................................16
Results................................................................................................................................20
Table 1...............................................................................................................................22
Figure 1..............................................................................................................................24
Figure 2..............................................................................................................................25
Figure 3..............................................................................................................................28
Discussion..........................................................................................................................29
References .........................................................................................................................34
Online Supplemental Materials..........................................................................................41
Table 1……………………………………..………………………………………..43
Paper 2.....................................................................................................................................44
Abstract..............................................................................................................................45
Introduction........................................................................................................................46
Figure 1..............................................................................................................................53

v
Method...............................................................................................................................55
Table 1...............................................................................................................................60
Table 2...............................................................................................................................61
Results................................................................................................................................62
Table 3...............................................................................................................................63
Figure 2..............................................................................................................................65
Figure 3..............................................................................................................................67
Discussion..........................................................................................................................68
References .........................................................................................................................73
General Discussion........................................................................................................................78
General Discussion References......................................................................................................86
Appendices…………….................................................................................................................89
Appendix A........................................................................................................................89
Appendix B........................................................................................................................91
Appendix C........................................................................................................................93
Appendix D........................................................................................................................94

vi
List of Tables
PAPER 1
Table 1: Descriptive Statistics and Bivariate Correlations for Main Study Variables……..……22

Online Supplemental Materials
Table 1: Parent-to-Child Aggression (PCA) as a Moderator of the Association between
Annoyance and Anger Words ……………………………………………………..…………….43

PAPER 2
Table 1: Descriptive Statistics for the Main Study Variables……………………………...…….60
Table 2: Correlations among Main Study Variables………………………………………...…...61
Table 3: Association between Hourly Partner Presence and Electrodermal Activity Controlling
for Covariates…………………………………………………………………………………… 63

vii
List of Figures
PAPER 1:
Figure 1: Actor-partner interdependence model of women’s and men’s annoyance predicting
women’s and men’s anger words……………………………………………………………….. 24
Figure 2: Actor-partner interdependence model of women’s and men’s parent-to-child
psychological aggression (PCA) predicting women’s and men’s anger words over one day….. 25
Figure 3: Women’s hourly annoyance and anger words moderated by women’s parent-to-child
psychological aggression (PCA)………………………………………………………………... 28

PAPER 2
Figure 1: Panels depicting hypothesized associations between hourly partner presence, romantic
attachment, and electrodermal activity…………………………………………………………. 54
Figure 2: Men’s anxious attachment as a moderator of partner presence and electrodermal
activity ………………………………………………………………………………………….. 65
Figure 3: Men’s avoidant attachment as a moderator of partner presence and electrodermal
activity…………………………………………………………………………………………... 67

viii
Abstract
Romantic partner interactions have long been considered central to the development of
psychological and physical health sequelae. However, much less is understood about the
microlevel processes, such as the day-to-day couple interactions, that contribute to such
outcomes over time. The first paper in this dissertation will seek to investigate how everyday
couple conversations are associated with fluctuating emotional dynamics in the relationship as
well as histories of family aggression. The second paper will explore how everyday partner
presence may be associated with attenuated physiological responses, which may point to one
pathway underlying the link between relationships and improved health. These findings
potentially add new information toward unraveling the important issue of how close relationships
contribute to overall well-being.

1
General Introduction
For better or for worse, romantic relationships are linked to health and well-being. For
instance, those who are married or more satisfied in their relationships are more likely to have
better health and live longer compared to those who are unmarried or dissatisfied (e.g., Coyne et
al., 2001; Holt-Lunstad, Smith, & Layton, 2010; Johnson, Backlund, Sorlie, & Loveless, 2000;
Robles, Slatcher, Trombello, & McGinn, 2014). On the other hand, those who are in
relationships characterized by high levels of conflict or aggression are at increased risk for
chronic mental and physical health problems (Campbell et al., 2002; Coker et al., 2002, Tjaden
& Thoennes, 2000). While it is well-established that romantic relationships are associated with
long-term consequences, much less is understood about the microlevel processes, such as the
day-to-day couple interactions, that contribute to such outcomes over time.
Romantic partner interactions have long been considered central to the development of
psychological and physical health sequelae. For instance, greater hostility during conflict
discussions in the laboratory is associated with increased autonomic, endocrine, and immune
responses, which over time contribute to the development of disease (e.g., see Robles & Kiecolt-
Glaser, 2003 for review). On the other hand, the presence of a romantic partner during stressful
lab tasks is associated with reduced physiological responses (e.g., Coan, Schaefer, & Davidson,
2006; Feeney & Kirkpatrick, 1996). Additionally, couples with a history of relationship
aggression demonstrate more anger and contempt during lab discussions compared to
nonaggressive couples (e.g., Cordova et al., 1993; Margolin, John, & Gleberman, 1988), which,
paired with other research, suggests that angry conversations are likely to set the stage for
aggression (Greenfield et al., 1998; Wilkinson & Hamerschlag, 2005).

2
While in-lab discussions are a valid and useful method of assessing couple interactions, a
limitation is that they only capture a brief snapshot of couple interactions and processes.
Evidence suggests that assessments in natural settings capture a far broader range of behaviors
compared to in the lab, as couples are not artificially restrained by standardized lab procedures
(e.g., Burman, Margolin, & John, 1993; Gottman, 1979; Laurenceau & Bolger, 2005).
Furthermore, emerging work indicates that physiological responses captured at home during
couple interactions are significantly larger and are more closely related to measures of
relationship functioning compared to those measured in the lab (e.g., Baucom et al., 2018).
Ambulatory assessment, or data collected in naturalistic settings, is increasingly used to capture
couples’ behaviors in everyday life. For example, the electronically activated recorder (EAR) has
been used to collect 30 second snippets of audio, which have been subsequently transcribed and
coded to reveal information about individuals’ social environments (e.g., Mehl & Pennebaker,
2003) and how couples cope with illness (Robbins, Lopez, Weihs, & Mehl, 2014). By utilizing
similar methodology to capture psychological, behavioral, and physiological processes as they
naturally unfold within their real-life contexts, we can better understand how relationship
interactions can incrementally contribute to far-reaching outcomes.
This dissertation utilizes innovative ambulatory assessment to investigate everyday
relationship processes that occur within the couple’s milieu. The data are a result of a USC
collaboration between psychologists in Margolin’s Family Studies Project (FSP) lab and
engineering colleagues in Narayanan’s Signal Analysis and Interpretation Lab (SAIL) (e.g., see
Timmons et al., 2017, for a description). Beyond several publications that have already come out
of that collaboration, this dissertation seeks to investigate and explore novel questions that have
yet to be examined. The first paper explores the connection between naturally-occurring feelings

3
of annoyance towards a romantic partner and the use of anger words in everyday conversations
(Han et al., revise and resubmit). Additionally, family-of-origin aggression is tested as a
predictor of anger words as well as a moderator of the association between feelings of annoyance
and anger words in order to investigate intergenerational transmission of aggression theories. To
assess everyday conversations, we employed smart phone technology to unobtrusively and
passively audio record 50% of the day, capturing both mundane and significant conversations as
they spontaneously occurred. These audio files were then transcribed and processed through
linguistic analyses in order to yield rich data regarding what romantic partners spontaneously say
to each other and when.
The second paper investigates one potential pathway underlying the association between
romantic relationships and improved health. Specifically, we investigate whether the presence of
a partner in everyday life is associated with reductions in sympathetic nervous system responses
in both partners. Additionally, we investigate adult attachment style as a moderator and also
examine gender differences. To assess ambulatory physiological responses, we outfitted couples
with wearable biosensors, which collected hundreds of physiological data points every hour to
detect small variations in electrodermal activity (EDA) throughout the day. With these two
papers, we aim to capture and investigate everyday relationship interactions between dating
partners. Though these microlevel processes may seem transient or trivial, we strive to
demonstrate how such processes may indeed map onto broader consequences, such as the
intergenerational transmission of aggression and the progression of health-relevant processes.
Additionally, an overarching aim of this dissertation is to better understand romantic
relationships among young adults (ages 18-25), who have been relatively understudied within the
relationship science literature. Young adulthood is a distinctive developmental period in which

4
many individuals navigate long-term romantic relationships, sometimes cohabitating with
partners for the first time (e.g., Settersten, Furstenberg, & Rumbaut, 2008). Relationship patterns
that emerge within these early relationships often set the stage for later marital relationships and
even parent-child relationships (Capaldi, Shortt, & Crosby, 2003; O’Leary et al., 1989). Thus,
studying young adult relationships can allow us to better understand how relationship patterns
become entrenched over time and may provide a natural window of opportunity for intervention
if needed.
In sum, by capturing a ‘day in the life’ of young dating couples, this dissertation has two
main objectives: 1) to investigate how everyday couple conversations are associated with
fluctuating emotional dynamics in the relationship as well as histories of family aggression; and
2) to explore how everyday partner presence may be associated with attenuated physiological
responses, which may point to one pathway underlying the link between relationships and
improved health. These findings potentially add new information toward unraveling the
important issue of how close relationships contribute to overall well-being.

5
References
Baucom, B. R., Baucom, K. J., Hogan, J. N., Crenshaw, A. O., Bourne, S. V., Crowell, S. E., ...
& Goodwin, M. S. (2018). Cardiovascular reactivity during marital conflict in laboratory
and naturalistic settings: Differential associations with relationship and individual
functioning across contexts. Family Process, 57, 662-678.
Burman, B., Margolin, G., & John, R. S. (1993). America's angriest home videos: Behavioral
contingencies observed in home reenactments of marital conflict. Journal of Consulting
and Clinical Psychology, 61, 28-39.
Campbell, J., Jones, A. S., Dienemann, J., Kub, J., Schollenberger, J., O'Campo, P., ... & Wynne,
C. (2002). Intimate partner violence and physical health consequences. Archives of
Internal Medicine, 162, 1157-1163.
Capaldi, D. M., Shortt, J. W., & Crosby, L. (2003). Physical and psychological aggression in at-
risk young couples: Stability and change in young adulthood. Merrill-Palmer
Quarterly, 49, 1-27.
Coan, J. A., Schaefer, H. S., & Davidson, R. J. (2006). Lending a hand: Social regulation of the
neural response to threat. Psychological Science, 17, 1032-1039.
Coker, A. L., Davis, K. E., Arias, I., Desai, S., Sanderson, M., Brandt, H. M., & Smith, P. H.
(2002). Physical and mental health effects of intimate partner violence for men and
women. American Journal of Preventive Medicine, 23, 260-268.
Cordova, J. V., Jacobson, N. S., Gottman, J. M., Rushe, R., & Cox, G. (1993). Negative
reciprocity and communication in couples with a violent husband. Journal of Abnormal
Psychology, 102, 559-564.
Coyne, J. C., Rohrbaugh, M. J., Shoham, V., Sonnega, J. S., Nicklas, J. M., & Cranford, J. A.

6
(2001). Prognostic importance of marital quality for survival of congestive heart failure.
The American Journal of Cardiology, 88, 526-529.
Feeney, B. C., & Kirkpatrick, L. A. (1996). Effects of adult attachment and presence of romantic
partners on physiological responses to stress. Journal of Personality and Social
Psychology, 70, 255-270.
Gottman, J. M. (1979). Marital interaction: Experimental investigations. San Diego, CA:
Academic Press.
Greenfield, L. A., Rand, M. R., Craven, D., Klaus, P. A., Perkins, C. A., Ringel, C., Warchol, G.,
Maston, C., & Fox, J. A. (1998). Violence by intimates. Washington, DC: U.S.
Department of Justice, Bureau of Justice Statistics.
Han, S. C., Schacter, H. L., Timmons, A. C., Kim, Y., Sichko, S., Pettit, C., & Margolin, G.
(revise and resubmit). Felt annoyance and anger words in dating couples’ daily lives: The
role of family-of-origin aggression.
Holt-Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social relationships and mortality risk: A
meta-analytic review. PLoS med, 7, e1000316.
Johnson, N. J., Backlund, E., Sorlie, P. D., & Loveless, C. A. (2000). Marital status and
mortality: The national longitudinal mortality study. Annals of Epidemiology, 10, 224-
238.
Laurenceau, J. P., & Bolger, N. (2005). Using diary methods to study marital and family
processes. Journal of Family Psychology, 19, 86- 97.
Margolin, G., John, R. S., & Gleberman, L. (1988). Affective responses to conflictual
discussions in violent and nonviolent couples. Journal of Consulting and Clinical
Psychology, 56, 24-33.

