UNDERSTANDING THE IN TERPERSONAL PROCESSE S ASSOCIATED WITH MARITAL SATISFACTION By Katherine M eredith Thomas A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Psychology Doctor of Philosophy 201 5 ABSTRACT UNDERSTANDING THE IN TERPERSONAL PROCESSE S ASSOCIATED WITH MARITAL SATISFACTION By Katherine Meredith Thomas Marital satisfaction is among the stron gest correlates of overall life satisfaction, whereas marital dissatisfaction is associated with a variety of physical and psychological difficulties. Although a relatively large body of research has described several interpersonal processes associated with marital satisfaction, a system atic theoretical and measurement model for conceptualizing and measuring adaptive interpersonal processes as they naturally unfold remains elusive. The primary aim of this study was to test the usefulness of moment - to - moment measurement of dominance and wa rmth, grounded in the Interpersonal Circumplex (IPC) as a theoretical and measurement model, to evaluate how ongoing adaptive processes relate to marital satisfaction (aim 1) and personality traits (aim 2) . To do this, I used the interpersonal joystick met hod to code moment - to - moment interpersonal behavior displayed by husbands and wives (n = 135 dyads) - P artner Interdependence Modeling (APIM) to exa mine individual and relational observed behaviors across task s and their self - reported marital satisfaction and personality characteristics . I also tested associations b etween dyadic patterns such as behavioral a nd dominance generally showed small effect s in predicting his satisfaction, but not his wives. Both partners tended to be most satisfied when moment - to - moment dominance reciprocity was high, although models examining interactions between individual warmth , dyadic dominance reciprocity, and marital satisfaction generally indicated that high dominance reciprocity was particularly associat ed with satisfaction when wives were warm, but not when wives were cold. In general, results examining personality traits revealed levels of warmth in both spouses. The data obtained using th is method also provide promising avenues for detailing how n egative spousal patterns, such as demand - withdraw and negative - reciprocity , unfold between partners from one moment to the next. These and ot her results highlight the value of using the IPC as a t heoretical and measurement model for understanding associations between moment - to - moment personality processes a nd relationship sa tisfaction. Copyright by Katherine Meredith Thomas 2015 To Grant Thomas Womack (1992 2014) You were here when I submitted my dissertation, so I never imagined you would be gone before I defended it. In your loss I feel, and for the first time fully grasp, the weight of the most consequential lesson I learned in my graduate training: Choose conne ction. vi ACKNOWLEDGEMENTS In retrospect, I began to pursue this path long before I realized it. With two parents with doctoral degrees, obtaining my own seemed the obvious path to follow. Now it seems like the definitive complement to the lives they cr eated for us, and to their embodiment of work as k ethic and temperament, my sensitivity, and I hope even network of extended family, including my aunts, uncles, cousins, and grandmother. In particular, I wish words could fully acknowledge my Aunt Carol, my unsung hero, whose will to grow and lov and a way to do the same. I cannot imagine I would have gone down this path, and certainly not with all the same wonderful turns, if it had not been for pivotal mentors throughout my education, beginning with my high school psychology teacher, Dr. Vanzant, who lit a fire in me that burning. As an undergraduate student, I had the pleasure of working with Dr s . Duke and Nowicki , who planted the interpersonal seed in my mind and my experiences of working with them long before I understood the formal model and its impl showed me ways the scientific method can be applied to study dynamic personality processes; that broad framework continues to inspire my research. Friends and mentors of the Grady Trauma Project also played pivotal and pos itive roles in my professional development. S everal MSU faculty were instrumental to my graduate training . In particular, Rick DeShon sparked my enthusiasm for learning statistics. Alytia Levendosky served on most of my committees and as an additional a dvisor whenever I sought insight or solace . Kelly Klump was a vii role model for me in essentially clinically, in the classroom, on research, and in my annual summer attempts to water ski! I am thankful t hat I had a dissertation guidance committee, Adrian Blow, Brent Donnellan, Emily Durbin, and Chris Hopwood, who not only made my final project richer, but also made my entire process enjoyable. I am especially grateful to Emily for providing the data that made this study possible, and to Brent, who served as a frequent collaborator and lunch mate and also as a valuable member on all of my major graduate committees. My graduate training was further enriched by many mentors and collaborators outside of MSU. I am especially grateful for the relationships I developed through the Society for Personality Assessment and the Society for Interpersonal Theory and Research . In particular I thank Pam Sadler for sharing so much of her time and insight with me, and for d eveloping the method which has inspired much of my work, including this study. I am also especially grateful for my many opportunities to work with Aaron Pincus and his students, particularly Emily Ansell, Nicole Cain, Mark Lukowisky, Aidan Wright, and Mik e Roche. Attending MSU to work with Chris and still getting to work so closely with the PSU contingency felt like having my cake and eating it too. I eagerly anticipate a career of our continued collaborations. My internship year has filled me in ways I n ever anticipated. It has been an honor to work with veterans and staff at the San Francisco VA. I am especially grateful to Keith Armstrong and It was also my priv ilege to work alongside fellow interns, Anna, Holly, Nana, and Steph, and my good fortune to consider you all among my closest friends this year. my relationships with fellow graduate students. I am especi ally appreciative of the opportunities I had and hope to continue viii having to work and connect with Audie, Britny, Charlotte, Hans, Heleen, Jess, Lisa, Mikhila, Natalie, Nick, Sarah, Shannon, Sharon, Xiaochen, and Zori. A few friends deserve further comment. Wains I suppose I could have survived t m certain testament to our friendship, flexibility, and passions that, despite not having an o bvious overlap to see the creative ways we continue to work together going forward. And Matt, my partner in life, love, and work, everything i s better with you by my side. found each other. This project simply would not have been possible without the help of a team of dedicated , Hannah, Jack, Josh, Kelsey, Kristin, Kyle, and Lexi for the time and dedica tion they put into this study. I learned more than I could have taught from you all and from the many RAs who worked in our lab over the years, most notably also including Chad, J essi, Sarah, Sindes, Taka, and Trevor. Last, but farthest from least, I spent every day of my graduate training grateful to be working with Chris, who, from the moment I arrived, treated me more like a collaborator than a student. You cultivated a combina tion of autonomy granting, broad knowledge, and constant curiosity that permitted me to pursue my passions regardless of how little or much they overlapped with yours. I will always be grateful that, with and because of you, my graduate training experience importantly, you embody the quintessential lesson you taught me: First communion, then . I ix TABLE OF CONTENTS LIST OF TABLES ................................ ................................ ................................ ........... x i LIST OF FIGURES ................................ ................................ ................................ ......... x ii INTRODUCTION ................................ ................................ ................................ ........... 1 Adaptive Processes and Marital Satisfaction ................................ ....................... 2 Interpersonal Theory and the Interpersonal Circumplex ................................ ..... 4 Integrating the VSA and IPC Models to Examine Marital Satisfaction .............. 9 Aim 1: Marital Satisfaction and Adaptive Processes ................................ ........... 1 0 Aim 2: Enduring Vulnerabilities and Adaptive Processes ................................ ... 1 1 METHOD ................................ ................................ ................................ ........................ 1 5 P articipants ................................ ................................ ................................ ........... 15 Procedure ................................ ................................ ................................ ............. 16 Measures ................................ ................................ ................................ .............. 2 0 Adaptive Processes ................................ ................................ .................. 20 Marital Satisfaction ................................ ................................ .................. 22 Enduring Vulnerabilities ................................ ................................ .......... 23 Data Analysis ................................ ................................ ................................ ....... 24 RESULTS ................................ ................................ ................................ ........................ 2 7 Reliabili ty and Descriptive Statistics ................................ ................................ .. 27 Hypothesi s 1.1: Observer ratings of warmth for both spouses will be associated - reported marital satisfaction ..................... 29 Hypothesi s 1. 2 : Complementarity on both the warmth and dominance dimensions will be a marital satisfaction ........... 30 Hypothesi s 1. 3 : M ean levels of dyad warmth will moderate the association between marital satisfaction and complementarity on the warmth and dominance dimensions ................................ ................................ ..................... 31 Hypothesi s 2.1: NEM, interpersonal problems, and total PD severity will be associated with lower levels of actor and partner warmth ............................... 33 Negative Emotionality ................................ ................................ ............. 33 Interpers onal Problems ................................ ................................ ............ 33 Personality Disorders ................................ ................................ ............... 34 Hypothesi s 2.2: PEM - C will be associated with higher levels of actor and partner warmth ................................ ................................ ................................ . 3 4 Exploratory Associations with Dominance ................................ ........................ 35 Marital Satisfaction ................................ ................................ .................. 35 Personality Trait s (NEM, PEM, & CON) ................................ ................ 35 Interpersonal Problems ................................ ................................ ............ 36 Personality Disorders ................................ ................................ ............... 36 x DISCUSSION ................................ ................................ ................................ .................. 37 Interpe rsonal Behaviors across Tasks ................................ ................................ .. 37 Interpersonal S tyle and Marital Satisfaction ................................ ........................ 39 Complementa rity and Marital Satisfaction ................................ .......................... 39 Mo mentary Behaviors and Personality Trai ts ................................ ...................... 41 Mo mentary Behavio rs and Interpersonal Problems ................................ ............. 43 Mo mentary Behaviors and Personality Disorders ................................ ............... 44 Mo mentary Me asurement of Demand - With draw and Negative Reciprocity ...... 45 Limi tations and Future Directions ................................ ................................ ....... 47 Conclusions ................................ ................................ ................................ .......... 49 APPENDICES ................................ ................................ ................................ ................. 52 Appendix A: Tables ................................ ................................ ............................. 53 Appendix B: Figures ................................ ................................ ............................ 65 REFERENCES ................................ ................................ ................................ ................ 79 xi LIST OF TABLES Table 1. Mean , (SD), Range, and Reliability of Warmth Time - Series across Tasks ....... 53 Table 2. Mean , (SD), Range, and Reliability of Dominance Time - Series across Tasks .. 54 Table 3. Mean , (SD), and Range of Dyadic Behaviors across Tasks .............................. 55 Table 4. APIM Associations between Husbands and Wives Marital Satisfaction and Individual and Dyadic Warmth and Dominance ................................ ............... 56 Table 5. APIM Associations between Marital Satisfaction and Individual Warmth, Dyadic Correspondence, and their Interaction ................................ .................. 57 Table 6 . APIM Associations between Husbands and Wives Warmth and Dominance and MPQ Negative Emotionality ................................ ................................ ....... 58 Ta ble 7. APIM Warmth and Dominance and IIP Interpersonal P roblems ................................ ................................ ........ 59 Table 8. and IPDE Personality Disorders, Actor E ffects ................................ .................... 60 Table 9 . and IPDE Personality Disorders, Partner Effects ................................ ................. 61 Table 10 . APIM Warmth and Dominance and MP Q Positive Emotionality Communion ................................ ............... 62 Table 11 . APIM Warmth and Dominance and MPQ Positive Emotionality Agency ................................ ....................... 63 Table 12 . APIM Warmth and Dominance and MPQ Constraint ................................ ................................ ........................ 64 xii LIST OF FIGURES Figure 1. The Vulnerability Stress Adaptation (VSA) M odel of Marriage ...................... 65 Figure 2. The Interpersonal Circumplex (IPC ) ................................ ................................ 66 Figure 3. Negative R eciprocity and D emand - W ithdraw Patterns Depicted on the IPC . 67 Figure 4. An Integrative Model of Adaptive Processes rooted in VSA and IPC .............. 68 Figure 5. Hypothesis 1.1: APIM Relating Husbands and Wives Warmth and Marital Satisfaction ................................ ................................ ................................ ........ 69 Figure 6. Hypothesis 1.2: APIM R elating Dyadic Correspondence and Marital Satisfaction ................................ ................................ ................................ ....... 70 Figure 7. Hypothesis 1.3: APIM Relating Marital Satisfaction with Dyadic Correspondence, Individual Warmth, and their Interaction ........................... 71 Figure 8 . Total Score Simple Slope Interactions between Marital Satisfaction , Mean Warmth, and Momentary Dominance Complementarity ........................ 72 Figure 9 . Husband Conflict Simple Slope Interactions between Marital Satisfaction , Mean Warmth, and Momentary Dominance Complementarity ........................ 73 Figure 10. Wife Conflict Simple Slope Interactions between Marital Satisfaction , Mean Warmth, and Momentary Dominance Complementarity ...................... 74 Figure 11. Best T hings Simple Slope Interactions between Marital Satisfaction , Mean Warmth, and Momentary Dominance Complementarity ...................... 75 Figure 12. Vacation Simple Slope Interactions between Marital Satisfaction , Mean Warmth, and Momentary Do minance Complementarity ...................... 76 Figure 13. Case Example of Negative Reciprocity: Dyad 111 , Best Things .................... 77 Figure 14. Case Example of Demand - Withdraw : Dyad 099 , Husband Conflict ............. 78 1 INTRODUCTION Relationships are a cornerstone of human health and happiness (Ainsworth, 1985; Baumeister & Leary, 1995; Bowlby, 1958; 1969; Ryan & Deci , 2000). Marital relationships in particular relate to a variety of physical, psychological, familial, and financial outcomes (e.g., Fincham & Beach, 2010; Huston, 2000; Levinger & Huston, 1990). Meta - analytic results indicate that marital satisfaction is related to personal wellbeing in cross - sectional and longitudinal studies (Proulx, Helms, & Buehler, 2007) and is among the stron gest correlates of overall life satisfaction (average r = .42; Heller, Watson, & Ilies, 2004). Conversely, marital dissatisfact ion is associated with greater health problems, lower reports of wellbeing (Bloom, Asher, & White, 1978; Burman & Margolin, 1992; Kiecolt - Glaser & Newton, 2001), and higher levels of criminal behavior ( Laub, Nagin, & Sampson, 1998) . Relationship satisfact ion varies substantially across couples and m arital dissatisfaction and dissolution are common ( Gottman & Notarius , 2000 ). In an attempt to synthesize and guide research on marital satisfaction and stability, Karney and Bradbury (1995) developed the Vulner ability Stress Adaptation (VSA) model. In this model ( Figure 1), marital satisfaction is influenced by the ongoing adaptive processes Bradbury propose that these processes are influenced by the enduring v ulnerabilities (e.g., personality, psychopathology) each partner brings to their relationship and the stressful experiences encountered by one or both partners. A considerable amount of research has been dedicated to articulating the adaptive processes as sociated with marital satisfaction (Gottman & Notarius, 2000; Fincham & Beach, 2010); however, a systematic model for conceptualizing and measuring adaptive processes remains elusive. This is scientifically problematic given that adaptive processes represe nt the 2 core psychological mechanism facilitating marital success in the VSA model. As such, the focus of this study is to test whether interpersonal theory, operationalized using a momentary assessment of behavioral processes as they unfold between spouses , can provide a systematic framework for understanding and examining the adaptive interpersonal processes that relate to marital satisfaction. Adaptive Processes and Marital Satisfaction Relative to nondistressed couples, distressed couples tend to display fewer positive behaviors, such as agreeing with and displaying physical affection toward one another, and more negative behaviors, such as hostility, criticism, and blame (Gottman, Markman , & Notarius , 19 7 7 ; Notarius & Markman, 1989 ). Less satisfie d couples also display higher rates of withdrawal, whining, anger, and disagreement compared to more satisfied couples (Gottman & Krokoff, 1989; Gottman & Levenson, 19 92; Pasch & Bradbury , 1998 ) , and increase in their displays of these negative communicati ons over the course of a discussion about a relationship problem ( Gottman & Levenson, 19 92). Controlling for marital satisfaction at baseline, Pasch and Bradbury ( 1998 ) found that levels of contemptuous behaviors during interactions predicted marital distr ess two years later. Whereas early marital research on adaptive processes was primarily focused on behaviors related to dissatisfaction , more recent research has also examined adaptive processes associated with marital satisfaction and stability (Fincham & Beach, 2010). This literature generally indicates that m ore satisfied couples display higher rates of affection, agreeableness, and interest in one another compared to less satisfied couples (e.g., Gottman & Levenson, 19 92). Longitudinal research has sho wn that p ositive communication behaviors (e.g., humor , affection , support) during a problem solving task predict marital satisfaction several years later ( e.g., Gottman, Coan, Carrere, & Swansan, 1998; Pasch & Bradbury, 1998; Rogge & Bradbury, 1999). 3 On a briefer time - scale, Laurenceau and colleagues (2005) found that higher daily ratings of closeness between spouses were associated with higher levels of global marital satisfaction. Positive communication behaviors may also protect couples from the otherwis e negative influence of their critical, hostile, and/or withdrawing behaviors (Fincham & Beach, 2010; Johnson, Maio, & Smith - McLallen , 2005; Margolin & Wampold, 1981). For instance, Johnson and colleagues ( 2005) found that, among couples who display high levels of negative communication behaviors, only those who failed to also display positive behaviors such as engagement and warmth experienced a decline in marital satisfaction in subsequent years. Research ide ntifying the types of behaviors that differentiate more and less satisfied couples is informative for beginning to understand the behavioral processes that relate to marital satisfaction. However, dyadic interactions involve more than the simpl e sum or agg regate of behaviors and examination of the interlocking patterns that characterize interpersonal behaviors is needed to account more fully for the complexity of dyadic interactions ( Gottman, Swanson, & Swanson, 2002; Shoda et al., 2002 ; Z ayas, Shoda, & Ayduk, 2002). An early study employing sequential analysis found that the frequency of non - negative (i.e., neutral or positive) communications by one partner immediately following a negative communication by the other partner incremented the total number of negative and positive communications in explaining variance in marital satisfaction (Margolin & Wampold, 1981). Subsequent researchers have identified two patterns, commonly referred to as negative reciprocity and demand - withdraw (a.k.a. p ursue - withdraw) , that distinguish more and less satisfied couples. Negative reciprocity involves negatively when faced with the negative behavior of the other, leading to a cycle of negative interact ion that may become stro ng enough to be self - sustaining (Gottman, 1979). Less satisfied 4 couples are more likely to engage in negative reciprocity than satisfied couples, who are relatively more likely to break this cycle by responding to negative behavior s with neutral or positive behaviors (e.g., Gottman, Markman, & Notarius , 1977; Gottman & Notarius , 2000; Margolin & Wampold, 1981 ). The demand - withdraw pattern involves one partner criticizing or commanding something from the other, who responds by disengaging from the discussion and avoiding confrontation (e.g., Gottman et al., 1997; Heyman, 2001; Notarius & Markman, 1989). Wives more frequently demand and husbands more frequently withdraw ( Bradb ury et al., 2000; Gottman et al., 1997; Heavey, Layne, & Chri stensen, 199 3 ); however, this pattern is bi - directional and can be initiated with a demand or withdrawal from either partner (Klinetob & Smith, 1996 ). A ccording to the demand - withdraw perspective cyclical nature of this process. Research indicates that demand - withdraw patterns are associated with decreased marital satisfaction ( Eldridge & Christensen, 2002; Heavey, Chri stensen, & Malamuth, 1995 ; Klinetob & Smith, 2006), although there is some evidence that warmth moderates this association (Caughlin & Huston, 2002). Interpersonal Theory and the I nterpersonal Circumplex The Interpersonal Circumplex (IPC) was derived by studying the structure of thousands of interactions (Leary, 1957) and its circular structure was replicated using a lexical approach to organize the taxonomy of interpersonal behavi ors (Wiggins, 1979). These and other independent inquiries into the structure of social processes converge to suggest that two fundamental dimensions, control (dominance to submission) and affiliation (warmth to coldness), account for much of the variability in relational functioning and behavior ( e.g., Leary, 1957; Luyten & Blatt, 5 2013; Wiggins, 1979; 1991). These dimensions are commonly operationalized using the IPC (Figure 2). The circular organization of this model has several advantages (Pincu s & Gurtman, 2006; Wright, et al., 2009), including the provision of an inclusive map of interpersonal behaviors that uses only two dimensions. For instance, trusting behavior represents a blend of warmth and submissiveness because it involves a desire to connect with and follow the lead of another. In this way, the IPC combines the advantages of a simple structure with the advantages of a comprehensive taxonomy (Gurtman, 1992). The IPC dimensions include behaviors that have commonly been found to relate t o marital satisfaction. For instance, a large body of research suggests warmth is associated with marital satisfaction . Gonzaga and colleagues (2001) coded four affiliative behaviors displayed by partners during discussions (head nods, gestures, forward le ans, and genuine [ Duchenne ] smiles) and found that higher levels of these behaviors were associated with higher levels of relationship satisfaction, even when controlling for trait levels of agreeableness and extraversion. Longitudinal research indicates t hat higher dyadic displays of disengaged behaviors, such as silence and disinterest, during problem - solving discussions predict lower marital satisfaction up Leary). Using ecological momenta ry assessment, Jani cki and colleagues (2006) found that higher rates of agreeable behavior during spousal interactions correlated with higher daily reports of marital satisfaction. Dominance ha s received less empirical attention than a ffiliative behaviors in marital research (Smith et al., 2009). Much of the research examining dominance in marital interactions has focused on extreme expressions of this behavioral dimension, such as intimate partner violence and coercive control. Not surprisingly, such beha viors are associated with lower levels of marital satisfaction (Fincham, 2003; Gray - Little & Burks, 1983). High ly dominant behavior s 6 by husbands during marital interactions have also been linked with lower levels of marital satisfaction for both partners ( Gottman & Levenson, 1984; Thomsen & Gilbert, 1998). Additional research regarding whether dominant behaviors are adaptive is mixed, and indicates the importance of examining the behaviors of both spouses. For instance, in one study Gray - Little (19 8 2) foun d that higher discrepancies between behavioral ratings of husbands and wives displays of dominance during a discussion were associated with higher levels of marital satisfaction; however, in a subsequent study (Gray - Little , Baucom, & Hamby, 1996 ), she foun d that husbands and wives who were most egalitarian in their displays of dominance had the highest levels of marital satisfaction. Although such results appear inconsistent, they can be integrated using an interactional perspective. For instance, it might be adaptive for spouses to express comparable levels of dominance overall, but to takes turns speaking and listening to one another during discussions. This notion is consistent with research indicating that momentary interpersonal behaviors, particularly dominance, tend to be cyclical (Sadler, Ethier, Gunn, Duong, & Woody, 2009 ; Thomas, Hopwood, Woody, Ethier, & Sadler , 2014). In additional to providing a comprehensive taxonomy of interpersonal behaviors that are relevant to marital satisfaction, the IPC may also provide a valuable tool for marital researchers because it is rooted in a theoretical model that makes specific predictions about how and under what conditions behaviors are likely to change. A core tenet of interpersonal theory is that individua ls continually assert influence on the responses they receive from others in their relationships ( Carson, 1969; Kiesler, 1996, Leary, 1957, Pincus & Hopwood, 2012). A specific pattern, referred to as complementarit y, describes a commonly identified process that unfolds during dyadic interactions ( Carson, 1969; Sadler, Ethier, & Woody, 2010 ). Based on this 7 principle, the behaviors of one individual invite particular behaviors from the other individual in dyadic interactions. Specifically, w armth invites warm th, whereas dominance invites submission. Empirical research has often found evidence for complementarity across a variety of relationships and interactions ( for a review, see Sadler et al., 2010), and h igher levels of complementarity are associated with interpersonal closeness (Ansell , Kurtz, & Markey , 2008; ; Yaughn & Nowicki, 1999) and relationship satisfaction (Dryer & Horowitz, 1997; Locke & Sadler, 2007). Among college students in long - term relationships, greater similarity in co - reported warmth and greater discrepancy in their self - reported dominance were associated with higher levels of relationship satisfaction (Markey & Markey; 2007) . Observations of interactions also indicate that higher levels of observed complementarity are associated with interactants liking one another more (Dryer & Horowitz, 1997; Mark ey , Lowmaster, & Eichler 2010; Nowicki & Manheim, 1991) , and deviations from complementarity may be indicative of problematic interpersonal functioning (P incus, Lukowitsky, Wright, & Eichler, 2009; Sadler et al. , 2010). T here are , however, important caveats to this research. For one, measuring complementarity at a global time scale may not be the most accurate way to assess its dynamic predictions (Sadler & Woody, 2003; Sadler et al., 2009; Tracey, 2004). Complementarity is a multi - faceted construct that may be more or less relevant depending on the temporal resolution used to measure it (Sadle r et al., 2010). R esearch examining complementarity across a vari ety of time scales (e.g., traits, situations, behaviors) has found that rates of complementarity are highest when measures at the level of real - time behaviors (Tracey, 2004). Secondly , it is unclear whether complementarity functions equivalently on the wa rm and cold halves of the IPC. Orford (1986) suggests that although complementarity is commonly 8 found on the warm half of the IPC (i.e., warm - dominance and