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Mehl, M. R., & Pennebaker, J. W. (2003). The sounds of social life: A psychometric analysis of
students' daily social environments and natural conversations. Journal of Personality and
Social Psychology, 84, 857-870.
O'Leary, K. D., Barling, J., Arias, I., Rosenbaum, A., Malone, J., & Tyree, A. (1989). Prevalence
and stability of physical aggression between spouses: A longitudinal analysis. Journal of
consulting and Clinical Psychology, 57, 263-268.
Robbins, M. L., López, A. M., Weihs, K. L., & Mehl, M. R. (2014). Cancer conversations in
context: Naturalistic observation of couples coping with breast cancer. Journal of Family
Psychology, 28, 380-390.
Robles, T. F., & Kiecolt-Glaser, J. K. (2003). The physiology of marriage: Pathways to health.
Physiology & Behavior, 79, 409-416.
Robles, T. F., Slatcher, R. B., Trombello, J. M., & McGinn, M. M. (2014). Marital quality and
health: A meta-analytic review. Psychological Bulletin, 140, 140-187.
Settersten Jr, R. A., Furstenberg, F. F., & Rumbaut, R. G. (2008). On the frontier of
adulthood: Theory, research, and public policy. University of Chicago Press.
Timmons, A. C., Baucom, B. R., Han, S. C., Perrone, L., Chaspari, T., Narayanan, S. S., &
Margolin, G. (2017). New Frontiers in ambulatory assessment: Big data methods for
capturing couples’ emotions, vocalizations, and physiology in daily life. Social
Psychological and Personality Science, 8, 552–563.
Tjaden, P., & Thoennes, N. (2000). Prevalence and consequences of male-to-female and female-
to-male intimate partner violence as measured by the National Violence Against Women
Survey. Violence against Women, 6, 142-161.
Wilkinson, D. L., & Hamerschlag, S. J. (2005). Situational determinants in intimate partner

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violence. Aggression and Violent Behavior, 10, 333-361.

9
Paper 1

Manuscript invited for revision

Feelings of Annoyance and Anger Words in Couples’ Everyday Lives:
The Role of Family-of-Origin Aggression

Sohyun C. Han,
1
Hannah Schacter,
2
Adela C. Timmons,
3
Yehsong Kim,
1
Stassja Sichko,
4

Corey Pettit,
5
& Gayla Margolin
1

1. University of Southern California
2. Wayne State University
3. Florida International University
4. University of California, Los Angeles
5. University of Virginia

Correspondence regarding this article should be addressed to Sohyun C. Han, Department of
Psychology, University of Southern California, 3620 S. McClintock Ave, SGM 501, Los
Angeles, CA 90089, sohyunha@usc.edu. This project is based on work supported by NSF Grant
No. BCS-1627272 (Margolin, PI), SC CTSI (NIH/NCATS) through Grant No. UL1TR000130
(Margolin, PI), NIH-NICHD Grant No. R21HD072170-A1 (Margolin, PI), and NSF GRFP
Grant No. DGE-0937362 (Han, PI). Its contents are the responsibility of the authors and do not
necessarily reflect the views of the NSF or NIH. The authors have no conflicts of interest that
might be interpreted as influencing this research.

10
Abstract
Little is known about the words that romantic couples use during emotionally heightened
moments, such as when feeling annoyed at their partner. To investigate this, young adult couples
were loaned mobile phones that audio-recorded 50% of their day and prompted hourly self-
reports of partner-related annoyance. Actor-partner models tested associations between
annoyance and anger words within the same hour and across the entire day; furthermore,
exposure to parent-to-child aggression (PCA) was examined as a moderator of these links. Men
reporting more annoyance across the day also used more anger words overall. Furthermore, men
reporting greater PCA used more anger words across the day. For women, hourly anger words
fluctuated in relation to men’s annoyance; moreover, greater PCA strengthened the link between
women’s own reported annoyance and anger words. Our findings highlight nuances in couples’
communication of everyday relationship distress and point to the role of PCA in next-generation
romantic relationships.

Keywords: Couples, ambulatory assessment, linguistic inquiry and word count, family
aggression

11
Introduction
Beyond literal meaning, the words people use convey aspects of personality (Mehl,
Gosling, & Pennebaker, 2006) and psychological well-being (Gortner & Pennebaker, 2003;
Mehl, Pennebaker, Crow, Dabbs, & Price, 2001). In the context of romantic relationships, subtle
features of word use can also convey how partners think and feel about their relationship (e.g.,
Sillars, Shellen, McIntosh, & Pomegranate, 1997). For example, greater use of the word “we”
(i.e., “we-talk”) during in-lab conflict discussions has been associated with higher relationship
satisfaction and less relationship distress, with we-talk thought to reflect a strong sense of joint
identity and affiliation (Simmons, Gordon, & Chambless, 2005; Williams-Baucom, Atkins,
Sevier, Eldridge, & Christensen, 2010). Relatedly, greater similarity in both pronouns and
articles (i.e., function words) during speed dating predicted which speed daters would mutually
match (Ireland et al., 2010). Overall, however, the immense potential for couples’ language use
to reveal important relationship phenomena is largely untapped. This study aims to advance
knowledge about word use and romantic relationships by capturing couples’ conversations in
their naturalistic interactions outside of the lab. We focus on the use of anger words and its
relation to on-going, fluctuating emotional dynamics in a relationship. Finally, to test
intergenerational transmission of aggression theories, this study investigates how exposure to
family-of-origin aggression may contribute to communication patterns with romantic partners.
Anger in Romantic Relationships
How romantic partners communicate and express their angry feelings with one another is
a common focus in theories of relationship distress and in interventions to improve couple
relationships (e.g., Benson, McGinn, & Christensen, 2012; Caughlin & Vangelisti, 2006; Ellis &
Malamuth, 2000; Snyder & Balderrama-Durbin, 2012). Most research, however, has focused on

12
anger and its association with relationship aggression and violence (e.g., Burman, Margolin, &
John, 1993; Liu, Lemay, & Neal, 2018; Wilkinson & Hamerschlag, 2005). Little is known about
more common everyday dimensions of anger, such as feelings of annoyance towards a romantic
partner, and how such annoyance might relate to angry word choice during conversations, which
may be a precursor to escalating arguments and aggression (Greenfield et al., 1998).
Anger Words using LIWC
The literature on word use has been made possible, to a large extent, by the development
of Linguistic Inquiry and Word Count (LIWC; Pennebaker, Francis, and Booth, 2007), a word
count program that has been validated in dozens of studies spanning more than three decades
(see Tausczik & Pennebaker, 2010 for a review). The premise of LIWC is that natural patterns of
language use are a window into a person’s psychological, social, and emotional worlds. Despite
the potential for LIWC to provide an in-depth look into romantic relationships, to date, only a
few studies have assessed couples’ naturalistic word usage in daily life. Researchers using
mobile technology (i.e., Electronically Activated Recorder; EAR; Mehl, 2017) captured brief
audio clips throughout the day and examined the extent of cancer-related conversations in
couples coping with breast cancer (Robbins, Karan, Lopez, & Weihs, 2018; Robbins, Lopez,
Weihs, & Mehl, 2014; Robbins, Wright, Lopez, & Weihs, 2019). Breast cancer patients’ use of
LIWC anger words in everyday conversations was associated with poorer relationship quality as
rated by both partners (Karan, Wright, & Robbins, 2017), suggesting that anger words may be
especially problematic within couple relationships.
It should be noted, however, that LIWC emotion words in daily life do not necessarily
relate with self-reported emotions. In a study of individuals wearing the EAR in daily life (Sun,
Schwartz, Son, Kern, & Vazire, 2019), self-reported negative and positive emotions were not

13
significantly related to spoken LIWC emotion words during 3-hour periods; yet LIWC anger
words were significantly associated with trained observer ratings of negative emotion based on
audio recordings. Thus, it appears that LIWC anger words, in comparison to other emotion
words, may have greater utility for assessing negative emotion. We build on this prior research to
investigate whether LIWC anger words map onto self-reported emotions within a smaller
timeframe (e.g., one hour). Furthermore, we broaden prior research on individuals to examine
LIWC anger words spoken within a relationship context.
Parent-to-Child Aggression
Intergenerational theories of aggression posit that children who are exposed to high levels
of aggression in their family-of-origin learn through social modeling to exhibit similar
aggression in their own adult romantic relationships (O’Leary, 1988; Widom, 1989). Notably,
exposure to family-of-origin aggression is also linked with the development of more angry and
hostile communication patterns with romantic partners. For example, greater exposure to
parental aggression is associated with greater negative affect and behavioral negativity among
men during in-lab conflict couple discussions (Halford, Sanders, & Behrens, 2000). Similarly,
men’s but not women’s retrospective reports of negativity within the family-of-origin is linked to
greater anger and contempt during in-lab conflict discussions with their spouses, which in turn is
associated with worse marital dissatisfaction and divorce four years later (Story, Karney,
Lawrence, & Bradbury, 2004). Moreover, more aversive communication (e.g., sarcasm and
criticism) by 17-year-olds and their parents during in-lab problem-solving discussions is
prospectively linked to more aversive communication between youth and their romantic partners
6 years later (Andrews et al., 2000). These examples of intergenerational transmission of
aggression (e.g., Cui, Durtschi, Donnellan, Lorenz, & Conger, 2010) appear to show that

14
exposure to parents’ hostile or verbally aggressive communications increases the likelihood of
more anger words in later adult romantic relationships, particularly for men. This transmission,
however, has not been tested with anger words spoken in daily life.
Beyond such direct intergenerational links, a history of parent-to-child aggression may
also moderate the association between feelings of annoyance and anger words. Conflict
sensitization theory posits that children exposed to high levels of marital conflict are more
sensitive to conflict and demonstrate greater emotional and behavioral reactivity to conflict, such
as arousal, anger, and anxiety (Cummings, Ballard, & El-Sheikh, 1991). Thus, anticipated or
perceived levels of conflict or threat may be particularly difficult to resolve or regulate.
Extending this theory to adults, individuals with a history of family-of-origin aggression may
react to minor annoyances in their romantic relationship with greater sensitivity and be more
likely to respond emotionally, such as with more anger words. Parent-to-child aggression may
thus strengthen links between one’s own annoyance and anger words in daily life.
Present Study
Our study uses innovative ambulatory methodology to audio record and assess couples’
use of anger words over the course of one day in their home environment. During the same day,
we also use ecological momentary assessments (EMA) to track participants’ feelings of
annoyance with their partner every hour. Given that no prior studies have simultaneously
assessed reported annoyance and anger words, we test several possibilities: annoyance and anger
words may show an actor effect at both the within-person level (i.e., hours of higher than average
annoyance may covary with higher than average anger words) or at the between-person level
(i.e., more annoyed people use more anger words overall). In addition to showing an actor effect,
annoyance and anger words may be linked across partners (i.e., a partner effect), such that one

15
person’s annoyance may covary with the other partner’s anger words. The growing literature on
emotion transmission indicates that romantic partners may be acutely attuned to one another and
that emotions may become synced (Butler & Randall, 2013; Butner, Diamond, & Hicks, 2007;
Hatfield, Cacioppo, & Rapson, 1994). Such cross-partner associations between annoyance and
aggression are presumed to be bi-directional, with one partner’s annoyance contributing to the
other’s anger words and one partner’s anger words contributing to the other partner’s annoyance.
However, for parsimony, the present study tests associations between annoyance (as the
predictor) and anger words (as the outcome) for both actor and partner effects.
Specifically, we first hypothesized that annoyance and anger words covary within person
(i.e., an actor link) as well as across partners (i.e., a partner link; HO1). We test this covariation
on an hourly basis (i.e., within-person) as well as across the whole day (i.e., between-person).
Second, we hypothesized that parent-to-child psychological aggression (PCA) assessed
retrospectively would be positively associated with overall anger word usage for both men and
women, but particularly for men as shown in prior research (HO2; e.g., Story et al., 2004). Third,
we anticipated that PCA would moderate the link between annoyance and anger words, such that
the association would be stronger for those growing up with higher exposure to PCA for both
men and women (HO3). We tested whether PCA has an effect on within-person or between-
person links between annoyance and anger words. We investigated these associations in an
ethnically diverse community sample of young adult couples, as young adulthood is an important
transitional stage between the family-of-origin and the establishment of long-term intimate
relationships and is a specific developmental period when aggression in close relationships can
have an insidious effect on later marital relationships (Capaldi, Shortt, & Crosby, 2003; O’Leary
et al., 1989).

16
Method
Participants
The study includes 77 young adult dating couples who were recruited for a larger study
and for whom we had complete audio transcriptions. Couples had to be dating at least 2 months
(Mmonths = 22.9, SD = 2.5), with both partners over age 18 and at least one partner less than 25.
Almost half (41.6%) reported living together. Most participants (73.4%) were employed part- or
full-time and 56.5% were students. The sample was ethnically and racially diverse, with 30.5%
self-identifying as Caucasian, 24.0% Hispanic/Latino, 14.3% Black/African-American, 12.3%
Asian-American, 18.2% multiple races, and 0.7% other.
Procedures
Home data procedures. Couples participated in comprehensive home data collection
procedures involving physiological measurements and survey items (Timmons et al., 2017). The
home data collection was scheduled when the couple could spend at least 5 hours together and
began with a 10 AM lab visit, where each person was loaned a mobile phone and instructed in its
use. The phones were programmed to: (a) alert participants each hour to take a short survey
regarding their mood and feelings towards the partner; and (b) to sample couple conversations by
automatically recording 3-minute segments every 12 minutes between 10 AM and 3 AM, or until
participants turned off the phones at bedtime. Recording times were staggered across the two
phones so that 6 minutes were recorded every 12 minutes. Couples were instructed to disable the
audio feature whenever they did not want to be recorded or if they were in a public setting,
though very few recordings were actually muted. In line with procedures developed for the EAR
(Mehl, 2017), participants were also given pin buttons that read, “This conversation may be
recorded” (Manson & Robbins, 2017), as well as informational handouts that explained the study

17
to people with whom they may be in frequent contact (e.g., roommates). Couples returned the
following day for debriefing, to return the phones, and to complete an exit interview. Couples
consented to the home data collection procedures as part of a larger study involving a pre-lab
survey, and an in-lab visit involving discussions and questionnaires. Participants were each
compensated $100 for the home data collection procedures. All study procedures were approved
by the university’s IRB.
Intrusiveness and Representativeness. Following data collection, participants
responded to questions on the intrusiveness of the procedures and the representativeness of the
day, through questions with a 5-point scale (Not at all, A Little, Some, A Lot, Extremely). Most
participants (74.0%) reported that the day of data collection was either A Lot or Extremely typical
of how they usually interact with their partner; 89.6% responded A Little or Not at All to whether
they changed their behaviors knowing that some of their conversations were recorded. Most
(74.7%) answered A Little or Not at All to whether filling out surveys changed their interactions
with their partner. See also Online Supplemental Materials for more information.
Measures
Hourly feelings of annoyance. Each hourly survey included the question, “In the last
hour, how irritated or annoyed did you feel towards your romantic partner?” Participants selected
a number between 0 (not at all) to 100 (extremely). Most participants reported at least some
annoyance during the day (women: 92.2%; men: 77.9%). To calculate day-long annoyance
scores (i.e., between-level measure of annoyance), we averaged hourly scores.
Hourly anger words. A total of 8,500 3-minute audio recordings were captured (M =
55.2 per individual phone). Each audio recording was manually transcribed and then checked by
two other research assistants. Transcripts from both phones were used to compile speech for each

18
partner. Human judgements of context cues were used to determine whether couples were
together or not for each audio file. When the other partner was not present, that segment was
removed from analysis to ensure that the data only included conversations in which the other
partner was present. Based on procedures used in prior research (e.g., Mehl & Pennebaker,
2003), hours when the participant spoke fewer than 20 words were also removed to ensure an
adequate representation of speech. Transcripts for each person were then edited for and
processed through LIWC2007 (Pennebaker, Booth, & Francis, 2007). Hourly counts of anger
words were derived from the LIWC pre-set dictionary of 230 anger words (e.g., “hate”, “mad”,
“stupid”) and then transformed into proportions of total words spoken per each hour. Almost all
participants (women: 97.4%; men: 96.1%) expressed anger words at some point during the day.
Parent-to-child psychological aggression. Parent-to-child psychological aggression
(PCA) was retrospectively assessed using 7-items from the Parent-Child Conflict Tactics Scale
(Straus, Hamby, Finkelhor, Moore, & Runyan, 1998; Cronbach’s alphas =.83 for women and .82
for men). For each item, participants reported how often a parent engaged in the behavior
towards them out of anger at any point during childhood (e.g., “Swore or cursed at you”,
“Insulted you or told you that you are not good enough or a failure”), using a 5-point scale (0 =
Never, 4 = more than 6 times). Most participants endorsed at least one instance of PCA (80.5%
of women and 71.1% of men).
Overview of Analyses
We used multilevel modeling for distinguishable dyads to account for dependencies in
the data due to repeated hourly measurements nested within persons and persons nested within
couples (Laurenceau & Bolger, 2012). This model allows for three conceptual levels of dyadic
data to be analyzed as a statistical model with two levels of analysis given that there is no

19
random variability at the person level. Thus, participants’ hourly within-person data (level-1) are
nested within couples (level-2). This modeling approach is an extension of the Actor-Partner
Interdependence Model (APIM; Cook & Kenny, 2005), with two sets of parameters estimated
per couple (one for the woman and one for the man). Analyses also controlled for relationship
length, a level-2 variable. In accordance with recommendations for cross-level interactions,
level-1 predictors were group-mean centered and level-2 variables were grand-mean centered
(Enders & Tofighi, 2007). Intercepts and slopes were estimated as random. Missing data were
estimated using full information maximum likelihood estimation (Muthen & Muthen, 1998–
2012). Based on multilevel simulation studies (Scherbaum & Ferreter, 2009), our level-2 sample
size comprising of 77 clusters and an average of 14 observations per cluster should have
adequate power to detect medium and large effects.
To test whether annoyance and anger words covary, we conducted one APIM model that
included both within-person and between-person actor-partner paths. The hourly within-person
paths tested women’s hourly annoyance and men’s hourly annoyance (group-mean centered
level-1 variables) as predictors of women’s and men’s anger words. These analyses examine
whether hourly fluctuations in participants’ annoyance (compared to their own average level of
annoyance across the day) predict relative fluctuations in anger words. The between-person paths
tested women’s average annoyance and men’s average annoyance (grand-mean centered level-2
variables) as predictors of anger words. In these analyses, we ask whether participants who
endorse greater annoyance with their partners across the day (compared to participants who
endorse less annoyance) also use more anger words across the day. In both within-person and
between-person models, actor paths (e.g., women’s annoyance to women’s anger words) and
partner paths (e.g., women’s annoyance to men’s anger words) were specified.

20
To test associations between PCA and average anger words, we conducted one APIM
model with actor and partner paths specified to test whether women’s and men’s PCA (grand-
mean centered level-2 variables) predicted their own and the partner’s anger words.
To test whether PCA moderated the within-person and between-person associations
between annoyance and anger words, we conducted separate analyses for within-person and
between-person interactions, separated by men and women (i.e., four total models). We tested
hourly within-person links between women’s annoyance and women’s anger words while
controlling for the partner path between men’s annoyance and women’s anger words. Women’s
PCA was added as a level-2 predictor of the association between women’s hourly annoyance and
women’s anger words. A parallel hourly model was run for men. For the between-person model,
we tested PCA as a moderator between average annoyance and average anger words, which were
both grand-mean centered, level-2 variables. Further details on multilevel equations are included
in the Online Supplemental Materials.
Results
Descriptive Statistics
Table 1 presents means, standard deviations, and minimum and maximum values for the
main study variables. Correlations based on hourly values appear above the diagonal and
correlations among day-long values appear below the diagonal. At the hourly level, women’s and
men’s annoyance were positively correlated and men’s annoyance was positively associated with
women’s anger words. Men’s and women’s anger words were positively correlated at both the
hourly and daily levels. Over the whole day, men’s, but not women’s, daily anger word total
was positively correlated with their own and the partner’s PCA.

21
T-tests of gender differences using mean scores showed that men (M = .97, SD = .78)
were significantly more likely to use anger words than women (M = .75, SD = .51), t(76) = -2.82,
p = .01. Main study variables were unrelated to age, length of their relationship, whether the
couple was cohabitating, and ethnic/racial status.

22
Table 1. Descriptive Statistics and Bivariate Correlations for Main Study Variables

Note. *p < .05; **p < .01; PCA = Parent-Child Psychological Aggression. Correlations calculated under the diagonal refer to day-
long scores whereas correlations over the diagonal refer to hourly scores. M = mean; SD = standard deviation.
a
Mean anger words
are higher for men compared to women.
b
Measured in months.

23
Hypothesis 1: Actor and Partner Associations between Feelings of Annoyance and Anger
Words
Hypothesis 1, that annoyance would be positively associated with anger words at both the
within- and between-person level, was partially supported. Figure 1 illustrates these actor and
partner effects at the hourly within-person level (top model) and day-long between-person level
(bottom model). For within-person (hourly) associations, one significant partner effect emerged:
men’s annoyance was positively associated with women’s anger words during the same hour (b
= .006, SE = .003, p < .047, 95% CI [0.000, 0.012], Proportional Reduction in Variance (PRV) =
12.44%). No other significant hourly actor or partner effects emerged. At the between-person
(day-long) level, we found the reverse pattern, such that men’s average annoyance was inversely
associated with women’s anger words across the day (b = -.007, SE = .003, p = .01, 95% CI [-
0.012, -0.001], PRV = -.11.84%
1
). Additionally, men’s average annoyance was positively
associated with their own anger words across the day (b = .009, SE = .004, p = .02, 95% CI
[0.002, 0.015], PRV = 10.48%).

1
In some instances, PRV can be a negative value when the association is negative in two-level random coefficient models. Interested readers can
refer to Snijders & Bosker (1994).

24

Figure 1. Actor-partner interdependence model of women’s and men’s annoyance predicting
women’s and men’s anger words. The figure in the top half displays hourly (within-person)
associations and the figure in the bottom half displays day-long (between-person) associations.
Standard errors are reported in parentheses. Analyses controlled for relationship length. *p <.05.

Women’s Day-long
Annoyance
Men’s Day-long
Annoyance
Women’s Day-long
Anger Words
Men’s Day-long
Anger Words
-.007(.003)*
.009(.004)*
.003(.006)
.015 (.011)
Women’s Hourly
Annoyance
Men’s Hourly
Annoyance
Women’s Hourly
Anger Words
Men’s Hourly
Anger Words
-.001(.003)
.000(.003)
.006(.003)*
-.002(.004)

25

Figure 2. Actor-partner interdependence model of women’s and men’s parent-to-child
psychological aggression (PCA) predicting women’s and men’s anger words over one day. The
analyses controlled for relationship length. *p < .05, **p < .001.

Women’s PCA
Men’s PCA
Women’s
Anger Words
Men’s
Anger Words
.290*
.205
.109
.187
.593**
.242

26
Hypothesis 2: Actor and Partner Associations between PCA and Anger Words
Hypothesis 2, that PCA would be associated with individual differences in anger words,
was supported for men. As shown in Figure 2, men reporting higher levels of PCA used more
anger words across the day (b = .29, SE = .13, p = .03, 95% CI [.021, 0.443, PRV = 14.41%).
Women’s PCA was not related to their own anger words. No significant partner effects emerged.
Hypothesis 3: PCA as a Moderator between Feelings of Annoyance and Anger Words
At the within-person level, we tested two cross-level interactions by adding PCA as a
level-2 moderator of the level-1 links between hourly feelings of annoyance and anger words –
one set of analyses predicting women’s anger words and another predicting men’s anger words.
For women, PCA was a significant moderator of hourly links between women’s annoyance and
women’s anger words (b = .008, SE = .003, p = .03, 95% CI [0.001, 0.014], PRV for level 1 =
11.84%; PRV for level 2 = -8.22%). In the presence of significant interactions, we tested for
regions of significance and found that the association between women’s hourly feelings of
annoyance and anger words became significant (and negative) at low levels of PCA (-.7 SD) and
became significant (and positive) at high levels of PCA (+2.3 SD). Figure 3 illustrates results
from regions of significance analyses with low PCA plotted at -.7 SD below the mean (dotted
line) and high PCA plotted at +2.3 SD above the mean (bolded line). There were no significant
interaction effects for men.
With parallel analyses at the between-person level, we also tested two interactions by
adding PCA as a moderator of the between-person links between average reported annoyance
and average anger words—one set of analyses predicting women’s anger words and another
predicting men’s anger words. There were no significant interactions for men or women. Table 1

27
in the Online Supplemental Materials contains additional statistical details regarding all four
interaction analyses.

28

Figure 3. Women’s hourly annoyance and anger words moderated by women’s parent-to-child
psychological aggression (PCA). Regions of significance analyses indicated that simple slopes
are significant for low PCA plotted at -.7 standard deviation below the mean (b = -.007, p = .04)
and high PCA plotted at 2.3 standard deviation above the mean (b = .015, p < .05). Low and high
hourly annoyance reflects +/- 1 standard deviation above and below the mean. Analyses
controlled for relationship length and men’s hourly annoyance.