warm - submissiveness elicit one another), cold - dominance elicits cold - dominance whereas cold - submissi veness elicits warm - dominance. However, the studies he reviewed largely measure d complementarity at a global time scale , and more recent research measuring interpersonal behaviors suggests that complementarity occurs on both the warm and cold halves of the IPC ( Sadler, Little, & Woody, 2014; Sadler & Woody, 2003). An important aim for future research is to examine whether complementarity functions as an adaptive process across the range of IPC and across a range of relationships and conversations. Research from the marital literature suggests that complementarity on the warmth half of the IPC might be more adaptive than complementarity on the cold half of the IPC. Indeed, the negative reciprocity and demand - withdraw patterns identified as maladaptive for mar ital satisfaction can be construed as complementarity that occurs on the cold side of the IPC. Specifically, negative reciprocity involves a complementary pattern whereby cold behavior from one spouse elicits cold behavior from the other spouse. As can be seen in Figure 3, this pattern is often cyclical and mutually reinforcing. In the demand - withdraw pattern (Figure 3), - dominance is met with cold - submissiveness from the other partner. This pattern may be especially common when one partne r is particularly rigid in his/her behavioral style, consistently pulling for a cold - complementarity response from the other partner. The demand - withdraw pattern may also be bi - directional, with partners taking turns demanding and withdrawing from one anot her over time (Klinetob & Smith, 1996). Given that negative reciprocity and demand - withdraw patterns involve cold complementarity, and that these patterns are associated with reduced marital satisfaction, it is possible that complementarity is most adaptiv e for couples when unfolds on the warm half of the IPC. 9 Integrating the VSA and IPC Models to Examine Marital Satisfaction Several marital researchers have drawn attention to the need for theoretical models and organizing principles capable of guiding our understanding of how a nd why marriages are experienced as satisfying ( Reis, 2007). In his review of the many methods that have been used to study marital interactions, Heyman ( 2001, p. 26) noted, One cannot establish procedures and measures as content and construct valid without a theory about what they should be measuring. Although this seems rather obvious, a large number of studies [examining behavioral processes in married dyads] seem to lack a theoretical structur e for their hypotheses or for their use of observational systems. used to study behavioral processes employ strategies that do not adequately capture the dynamic comple xities inherent in interpersonal exchanges. These conclusions suggest that it would be beneficial to measure adaptive processes using an empirically supported model that can capture dynamic complexities. In their development of the VSA model, Karney and Br adbury (1995) evaluated common theoretical perspectives of marriage against three important criteri a know n to account for meaningful variance in marital satisfaction. Specifically, they evaluated whether each model: 1) could be used to examine both macro - and micro - level vari ables, 2) s pecified mechanisms of change and influence on marital satisfaction, and 3) could account for both within and between - couple variations in marital outcomes. The interpersonal model meets each of these criteria. Specifically, interpersonal theory and the IPC can be used: 1) to organize the study warmth and dominance at high levels of abstraction (e.g., the extents to which a person values connection and achievement), to study moment - to - moment behaviors as they unfold in real ti me, and to examine varying levels of specificity in between (e.g., average behavioral tendencies); 2) to test 10 theoretically - grounded mechanisms presumed to influence behavioral processes, such as complementarity; and 3) to measure behavioral processes that unfold both within and between individuals ( Leary, 1957; Fournier, Moskowitz, & Zuroff, 2008; 2009 ; Sadler et al., 2009 ; Thomas et al., 201 4; Wiggins, 1996). In the VSA model, Karney and Bradbury (1995) propose that individual vulnerabilities and stressf ul situations adversely influence behaviors, which in turn adversely influences marriages. The IPC provides a map for measuring real - time behavioral interactions (G ifford, & O'Connor, 1987), and t he principle of complementarity provides a testable theoreti cal prediction . In the following section I will discuss specific aims for testing the integrative potential of the IPC as a measurement model of adaptive processes (see also Figure 4). Aim 1: Marital Satisfaction and Adaptive Processes The first aim of this study wa s to examine associations between momentary interpersonal behaviors and marital satisfaction. Given research indicating warmth is associated with marital satisfaction, I expect ed mean ratings of warmth for both spouses to be associated with both - reported marital satisfaction. Because it is less clear whether dominant behaviors relate to marital satisfaction, I explored associations between mean ratings of dom inance and marital satisfaction, but did not make a specific prediction about these potential associations. Complementarity involves dynamically stable behavioral pulls that unfold between partners. This principle provides a baseline prediction for behavio ral exchanges, but dyads display considerable differences in the extent to which their behaviors are complementary ( Pincus, 1994; Sadler et al., 2009) and the potentially adaptive role of complementarity remains unclear. Whereas much research has linked co mplementarity with relationship satisfaction, other 11 research suggests that complementary cold behaviors are associated with less relationship satisfaction. Thus, the potential adaptivity of complementarity may differ depending on whether it unfolds in the context of warmth or coldness. I predicted that complementarity on both the warmth and dominance dimensions would relate to marital satisfaction, but that this association would be stronger for couples who displayed more warm behaviors. Hypothes is 1.1: O b server ratings of warmth for both spouses will be associated with - reported marital satisfaction. Hypothesis 1.2: Complementarity on both the warmth and dominance dimensions will be associated with marital satisfaction . H ypothesis 1.3 : Mean levels of dyad warmth will moderate the association between marital satisfaction and complementarity on the warmth and dominance dimensions. Aim 2: Enduring Vulnerabilities and Adaptive Processes Research ers using the VSA model have found that personality characteristics systematically relate to marital satisfaction (Bradbury, Finch am , & Beach, 20 0 0; Malouff, Thorsteinsson, Schutte, Bhullar, & Rooke). One of the most robust personality predictors of poor marital satisfaction is Nega tive Emotionality (NEM) 1 , which involves the tendency to experience negative emotional states such as anger, anxiet y, depression, and vulnerability (Clark, 2005; Rothbart, Ahadi, & Evans, 2000 ; Tellegen & Waller, 2008 ). NEM has consistently been linked wit h lower self - and partner - reports of satisfaction in romantic relationships in cross - sectional ( Cundiff, Smith, & Frandsen, 2012 ; Donnellan, Assad, Robins , & Conger, 200 7; Donnellan, 1 To maintain consistent terminology throughout the paper, I use the terms NEM, PEM, and CON to describe results MPQ ( Tellegen, 1982), and the FFM, as measured by the NEO - PI - R (Costa & McCrae, 1992), are as follows: NEM is associated primarily with neuroticism and somewhat with agreeableness; PEM is highly associated with extraversion and moderately associated with agreea bleness; and CON primarily overlaps with conscientiousness ( Church, 1994; Markon, Krueger, & Watson, 2005). 12 Conger, & Bryant, 2004 ; Karney Bradbury, 1997 ; Malouff et al., 2010; Rob ins, Caspi, & ; Stroud et al., 2010) and longitudinal ( Caughlin, Huston, & Houts, 2000 ; Kelly & n with a host of other negative outcomes, including poor physical health (Lahey, 2009) most forms of psychopathology (Kotov, Gamez, Schmidt, & Watson, 2010; Samual & Widiger, 2008), and less satisfaction in work and relationships (Ozer & Benet - Martinez, 20 06). Karney and Bradbury (1995) proposed that personality characteristics such as NEM interactions (Figure 1). Indeed, t here is widespread evidence that personality t raits influence behavior, although the correspondence between broad traits and specific behaviors is modest ( Back, Schmukle, & Egloff, 2009 ; Fleeson & Gallagher, 2009; Paunonen & Ashton, 2001; Sadler & Woody, 2003). NEM has been associated with general int erpersonal distress (Tracey, Rounds, & Gurtman, 1996) and with cold interpersonal characteristics in particular (Trapnell & Wiggins, 1990). Higher levels of self - reported NEM have also been associated with lower levels of observed warmth during dyadic inte ractions ( Côté & Moskowitz, 1998; Moskowitz, 1994). characteristics influence spousal behaviors. NEM has consistently demonstrated associations with other enduring vulnerabilities such as general interpersonal problems (Ozer & Benet - Martinez, 2006) and p ersonality d isorders (PDs; Samuel & Widiger, 2008). Cross - sectional and ambulatory assessments of spousal PD onality pathology is associated with reduced marital satisfaction for both partners (Knabb, Vogt, Reist Gibbel, & Brickley, 2012; 13 South, 2014; South, Turkheimer, & Oltmanns, 2008). These findings are not surprising given that difficulties in interpersonal relationships are a defining characteristic of PDs (e.g., Benjamin, 1996; Krueger, Skodol, Livesley, Shrout, & Huang, 2007; Pincus, 2005; Pincus & Hopwood, 2012). Evidence suggests that interpersonal problems and PDs also negatively influence adaptive proc esses. For instance, PDs are typically associated with cold and disagreeable behavior (Samuel & Widiger, 2008), suggesting that maladaptive behavioral processes may be one reason for relationship difficulties among individuals with PDs. The severity of ind pathology (i.e., the total number of PD symptoms endorsed) is also associated with higher rates of verbal and physical aggression toward their spouse ( Schumacher, & Leonard, 2005 ). Likewise, there is a large body of evidence linking i nterpersonal problems and PDs to rigid interpersonal behavior (Chen et al., 2004; Johnson, Chen, & Cohen, 2004; Pincus & Wiggins, 1990). Given that interpersonal rigidity is associated with lower levels of complementarity (Tracey, 2005), individuals with P Ds appear less likely to adhere to normative behavioral patterns when interacting with others. Although Karney and Bradbury (1995) referred to the characteristics that contribute to adaptive processes and marital satisfaction as vulnerabilities , individual characteristics can also mai ntain and improve marital satisf action (Fincham & Beach, 2010). S ome evidence suggest s marital satisfaction is associated with personality traits related to achievement and social closeness. For instance, self - reporte d Positive Emotionality (PEM), which involves tendencies to experience positive emotions such as excitement and joy and to be activ ely engaged with the environment, relates to higher levels of self - and partner - reported relationship satisfaction ( Donnellan et al., 2005; Malouff et al., 2010; Robins et a l., 2000; Stroud et al., 2010) and to higher observer - rated warm th behaviors during dyadic interactions ( Côté & Moskowitz, 1998). 14 M ost of the variance in marital satisfaction associated with PEM appears to b e captured by facets related to social connection, referred to as communal PEM (PEM - C), rather than by facets associated with agentic PEM (PEM - A), which include achievement and social potency ( Donnellan et al ., 2007; Robins et al., 2000). Given association s between PEM - C and Five Factor Model (FFM) agreeableness and PEM - A and extraversion (Markon et al., 2010), this finding is consistent with meta - analytic results (Malouff et al., 2010) indicating that agreeableness is more strongly associated with marital satisfaction than extraversion . In sum, individuals who characteristically experience negative emotions and relational problems tend to also have less satisfying marriages, whereas highly communal individuals tend to have more satisfactory marriages (Smith, Traupman, Uchino, & Berg, 2010 ; Whisman , Uebelacker, & Weinstock, 2004 ). In the VSA model, such enduring vulnerabilities are p roposed to reduce marital satisfaction via their influence on adaptive interpersonal processes (Karney & Bradbury, 1995). Whereas my first study aim was to test associations between interpersonal processes and marital satisfaction, my second aim was to exa mine whether the interpersonal processes that are relevant for marital satisfaction are influenced by personality characteristics. Based on past research, I expected e nduring vulnerabilities related to negative emotional experiences and interpersonal probl ems to be associated with maladaptive interpersonal processes , specifically warmth . In contrast, I expected positive emotional experiences and closeness in relationships to be associated with observed warmth. Hypothesis 2. 1 : NEM, interpersonal problems, a nd total PD s everity will be associated with lower levels of actor and partner warmth. Hypothesis 2. 2 : PEM - C will be associated with higher levels of actor and partner warmth. 15 METHOD Participants The majority (93.2%) of participants were married (mean marriage length = 8.81 years; SD = 3.96 years) and raising approximately two children ( M = 2.32; SD ranged from 23 - 49 ( M = 36.91 ; SD = 5.17 - 57 ( M = 38.27 ; SD = 5.79 ). Approximately 90% of participants provided information on their race/ethnicity and described themselves as Caucasian/White (75.9% women , 75.5% men ), Hispanic/Latino (10.2% women , 11.3% men ), African American/Black (9.3% women , 9.4% men ) , Asian (8.3% women , 4.7% men ), Native American (2.8% women , 1.9% men ), other (1.9% women, 4.7% men), and bi/ multi - racial (1.9% women , 3.8% men ) 2 . The majority ( 80.4% ) of partners within a couple endorsed the same race/ethnicity. Among couples who provided information on their family income (86.4%) , 1.9% reported income below 10,000; 17.8% reported income between 21,000 and 40,000; 15.0% reported income between 41,000 and 60,000; 31.8% reported income between 61,000 and 100,000; and 33.6% reported income ab ove 100,000. This study will use existing data collected from heterosexual couples (n = 140 dyads) recruited from the Chicago, IL area for a study of family relationships, temperament, and psychopathology (Stroud et al., 2010; Stroud, Durbin, Wilson, & Mendelsohn, 2011; Wilson & Durbin, 2012a; Wilson & Durbin, 2012b). Eligible couples cohabitated and had a biological child between the ages of three and six. The full study involved two laboratory visits, the first to assess child temperament and the secon d to assess parent personality [traits, problems, and disorders] and relationship satisfaction as well as f amily and spousal interactions . Only data from the second laboratory visit will be used for the present study. All procedures were approved by 2 Race/ethnicity values do not sum to 100% because participants could endorse multiple categories. 