0
0.2
0.4
0.6
0.8
1
1.2
1.4
Low High
Women's Hourly Anger Words
Women's Hourly Annoyance at Partner
Parent-to-Child Aggression as a Moderator of Women's Hourly
Annoyance at Partner and Hourly Anger Words
Low PCA High PCA

29
Discussion
The present study is the first to investigate how naturally-occurring feelings of annoyance
and anger words covary among young adult dating couples in everyday life. We tested actor and
partner hourly fluctuations between annoyance and anger words as well as between-person links
between overall reported annoyance and expressed anger words over the course of one full day.
First, as hypothesized (HO1), at the between-person level, men who were on average more
annoyed with their partner expressed more anger words across the day. Also, as expected for
cross-partner effects, men’s annoyance scores were associated with an increase in women’s
anger words during the same hour, i.e., at the within-person level. Unexpectedly, cross-partner
between-person associations showed the opposite effect such that men’s average annoyance was
associated with fewer women’s anger words across the day. Second, the hypothesis that parent-
to-child aggression (PCA) is an individual factor that is associated with increased use of anger
words (HO2) was supported for men but not for women. Third, when tested as a moderator of
the links between annoyance and anger words (HO3), high PCA significantly strengthened
within-person associations between women’s hourly annoyance and their own anger words
whereas low PCA lessened the association. Overall, these findings portray nuances in the way
men and women communicate their everyday relationship annoyances at home and show that
family-of-origin aggression is a relevant factor that can influence word usage.
The fundamental hypothesis of this study is that experiencing more annoyance than usual
towards a partner would be accompanied by increases in anger words. Our results suggest that
there are patterns among individuals who use more anger words based on their own or their
partner’s report of annoyance. Men who were generally more annoyed with their partners over
the whole day expressed more anger words overall in conversations with their partner. On the

30
other hand, women’s anger words show cross-partner effects; that is, women used anger words
concurrent to men’s annoyance at the hourly and day-long level—but in opposite directions. The
more immediate, hourly correspondence may, on the one hand, reflect women’s sensitivity to
their partners’ annoyance, in line with meta-analyses showing that women are more accurate
than men at identifying and interpreting emotions in others (i.e., Brody & Hall, 2010). The more
unexpected inverse association between men’s annoyance across the day and women’s fewer
anger words may represent a different type of sensitivity, that is, a more general modulation of
anger words. This restraint in overt anger words dovetails with findings that women are more
likely than men to engage in behaviors that serve to maintain peace and stability in relationships
(Ogolsky & Bowers, 2012).
Our findings also illustrate the importance of incorporating prior exposure to PCA when
understanding couple communication patterns. In line with intergenerational transmission
theories of aggression, men with high PCA exposure used more anger words overall in their
conversations with the partner. That this finding applies only to men and not women echoes a
gender-based pattern from lab-based conflict discussions in which men’s but not women’s PCA
was associated with angry and hostile communication patterns (Halford et al., 2000; Story et al.,
2004). However, our findings across the day portray more of a generalized speech pattern rather
than a response when upset with the partner. In the present sample, men overall use more anger
words than women, perhaps reflecting gender socialization whereby men are encouraged to
express and women are encouraged to conceal their anger (i.e., Kocur & Deffenbacher, 2014;
Sharkin, 1993).
For women, PCA emerged as a contextual factor that moderates within-person
associations between women’s annoyance at their partner and their use of anger words.

31
Unexpectedly, women who grew up with low levels of PCA (-.7 SD) actually used significantly
fewer anger words during hours when they were annoyed with the partner, perhaps another sign
of efforts to preserve relationship harmony. Yet, conversely and as expected, high exposure to
PCA strengthened the likelihood of women using anger words specifically during hours when
they were annoyed with their partners. High exposure to PCA thus appears to make feelings of
annoyance towards a partner especially salient and difficult to regulate. In line with conflict
sensitization theory (e.g., Davies & Sturge-Apple, 2007), these results suggest that PCA
increases women’s risk for behavioral reactivity (i.e., anger words) when they feel more annoyed
than usual. Notably, women demonstrate this sensitivity only at particularly high levels of PCA.
Limitations and Future Directions
An important consideration is that anger can be communicated in a number of ways.
First, although LIWC analyses offer the benefit of reliably quantifying anger words in relation to
the total number of words spoken, a limitation of this method is that it captures only one channel
of communication—spoken words—and does not capture nonverbal or paraverbal forms (i.e.,
tone) of anger communication. Prior literature suggesting that men use more direct forms of
anger expression whereas women use more indirect forms, such as an irritated tone of voice
(Fischer & Evers, 2011), may partially account for findings specific to men or women reported
here. Combining linguistic analyses with behavioral coding would be useful to capture the full
array of anger expression for men and women.
Second, another limitation of LIWC analysis is that anger words are counted without
regard for the larger context of the sentence. With no distinction between “I hate traffic” and “I
hate you” in the anger word count, we do not actually know how much of the anger is directed at
the partner. Nonetheless, a strength of LIWC is that we also captured anger words that may not

32
be directed at their partner but were still linked to annoyance. Partners in close relationships may
interpret the partner’s angry words as personal attacks, even if not intended as such.
Additionally, it is possible that using anger words such as “I feel angry” may reflect adaptive
communication processes in which partners share how they feel in a constructive manner.
However, positive associations between PCA and anger words suggest that speaking more anger
words is less likely to be an adaptive process overall. To address limitations in LIWC anger
words categories, future research may even conduct an open-vocabulary analysis (e.g., Schwartz
et al., 2013), an innovative data-driven approach in which a priori word or category judgements
are not made. Though beyond the scope of this paper, this approach would explore the words
men and women use when they feel annoyed rather than relying on closed categories.
Third, with annoyance and anger words tested within the same hour or the same day,
none of these results imply causality. Although we had theoretical reasons for testing models
where annoyance ‘leads to’ anger words, the alternate direction also is possible: one person’s
anger words might increase the other partner’s annoyance. Though time-lagged analyses could
better discern whether anger or annoyance are prompting the other, the 1-hour segmentation of
our data is limited in capturing what might be moment-to-moment sequences. Fourth, though a
full day of data is substantially longer than prior research relying on 10-minute lab-based
discussions, and though participants rate the days as relatively ‘typical’, we still may not be
capturing the full range of a couple’s interactions. Finally, because this study was conducted with
young adult dating couples, generalizability to couples in a different life stage cannot be
assumed. Nonetheless, information about young couples is important in that relatively little is
known about the early stages of romantic relationships where nascent interaction patterns might
get entrenched and affect longer-term interpersonal outcomes.

33
Conclusion
Our findings demonstrate that the words romantic partners use to communicate with one
other are a window into the fluctuating emotional dynamics of their relationship. Investigating
the connection between everyday relationship distress and language use can one day be used to
identify potentially maladaptive relationship processes, such as the escalation of annoyance into
full-blown conflict or aggression. Furthermore, exploring familial influences on language use
can pinpoint one pathway by which exposure to family-of-origin aggression may lead to more
maladaptive relationship patterns in the next generation.

34
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Online Supplemental Materials
Representativeness and Compliance
Couples were given the option of reviewing and deleting any of their recordings; only
one couple chose to review their files and decided not to delete any recordings after listening.
Participants were generally compliant with the survey procedures. Most surveys were completed
within 15 minutes of the prompt (women: 92.5%; men: 91.3%). On average, women completed
11.9 surveys (SD = 2.1, range = 5 to 15) and men completed 12.9 (SD = 1.8, range = 6 to 16). In
terms of the audio files, recordings were made for an average of 14.0 hours and, on average,
couples were together 11.6 of those hours.
Multilevel Equations
HO1. The following equations represent the multilevel APIM models for predicting
women’s and men’s anger words. We only illustrated the equations predicting women’s anger
words for simplicity:
Level 1: Women’s anger words = b0j + b1j (women’s hourly annoyance1ij)
+ b2j (men’s hourly annoyance1ij) + eij
Level 2: b0j = g00 + u0j
b1j = g10+ u1j
b2j = g20 + u2j
HO3. For each cross-level interaction, we tested hourly links between annoyance and
anger words while controlling for the partner path between annoyance and anger words. The
following equation illustrates only the model predicting women’s anger words for simplicity:
Level 1: Hourly Women’s Anger Wordsij = b0j + b1j (Women’s Hourly Annoyance1ij) + b2j
(Men’s Hourly Annoyance) + eij

42
Level 2: b0j = g00 + g01(Women’s Parent-to-Child Aggression1j) + u0j
b1j = g10 + g11(Women’s Parent-to-Child Aggression1j) + u1j
Additional Interaction Table
The following table provides additional statistical detail regarding HO3 interaction
analyses reported in the manuscript.

43
Table 1. Parent-to-Child Aggression (PCA) as a Moderator of the Association between
Annoyance and Anger Words

b SE p
Women’s Anger Words (Cross-level interaction)
Fixed: Intercept .738 .060 < .001
Fixed: Women’s Annoyance -.002 .002 .502
Fixed: Women’s PCA .128 .060 .034
Fixed: Women’s Annoyance x Women’s PCA .008 .003 .026
Random: Residual .782 .168 < .001
Random: Intercept .158 .043 < .001
Random: Women’s Annoyance .000 .001 .917
Men’s Anger Words (Cross-level interaction)
Fixed: Intercept .944 .081 < .001
Fixed: Men’s Annoyance .002 .005 .710
Fixed: Men’s PCA .273 .103 .008
Fixed: Men’s Annoyance x Men’s PCA -.001 .006 .898
Random: Residual .941 .173 < .001
Random: Intercept .388 .083 < .001
Random: Annoyance .000 .001 .770
Women’s Anger Words (Between-level interaction)
Fixed: Intercept .737 .054 < .001
Fixed: Women’s day-long annoyance .004 .006 .551
Fixed: Women’s PCA .093 .057 .103
Fixed: Women’s day-long annoyance x PCA .002 .009 .787
Random: Residual .886 .155 < .001
Random: Intercept .134 .054 < .001
Men’s Anger Words (Between-level interaction)
Fixed: Intercept .979 .084 < .001
Fixed: Men’s day-long annoyance .019 .011 .070
Fixed: Men’s PCA .259 .107 .016
Fixed: Men’s day-long annoyance x PCA -.010 .007 .140
Random: Residual 1.041 .165 < .001
Random: Intercept .364 .086 < .001

44

Paper 2

Romantic partner presence and physiological responses in daily life:
Attachment style as a moderator

Sohyun C. Han,
1
Hannah Schacter,
2
Adela C. Timmons,
3
Yehsong Kim,
1
Stassja Sichko,
4

Corey Pettit,
5
Theodora Chaspari,
6
Shrikanth Narayanan,
1
& Gayla Margolin
1

1. University of Southern California
2. Wayne State University
3. Florida International University
4. University of California, Los Angeles
5. University of Virginia
6. Texas A&M University

Correspondence regarding this article should be addressed to Sohyun C. Han, Department of
Psychology, University of Southern California, 3620 S. McClintock Ave, SGM 501, Los
Angeles, CA 90089, sohyunha@usc.edu. This project is based on work supported by NSF Grant
No. BCS-1627272 (Margolin, PI), SC CTSI (NIH/NCATS) through Grant No. UL1TR000130
(Margolin, PI), NIH-NICHD Grant No. R21HD072170-A1 (Margolin, PI), and NSF GRFP
Grant No. DGE-0937362 (Han, PI). Its contents are the responsibility of the authors and do not
necessarily reflect the views of the NSF or NIH. The authors have no conflicts of interest that
might be interpreted as influencing this research.

45
Abstract
Although romantic relationships have long been connected to better health, little is known about
the daily physiological processes that may contribute to such outcomes over time. Guided by
attachment and social baseline theory, the present study investigated the presence of a romantic
partner in daily life as one such factor contributing to attenuations in sympathetic nervous system
responses, which over time may lead to improved health outcomes. Additionally, romantic
attachment style was tested as a moderator. The sample included 106 heterosexual young adult
dating couples who were outfitted with ambulatory sensors that continuously monitored
electrodermal activity (EDA), an index of the sympathetic nervous system, over the course of
one day. The next day, couples were interviewed regarding whether they were together or apart
for every hour of the day. Multilevel models indicated that for both men and women, hours in
which their partner was present was associated with lower within-person levels of EDA
compared to hours in which their partner was absent. Additionally, romantic attachment style
moderated this association for men such that men who had high anxious attachment showed
significantly higher EDA when partners were absent compared to those with lower anxious
attachment. On the other hand, men who had high avoidant attachment showed significantly
lower EDA when partners were present compared to those with lower avoidant attachment.
These findings suggest that the simple presence of a romantic partner can facilitate everyday
health-promoting physiological processes.