16 local Institutional Review Boards and families were financially compensated for their participation. P rocedure During their lab visit, participants completed a variety of self - report measures assessing marital satisfaction and personality functioning. Followi ng the completion of these questionnaires, couples participated in six discussion tasks, four of which were coded in this - vacation (5 minutes), discussing the location, length, and activities involved with the potential trip. For their - best things about their relationship (5 minutes). In between these tasks, couples engaged in two conflict discussions (8 minutes each), o ne identified as the with her husband and the other identified as the conflict with his wife. For these discussions, research assistants told couples what to discuss sagreement on the Dyadic Adjustment Scale (DAS; Spanier, 1976) . Couples were instructed to discuss the conflict and try to reach a solution. These discussions were designed to elicit common emotional experiences that occur between couples (Foster, Caplan, & Howe, 1997). To behaviorally anchored ratings of each spouse during each discussion ( Lizdek, Sadler, Woody, Ethier, & Malet, 2012 ; Sadler et al., 2009). Ratings were ma de by simultaneously viewing a discussion task and using a computer joystick ( the Microsoft SideWinder Force Feedback 2 ) to The computer monitor displayed the video being viewed and a Cartesi an plane depicting dominance and warmth (i.e., 17 the IPC dimensions). A dot moved within the Cartesian plane in accordance with joystick movements, allowing coders to view the placement of their ratings as they watched videos. Joystick data were scaled from - 1,000 to 1,000 on both dimensions, with 1,000 on the y - axis representing extreme dominance and 1,000 on the x - axis representing extreme warmth. Consistent with past research, the program software recorded rater s joystick placement along both axes twice p er second . Coders were instructed to make behaviorally anchored ratings by moving the joystick in accord with all tatements, nonverbal behaviors, fluctuations in tone, etc., that constitute d an increase or decrease in dominance and/ or warmth . As such, raters moved the joystick in a relatively continuous manner in accord with their perceptions of changes in the interpersonal behavior . Examples of dominant behaviors included directing the conversation, asserting authority, and speaking first during conversational lulls, whereas requests, and not speaking during conversational lulls. Examples of warm behaviors included physical gestures such as moving closer to the other person, eye contact, and affectionate touching, and verbal communications such as laughing, praising, supporting, or complimenting the other person. In contrast, examples of cold behaviors included physical gestures like looking away or aggressive touch, verbal communications such as mean and sarcastic comments, and an absence of reciprocated warmth, such as not laughing when the other person employs humor or withdrawing from physical affection. Because many behaviors refl ect a blend of dominance and warmth (e.g., interruptions are often both dominant and cold), horizontal and vertical joystick movements often occur simultaneously to varying degrees and coders were instructed to move the joystick in a manner that concurrent ly represents dominance and warmth. Raters were also 18 instructed to move the joystick to represent any times in which the absence of a be havior signified a meaningful interpersonal action (e.g., if the target remained silent after being asked a question). W hen no discernible changes in interpersonal behavior were displayed, raters were instructed to maintain their most recent joystick position until the target displayed a meaningful interpersonal behavior. Importantly though, raters were instructed to code e ven slight gestures like eye contact, head nods, and changes in tone to ensure that they capture fine - grained variations in behavior. Eight undergraduate research assistants (fours males and four females) underwent training on the joystick method using pr otocol outlined by Sadler and colleagues (2009). Training began with an introduction to the IPC, familiarization with the joystick apparatus, and practice moving the joystick to represent various interpersonal descriptors. Raters then watched the study aut to ask any clarifying questions before coding videos themselves and receiving live observation and feedback. To practice rating momentary interpersonal behaviors, coders watched and rated several parent - adolescent conflict discussions (10 minutes). These data were selected for training purposes for several reasons. For one, these videos were previously rated by seven reliably trained coders, and their averaged ratings prov ided a reliable composite against which to assess the reliability of new coders. Additionally, the dyads in these training videos also involved y, participants in the training videos discussed and attempted to resolve a disagreement. This provided novice coders with opportunities to train on a task similar to tasks they rated in this study. Sadler and colleagues (2009) found that cross correlation s of at least .40 - 19 .50 are sufficient for obtaining good reliability of the moment - to - moment ratings once they are aggregated across raters. Thus, coders only bega n coding videos for this study when they consistently obtained cross - 5 0 with t rained raters on at least five training videos. Once trained, four coders were assigned to code each dyad. Coder assignments were quasi - randomized such that: 1) two males and two females were assigned to code each dyad, and 2) each coder was assigned to c ode either vacation or best things and either husband conflict or wife conflict. Coders assigned to a discussion for a dyad coded both the husband and the wife (one male and female coded the wife first and one male and female coded the husband first). This approach to coding should reduce the likelihood that observed differences in interpersonal Coders received four coding assignments, completed by task in the following order: 1) vacation , 2) husband conflict , 3) wif e conflict, and 4) best things . Each coder was assigned to approximately half of the videos in each category. The order in which coders watched videos was randomized to reduce the influence of potential coder drift systematicall y affecting some videos more than others. Across participants, if one coder demonstrate d low reliability ( i.e., most cross - 0) with other coders on both IPC dimen time - series . When multiple coders demonstrate d poor reliability on both dim ensions for a given dyad, we review ed that video with one another in bi - weekly team meetings with all coders and the study author, and coders then re - rate d the interaction. During m eetings, coders took turns rating videos and receiving live observation and feedback from the group. T o obtain the final time series data for each participant on both IPC dimensions, I averaged joystick data across reliable coders at each time point . These half - second ratings of 20 dominance and warmth yield ed eight bivariate time - series for each participant (two IPC dimensions, four discussions). The average time - series for the vacation and best things discussions (five minutes) include d approximately 600 dat a points, and the average time - series for the husband and wife conflict discussions (eight minutes) include d approximately 960 data points. The reliability of t he aggregated time series was assessed by comparing the shared variance to the total variance f or each time series, as described by Sadler and colleagues (2009). The shared (i.e., true scor e) variance was estimated as the mean of the cross covariances of the individual raters' times series, and the total variance was estimated as the variance of the aggregated time series. In past research, t his approach has yielded reliability estimates ranging from .61 to .89 for dominance and .58 to .82 for warmth (Klahr, Thomas, Hopwood , Klump, & Burt, 2013; Markey et al. , 2010 ; S adler et al., 2009 ; Thomas et al. , 2014 ). Measures Adaptive Processes Several indices wer e com puted from joystick data to examine the associations between interpersonal behaviors and marital satisfaction (Aim 1) and between interpersonal behaviors and personality characteristics (Aim 2). These indices include the overall levels of warmth and dominance displayed by each participant and the moment - to - moment correspondence of these behaviors between spouses . To examine how overall tendencies in interperso nal behavior were associated with the VSA model , I compute d global ratings of dominance and warmth for each participant by averaging these values across reliable raters for each time - series. Averaged joystick data provides an equal weighting of all moments in an interaction, and should therefore reduce biases 21 associated with recency and primacy effects common to other global coding schemes (e.g., Stone & Shiffman, 1994). As such, this method i average behavioral tendencies. I examined complementarity in two ways. First, I computed the absolute difference between a total mean correspondence score. Second, I computed the moment - to - moment cross - correlation between - series in each task, and averaged across tasks for a total momentary complementarity score. Cross - correlations are a n intuitively accessible analysis of the momentary correspondence between partners (Sadler et al., 2009; Thomas et al., 2014). They provide a direc tional value indicating the strength of association between behaviors throughout the course of an interaction . A positive cross - correlation for warmth indicates that as one individual increases in warmth at a given time, the other individual also increases in warmth at that time. Stronger cross - correlations for warmth are more consistent with the theory of interp ersonal complementarity. C onversely, stronger negative cross - correlations on the dominance dimension are more consistent with the theory of complementarity because negative values indicate that as one individual increase d in dominance at a given time, the other individual increase d in submissiveness at that time . Overall complementarity scores were computed by averaging the complementarity values obtained for each of the four interactions. Although c ross - correlations provide a useful estimate of momentary correspondence between partners, these values are often inflated in the presence of linear trends ( Warner, 1998 ; Yule, 1926 ). Thus, if a dyad has a high cross - correlation for affiliation and both members of the dyad also became warmer from the start to the end of the interaction, it is unclear to what degree the high cross - correlation is artificially inflated due to the linear trend versus a function of genuine correspondence between partners. To test this distinction, I computed the momentary 22 correspondenc e between residual cross - correlations for all interactions in which either partner had a significant linear trend in his/her warm or dominant behaviors throughout the interaction. I computed values for residual cross - correlations by first conducting a lin ear regression interaction as the dependent variable and time as the independent variable. Cross - correlations were then re - computed using the unstandardize d residuals from these analyses ( i.e., cross - for unstandardized residuals of bo These values provide an index of momentary correspondence irrespective of systematic linear changes in interpersonal behavior over time. Marital Satisfaction I used scores from two different measures, the DAS and the Marital Satisf action Inventory Revised (MSI - R; Snyder & Aikman, 1999), to evaluate marital satisfaction. T he DAS is a 32 - item self - report inventory designed to assess relationship distress . Meta - analytic results indicate that the total DAS score is a reliable indicator of relationship dissatisfaction across diverse samples (Graham, Liu, & Jeziorski, 2006). Mean DAS scores averaging all items were used as an indicator of marital dissatisfacti on 3 . The MSI - R is a 150 - item true - false measure designed to assess global and specific aspects of relationship distress. The MSI - R has demonstrated good discriminant validity, internal consistency, and test retest reliability across several studies (Snyde r & Aikman, 1999). In the 3 Consistent with past research using these data (Stroud et al., 2011), only participants who answered at lea st 26 of the 32 DAS items wer e included in this study. 23 present study, the 22 - marital dissatisfaction . I computed a total marital satisfaction score for each participant by averaging their total DAS and MSI - R distress scores. Mean scores from both measures should provide a more reliable estimate of marital satisfaction than either measure would provide in isolation. Because these measures are scaled in opposite directions (lower DAS scores indicate higher lev els of diss atisfaction, whereas higher MSI - R scores indicate higher levels of dissatisfaction ), MSI - R scores were recoded so that higher scores on both measures indicated greater marital satisfaction . Marital satisfaction was computed and examined independ ently for husbands and wives. Enduring Vulnerabilities Participants also completed three personality - relevant measures to assess their standing on broadband personality traits, interpersonal problems, and personality disorder symptoms. The Multidimensio nal Personality Questionnaire (MPQ; Tellegen, 1982 ) is a 300 - item true - false measure that assesses th e temperament traits NEM, PEM, and constraint (CON), each comprised of lower - order facets. NEM includes aggression, alienation, and stress reaction. PEM includes four scales that can be further divided to index PEM - A (a chievement and social potency) and PEM - C (s ocial c loseness and w ellbeing ). CON includes control, harm avoidance, and traditionalism scales. I used these MPQ scales to test associations betwe en self - reported vulnerabilities and observer - rated interpersonal behaviors. The Inventory of Interpersonal Problems (IIP ; Horowitz, Rosenberg, Baer, Ureno, & Villasenor, 1988) assesses the severity and types of difficulties that individuals commonly expe rience in their relationships. The IIP total score (elevation) indexes the severity of problems in their social lives . Items of the IIP can also be organized around the 24 ems related to warmth and problems related to dominance. Total, warmth, and dominance problem scores were used to test associations between self - reported interpersonal problems and observer - rated interpersonal behaviors. The International P ersonality Disor der Examination - Screener (IPDE - S ; Loranger et al., 1994) is a 77 - item true - false questionnaire designed to assess the ten Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition ( DSM - IV ; American Psychiatric Association, 2000) PDs. The IPDE i s comprised of scales assessing each DSM - IV PD (p aranoid, s chizoid, schizotypal, a ntisocial, b orderline, h istrionic, n arcissistic , avoidant, d ependent, and obsessive c ompulsive ). Common criticisms of the DSM - IV PDs have included poor reliability of the cat egorical rating system and high levels of co - occurrence among disorders (e.g., Clark, 2007; Widiger & Trull, 2007). However, because DSM - based measures are commonly employed, researchers have examined how these measures can be reliably used. This work indi cates that dimensional counts of PD symptoms provide reliable estimates and explain variance in several outcomes related to functional severity (Morey et al., 2007; Skodol, et al., 2005). Thus, I used the total IPDE - S score (i.e., sum of all endorsed sympt oms) and PD symptom counts to test associations between self - reported personality pathology and observer - rated interpersonal behaviors. Data Analysis A preliminary question is whether mean warmth and dominance ratings significantly differ ( p < .05) across interactions. Frequent marital disagreements are associated with lower marital satisfaction (McGonagle, Kessler, & Schilling, 1992), and daily reports of marital disagreement are associated with lower daily reports of satisfaction, suggesting that disagreement 25 and dissatisfaction may co - occur (Bolger et al., 1989; Campbell, Simpson, Boldry, & Kashy, 2005). On a briefer time - scale, the manner in which couples respond to one another during conflict discussions has been associated with marital sat isfaction (Argyle & Furnham, 1983; Schneewind & Gerhard, 2002; Story & Bradbury, 2003). Such findings suggest that discussions discussions to have lower levels of average warmth than the best things and vacation discussions. To examine this I tested differences in mean warmth and dominance across interactions using a one - way ANOVA. If these values do not significantly differ across interactions, it would suggest th dominance across interactions may be the most reliable way to operationalize interpersonal behaviors. In contrast, if these values do significantly differ across interactio ns, it would suggest that it is worth examining associations between the VSA model and interpersonal behaviors distinctly across interactions. Following these analyses, m y first aim wa s to examine association s between momentary interpersonal behaviors an d marital satisfaction. I expected adaptive processes to be associated with mean warmth and with complementarity, and I employed multilevel modeling (MLM) using the Actor - Partner Interdependence Model (APIM) fo r distinguishable dyads (Kenny, Kashy , & Cook , 2006 ) to test associations between momentary interpersonal behaviors and marital satisfaction. This model accounts for the inherently dependent nature of marital satisfaction within dyads (i.e., husbands and wives reports of marital satisfaction tend to b e highly correlated; Cook & Snyder, 2005). The APIM model also allows researchers to estimate the influence of one spouses standing on a particular construct with his/her own marital tisfaction ( a partner effect). 26 To test associations between adaptive processes and marital satisfaction, I first examined - reported marital satisfaction. I next examined p athways between dyadic predictors (e.g., would moderate dominance complementarity in predicting marital satisfaction, I tested associations between each partners complementarity, and the interaction between warmth and complementarity in a single model. My second study aim wa s to examine association s between enduring vulnerabilities and momentary interpersonal behaviors . I expected NEM, IIP elevation, and PD severity to be associated with maladaptive interpersonal processes, whereas I expected PEM to be associated with adaptive interpersonal processes. Given characteristics wo uld influence the behavior of the other spouse (e.g., Sadler & Woody, 2003), I tested both actor and partner effects using APIM. 27 RESULTS Reliability and Descriptive Statistics Reliability of joystick time - series data was comparable to what has been observed in past studies, with mean inter - rater reliabilities for dominance averaging .77 and mean inter - rater reliabilities for warmth averaging .64 ( see Table 1 for full results ). The within dyad correlation between husbands and wives reliability was small for warmth and moderate for dominance, suggesting that dominance is relatively more or less reliably coded within dyads. Mean levels of warmth, as well as variability in warmth, tended to be highly correlated within dyads (Table 1). Husbands and wives did not significantly differ from one another in their mean levels of warmth displayed in any task. Unsurprisingly, however, spouses did display less warmth during the conflict discu ssions relative to the vacation and best things discussions ( F ( 3,531 ) = 28.35, p < .05). and vacation tasks (157.77; SD = 120.61) was higher than their mean warmth averaged across both confli ct tasks (62.32; SD = 140.86); the effect size difference in mean warmth between conflict and non - conflict tasks was moderately large ( = .73). This finding suggests that there is value in examining potential effects across tasks, rather than only in aggregate. In general, mean levels of spouse dominance tended to be moderately to strongly negatively associated within dyads, and variability in dominance was generally highly associated within dyads, although a bit lower in the vacation task (see Ta ble 2). Husbands and wives did not significantly differ from one another in their levels of dominance with one exception: wives were more dominant than husbands during their conflict discussion ( t (266) = 2.26, p < .05). Across dyads, t he highest levels of mean dominance were observed in the vacation task (65.38; SD = 28 153.21), whereas the lowest was observed in the best things task (1.93; SD = 167.47). The effect size difference between these means was moderate ( = .40). Overall, these results high light that joystick data are as reliable as we would expect based on past research, husbands and wives were generally correlated in the extent to which they were warm within tasks, and they tended to correlate negatively in the extent to which they were ge nerally dominant within tasks. These results are consistent with concept of mean - level complementarity in dyads, which has frequently been studied in past literature. Also, consistent with expectable contextual factors, participants tended to be warmer whe n discussing the best things in their relationship and when planning a vacation than when discussing their relationship conflicts. Participants were also the most dominant when asked to plan something. Past research indicates that dyads are often most sat isfied when their warmth correspondence is high, but when their dominance correspondence is low , a dyadic pattern referred to as complementarity. Descriptive statistics for complementarity, examined as both mean - level correspondence between spouses as well as moment - to - moment correspondence between spouses, are presented in Table 3. Additionally, correlations between moment - to - moment and mean - levels of interpersonal behaviors are small to null, indicating that complementarity differs depending on how it is measured. For instance, across dyads, the degree to which couples displayed mean - level correspondence in dominance was generally unrelated to the degree to which they displayed momentary correspondence in dominance. This suggests that spouses can have low dominance complementarity across an interaction (e.g., both may behave somewhat dominantly), while simultaneously displaying high dominance complementarity within the interaction (e.g., they may take turns displaying more and less dominance, while still re maining actively engaged). 29 In addition to these results, I examined the consistency of dyadic behaviors across tasks stability in mean - level dyadic correspondence .64). In contrast, consistency in moment - to - .80), whereas consistency in moment - to - These results suggest that dyad co rrespondence between pa and mean dominance appears to be a somewhat stable feature of relatio nships across contexts . In contrast, momentary correspondence in warmth was not a stable attribute in couples over time, although momentary domi turns, or not, in conversations may be a relatively stable aspect of their relationship. Hypothesis 1.1: Observer ratings of warmth for both spouses will be associated with both f - reported marital satisfaction . associated with their marital satisfaction, actor effects , as well as their satisfaction, partner effects ( see Figure 5 lly associated marital satisfaction may be more associated with th is moderately associated with his own satisfaction. 30 Hypothesis 1.2: Complementarity on both the warmth and dominance dimensions will be associated with marital satisfaction. I first tested the hypothesis that complementarity would r elate to marital satisfaction by using APIM to examine mean correspondence as a dyadic (level 2) variable in predicting (see Figure 6) . As indicated in Table 4, dyadic correspondence in mean warmth, averaged across tasks, was significantly associated with marital satisfaction for husbands and wives. Mean - level dominance correspondence, in contrast, was not associated with marital satisfaction in any tasks. Next I tested the hypothesis that complementarity would relate to m arital satisfaction by using APIM to examine momentary correspondence as a dyadic (level 2) variable in predicting - level warmth correspondence was associated with marital satisfaction for both partners, momentary warmth correspondence was unrelated to marital satisfaction. In contrast, momentary dominance across all tasks. Results did not significantl y differ when using residual cross - correlations for warmth or dominance. As such, I used raw cross - correlations, which are more intuitively appealing as they reflect observed data, for all remaining analyses. Taken together, results from these analyses s uggest that satisfactory relationships are characterized by high levels of general dyadic correspondence in warmth, and moment - to - moment reciprocity in dominance. These results raise the possibility that dominance complementarity only characterizes satisfy ing relationships when both partners are warm, the next study hypothesis. 31 Hypothesis 1.3: Mean levels of dyad warmth will moderate the association between marital satisfaction and complementarity on the warmth and dominance dimensions. The association between complementarity and marital satisfaction was significant for two dyadic variables: mean - level warmth and moment - to - moment dominance. However, given that negative behavioral patterns such as negative reciprocity and demand - withdraw can be characteri zed as warmth and dominance complementarity, respectively, on the cold half of the IPC (see Figure 3), I also anticipated that mean warmth would moderate the association between complementarity and marital satisfaction. I tested this hypothesis using the A PIM simple slopes method (Kenny, Kashy, & Cook, 2006), in which estimates of the influence of warmth and complementarity were computed at both high and low levels of husband, and wife, warmth (defined as 1z and - 1z, respectively ; see Figure 7 ). Table 5 displays two models. The left side of the table shows main effects and interaction effects of mean warmth and mean - level warmth complementarity on marital satisfaction. Results of the full model indicate that individual warmth, but not dyadic warmth corres pondence nor their interaction, is associated with marital satisfaction. Thus, consistent with the simple individual model, results indicate that only actor effects for husbands and wives, and partner nt variance in marital satisfaction. The right side of Table 5 shows main effects and interaction effects of mean warmth and momentary dominance complementarity. In this model, mean - level warmth remains a significant predictor of marital satisfaction for satisfaction. Although dominance complementarity does not remain a significant predictor of marital satisfaction in this model, interactions between mean warmth and momentary dominance 32 complementarity significantly predicted marital satisfaction. Graphs of the s e moderation effects are presented in Figure s 8 12 . Results from the total scores (Figure 8) indicate that if both par tners are generally warm, or if both partners are generally cold, dominance c omplementarity does not influence marital satisfaction for either partner. In the latter instance, marita l satisfaction tends to be low for both partners . This follows given that, in the case of low dominance complementarity, couples are either both engagi ng in demanding/critical/quarrelsome behavior (both generally cold - dominant) or both withdrawing/distancing (cold - submissive) , an d in the case of high dominance complementarity, couples are likely e ngaging in demand - withdraw patterns (see Figure 3 ). In cas es characterized by generally high husband warmth but low wife warmth, higher dominance complementarity decreases satisfaction for both partners. In contrast, in cases with higher wife warmth but lower husband warmth, higher dominance complementarity incre ases satisfacti on for both partners. T he most satisfied individuals are in relationships characterized by high wife warmth, relatively lower husband warmth, and high dominance complementarity . It is important to remember that given characteristics of the t otal score (i.e., mean and variability of total warmth scores hovering around the center (i.e., 0) of the warmth scale. Thus, these results suggest that when husbands a re neutral, wives are warm, and they show moment - to - moment reciprocity in dominance, marital satisfaction is likely to be high. In contrast, when the wife is neutral or colder, neither husband warmth nor moment - to - moment reciprocity in dominance seems to c ounter this effect and marital satisfaction is likely to be low for both partners. These results are based on total scores; Figures 9 - 12 examine whether this pattern is also observed across each of the four tasks. For both of the conflict discussions , th e same patter n of 33 significant interactions was found (see Table 5 and Figures 9 and 10 ). F or the b est t hings discussion s satisfaction (Table 5). This simple bivariat e effect can be seen in Figure 11 . Finally , for the v acation discussion, mean warmth was relate d to satisfaction for both patterns, irrespective of dominance complementarity; however, unlike with the total scores, in this task high d ominance complementarity was related to high er levels of satisfaction when bother partners display ed less warmth. Also unlike the total scores, d ominance complementarity was related to increased satisfaction when the husband wa s warmer and the wife wa s colder, but was related to decreased satisfact ion when the wife is warmer and the husband is colder (see Figure 12) . The differential results found in this task may be related to it being the first and/or generally the least emotionally evocative discussion couples engaged in during their lab visit. Hypothesis 2.1 : NEM, interpersonal problems, and total PD severity will be associated with lower levels of actor and partner warmth. Negative Emotionality Husband actor and partner effects for NEM and its lower - order facets were all significant in the predicted direction (Table 6). Husbands