Keywords: partner presence, couples, sympathetic nervous system, attachment style

46
Romantic partner presence and physiological responses in daily life:
Attachment style as a moderator
Romantic relationships are an important component of health and well-being. Being
married or having a high-quality romantic relationship is associated with living longer and
having fewer health problems compared to those who are unmarried or have poor-quality
relationships (Coyne et al., 2001; Holt-Lunstad, Smith, & Layton, 2010; Johnson, Backlund,
Sorlie, & Loveless, 2000; Robles, Slatcher, Trombello, & McGinn, 2014). One pathway by
which romantic relationships are thought to affect long-term health is through attenuating
physiological responses to stress (e.g., Butler & Randall, 2013; DeVries, Glasper, & Detillion,
2003; Robles, 2014; Uchino, Cacioppo, & Kiecolt-Glaser, 1996). Notably, research has shown
that simply the presence of a romantic partner beyond any supportive interactions is associated
with reduced physiological and neural responses when one partner experiences a laboratory-
induced stress (e.g., Coan, Schaefer, & Davidson, 2006; Feeney & Kirkpatrick, 1996). Despite
these intriguing findings, considerably less is known about the presence of a partner and
physiological responses in naturalistic circumstances. By tracking the hourly comings and goings
of young adult romantic partners over the course of an entire day, the current study investigates
associations between partner presence and physiological responses in both partners.
Additionally, in response to calls to identify individual characteristics that account for variation
in the health benefits accrued from romantic relationships (Robles, 2014), we investigate adult
attachment style as a moderator and also examine gender differences.
Theories on Relationship Regulation
Humans are biologically predisposed to be physiologically regulated by others (e.g.,
Butler, 2011). Attachment theory posits that beginning in infancy, humans have an innate drive

47
to maintain proximity to a caregiver, which helps promote regulation of affect, physiology, and
behavior (Bowlby, 1982). When infants are even briefly separated from their caregivers, they
demonstrate heightened behavioral and emotional distress accompanied by physiological stress
reactivity (e.g., Gunnar, Brodersen, Nachmias, Buss, & Rigatuso, 1996). Beyond childhood,
Bowlby (1988) theorized that attachment persists across the lifespan, with adult attachment most
commonly conceptualized within the context of romantic relationships (e.g., Fraley & Shaver,
2000; Mikulincer & Shaver, 2007). Similar to attachment figures in childhood, adult romantic
partners are thought to help regulate physiological responses, though some studies suggest that
individual differences in attachment style influence the extent of physiological regulation
(Pietromonaco, DeBuse, & Powers, 2013; Pietromonaco, Uchino, & Dunkel Schetter, 2013).
Complementing attachment theory, social baseline theory holds that humans were
evolved within a social context and are predisposed to rely on others to share resources and help
stay vigilant for risks (Beckes & Coan, 2011; Coan & Maresh, 2014). Thus, when others are in
close proximity, the brain automatically becomes less vigilant for threats, which helps conserve
and regulate one’s own resources. For instance, participants who were in the presence of a friend
estimated hills to be less steep compared to those who were alone (Schnall, Harber, Stefanucci,
& Proffitt, 2008). These findings suggest that the brain intrinsically modified sensory perception
in calculating the cost of climbing a hill, as it is less difficult to climb a hill when a friend can
help share the load. Additionally, the same experimenters found that the effect was moderated by
friendship duration – that is, the longer participants knew their friend, the less steep they
perceived the hill to be, indicating that the proximity of more intimate relationships showed
stronger physiological effects.
Romantic Partner Presence and Physiology

48
In support of both attachment and social baseline theories, a number of experimental
studies have shown that the presence of a romantic partner is associated with reduced
physiological stress responses. In a landmark study (Coan et al., 2006), women were told they
were going to receive an electric shock; conditions varied by whether they were in the presence
of their partner, a stranger, or alone. Threat-related neurological activity was most attenuated
when holding their romantic partner’s hand followed by holding a male stranger’s hand, which
was followed by being alone. Notably, among those who held their partner’s hand, individuals in
higher quality relationships demonstrated the largest threat reductions, illustrating individual
variability in stress responses. Additionally, women completing a lab-based stress-inducing task
(i.e., mental arithmetic) demonstrated lower heart rate and blood pressure when they were in the
presence of their romantic partner compared to women who were alone (Feeney & Kirpatrick,
1996). This effect was moderated by attachment style such that those who were insecure in their
attachment to their partners demonstrated higher heart rate and blood pressure when the partner
was absent compared to those with more secure attachment. Similarly, another experiment found
that the presence of a romantic partner was associated with attenuated blood pressure reactivity
during a cold pressor task compared to those who were in an active control (e.g., thought about
their day), though significant attenuation was also found for simply thinking about a romantic
partner (Bourassa, Ruiz, & Sbarra, 2019).
Partner Presence in Daily Life
Although partner presence has shown time-limited physiological effects in the laboratory,
ambulatory physiological assessments are thought to capture a more accurate representation of
an individual’s physiology relative to in-lab measures (e.g., Stone & Shiffman, 1994). Emerging
evidence also indicates that physiological responses captured at home are larger and correspond

49
more closely to measures of relationship functioning than physiological responses in the lab
(e.g., Baucom et al., 2018). Thus, identifying physiological patterns in daily life is crucial to
understanding the natural progression of health-relevant processes. To our knowledge, only two
studies have investigated the effects of romantic partner presence outside of the laboratory. In
one study, married individuals wore an ambulatory blood pressure monitor that assessed blood
pressure every 45 minutes for a 6-day period (Gump, Polk, Kamarck, & Shiffman, 2001).
Immediately after each reading, participants rated whether they were interacting with their
partner or with others. Results indicated that interactions with a romantic partner were associated
with reduced blood pressure relative to interactions with others. Another study used daily diary
methodology for 21 days among couples who anticipated a 4 to 7-day natural separation from
their partner due to work-related travel (Diamond, Hicks, & Otter-Henderson, 2008). On days
that partners were separated, couples evidenced declines in feelings of closeness and
appreciation towards each other and increases in sleeping problems. Further, partners who did
not travel were assessed for fluctuations in diurnal cortisol responses. Those with high anxious
attachment showed heightened average diurnal cortisol responses across the day (e.g., increased
physiological stress reactivity) during the separation compared to the days leading up to and
following the separation.
Though these daily life findings are informative, further investigation is needed to
understand these physiological processes on a more fine-grained level. First, measuring
physiology in both members of the couple is crucial to understanding how these processes unfold
across the dyad. To our knowledge, no studies either in the lab or at home have examined
physiological effects of partner presence in both partners. Measuring physiology in both
romantic partners is important to account for the possible physiological covariation between

50
partners, as physiological responses can become synchronized or linked (see Timmons,
Margolin, & Saxbe, 2015 for review), though the focus of this paper is not on examining linkage.
Additionally, monitoring fine-grained physiological processes on an hourly basis can be
informative for understanding within-person changes in physiology associated with partner
presence versus absence over the course of the day. In order to examine changes in physiological
responses, it may be especially informative to rely on a measure such as electrodermal activity
(EDA) that is exclusively innervated by the sympathetic nervous system and quick to respond
(i.e., in 2-3 seconds), thus allowing for a clearer approximation of continuous physiological
arousal (Dawson, Schell, Filion, 2007; Hugdahl, 1995).
Attachment Style and Gender
Several studies have found partner presence effects to vary depending on individual
attachment style (e.g., Diamond et al., 2008; Feeney & Kirpatrick, 1996). Adult attachment is
generally distinguished by secure and insecure attachment (Fraley & Shaver, 2000; Mikulincer &
Shaver, 2007). Adults who are securely attached believe that their partners will be accessible and
responsive (Hazan & Shaver, 1987). On the other hand, those are insecurely attached are likely
to perceive and expect threats in their relationship. Individuals characterized by high anxious
attachment tend to fear rejection and abandonment whereas those characterized by high avoidant
attachment may experience discomfort with intimacy and desire independence. While partner
presence may have a main effect on physiological responses among all individuals, the extent to
which partner presence has an effect is likely to vary depending on individual attachment style.
Specifically, individuals with high anxious attachment who may fear abandonment are
likely to evidence higher levels of physiological arousal when their partner is absent compared to
those with more secure attachment. On the other hand, those with high avoidant attachment often

51
seek independence from their partner and thus partner presence may show a weaker effect on
attenuating physiological responses compared to those with secure attachment. Additionally, we
investigate how the effects of partner presence may vary by gender. Prior evidence suggests that
men compared to women receive more health benefits from romantic relationships (e.g., see
Kiecolt-Glaser & Newton, 2001 for review), thus we run analyses separately by gender to
explore potential gender effects.
The Present Study
The present study uses ambulatory assessment in young adult couples to investigate the
naturally-occurring association between romantic partner presence and physiological responses
in daily life. Specifically, we investigate whether hours in which romantic partners are present
versus absent are associated with within-person differences in EDA, an index of the sympathetic
nervous system. Additionally, we test whether the association between partner presence and
EDA may differ depending on intrapersonal variables, specifically romantic attachment style.
Figure 1 illustrates our three hypotheses in separate panels. As shown in Panel A, we first
hypothesized a main effect in which partner presence (compared to absence) is concurrently
associated with lower EDA during the same hour for both men and women (HO1). Second, as
shown in Panel B, we hypothesized that anxious attachment style would strengthen the link
between partner presence and EDA (HO2). Those with high anxious attachment may show
heightened effects such that their EDA is higher when partners are not present compared to those
with low anxious attachment. Lastly, as shown in Panel C, we expected that avoidant attachment
style would weaken the association between partner presence and EDA (HO3). Those with high
avoidant attachment may not depend as much on their partners for regulation and thus may show
a weaker link between partner presence and EDA compared to those with low avoidant

52
attachment. Each of the three models also control for the covariation between men and women’s
EDA, which are depicted in Figure 1 using grey lines. We conduct analyses separately by men
and women to investigate exploratory gender differences.

53

54

Panel C

Figure 1. Panel A: Main effect between hourly partner presence and electrodermal activity (EDA). Panel
B: Anxious attachment style as a between-person moderator of the hourly within-person link between
partner presence and EDA; anxious attachment is hypothesized to strengthen the link between partner
presence and EDA. Panel C: Avoidant attachment style as a between-person moderator of the hourly
within-person person link between partner presence and EDA; avoidant attachment is hypothesized to
weaken the link between partner presence and EDA. Dashed lines represent cross-level moderation paths.
Grey lines represent the covariation between men’s and women’s EDA as well as between men’s and
women’s moderators. Covariates are not depicted for parsimony.

Women’s
Electrodermal
Activity
Women’s
Avoidant
Attachment Style
Partner Presence
Men’s
Electrodermal
Activity Men’s Avoidant
Attachment Style
(-)
(-)
(-)
(-)

55
Method
Participants
Participants were young adult dating couples participating in a larger study of family-of-
origin experiences and couples’ functioning (Timmons et al., 2019). The majority of the couples
were recruited through online and paper postings and qualified for the study if they were
between the ages of 18 and 25 and dating for at least 2 months. A smaller subset of participants
(n = 29) had previously participated in earlier waves of a longitudinal study that began when
participants were children and early adolescents; those who had a dating partner were recruited
for this study. Three same-sex couples were excluded in the present study due to the interest in
studying within-couple gender differences.
The final sample consisted of 212 young adults (106 heterosexual couples; M Age =
22.68; SD = 2.46). Couples had been dating for an average of two and a half years (M months =
30.49; SD = 24.11); 46 couples (43.40%) were cohabitating. Participants were ethnically and
racially diverse, with 27.83% self-identifying as Caucasian, 23.58% Hispanic/Latinx, 15.57%
Black/African-American, 13.21% Asian, 16.04% multiracial, and 3.77% other race. Most
participants (74.58%) were employed and about half (50.94%) were students.
Procedures
Overview. Couples responded to flyers posted online and in the community that
requested participation in a study on “how young dating couples talk to each other.” After being
screened for eligibility, couples initially participated in a laboratory visit in which they filled out
questionnaires and engaged in discussions with each other (Corner et al., 2019). During the lab
visit, couples additionally consented to participate in home data collection procedures. Couples
were asked to pick a day when they could spend at least five waking hours together in order to

56
ensure that the day included at least a moderate amount of time spent in the presence of their
partner. Most couples chose to participate on a day when neither partner worked. Participants
were each compensated $100 for the home data procedures. All study procedures were approved
by the university’s IRB.
Home data procedures. On the day of home data collection, couples attended a brief
meeting in our lab at 10 AM to consent to procedures, receive instructions, and pick up
equipment (see Timmons et al., 2017 for additional procedural details). Each participant was
outfitted with a small, wireless wrist monitor that continuously collected EDA. They were
instructed to wear the monitors at all times (except when they showered) and to go about their
day as usual. Additionally, they were each lent a smartphone that alerted them to complete short
surveys at the beginning of every hour from 10 AM until 3 AM, or until they turned their phone
off at bedtime. The next day, couples came back to the laboratory to return the equipment,
participate in a short interview regarding activities they engaged in during the previous day, and
report on the extent to which participating in the study changed their behaviors or interrupted
normal daily activities.
Intrusiveness and Representativeness. Participants individually answered an online
questionnaire on the intrusiveness of the procedures and the representativeness of the day, which
were rated on a 5-point scale including Not at All, A little, Some, A Lot, and Extremely. Most
participants (72.17%) reported that the day was typical of how they usually interact with their
romantic partner (either A Lot or Extremely). Furthermore, most participants (78.67%) reported
that wearing the wrist monitor generally did not interfere with their daily activities (either Not at
All or Slightly).
Equipment