who described themselves as high in stress, alienation, and aggression behaved more coldly, and were with wives who beh aved more coldly, across most tasks. Actor effects for wives were only observed for the NEM factor and the aggression facet, such that higher levels of these variables were associated with lower levels of Interpersonal Problems however, this effect was not found for wives. Husbands and wives with higher self - reported 34 problems related to bein g overly dominant behaved more coldly across tasks, particularly the conflict discussions. Husbands who reported more problems related to warmth behaved more - reported warmth problems and warmth behaviors were unrela ted. The only partner effect observed was that wives who reported higher levels of problems related to dominance were with husbands who behaved more coldly across tasks. Personality Disorders ll coldness. Actor effects of antisocial, borderline, histrionic, and obsessive - antisocial) across interactions (Table 8). Wives who reported more symptoms of antisocial PD were with husbands who behaved more coldly, particularly during his conflict discussion; however no other partner effects for wives PDs were observed (Table 9). In contrast, partner effects were (total, paranoid, schizotypal, antisocial, borderline, histrionic, avoidant, and obsessive - rally Hypothesis 2.2 : PEM - C will be associated with higher levels of actor and partner warmth. As predicted, wives who reported hi gher levels of PEM - C were warmer across tasks - C, driven by the wellbeing facet, was also associated with higher levels of husband warmth. Husbands who reported higher levels of social closeness were warmer across tasks; however, no o - C. As expected, no effects between PEM - A and warmth were observed (Table 11). Taken together, 35 encies to behave warmly during interactions. Exploratory Associations with Dominance Lastly, given mixed past research, I did not predict associations between observed dominance and self - reported satisfaction or personality; however, I did explore possib le associations among these variables. Marital Satisfaction First I examined associations between observed dominance and self - reported marital dominance significantly predicted his marital satisfaction, and in fact, demonstr ated stronger total associations with marital satisfaction than his actor effect of warmth. The partner effect of partner effects of dominance were associated w husbands who exhibit higher levels of mean dominance, and husbands with wives who also tend to exhibit higher levels of mean dominance, report greater satisfaction in their marriages. Personality Traits (NEM, PEM, & CON) APIM associations between observed dominance and self - reported NEM and its facets are presented in Table 6. Wives with higher self - reported levels of aggression tended to behave more dominantly across tasks. No other actor effects were co nsistently observed. Partner effects indicated that husbands who reported higher levels of NEM, specifically alienation and aggression, were with wives who tended to behave more submissively. As expected, no significant actor or partner effects were observ ed between dominance and PEM - C (Table 10). With respect to PEM - 36 - reported traditionalism and control and their observed dominance. Interpersonal Problems No actor or pa rtner effects were observed between total interpersonal problems and dominance, however, actor effects were observed between higher levels of self - reported dominance problems and higher levels of observed dominance for both husbands and wives (Table 7). Co mplimentary partner effects for these variables were also observed, such that - reported dominance problems were with spouses observed to behave submissively. Wives with self - reported problems related to warmth were also o bserved to behave more dominantly across tasks. Personality Disorders Lastly, I examined associations between observed dominance and self - reported PD symptoms among spouses - compulsive PD were associated with their tendency to behave dominantly across tasks. Wives who rated themselves as relatively high in borderline and histrionic PD symptoms were observed to behave more dominantly, especially when discussing their own conflict. Partner effects indicated that husbands with higher self - reported PD symptoms, particularly of borderline and paranoid PDs, - reported PDs and behavioral displays of dominance were observed. 37 DISCUSSION synthesis among researchers interested in better understanding the factors that lead to marital satisfaction. Despite many advantages, the VSA model has been critic ized on the grounds that its most important proposed mechanism, adaptive processes , is hard to define and perhaps harder to measure. The overarching aim of this study was to test the potential of a momentary assessment technique, guided by interpersonal th eory and structured by the IPC, as an effective means of and data obta ined from this method were used to test the hypotheses that overall warmth, momentary correspondence in warmth, and momentary reciprocity in dominance would relate to actor and partner marital satisfaction and personality characteristics. Interpersonal Beh aviors across Tasks Although the primary focus of this study was to test associations between momentary behaviors and marital satisfaction and personality characteristics , researchers have not previously used the joystick method to obtain ratings of inter personal behaviors across multiple interactions. Not surprisingly, spouses were colder during the conflict discussions than they were during the vacation and best things discussions, suggesting that the joystick method is sensitive to capturing contextual differences across varied environments. Relatively minimal differences were observed in mean levels of dominance across tasks or displayed by husbands and wives. and w 38 of warmth and dominance were moderately consistent across tasks. These fi ndings suggest that the dyadic pattern of complementarity emerged at the level of characteristic behaviors across tasks, and that mean - level complementarity in warmth and dominance is a relatively stable feature among married dyads. Additionally, the amoun t that husbands and wives varied in their warm (and dominant) behaviors also tended to be highly correlated within dyads, indicating that some dyads tend to be more variable in their behaviors whereas other dyads tend to be more rigid. For instance, severa l couples displayed both warm and cold behaviors during the conflict tasks, but some couples engaged almost exclusively in cold behaviors, whereas other couples avoided engaging in critical behaviors even during discussions of a conflict. At the moment - t o - moment level, dominance complementarity within dyads was highly consistent across tasks , indicating that the extent to which dyads negotiate power is typically consistent across conversations. Importantly, however, mean - level discrepancies in dominance and momentary correlations in dominance were not related within dyads. That is, the extent to which dyads differed in their overall levels of dominance was unrelated to their moment - to - moment dom inance reciprocity. This suggests that mean - level dominance complementarity may be orthogonal to moment - to - moment dominance complementarity. In contrast to momentary dominance correspondence, momentary correspondence in warmth was not stable across tasks. Within tasks, dyad discrepancies in warmth were modestly associated with momentary warmth correspondence. Thus, the extent to which dyads were generally similar in their overall levels of warmth was associated with the extent to which they tended to vary together from one moment to the next in warmth within a task, even though momentary warmth complementarity was less consistent than mean - level complementary within dyads from one discussion to the next. These results further illustrate that different measu rements 39 of complementarity reveal different types of dyadic processes (e.g., Sadler, Ethier, & Woody, 2010 ) , and these processes may have differing effects on relationship functioning. Interpersonal Style and Marital Satisfaction The first aim of this study was to understand better the associations between ongoing interpersonal processes and marital satisfaction. I tested the hypothesis that observed levels of - reported marital satisfaction. Across warmth was associated with his own satisfaction, although this association was . Surprisingly, his warmth was not associated with his satisfaction. I also explored associations between dominance and marital satisfaction and found that alt Taken together, results from this study indicate are also associated with Complementarity and Marital Satisfaction The next set of study hypotheses involved the prediction that higher levels of complementarity would be associ ated with higher levels of marital satisfaction. Mean - level results indicated that correspondence in warmth between members of a dyad related to marital satisfaction for both partners, but this effect washed out after accounting for individual warmth. That is, dyadic similarity in warmth was unrelated to marital satisfaction after accounting for the 40 overall effects of warmth on satisfaction. Momentary correspondence in warmth did not relate to satisfaction for either partner, and was not consistent within d yads across tasks. Mean - level correspondence in dominance was unrelated to satisfaction for husbands or wives; however, momentary reciprocity in dominance predicted satisfaction for both partners. These results were more nuanced when considering interactio warmth. Results from analyses examining potential moderating effects of mean warmth indicated that, when both partners were warm, marital satisfaction was generally high and dominance complementarity did not further increa se satisfaction. Likewise, if neither partner was warm, satisfaction was generally low and dominance complementarity did not improve it. Interaction wives were warm and h usbands somewhat colder, higher dominance complementarity was associated with greater marital satisfaction for both partners. This suggests that satisfaction tends to be highest when wives are warm and husbands and wives reciprocate their levels of dominan ce from one moment to the next. The reverse pattern was found in couples characterized by high husband warmth and relatively low wife warmth. In these cases, higher dominance complementarity was associated with lower levels of satisfaction for both partne rs. It is possible that, in many of these dyads, low wife warmth and high dominance complementarity manifests as a form of demand - withdraw. This pattern might partially explain why relatively low wife warmth, even in the context of high husband warmth and high dominance complementarity, is associated with low levels of both 41 Momentary Behaviors and Personality Traits The second aim of the study was to better understand how warmth, an adaptive process in marriages, relates to personality characteristics. Results from analyses using temperament - reported NEM wa s associated with less observed warmth among both husbands and wives. Thus, h usbands who reported higher leve ls of NEM, including stress, alienation, and aggression, not only tended to behave more coldly across all discussions, particularly their own conflict, but these husbands were also with wives who tended to behave more coldly across tasks, particularly the conflict discussions. With respect to dominance, wives of husbands who reported higher levels of alienation and aggression tended to behave more traits influence not only their own behavior in close relationships, but also their p behaviors. More specifically, study results suggest that dyads characterized by higher levels of distress, particularly among husbands, are also characterized by lower levels of w armth. Results from this study are cross - sectional, and thus do not permit distinctions regarding the directionality of associations between traits, behaviors, and satisfaction. However, altogether the pattern of results obtained is suggestive of a viciou vulnerabilities, such as frequently feeling stressed, angry, and isolated, can lead both partners to behave more coldly in their ongoing interactions. This coldness, particularly when displayed by wives, is associated wit h lower levels of marital satisfaction for both partners , which likely leads them to feel increasingly stressed, angry, and isolated, thereby reaffirming a maladaptive cycle. Additional analyses of temperament traits indicate that wives with higher self - r eported levels of communal positive emotionality were warmer, and were also with husbands who were warmer, across discussions. Husbands who reported higher levels of social closeness were 42 warmer across tasks, but in contrast to negative emotionality, no pa rtner associations were observed for husbands with regards to their positive emotionality. As expected, no associations were observed between communal positive emotionality and dominance. ive emotionality, particularly her self - reported tendencies to be persuasive and noticed (social potency), was Unexpectedly, however, no associations were observed b positive emotionality and either husbands positive emotionality. This is in contrast to negative With respect to self - reported tendencies to i nhibit impulses and engage in conventional, conservative behavior, results from this study suggest that wives who report higher levels of constraint themselves as generally cautious and relatively traditional in their personal and family values tended to behave less dominantly across tasks. Wives who reported higher levels of harm avoidance tended to behave more coldly, particularly when discussing their conflict and the best things in their relationship. Given that the MPQ harm avoidance scale primarily assesses content involving a preference for tedium over risk, this finding may suggest that wives with high proclivities to avoid harm may be less likely to risk the vulnerabi lity that is often associated with avoidant are also colder, and that coldness tends to increase harm within marriages. 43 Momentary Behaviors and Interpersonal Probl ems Examinations of self - reported interpersonal problems indicated that h problems was related to their coldness across tasks; however, this effect was not found for wives. Husbands with higher self - reported problems related to being overly warm were also observed to behave more warmly across tasks; however, wives self - reported warmth problems were unrelated to their observed warmth, but were instead associated with their obs erved levels of dominance. These results suggest that associations between problematic variants of a behavior, like warmth, can have complicated associations with normative variants of that same behavior , and that one moderator of this complex association can be gender . For instance, wives may perceive their dominant behavior as a way in which they express care and concern for their partner, and thus are too kind, whereas husbands may tend to perceive their displays of warmth as ways in which they seek to p lease others too much. Results were more similar across husbands and wives with respect to self - reported dominance problems and observed behavior. For both partners , self - reported problems related to being overly dominant were associated with higher levels of observed dominance and lower levels of observed warmth. These results indicate that the general tendency to have problems related to assertiveness may manifest specifically as distant and disconnected behavior in spousal relationships. - reported dominance problems were associated with lower levels of husband warmth and dominance. Thus, wives who reported high er levels of relationship problems related to dominance were with husbands who tended to behave more coldly and submissively across tasks. Considered with results of actor effects, this finding may reflect a global level demand - withdraw pattern, whereby wi ves who report 44 dominance problems indeed behave more dominantly, but also behave more coldly, and in turn have husbands who tend to compliment this position with cold and submissive behavior, thereby reinforcing the demand - withdraw pattern. Momentary Beha viors and Personality Disorders Analyses examining associations between self - reported personality pathology and associated with a higher total number of self - reported PD symptoms. Likewise, whereas actor indicate d - antisocial own coldness, suggesting a complementary pattern). Compared to results for warmth, relatively few actor or partner effects were observed between PD symptoms and dominance . Actor effects indicated that husbands who reported higher levels of obsessive - compulsive PD, and wives who reported higher levels of borderline borderline PD symptom All together, results examining associations between PDs and spousal behaviors indicate that hus bands who report more total PD symptoms tend to behave coldly and to have wives who withdraw (i.e., behave coldly and submissively). These results, which indicate stronger 45 associations between self - reported PDs and observed coldness for husbands than for w ives, mirror aforementioned - reported interpersonal problems and their observed coldness , and in this way are consistent with a broad literature which describes interpersonal problems as the core impairment in personality d isorders (e.g., Benjamin, 1996; Hopwood, Wright, Ansell, & Pincus, 2013). Momentary Measurement of Demand - Withdraw and Negative Reciprocity Descriptions of demand - withdraw and negative reciprocity in clinical theory and research describe these processes as dyadic patterns that unfold over time. I have argued that these patterns can be conceptualized as dominance complementarity (demand - withdra w) and warmth complementarity (negative reciprocity) when these processes unfold on the cold half of the IPC. In this study I examined complementarity as a pattern that can emerge at two levels: overall and moment - to - moment. To highlight how demand - withdra w and negative reciprocity can unfold as complementarity on the cold half of the IPC, I graphed and examined complementarity at both of these levels in two couples who report high levels of marital dissatisfaction. Figure 13 displays the interaction betwe en a dyad (marital satisfaction z - scores = - 2.32 [husband] and - 2.00 [wife]) discussing the best things in their relationship. Data from this interaction are displayed in two ways: Panels A and B are density plots which provide information about this husba interaction; Panel C is a time - next. Panels A and B indicate that both partners tended to behave in a cold and submissive manne r, and that for both of them, colder behavior tended to co - occur with more submissive behavior. It becomes more evident that this couple engaged in a negative - reciprocity pattern when looking at the time - series graph for this interaction, which indicates t hat increases in 46 warmth during this discussion was - 152.18 (SD = 184.24), indicating that their behaviors generally unfolded on the cold half of the IPC. Their war mth complementarity measured as mean - level correspondence was 6.44, which suggests that were considerably more similar in their mean levels of warmth than most dyads in this sample were during this discussion (see y complementarity in warmth was high (cross - correlation = .96 using raw data and .70 using residual data removing the linear time trend). These values were also much higher than the sample averages (Table 3), indicating that not only milarly cold, but also that their momentary expressions of coldness tended to fuel one another and co - occur. Figure 14 displays the interaction between a different dyad (marital satisfaction z - scores = - 1.92 [husband] and - 2.22 [wife]) discussing the hus are displayed in the manner described above except, for this dyad, the time - series data in Panel C that the husband w as generally cold and dominant during this discussion, whereas Panel B shows that his wife tended to behave in a cold and submissive manner. These graphs illustrate mean - level dominance complementarity unfolding on the cold half of the IPC. Indeed, this co average warmth during this discussion was - 290.92 (SD = 177.21) and both remained almost exclusively on the cold half of the IPC during this interaction. Panel C further highlights how this couple engaged in a demand - withdraw process by showing how momentary dominance correspondence was comparable to the sample mean (raw cross - correlation = - .43; detrended cross - correlation = - .63). These graph s are useful for highlighting 47 how this otherwise normative pattern of complementarity can unfold as a demand - withdraw process when it occurs on the cold half of the IPC, and particularly when one partner is consistently the more dominant partner, rather th situation. Limitations and Future Directions This wa s the first study to use the interpersonal joystick method to examine behaviors across multiple interactions in married dyads, and thus the expected stability of th ese measurements over time is unknown. This study also included discussion tasks that involved different content, making it difficult to discern to what extent behavioral instability may have been a function of: context, predictable instability in behavior over time and across situations, and/or measurement error. Future research will benefit from continued examination of the stability of momentary interpersonal behaviors over time and across contexts and partners. Observational coding methods such as the one used in this study offer many advantages compared to other methods, however, they are not without their own set of limitations. For instance, a lthough I took several steps to minimize coder fatigue, code rs were assigned an average of eight hours of vi deo s to code per week, and they were likely fatigued and less attentive to the task a t some points (e.g., when other academic and/or personal stressors were high). I attempted to counter such sources of error by giving coders one - month notice on their dead lines so they could plan their coding schedule in accord with their other obligations. I also conducted bi - weekly meetings during which coders could share effective strategies for maximizing attention, particularly when videos were difficult to hear, borin g to watch, etc. Nonetheless, error as a result of various forms of coder bias and unreliability was inevitably present in this data and may have minimized our ability to find true and meaningful effects. 48 This study involved testing a number of hypothese s. Conventional cut - offs of null - hypothesis significance testing were employed (i.e., p - values less than .05 were regarded significant), and given the large number of analyses, it is likely that some significant results were obtained by chance. Generally, however, results were only interpreted when they consistently demonstrated at least sma ll effect sizes across tasks. In many respects, this sample was healthy and high functioning. The majority of participants were married, employed, and effectively raising their children. Spouses endorsed relatively low rates of some variables of interest, most notably symptoms of personality disorders. Relationship satisfaction was also relatively high, particularly compared to treatment seeking samples. As such, research on more severely distressed populations will be informative for better understanding associations between psychopathology and marital satisfaction. More research on momentary expressions of patterns such as demand - withdraw could also valuably inform treatment of these patterns in therapy. A lthough I collected intensive longitudinal data, I conducted most analyses using cross - sectional designs. This limited my ability to determine the direction of influence between behaviors and satisfaction. Figures 13 and 14 provide preliminary indications regarding how data obtained using the joystick me thod can be used to model the vast range of behavioral patterns displayed across couples. Consistent with interpersonal theory, complementarity was generally observed across tasks. However, couples ranged considerably in the extent to which they adhered to patterns of complementarity versus the extent to which they displayed different behavioral patterns (see Table 3). Future directions with this data could utilize idiographic, time - series analyses capable of answering questions like: how does a wife who re ports high levels of negative emotionality and borderline personality and low levels of constraint react when her 49 husband withdraws from her? Criticizes her? Nurtures her? Are these reactions consistent pattern s within a given person or d yad? Are they cons istent over time and across contexts? When they differ, is there a consistent pattern to these deviations? These questions provide a glimpse into some of the many possible future directions for applying momentary assessment of interpersonal behavior to the improved understanding of marital satisfaction. Conclusion s This study highlights the value of using the interpersonal joystick m ethod (Lizdek et al., 2012) to measure more and less adaptive process es in relationships as they unfold from one moment to t he next . In addition to reliably capturing real - time behavior, this method is rooted in an empirically based model of interpersonal behavior that is tethered to a rich network of that commonly unfolds, even though the degree to which this is true varies considerably across dyads). Further, this model accommodates existing conceptualizations of detrimental relational processes such as demand - withdraw and negative reciprocity, and pr ovides a map and a method for measuring these processes as they occur in real - time interactions. Despite these advantages of the joystick method, relative to methods such as self - report, it is a time - intensive assessment tool. However, results from this s tudy highlight the numerous and often complex ways in which joystick rated behaviors display differential associations with self - reported behaviors across gender and across actors and partners. Thus, these behavioral data provide additional insight into wa ys in which self - reported behaviors manifest in important interpersonal relationships. Perhaps more importantly, unlike traditional assessment methods which tend to aggregate data over time, joystick data permit analyses that examine change over time, and thus these data can be used to model and elucidate processes as they unfold between 50 individuals. Future researchers are encouraged to continue exploring optimal methods for quantifying the time - series data generated by the joystick method. Promising method s include dynamic systems modeling (e.g., Boker & Laurenceau , 2006 ), state - space grids (Hollenstein, 2013) and time - varying effect models (e.g., Tan, Shiyko, Li, Li, & Dierker, 2012 ). This study adds to an expanding body of literature which highlights t he usefulness of measuring not just individual pathways between variables, but also pathways between individuals (i.e., relational effects). The current study replicates existing research indicating that behaviors are influenced not only by the personality of actors, but also by the personality and behaviors of their partners. Simply put, relationships are bi - directional experiences. However, this study and others also suggest that relationship processes are not necessarily gender or context invariant. For gs point more highly associated with bi - adaptive characteristics and behaviors will be more strongly as sociated with relationship This study provides an initial illustration of how interpersonal theory and momentary assessment can be usefully integrated with existing models o f marital functioning, and several results merit replication and more nuanced attention in future research. The consistent questions regarding the overlap between momentary expressions of warmth and momentary expressions of 51 examined and offer exciting avenues for future research . In general, ongoing research aimed at further testing these and other hypotheses would provide a valuable contribution to our understanding of marital satisfaction. Overall, results from this study highlight the value of measuring ongoing interpersonal processes as they unf old in real - time, and of tethering these measurements to well - validated theoretical models. Specifically, this study suggests that the IPC and interpersonal theory can be usefully integrated into the VSA model to provide a framework for measuring adaptive processes as they unfold between couples. These results also highlight the importance of considering individual, partner, and dyadic pathways when seeking to understand relationship processes. Although the present study focused on these pathways using nomo thetic analyses, I also used two case studies to demonstrate how interpersonal joystick data can also be used to examine idiographic and relational patterns that unfold across specified dyads. This method could have valuable implications for studying inter personal processes as they unfold over multiple sessions in the course of counseling among treatment seeking dyads. Ultimately, the pattern of results obtained in this study not only adds to a large body of literature indicating that personality characteri stics, interpersonal behaviors, and relationships satisfaction are intricately associated, but also highlights promising avenues for future investigations of nomothetic, dyadic, and idiographic processes associated with satisfaction in important relationsh ips. 52 APPENDICES 53 APPENDIX A: TABLES Table 1. Mean , (SD), Range, and Reliability of Warmth Time - Series across Tasks Warmth Mean Warmth Variability (SD) Discussion r r r Total 102 . 86 (109.65) - 227.39 274.73 116 . 16 (106.93) - 437.79 275.61 .69 64.42 (24.98) 33.63 169.73 6 8 . 57 (26.44) 34.34 190.69 .63 .63 (.08) .45 .84 .65 (.08) .42 .89 .21 Best Things 152.78 (108.44) - 223.73 347.40 162.76 (123.57) - 334.47 334.47 .70 60.08 (28.64) 21.91 204.87 61.92 (31.19) 23.70 197.51 .60 .66 (.14) .26 .96 .65 (.15) .27 .98 .24 Vacation 143.53 (137.74) - 515.12 390.42 172.01 (112.68) - 380.87 365.69 .52 62.13 (33.61) 16.25 214.83 62.06 (29.07) 20.25 225.05 .63 .63 (.14) .24 .92 .65 (.13) .30 .94 .33 Husband Conflict 58.83 (156.08) - 403.45 365.16 65.44 (153.88) - 564.07 358.30 .68 75.41 (40.44) 24.64 220.12 83.41 (46.81) 19.65 240.81 .63 .63 (.16) .18 .92 .64 (.15) .24 .96 .33 Wife Conflict 56.74 (128.06) - 388.51 293.24 68.27 (125.41) - 471.77 315.75 .71 58.57 ( 33 . 