57
Q sensor. The Q sensor is a small, wireless wrist monitor that continuously collects EDA
(Poh, Swensen, & Picard, 2010). The Q sensor has shown adequate reliability with EDA
collected in the lab and correlates with relevant psychological and physical health constructs in
daily life (Poh et al., 2012; Timmons et al., 2017). The Q sensor was worn on the inside of the
wrist and applied to the non-dominant hand in order to reduce movement artifacts. Consistent
with current standards for wearable EDA devices (Poh et al., 2010), sampling rate for the Q
sensor was set to 8 hertz.
Smartphones. Each participant was lent a 5-inch Nexus 5 Android phone that was used
to take hourly surveys on several emotional and behavioral dimensions, including potential
confounds of EDA (e.g., intake of caffeine). The phones were programmed to set off an alarm at
the top of every hour, which alerted participants to take the hourly survey based on activity
during the previous hour. All data were uploaded automatically to a secure online server.
Measures
Partner Presence. When participants returned to the lab the following day, a trained
research assistant interviewed both partners together on the various activities they engaged in
during the day of home data collection. For every hour of the day starting at 10 AM, couples
were asked whether they were together or apart and what activity they each engaged in. During
hours together, we characterized the types of activities couples engaged in and found that the
following were the most common among all hourly activities reported when couples were
together: driving/walking (22.15%), cooking/eating (15.95%), using media, i.e., TV, phone,
computer (13.74%), doing errands (9.81%), and “hanging out” at home (9.45%).
Hourly covariates. To account for a number of confounding factors related to EDA,
participants answered hourly reports via the lender phone on their engagement in physical

58
activity (e.g., exercise), and consumption of caffeine, alcohol, or tobacco during the past hour.
During the interview the next day, they also reported when they napped or were interacting with
others (e.g., roommates, family members) for every hour of the day of data collection. All of
these variables were rated on a dichotomous (yes/no) scale. These variables were then included
as potential confounding variables every hour.
Electrodermal Activity. EDA data collected on the Q sensors were downloaded onto a
computer for processing. Matlab scripts were used to automatically detect artifacts in EDA
signals. Research assistants inspected all artifacts and flagged additional artifacts when
necessary. Matlab scripts were then used to remove all identified artifacts. Each participant’s
EDA values (measured in microsiemens) were averaged across each hour to obtain one mean
EDA score for each person every hour. An average of 14.35 hours (SD = 1.47) were captured per
couple.
Attachment style. The Experiences in Close Relationships-Revised Questionnaire (ECR-
R; Fraley, Waller, & Brennan, 2000) was used to assess anxious and avoidant attachment style.
This measure includes 18 items assessing anxious attachment (e.g., “I am afraid that I will lose
my partner’s love”) and 18 items assessing avoidant attachment (e.g., “I prefer not to show a
partner how I feel deep down”). Participants answered items on a 7-point Likert scale ranging
from 1 (Strongly Disagree) to 7 (Strongly Agree). Mean anxious attachment and mean avoidant
attachment were calculated for each participant, with higher scores indicating higher levels of
anxious or avoidant attachment.
Overview of Analyses
We used multilevel modeling in which observations are nested in people and people are
nested in couples. For the main effect model (HO1), we tested hourly partner presence (Level-1)

59
as a predictor of men and women’s EDA (Level-1). For the moderation analyses (HO2-HO3), we
additionally added between-person moderators as Level-2 predictors of Level-1 slopes between
partner presence and EDA. All models accounted for the covariation between men and women’s
EDA as well as between men and women’s moderators (e.g., men’s anxious attachment and
women’s anxious attachment). To determine whether the effects of partner presence on EDA
varied by gender, we used tests of model constraint.
In all our models, we statistically accounted for the following time-varying Level-1
covariates: whether or not they engaged in physical activity, slept, interacted with others, drank
alcohol, smoked tobacco, or consumed caffeine. Additionally, we accounted for Level-2
covariates including age, race/ethnicity, relationship length, and whether or not the couple was
cohabitating. In accordance with guidelines for cross-level interactions (Enders & Tofighi,
2007), continuous Level-2 variables were grand-mean centered; Level-1 variables were not
centered given that they were all dichotomous. Intercepts and slopes were estimated as random.
Missing data were handled with Full Information Maximum Likelihood Estimation in Mplus
Version 7 (Enders, 2010; Muthén & Muthén, 2017).

60
Table 1. Descriptive Statistics for the Main Study Variables

Women Men Entire Sample
M SD Min-
Max
M SD Min-
Max
M SD Min-
Max
1. Partner Presence -- -- -- -- -- -- 0.85 0.19 0.23-
1.00
2. Electrodermal
Activity (ms)
a

4.64 5.83 0.02-
28.93
6.83 6.62 0.07-
32.21
-- -- --
3. Mean Anxious
Attachment
b

3.19 1.30 1.00-
6.50
2.75 1.24 1.00-
5.94
-- -- --
4. Mean Avoidant
Attachment
2.23 0.95 1.00-
4.83
2.20 0.96 1.00-
4.28
-- -- --

Note. Hourly variables (i.e., partner presence and electrodermal activity) were averaged across the entire day.
a
significant gender difference (men > women);
b
significant gender difference (women > men).

61
Table 2. Correlations among Main Study Variables

1. 2. 3. 4. 5. 6.
1. Women’s EDA --
2. Men’s EDA .49** --
3. Women’s Anxious Attachment .02 .01 --
4. Men’s Anxious Attachment -.08 .04 .26* -
5. Women’s Avoidant Attachment -.03 .05 .55** .29* --
6. Men’s Avoidant Attachment -.09 .00 .17 .52** .21* --

Note. EDA = electrodermal activity, measured in microsiemens. EDA was averaged across the day. * p < .05, ** p <
.001

62
Results
Descriptive Statistics
Table 1 presents descriptive statistics for the main study variables. On average, couples
spent about 85% of the day together. Approximately 46 couples spent the whole day together.
Paired sample t-tests showed that men on average (M = 6.83) had significantly higher levels of
EDA than women across the day (M = 4.68), t(105) = -3.47, p < .001). On the other hand,
women reported higher anxious attachment (M = 3.21) than men on average (M = 2.75), t(105) =
3.07, p < .01). There were no significant gender differences in avoidant attachment.
Table 2 presents correlations among continuous study variables separately by men and
women. EDA scores were averaged to obtain one score across the day. Significant positive
correlations were found between men and women’s EDA, anxious attachment, and avoidant
attachment.
HO1: Associations between Partner Presence and EDA
First, we conducted tests of model constraint using a Wald Test to assess whether the
effect of partner presence on EDA was significantly different for men compared to women.
Women’s and men’s effects did not significantly differ from one another (c
2
(1) = .91, p = .34),
thus paths were constrained to equality for parsimony. This effect was constrained in all
subsequent analyses.
As shown in Table 3, as hypothesized, both men’s and women’s EDA levels were
significantly lower during hours they were with their partner relative to hours when they were
apart.

63
Table 3. Association between Hourly Partner Presence and Electrodermal Activity Controlling for
Covariates

b SE p Lower CI Upper CI
Within-Level
Women’s EDA
Women with others 0.64 0.52 .22 -0.38 1.66
Women’s sleep 0.42 0.84 .62 -1.22 2.06
Women’s physical activity 0.96 0.36 .01* 0.25 1.66
Women’s caffeine -1.03 0.59 .08 -2.19 0.14
Women’s alcohol 0.05 1.20 .97 -2.31 2.41
Women’s tobacco -0.29 0.40 .47 -1.08 0.50
Partner present -1.44 0.61 .02* -2.63 -0.25
Men’s EDA
Men with others 0.56 0.52 .28 -0.46 1.59
Men’s sleep 0.14 0.92 .88 -1.67 1.93
Men’s physical activity 1.21 0.42 .01* 0.39 2.04
Men’s caffeine 0.32 0.71 .66 -1.07 1.70
Men’s alcohol 2.02 0.91 .03* 0.24 3.80
Men’s tobacco -0.10 0.53 .86 -1.13 0.94
Partner present -1.44 0.61 .02* -2.63 -0.25
Between-Level
Women’s EDA
Intercept 2.64 5.46 .63 -8.06 13.34
Relationship length -0.03 0.02 .16 -0.08 0.01
Cohabitation 0.50 1.11 .65 -1.66 2.67
Women’s age 0.12 0.26 .66 -0.40 0.63
Women’s ethnicity: White -0.11 1.38 .44 -3.78 1.63
Women’s ethnicity: Latina 1.10 1.60 .49 -2.04 4.24
Women’s ethnicity: Black -2.69 1.16 .02* -5.00 -0.42
Men’s EDA
Intercept 8.68 3.38 .01* 2.05 15.32
Relationship length -0.01 0.02 .58 -0.06 0.03
Cohabitation -0.77 1.30 .55 -3.31 1.77
Men’s age -0.05 0.15 .72 -0.35 0.24
Men’s ethnicity: White 0.14 1.78 .94 -3.34 3.62
Men’s ethnicity: Latino 0.82 1.58 .60 -2.28 3.92
Men’s ethnicity: Black -1.34 1.34 .32 -0.40 1.20
Note. EDA = electrodermal activity measured in microsiemens. Variables were included simultaneously
in one model. Men’s and women’s paths between partner presence and EDA were constrained to equality.
Ethnicity was dummy-coded and compared to “other” ethnicity category comprised of all other ethnic
groups. Analyses also accounted for the correlation between men’s and women’s EDA at both the within
and between-level. * p < .05

64
HO2: Anxious Attachment Style as a Moderator between Partner Presence and EDA
To test whether the link between partner presence and EDA varies as a function of
anxious attachment style, anxious attachment was added as a between-person moderator of
within-person slopes between hourly partner presence and EDA. Men’s anxious attachment
significantly moderated the hourly association between partner presence and men’s hourly EDA
(b = -.24, SE = .01, p < .001, 95% CI [-.26, -.22], Proportional Reduction in Variance (PRV) =
.28%). Figure 2 illustrates mean levels of EDA when partner is not present (white bars) and
when partner is present (shaded bars). Partner presence was associated with lower EDA across
all groups; however, simple slopes between partner presence versus partner absence were
steepest among those with high anxious attachment (+1 SD above the mean; b = -1.79, p < .01)
compared to those with average anxious attachment (b = -1.50, p = .01), and low anxious
attachment (-1 SD below the mean; b = -1.02, p < .05). No significant moderation effects were
found for women (b = .84, SE = 1.01, p = .41, 95% CI [-1.14, 2.81], PRV = -.15%).

65

Figure 2. The association between partner presence and men’s electrodermal activity moderated by men’s
anxious attachment style. Simple slopes were probed and found significant at low anxious attachment (-1
SD below the mean; b = -1.02, p < .05), mean anxious attachment (b = -1.50, p = .01), and high levels of
anxious attachment (+1 SD above the mean; b = -1.79, p < .01). µS = microsiemens.

8.43
9.07
9.72
7.23
7.58
7.92
3.00
5.00
7.00
9.00
11.00
Low Anxious Mean Anxious High Anxious
Electrodermal Activity (𝜇S)
Men's Anxious Attachment as a Moderator of Partner Presence and
Electrodermal Activity
Partner Not Present Partner Present

66
HO3: Avoidant Attachment Style as a Moderator between Partner Presence and EDA
To test whether the link between partner presence and EDA depends on avoidant
attachment style, avoidant attachment was added as a between-person moderator of within-
person slopes between hourly partner presence and EDA. Men’s avoidant attachment
significantly moderated the hourly link between partner presence and men’s EDA (b = .23, SE =
.02, p <.001, 95% CI [.18, .27], PRV = 1.72). Figure 3 illustrates mean levels of EDA when
partner is not present (white bars) and when partner is present (shaded bars). Partner presence
was associated with lower EDA across all groups; however, simple slopes between partner
presence versus partner absence were flattest among those with high avoidant attachment (+1 SD
above the mean; b = -1.23, p = .04) compared to those with average avoidant attachment (b = -
1.45, p = .02) and low avoidant attachment (-1 SD below the mean; b = -1.66, p = .01). No
significant moderation effect emerged for women (b = .08, SE = .85, p = .92, 95% CI [-1.59,
1.75], PRV = .83%).

67

Figure 3. The association between partner presence and men’s electrodermal activity moderated by men’s
avoidant attachment style. Simple slopes were probed and found significant at low avoidant attachment (-
1 SD below the mean; b = -1.66, p = .01), mean avoidant attachment (b = -1.45, p = .02), and high levels
of avoidant attachment (+1 SD above the mean; b = -1.23, p = .04). µS = microsiemens.

9.04
8.98
8.92
7.37
7.53
7.69
5.00
5.50
6.00
6.50
7.00
7.50
8.00
8.50
9.00
9.50
Low Avoidant Mean Avoidant High Avoidant
Electrodermal Activity (𝜇S)
Men's Avoidant Attachment as a Moderator of Partner Presence
and Electrodermal Activity
Partner Not Present Partner Present

68
Discussion
The present study investigated naturally-occurring associations between partner presence
and physiological arousal among young adult couples in daily life. During hours that romantic
partners were present versus absent, men and women evidenced lower physiological responses as
measured via electrodermal activity (EDA). As hypothesized, this main effect was moderated by
romantic attachment style such that individuals with high anxious attachment showed a steeper
slope between partner presence and EDA whereas those with high avoidant attachment showed a
flatter slope between partner presence and EDA; however, effects were only found for men.
Overall, these findings suggest that romantic partner presence in daily life may be one
physiological pathway underlying the link between romantic relationships and health. To our
knowledge, this was the first study to examine physiological effects of partner presence in both
members of the dyad in a naturalistic setting, allowing for a more comprehensive investigation of
health processes in romantic relationships.
Our findings build on prior research showing that the presence of a romantic partner is
associated with attenuated physiological and neurological responses at home and in daily life
(e.g., Bourassa et al., 2019; Coan et al., 2006; Diamond et al., 2008; Feeney & Kirpatrick, 1996).
As posited by attachment and social baseline theories, the day-to-day proximity of a significant
other is likely to promote feelings of safety and facilitate greater regulation of physiology (e.g.,
Gunnar et al., 1996). Our study found that for both men and women, being in the presence of a
romantic partner resulted in within-person reductions in EDA compared to when partners were
absent. Though prior research has focused on how partner presence may attenuate physiology
during situations in which stress was intentionally elicited (e.g., Coan et al., 2006), we found that
partner presence was associated with reductions in physiological arousal even during days

69
characterized by leisure, as most couples in our sample participated on days in which neither
partner worked. Our findings point to subtle ways in which romantic partners may help facilitate
physiological regulation during the ups and downs of everyday life.
As expected, there was individual variability in the extent to which partner presence was
associated with physiological responses. Men who had high anxious attachment demonstrated a
steeper slope between partner presence and EDA compared to those with average or low levels
of anxious attachment. In other words, anxiously attached men evidenced higher physiological
responses when partners were absent compared to those with lower anxious attachment. Those
with high anxious attachment are likely to be more sensitive to and perceive more threats when
their partners are not physically available whereas they may not differ from more securely
attached individuals when partners are present (e.g., Hazan & Shaver, 1987). Similarly, other
studies found that anxious attachment was related to higher physiological reactivity when
partners were absent but not when partners were present (e.g., Diamond et al., 2008; Feeney &
Kirkpatrick, 1996), though these studies found effects for women in addition to men. On the
other hand, men who were high in avoidant attachment showed a flatter slope between partner
presence and physiological responses such that they evidenced relatively smaller reductions in
physiology when partners were present compared to when they were absent. Although partner
presence still has a main effect on physiology, its effect appears weaker among men with high
avoidant attachment, as they may feel discomfort during times of intimacy when their partners
are present.
At this time, it is unclear why moderation effects for attachment style were only found for
men and not for women. More generally, men have been found to derive more health benefits
from relationships, whereas women appear to be more negatively impacted by relationship

70
conflict (e.g., Kiecolt-Glaser & Newton, 2001), though a recent meta-analysis did not find clear
gender moderation effects in the link between marital quality and health (Robles et al., 2014).
Our findings suggest that while partner presence may be similarly associated with lower EDA
among men and women, the effects may be less beneficial for men who have high anxious or
avoidant attachment, which is counter to some prior studies that found men to benefit more from
romantic relationships (e.g., Kiecolt-Glaser & Newton, 2001). Perhaps for women, the presence
of a partner is most important regardless of individual differences in attachment style. As others
have theorized, women may be more relationally oriented than men, and thus knowing that their
partner is “there” shows physiological effects whereas for men it depends on other factors (e.g.,
Kiecolt-Glaser & Newton, 2001). Given that very few studies have examined positive aspects of
relationships and physiological processes, more research into gender differences in this area is
needed.
Despite the unpredictability of collecting data “in the wild,” we were able to find
significant, albeit, small effects in the hypothesized directions. Though small changes may seem
trivial, repeated activation of physiological stress systems can lead to “wear and tear” over time,
which can accumulate into long-term health consequences such as chronic diseases (e.g.,
McEwan, 1998; Repetti, Robles, & Reynolds, 2011). Thus, partner presence may represent one
such pathway that may help decrease such wear and tear. Furthermore, partner presence had an
effect over and beyond being the presence of others such as friends and family members,
suggesting that romantic partner may play a particularly important role in regulating
physiological responses. Though young adult dating relationships are often considered to be less
established than marital relationships, our findings indicate that even emerging relationships are
effective in contributing to physiological processes.

71
Though the current study has a number of strengths, several limitations must also be
noted. First, we did not assess for the quality of romantic partner interactions when couples were
together. Though we qualitatively interviewed participants regarding what they did every hour, it
is unclear whether couples interacted in a positive or negative way. It is well-established that
negative, stressful couple interactions are likely to activate physiological stress response systems,
particularly in low quality relationships (e.g., Robles & Kiecolt-Glaser, 2003), thus it is possible
that partner presence was associated with greater physiological arousal in some instances. Future
research may explore whether other relationship variables such as relationship quality and
conflict may moderate the link between partner presence and EDA. Second, it is possible that
there are “third variables” that account for the link between partner presence and EDA; for
instance, the type of activity that couples engaged in together could have helped attenuate EDA.
Though the range in activities was too wide to quantitively assess the correlation between
activity type and EDA, there appeared to be a significant amount of variability in the types of
activities couples engaged in. For instance, some couples chose to “hang out at home” during
their time together while others chose to run errands or do an outdoor recreational activity.
Another limitation is that we required couples to be together at least 5 hours in order to ensure at
least a moderate amount of time together; though this may have placed an artificial constraint,
most couples chose to spend more than 5 hours together on the day of the study. Future research
should assess couples over multiple days to capture more variability in patterns of partners’
presence versus absence. Lastly, we used retrospective interview reports and segmented their
activities into hours, which may have placed artificial constraints on their actual time together.
While couples typically consulted each other and their phones to determine the exact time in
which they engaged in certain activities, future research using ecological momentary assessments

72
to track the exact time they were together and apart would be more accurate, although that would
significantly increase participant burden. Moreover, even if partners are not in one another’s
physical presence, they may be connected virtually through social media, texting, and video or
phone calls, which some research suggests may have an effect on physiological responses (e.g.,
Bourrassa et al., 2019; Diamond et al., 2008).
The current study utilized innovative ambulatory assessment methodology to investigate
everyday associations between partner presence and physiological responses among young adult
couples. Our findings point to subtle ways in which romantic relationships can facilitate
everyday health processes to cumulatively impact long-term health outcomes. Additionally, the
presence of a romantic partner may be more or less beneficial depending on individual
differences in romantic attachment style. Exploring everyday relationship factors that help
mitigate the effects of physiological stress can be key to preventing adverse health problems.

73
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General Discussion
The overall aim of this dissertation was to investigate ‘a day in the life’ of young adult
dating couples to better understand how everyday relationship processes contribute to overall
health and well-being. Toward these aims, we used innovative methodology to collect multi-
modal data (e.g., audio, self-report, physiology) every hour in dating couples as they went about
their day. Across two studies, we discovered that small changes in moods, behaviors, and
physiology in daily life related to broader outcomes such as aggression and indicators of health.
Substantive Contributions
The first study investigated links between self-reported feeling states (e.g., annoyance)
and spontaneously spoken anger words every hour, with parent-to-child aggression tested as a
moderator of these links. As contrasted with previous studies where couples were instructed to
discuss conflictual topics, we were able to observe how partners naturally communicated over
the course of the day and how irritations with a partner spontaneously emerged. As anticipated,
fluctuations in feelings of annoyance corresponded with spoken anger words but specific
findings differed by gender. For men, overall annoyance and anger words were positively related
but, for women, their anger words were surprisingly linked with their partner’s annoyance and
not their own. These results raise questions about whether women are highly attuned to their
partner’s annoyance (e.g., Brody & Hall, 2010) or whether women express their annoyance in
other ways, such as using an irritated tone of voice or ignoring (e.g., Fischer & Evers, 2011),
though other forms of anger communication were not measured in this study.
Additionally, to explore intergenerational transmission of aggression theories, we tested
whether fluctuations in anger words corresponded with family histories of growing up with
parents who communicated in aggressive ways for both men and women. As hypothesized, men

79
who had greater exposure to parent-to-child aggression (PCA) spoke more anger words
throughout the day. On the other hand, for women, a history of PCA moderated the within-
person link between annoyance at the partner and the use of anger words. That is, annoyance
appeared particularly difficult to regulate for those women with especially high exposure to
PCA. These results support conflict sensitization theory, which posits that children exposed to
marital conflict become attuned to conflict and show greater behavioral reactivity, measured here
in anger words, compared to those with less exposure (e.g., Davies & Sturge-Apple, 2007). For
men, perhaps conflict sensitization led to direct links with anger words without the
accompanying fluctuations in annoyance. Overall, our data show that the choice of words that
couples use in their conversations are meaningfully linked to ongoing emotional dynamics in the
relationship and to familial patterns of communication. Given that prior research has shown how
angry communication during conflict discussions can be maladaptive (Halford, Sanders, &
Behrens, 2000; Story et al., 2004), our data are the first to show that even everyday annoyances
and anger words spoken throughout the day may be important to monitor. These results further
suggest that the clinical focus on angry communications within controlled settings, such as in
therapy, should also address the use of angry words in more naturalistic settings. Such
interventions may be particularly important for those who have family histories of aggression.
The second study investigated whether the presence of a romantic partner may help
explain the well-established link between romantic relationships and improved health outcomes
(e.g., Robles et al., 2014). By not restricting the activities or locations of couple interactions, we
were able to investigate how partner presence is linked with physiological responses as couples
went about their daily lives. Across the wide variety of activities that couples engaged in over the
course of the day, being physically together was associated with lower levels of sympathetic

80
nervous system responses (e.g., electrodermal activity; EDA) for both men and women. We
additionally found individual differences based on romantic attachment style; men with high
anxious attachment showed higher physiological responses when partners were absent compared
to those with lower anxious attachment, suggesting that they may be more sensitive to threats
when partners are not available. On the other hand, men with high avoidant attachment showed
relatively lower physiological responses when partners were present compared to those with low
avoidant attachment, indicating that they may feel discomfort with intimacy when partners are
present.
Though it is well-established that romantic relationships have an effect on physical
health, it is still unclear exactly how relationships can get “under the skin” (e.g., Slatcher &
Selcuk, 2017). Furthermore, the vast majority of research has examined how negative aspects of
relationships, such as conflict, lead to greater physiological stress reactivity (e.g., Robles &
Kiecolt-Glaser, 2003), with little known about how beneficial aspects of relationships relate to
physiological processes that promote health. By examining what naturally transpires between
romantic partners in their daily lives, we contribute novel findings to understanding the daily
physiological processes that underlie overall long-term positive links found between
relationships and health. Grounded in attachment and social baseline theories, our findings
suggest that minor fluctuations in physiological responses associated with partner presence are
one such mechanism by which aspects of romantic relationships can contribute to health-
promoting physiological processes. Furthermore, individual differences in attachment style can
affect how one perceives the presence or absence of a partner, further contributing to
understanding how close, attachment relationships contribute to health. As social relationships
have a similar effect size on health as other risk factors, such as diet and sedentary activity (e.g.,

81
Robles, Slatcher, Trombello, & McGinn, 2014), unpacking relationship factors that promote
health is an important aim for improving public health.
Our data highlight that gender differences are particularly important to consider when
investigating relationship functioning in heterosexual romantic relationships. In both studies,
findings tended to differ by gender, for instance, with romantic partner attachment moderating
the association between partner presence and EDA for men but not for women. Though our two
studies draw from different literatures (e.g., aggression, health), they suggest that men and
women appear to communicate and respond physiologically differently from one another,
perhaps reflecting differences in socialization and biological processes. The early health
literature indicated that men tend to reap more health benefits from marriage than women (e.g.,
see review by Kiecolt-Glaser & Newton, 2001), though more recent literature fails to find gender
differences across most health outcomes (see review by Robles et al., 2014). Our results did not
find that men and women differed in the effect of partner presence on physiological processes.
However, our interaction results suggest that men may not have a health advantage if they have
high insecure attachment. Theorists suggest that future research into gender-related moderators,
such as who takes on certain roles or who has relatively more power in the relationship, may be
fruitful areas for further exploring whether it is gender that is truly associated with health
outcomes or whether it is based on other factors such as relative power in the relationship (Wanic
& Kulik, 2011).
Our study focused on young adult dating couples, who have been largely overlooked
within the romantic relationship literature compared to middle or older adult couples (e.g., see
Robles et al., 2014 for review). In contrast to many married couples (e.g., Saxbe, Repetti, &
Graesch, 2011), most young adult dating couples did not share responsibilities of caring for

82
children or taking care of housework, as 60% of couples in our study did not cohabitate. While
their day-to-day responsibilities may be somewhat different than married couples, young adult
dating couples showed emerging relationship processes that dovetail with prior research. For
instance, our findings regarding the use of anger words throughout the day mirror prior in-lab
studies (e.g., Halford et al., 2000) showing that men with histories of PCA tend to use more
anger words in conflict discussions. Our results with young dating couples are important in the
context of other research showing that relationship processes in earlier young adult relationships
are associated with later patterns in marital and parent-to-child relationships (Capaldi, Shortt, &
Crosby, 2003; O’Leary et al., 1989). More research is needed to better understand continuities
and discontinuities in relationship patterns across the life span as well as to identify possible
points of intervention.
Methodological Contributions
Beyond these substantive contributions to the literature, these two studies utilized novel
ambulatory assessment methods to contribute methodologically to research on dyads in
naturalistic settings. Though many prior studies of daily life collected individual streams of data
in home environments through audio recordings (e.g., Mehl, 2017; Mehl & Pennebaker, 2003),
and daily diary self-reports (e.g., Laurenceau & Bolger, 2005), our studies are unique in the
collection, analysis, and combination of multi-modal data types across dyads. For instance, our
first paper combined ecological momentary assessments of self-reported feelings with linguistic
analyses of spoken words during the same hour, which has never previously been done before
apart from our data. We also collected multi-modal data on both members of the dyad,
expanding upon prior research that has focused on individuals (e.g., Mehl, 2017) or one member
of the dyad (e.g., Diamond et al., 2008). That is, in our second paper, both partners of the dyad

83
wore biosensors to track physiological responses as they went about their daily lives. As
technological advances and commercial products have made it easier to collect data at home via
smartphones and wearable biosensors (e.g., FitBits, Apple Watches), psychological research can
capitalize on such innovations to investigate phenomena in daily life.
Limitations and Future Directions
Despite these strengths, several limitations must be noted. First, although our aim was to
collect data on couples in their everyday lives, participating in these home data collection
procedures introduced some confounds. For instance, we asked couples to meet experimenters in
the lab in the morning to pick up equipment, which resulted in couples starting their day by
driving to our lab and spending an hour with our experimenters—which is a deviation from their
normal activities. In order to gather a moderate amount of data when couples were together, we
also asked couples to spend at least five waking hours together, which may have introduced an
artificial constraint, though most couples naturally spent more than five hours of the data
collection day together. Furthermore, given that we only examined one day, most often on a day
when neither romantic partner worked, our data may not be generalizable to workdays or to other
days in general. Future research may collect data across several days to reduce these limitations
and to better generalize the findings.
Additionally, given that the physiological data were collected “in the wild,” they are
inextricably “noisier” and more prone to artifacts. We attempted to control for various
confounds, such as substances consumed (e.g., caffeine, alcohol) and exercise, however, it is
possible that fluctuations in EDA were due to other emotional experiences or situational factors.
Third, though changes in autonomic nervous system activity are theoretically considered to be
part of the allostatic process associated with health outcomes (e.g., Repetti, Robles, & Reynolds,

84
2011), we did not measure indices of health directly. Thus, a direction for future research would
be to explore how daily physiological changes are linked with clinical endpoints (e.g., disease)
and/or surrogate endpoints (e.g., biological markers that predict disease, such as blood pressure).
Also, is possible that there are “third variables” that give rise to the associations found in these
studies, such as personality characteristics that contribute to both increased annoyance and anger
words.
Given the richness of the data, there are a number of areas for future research. First, while
we used traditional hypothesis testing to investigate questions in this dissertation, future research
can use exploratory methods without relying on a priori hypotheses. For instance, instead of
using LIWC dictionaries, which were determined by human raters, we can develop open
dictionaries that determine words couples actually say during hours they reported feeling angry,
sad, happy, etc.. Such methods can capture communication patterns that are unexpected but more
closely map onto how people actually communicate. Second, we plan to use behavioral coding to
quantify couples’ activities, locations, and the people they interact with. We also aim to code
romantic partner’s affect and interactions with each other, such as their tone of voice and both
positive (e.g., humor, words of affirmation) and negative interactions (e.g., insulting, defending).
Coding of audio files in the home environment can reveal novel observational data, such as
whether anger words are spoken in the context of an argument and how conflicts naturally begin
and end. Additionally, we can expand upon the concurrent associations reported in this study to
test time-lagged analyses, which will clarify the direction of effects from one moment to the
next. Though the current studies chunked data into one hour “bins,” future research can also
examine different time intervals such as every 5, 10, or 15 minutes. Phenomena can also be

85
examined when they occur, such as tracking the actual times couples are together and apart,
rather than exploring what happens each hour.
Conclusion
This dissertation is a demonstration of how day-to-day relationship phenomena enhance
our understanding how romantic relationships contribute to broader outcomes, such as the
intergenerational transmission of aggression and the development of disease. The two papers
highlight new substantive and methodological directions for exploring mechanisms by which
close relationships contribute to health and well-being.

86
References
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NY: Springer. http://dx.doi.org/10.1007/978-1-4419-1465-1_21
Capaldi, D. M., Shortt, J. W., & Crosby, L. (2003). Physical and psychological aggression in at-
risk young couples: Stability and change in young adulthood. Merrill-Palmer
Quarterly, 49, 1-27.
Davies, P. T., & Sturge-Apple, M. L. (2007). The impact of domestic violence on children’s
development. In J. Hamel & T. L. Nicholls (Eds.), Family interventions in domestic
violence: A handbook of gender inclusive theory and treatment, (pp.165-190). New York,
NY: Springer.
Diamond, L. M., Hicks, A. M., & Otter-Henderson, K. D. (2008). Every time you go away:
Changes in affect, behavior, and physiology associated with travel-related separations
from romantic partners. Journal of Personality and Social Psychology, 95, 385.
Fischer, A. H., & Evers, C. (2011). The social costs and benefits of anger as a function of gender
and relationship context. Sex Roles, 65, 23-34. http://dx.doi.org/10.1007/s11199-011-
9956-x
Halford, W. K., Sanders, M. R., & Behrens, B. C. (2000). Repeating the errors of our parents?
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McEwen, B. S. (1998). Stress, adaptation, and disease: Allostasis and allostatic load. Annals of
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Kiecolt-Glaser, J. K., & Newton, T. L. (2001). Marriage and health: His and hers. Psychological

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Mehl, M. R., & Pennebaker, J. W. (2003). The sounds of social life: A psychometric analysis of
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89
Appendix A: Hourly Phone Surveys
1. How stressed were you in the last hour? 0-100 (not at all-extremely)
If anything other than zero is endorsed for Q1, participants will receive Q2:
2. What was the source of stress? Please check all that apply.
a. The romantic partner
b. Another person
c. Work or school
d. Other events/news
3. Did you consume any of the following in the last hour? Please check all that apply.
a. Coffee, tea, or energy drinks
b. Alcohol
c. Tobacco
d. None of the above
4. Did you engage in any physical activity in the last hour?
a. Not at all
b. Moderate intensity
c. High intensity
5. In the last hour, how happy were you? 0-100 (not at all-extremely)
6. In the last hour, how sad were you? 0-100 (not at all-extremely)
7. In the last hour, how nervous were you? 0-100 (not at all-extremely)
8. In the last hour, how angry were you? 0-100 (not at all-extremely)
9. In the last hour, how close or connected did you feel towards your romantic partner? 0-100
(not at all-extremely)

90
10. In the last hour, how irritated or annoyed did you feel towards your romantic partner? 0-100
(not at all-extremely)
If anything other than zero is endorsed for Q10, they will receive Q11:
11. Did you express this irritation to your romantic partner (speaking, texting, etc.)?
a. Yes
b. No
12. In the last hour, have you had any contact with your romantic partner via text or phone?
a. Yes
b. No

91
Appendix B: Exit Interview

92

93
Appendix C: Parent-Child Conflict Tactics Scale

Directions: Please indicate if at any time in your life a parent or stepparent did any of the
following out of anger to you…

Never Once Twice
3 to 5
times
More
than 6
times
1. Swore or cursed at you
2. Kick you out of the house or car
3.
Insulted you or told you that you are
not good enough or a failure

4.
Insulted or shamed you in front of
other

5.
Threatened to stop supporting you
financially

6.
Sent you a threatening text, email,
tweet, etc.

7.
Posted something embarrassing or
upsetting about you online

94
Appendix D: The Experiences in Close Relationships-Revised (ECR-R) Questionnaire

Instructions: The statements below concern how you feel in emotionally intimate relationships.
We are interested in how you generally experience relationships, not just in what is happening in
a current relationship. Respond to each statement by selecting the number to indicate how much
you agree or disagree with the statement.

Items are rated on a 7-point scale:
1 = Strongly disagree
7 = Strongly agree

1. I'm afraid that I will lose my partner's love.
2. I often worry that my partner will not want to stay with me.
3. I often worry that my partner doesn't really love me.
4. I worry that romantic partners won’t care about me as much as I care about them.
5. I often wish that my partner's feelings for me were as strong as my feelings for him or her.
6. I worry a lot about my relationships.
7. When my partner is out of sight, I worry that he or she might become interested in someone
else.
8. When I show my feelings for romantic partners, I'm afraid they will not feel the same about
me.
9. I rarely worry about my partner leaving me.
10. My romantic partner makes me doubt myself.

95
11. I do not often worry about being abandoned.
12. I find that my partner(s) don't want to get as close as I would like.
13. Sometimes romantic partners change their feelings about me for no apparent reason.
14. My desire to be very close sometimes scares people away.
15. I'm afraid that once a romantic partner gets to know me, he or she won't like who I really am.
16. It makes me mad that I don't get the affection and support I need from my partner.
17. I worry that I won't measure up to other people.
18. My partner only seems to notice me when I’m angry.
19. I prefer not to show a partner how I feel deep down.
20. I feel comfortable sharing my private thoughts and feelings with my partner.
21. I find it difficult to allow myself to depend on romantic partners.
22. I am very comfortable being close to romantic partners.
23. I don't feel comfortable opening up to romantic partners.
24. I prefer not to be too close to romantic partners.
25. I get uncomfortable when a romantic partner wants to be very close.
26. I find it relatively easy to get close to my partner.
27. It's not difficult for me to get close to my partner.
28. I usually discuss my problems and concerns with my partner.
29. It helps to turn to my romantic partner in times of need.
30. I tell my partner just about everything.
31. I talk things over with my partner.
32. I am nervous when partners get too close to me.
33. I feel comfortable depending on romantic partners.

96
34. I find it easy to depend on romantic partners.
35. It's easy for me to be affectionate with my partner.
36. My partner really understands me and my needs.

Abstract (if available)

Abstract Romantic partner interactions have long been considered central to the development of psychological and physical health sequelae. However, much less is understood about the microlevel processes, such as the day-to-day couple interactions, that contribute to such outcomes over time. The first paper in this dissertation will seek to investigate how everyday couple conversations are associated with fluctuating emotional dynamics in the relationship as well as histories of family aggression. The second paper will explore how everyday partner presence may be associated with attenuated physiological responses, which may point to one pathway underlying the link between relationships and improved health. These findings potentially add new information toward unraveling the important issue of how close relationships contribute to overall well-being.

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Asset Metadata

Creator Han, Sohyun Christine (author)

Core Title Young adult dating couple interactions in daily life: links to family aggression and physiological processes

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Publication Date 07/23/2020

Defense Date 04/22/2020

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag ambulatory assessment,Couples,ecological momentary assessment,family aggression,OAI-PMH Harvest,Physiology,romantic relationships

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Margolin, Gayla (committee chair), John, Richard (committee member), Meyerowitz, Beth (committee member), Narayanan, Shri (committee member), Saxbe, Darby (committee member)

Creator Email sohyunha@usc.edu,sohyunhan@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-343555

Unique identifier UC11663333

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Legacy Identifier etd-HanSohyunC-8733.pdf

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Rights Han, Sohyun Christine

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Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

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