12 ) 16.82 1 9 5 . 09 66.54 (35.73) 17.79 195.32 .55 .61 (.14) .23 .92 .64 (.15) .24 .92 .21 Note: Joystick data range from - 1000 to 1000 for warmth and dominance. Within each column, bold values indicate sample means, parenthetical values are sample standard deviations, and beneath are sample ranges. The r values in each section of the table represent Pearson correlation values between husbands and wives across the sample. 54 Table 2. Mean , (SD), Range, and Reliability of Dominance Time - Series across Tasks Dominance Mean Dominance Variability (SD) Dominance Discussion r r r Total 37 . 90 (136.14) - 304.94 394.76 4 0 . 57 (127.31) - 403.70 324.53 - .62 1 48.57 (30.40) 98.05 248.39 147 . 42 (34.09) 75.87 270.84 .71 .7 7 (.06) .59 .89 .77 (.06) .55 .89 .49 Best Things 5.08 (155.21) - 532.10 377.52 - 1.22 (151.21) - 463.05 313.18 - .49 151.11 (47.43) 69.04 320.83 149.46 (49.62) 63.67 332.00 .74 .81 (.09) .55 .93 .81 (.09) .34 .96 .41 Vacation 64.81 (167.26) - 322.04 407.36 65.95 (167.67) - 384.69 414.42 - .65 121.60 (36.14) 60.34 243.54 119.00 (38.10) 61.74 296.85 .48 .72 (.11) .32 .91 .72 (.12) .32 .91 .37 Husband Conflict 48.48 (179.63) - 444.42 551.80 36.00 (176.03) - 552.03 536.12 - .63 166.36 (52.31) 72.23 392.35 168.93 (53.51) 70.32 316.01 .71 .78 (.11) .46 .95 .78 (.09) .40 .93 .48 Wife Conflict 25.79 (152.42) - 466.93 500.17 65.31 (132.80) - 368.65 359.04 - .42 155.80 (57.10) 62.89 339.17 150.97 (58.36) 64.20 354.41 .72 .78 (.10) .37 .96 .77 (.10) .48 .97 .45 Note: Joystick data range from - 1000 to 1000 for warmth and dominance. Within each column, bold values indicate sample means, parenthetical values are sample standard deviations, and beneath are sample ranges. The r values in each section of the table represent Pearson correlation values between husbands and wives across the sample. 55 Table 3. Mean , (SD), and Range of Dyadic Behaviors across Tasks Warmth Complementarity Dominance Complementarity Discussion Cross - Correlation Mean Discrepancy r Cross - Correlation Mean Discrepancy r Total . 46 (.18) - .01 .88 59 . 89 (62.71) 0.24 362.46 - .27 - . 52 (.26) - .86 .81 187 . 91 (144.22) 5.24 746.59 - .10 Best Things . 4 7 (.34) - .49 .96 6 3 . 15 (66.59) 0.65 383.96 - .32 - . 62 (.33) - .98 .89 206.44 (163.83) 4.14 713.89 .05 Vacation . 48 (.31) - .49 .96 8 6 . 3 2 (94.05) 0.11 763.05 - .17 - . 52 (.35) - .96 .74 245.58 (178.94) 0.38 791.00 - .10 Husband Conflict . 48 (.31) - .49 .96 85 .1 9 (89.73) 0.35 576.28 - .25 - . 50 (.36) - .96 .89 188.70 (153.28) 0.65 677.82 - .18 Wife Conflict . 45 (.29) - .58 .93 67 . 03 (70.54) 0.09 341.73 - .16 - . 47 (.31) - .94 .88 245.58 (178.94) 0.38 791.00 - .09 Note: Within each column, bold values indicate dyad means, parenthetical values are dyad standard deviations, and beneath are dyad ranges. The r values in each section of the table represent across sample Pearson correlation values between cross - correlations an d mean discrepancies across dyads. 56 Table 4. Warmth Dominance Total Best Things Vacation Husband Conflict Wife Conflict Total Best Things Vacation Husband Conflict Wife Conflict H intercept - .03 (.06) - .03 (.06) - .04 (.07) - .03 (.07) - .05 (.07) - .02 (.08) - .03 (.08) - .02 (.08) - .03 (.08) - .04 (.08) W intercept - .04 (.07) - .04 (.07) - .01 (.08) - .03 (.07) - .04 (.07) - .02 (.08) - .03 (.08) .00 (.08) - .03 (.08) - .02 (.08) Actor Effects MarSat .20 (.08) .18 (.10) .17 (.08) .28 (.09) .06 (.10) .30 (.10) .22 (.09) .29 (.11) .16 (.11) .24 (.08) MarSat .50 (.09) .48 (.09) .30 (.10) .38 (.09) .46 (.10) .09 (.11) - .01 (.10) .15 (.11) - .02 (.11) .11 (.10) Partner Effects MarSat .45 (.09) .39 (.09) .36 (.10) .34 (.09) .43 (.10) .22 (.10) .10 (.09) .21 (.11) .03 (.11) .27 (.09) MarSat .13 (.09) .08 (.10) .18 (.08) .21 (.09) .02 (.10) .19 (.10) .13 (.09) .24 (.11) .07 (.11) .18 (.09) Dyad Effects D mean MarSat .20 (.08) .18 (.08) .16 (.08) .17 (.08) .10 (.08) .02 (.08) .03 (.08) - .03 (.08) .03 (.08) .07 (.08) D mean MarSat .20 (.08) .13 (.08) .17 (.08) .13 (.08) .12 (.08) - .01 (.08) - .02 (.08) - .01 (.08) .05 (.08) .02 (.08) D moment MarSat .04 (.08) .13 (.08) .01 (.08) - .07 (.08) .00 (.08) - .31 (.08) - .28 (.08) - .36 (.08) - .17 (.08) - .17 (.08) D moment MarSat .04 (.08) .08 (.08) .00 (.08) - .05 (.08) .04 (.08) - .24 (.08) - .20 (.08) - .31 (.08) - .14 (.08) - .14 (.08) Note: D = Dyad, H = Husband, W = Wife , Mean = mean - level correspondence between spouses, and Moment = moment - to - moment correspondence between spouses. Values represent standardized Beta ( ) estimates from multi - level regression models. Standard Errors ( SE ) of these estimates are in parenthesi s. Bold values indicate p < .05. 57 Table 5. APIM Associations between Marital Satisfaction and Individual Warmth, Dyadic Correspondence, and their Interaction D=Dyad Mean Warmth Correspondence D=Dyad Momentary Dominance Correspondence Total Best Things Vacation Husband Conflict Wife Conflict Total Best Things Vacation Husband Conflict Wife Conflict H intercept - .06 (.07) - .04 (.08) - .06 (.08) .01 (.07) - .06 (.08) - .01 (.07) - .01 (.07) .02 (.08) - .03 (.07) - .02 (.07) W intercept - .07 (.07) - .04 (.08) - .04 (.08) - .02 (.08) - .06 (.08) - .05 (.07) - .03 (.07) .05 (.08) - .04 (.07) - .04 (.08) Actor Effects H warm MarSat .41 (.14) .30 (.16) .25 (.12) .46 (.14) .14 (.18) .18 (.09) .15 (.11) .19 (.08) .25 (.09) .05 (.10) W warm MarSat .40 (.14) .50 (.16) .20 (.12) .35 (.15) .55 (.17) .57 (.10) .48 (.10) .18 (.10) .42 (.10) .47 (.11) Partner Effects W warm MarSat .28 (.14) .33 (.15) .26 (.12) .20 (.15) .39 (.17) .45 (.09) .36 (.10) .22 (.10) .35 (.09) .40 (.11) H warm MarSat .27 (.14) .12 (.17) .24 (.12) .33 (.14) - .05 (.18) .12 (.09) .07 (.12) .19 (.08) .18 (.09) .01 (.10) Dyad Effects MarSat - .02 (.11) .06 (.10) - .23 (.13) .17 (.11) .05 (.10) - .06 (.08) .01 (.09) .22 (.09) .02 (.08) - .02 (.08) MarSat - .05 (.11) .12 (.10) - .25 (.13) .17 (.11) .02 (.10) - .09 (.08) - .04 (.09) .17 (.09) - .03 (.08) - .01 (.08) Interaction Effects D*H warm MarSat - .11 (.06) - .04 (.07) - .07 (.03) - .02 (.06) - .04 (.08) - .23 (.10) .00 (.07) .16 (.08) - .26 (.08) - .24 (.11) D*W warm MarSat .02 (.06) - .01 (.07) .20 (.10) .04 (.06) - .07 (.08) .27 (.11) .00 (.08) - .30 (.11) .21 (.10) .25 (.10) D*W warm MarSat .05 (.06) .03 (.07) .17 (.10) .11 (.06) .00 (.08) .14 (.10) - .01 (.08) - .30 (.11) .19 (.10) .10 (.10) D*H warm MarSat - .10 (.06) .01 (.07) - .07 (.04) - .02 (.06) .03 (.08) - .29 (.10) - .03 (.07) .15 (.09) - .24 (.08) - .31 (.11) Note: H = Husband, W = Wife. Values on the left side of the table are from analyses in which the dyadic variable was mean warmth correspondence and values on the right side of the table are from analyses in which the dyadic variable was momentary dominan ce corre spondence. Values represent standardized Beta ( ) estimates from multi - level regression models. Standard Errors ( SE ) of these estimates are in parenthesis. Bold values indicate p < .05. 58 Table 6. Warmth and Dominance and MPQ Negative Emotionality Mean Warmth Mean Dominance Total Best Things Vacation Husband Conflict Wife Conflict Total Best Things Vacation Husband Conflict Wife Conflict H intercept - .04 (.08) - .08 (.08) - .05 (.08) .03 (.08) - .03 (.09) - .01 (.10) - .01 (.10) .03 (.09) - .01 (.09) - .11 (.10) W intercept .04 (.09) .00 (.09) .10 (.08) .05 (.09) .03 (.09) .04 (.08) .04 (.09) - .01 (.09) .02 (.09) .17 (.08) Actor Effects H NEM - .40 (.09) - .31 (.09) - .25 (.09) - .42 (.08) - .34 (.10) .02 (.10) - .04 (.10) .12 (.10) .00 (.10) - .06 (.10) W NEM - .20 (.09) - .19 (.10) - .15 (.09) - .21 (.09) - .14 (.10) .13 (.09) .21 (.09) .01 (.10) .12 (.10) .06 (.09) H stress - .27 (.09) - .18 (.09) - .20 (.09) - .30 (.09) - .22 (.10) - .03 (.10) - .03 (.10) .04 (.10) - .04 (.10) - .08 (.11) W stress - .11 (.10) - .18 (.10) - .07 (.09) - .10 (.09) - .03 (.10) - .01 (.09) .08 (.09) - .07 (.10) - .06 (.10) - .01 (.09) H alienation - .46 (.09) - .41 (.09) - .24 (.09) - .49 (.09) - .37 (.10) .00 (.11) - .11 (.11) .13 (.11) .07 (.11) - .12 (.11) W alienation - .19 (.09) - .12 (.10) - .19 (.09) - .24 (.09) - .11 (.10) .13 (.10) .15 (.10) - .02 (.10) .25 (.10) .07 (.10) H aggression - .31 (.09) - .26 (.08) - .15 (.09) - .34 (.09) - .27 (.10) .07 (.10) - .03 (.10) .14 (.09) .03 (.09) .01 (.10) W aggression - .33 (.09) - .31 (.10) - .17 (.09) - .30 (.09) - .30 (.09) .23 (.08) .25 (.09) .12 (.10) .21 (.09) .14 (.08) Partner Effects W NEM - .12 (.09) - .07 (.09) - .12 (.09) - .16 (.09) - .05 (.10) - .03 (.11) - .03 (.10) - .07 (.10) - .03 (.10) .10 (.11) H NEM - .35 (.09) - .29 (.10) - .24 (.08) - .30 (.09) - .34 (.09) - .21 (.09) - .16 (.09) - .22 (.10) - .15 (.10) - .14 (.09) W stress - .09 (.09) - .08 (.09) - .10 (.09) - .11 (.09) .00 (.10) .02 (.10) .03 (.10) - .04 (.10) .04 (.10) .09 (.11) H stress - .26 (.10) - .18 (.10) - .18 (.09) - .26 (.09) - .22 (.10) .09 (.09) - .10 (.09) - .11 (.10) - .02 (.10) - .07 (.09) W alienation - .06 (.09) .04 (.09) - .10 (.10) - .11 (.09) - .04 (.11) .05 (.11) .05 (.11) .01 (.11) - .02 (.11) .14 (.12) H alienation - .42 (.09) - .41 (.10) - .22 (.09) - .35 (.09) - .42 (.10) - .23 (.09) - .10 (.10) - .23 (.10) - .25 (.10) - .16 (.09) W aggression - .15 (.09) - .09 (.09) - .12 (.09) - .15 (.09) - .13 (.10) - .20 (.10) - .16 (.10) - .18 (.10) - .16 (.10) - .13 (.10) H aggression - .20 (.09) - .13 (.10) - .13 (.08) - .19 (.09) - .20 (.09) - .25 (.08) - .18 (.09) - .18 (.09) - .25 (.09) - .18 (.08) Note: H = Husband, W = Wife. Values represent standardized Beta ( ) estimates from multi - level regression models. Standard Errors ( SE ) of these estimates are in parenthesis. Bold values indicate p < .05. 59 Table 7. Warmth and Dominance and II P Interpersonal Problems Mean Warmth Mean Dominance Total Best Things Vacation Husband Conflict Wife Conflict Total Best Things Vacation Husband Conflict Wife Conflict H intercept - .02 (.08) - .04 (.08) - .04 (.09) .03 (.09) - .05 (.09) - .01 (.10) .02 (.10) .03 (.09) .00 (.09) - .12 (.10) W intercept .06 (.09) .03 (.10) .12 (.08) .03 (.09) .02 (.09) .01 (.09) - .04 (.09) - .01 (.09) .00 (.09) .12 (.08) Actor Effects H total.probs - .29 (.08) - .23 (.08) - .33 (.09) - .24 (.09) - .16 (.09) - .03 (.10) .02 (.09) - .11 (.09) .02 (.09) .01 (.10) W total.probs - .16 (.12) - .16 (.10) - .05 (.08) - .18 (.09) - .12 (.09) .09 (.09) .15 (.09) - .06 (.09) .08 (.09) .15 (.09) H warm.probs .24 (.09) .20 (.08) .18 (.10) .18 (.09) .27 (.09) .05 (.10) .11 (.09) .03 (.09) .03 (.09) - .03 (.10) W warm.probs .04 (.09) .07 (.10) .10 (.08) - .04 (.09) .01 (.09) .24 (.09) .20 (.09) .21 (.09) .10 (.09) .29 (.08) H dom.probs - .25 (.08) - .20 (.08) - .08 (.09) - .25 (.08) - .27 (.09) .22 (.09) .10 (.09) .16 (.09) .21 (.09) .23 (.09) W dom.probs - .40 (.09) - .32 (.10) - .26 (.08) - .41 (.09) - .38 (.09) .40 (.08) .33 (.09) .29 (.09) .39 (.08) .26 (.08) Partner Effects W total.probs - .16 (.09) - .11 (.08) - .11 (.09) - .18 (.09) - .13 (.09) - .05 (.10) .00 (.10) .00 (.09) .05 (.09) .12 (.10) H total.probs - .17 (.09) - .25 (.10) - .14 (.08) - .13 (.09) - .04 (.09) - .14 (.09) - .14 (.09) - .01 (.09) - .11 (.09) - .17 (.08) W warm.probs - .01 (.09) .04 (.09) - .07 (.09) .05 (.09) - .04 (.09) - .12 (.10) - .05 (.10) - .16 (.09) - .05 (.09) - .10 (.10) H warm.probs - .01 (.09) .01 (.10) - .07 (.09) .01 (.09) .07 (.09) .06 (.09) - .04 (.09) .07 (.09) .11 (.09) .05 (.08) W dom.probs - .31 (.08) - .28 (.08) - .24 (.09) - .30 (.08) - .24 (.09) - .36 (.09) - .33 (.09) - .30 (.09) - .31 (.09) - .24 (.10) H dom.probs - .10 (.09) - . 11 (.10) .00 (.08) - .06 (.09) - .12 (.09) - .24 (.08) - .13 (.09) - .20 (.09) - .25 (.08) - .21 (.08) Note: H = Husband, W = Wife. Values represent standardized Beta ( ) estimates from multi - level regression models. Standard Errors ( SE ) of these estimates are in parenthesis. Bold values indicate p < .05. 60 Table 8. Warmth and Dominance and IPDE Personality Disorders, Actor Effects Actor Effects Mean Warmth Mean Dominance Total Best Things Vacation Husband Conflict Wife Conflict Total Best Things Vacation Husband Conflict Wife Conflict H total.PD - .34 (.09) - .34 (.09) - .18 (.09) - .33 (.09) - .28 (.10) .00 (.11) - .04 (.10) .08 (.10) .00 (.10) - .03 (.11) W total.PD - .06 (.10) - .03 (.11) - .06 (.09) - .10 (.10) - .01 (.10) .14 (.09) .16 (.09) .00 (.10) .12 (.10) .18 (.09) H paranoid - .38 (.10) - .35 (.09) - .19 (.10) - .37 (.09) - .35 (.11) .03 (.11) - .16 (.10) .13 (.11) .10 (.11) .04 (.12) W paranoid - .29 (.10) - .22 (.11) - .29 (.09) - .31 (.10) - .19 (.11) .10 (.10) .08 (.10) - .07 (.11) .23 (.10) .06 (.10) H schizoid - .13 (.09) - .20 (.08) - .08 (.09) - .08 (.09) - .10 (.09) - .12 (.09) - .14 (.09) .01 (.09) - .14 (.09) - .19 (.09) W schizoid - .13 (.11) - .22 (.12) - .11 (.10) - .08 (.11) - .09 (.11) - .17 (.10) - .07 (.10) - .27 (.10) - .08 (.11) - .13 (.09) H schizotypal - .26 (.09) - .23 (.08) - .23 (.09) - .22 (.09) - .19 (.10) - .07 (.10) - .07 (.09) - .03 (.09) - .02 (.09) - .12 (.10) W schizotypal - .09 (.11) - .09 (.12) - .09 (.10) - .15 (.11) .02 (.11) .02 (.10) .03 (.10) - .14 (.11) .16 (.11) .03 (.10) H antisocial - .24 (.08) - .30 (.08) - .08 (.08) - .20 (.08) - .22 (.09) - .05 (.09) - .10 (.09) .06 (.09) - .06 (.09) - .11 (.10) W antisocial - .32 (.11) - .28 (.12) - .28 (.10) - .26 (.11) - .23 (.11) .07 (.11) .13 (.11) - .08 (.11) .10 (.11) .04 (.10) H borderline - .36 (.10) - .29 (.10) - .12 (.10) - .42 (.09) - .32 (.11) - .01 (.11) - .06 (.11) .08 (.11) .01 (.11) - .05 (.12) W borderline - .17 (.09) - .11 (.10) - .19 (.09) - .15 (.09) - .08 (.10) .22 (.09) .23 (.09) .06 (.10) .20 (.09) .25 (.08) H histrionic - .32 (.09) - .19 (.09) - .20 (.10) - .32 (.09) - .34 (.10) .07 (.11) .05 (.10) .05 (.10) .10 (.10) .07 (.11) W histrionic - .08 (.09) - .02 (.11) - .05 (.09) - .12 (.09) - .06 (.09) .21 (.08) .18 (.09) .09 (.09) .15 (.09) .26 (.08) H narcissistic .14 (.10) .03 (.10) .19 (.10) .18 (.10) .10 (.11) .11 (.11) .11 (.10) .09 (.10) .08 (.10) .11 (.11) W narcissistic - .11 (.10) .03 (.11) - .12 (.09) - .12 (.10) - .15 (.11) .08 (.09) .07 (.09) .05 (.10) .03 (.10) .07 (.09) H avoidant - .20 (.10) - .25 (.10) - .21 (.10) - .14 (.10) - .10 (.11) - .19 (.11) - .10 (.11) - .10 (.11) - .24 (.10) - .20 (.11) W avoidant .05 (.10) - .01 (.11) .03 (.09) .03 (.09) .09 (.10) - .06 (.09) - .01 (.09) - .08 (.09) - .04 (.09) - .04 (.09) H dependent - .13 (.11) - .13 (.10) - .03 (.11) - .17 (.11) - .06 (.12) - .03 (.12) - .06 (.11) .02 (.11) - .02 (.11) .00 (.12) W dependent - .07 (.09) - .02 (.09) - .02 (.08) - .11 (.09) - .06 (.09) .06 (.08) .06 (.08) .06 (.08) - .02 (.09) .09 (.08) H obsess - comp - .27 (.09) - .20 (.09) - .15 (.09) - .32 (.09) - .22 (.10) .23 (.10) .14 (.10) .19 (.09) .21 (.10) .25 (.10) W obsess - comp .09 (.10) .05 (.11) .11 (.09) .02 (.09) .07 (.10) .08 (.09) .11 (.09) .02 (.10) .09 (.10) .09 (.08) Note: H = Husband, W = Wife. Values represent standardized Beta ( ) estimates from multi - level regression models. Standard Errors ( SE ) of these estimates are in parenthesis. Bold values indicate p < .05. 61 Table 9. Warmth and Dominance and IPDE Personality Disorders , Partner Effects Partner Effects Mean Warmth Mean Dominance Total Best Things Vacation Husband Conflict Wife Conflict Total Best Things Vacation Husband Conflict Wife Conflict W total.PD - .04 (.10) .03 (.09) - .06 (.10) - .10 (.10) .03 (.11) - .04 (.11) .03 (.11) - .06 (.10) - .08 (.11) .00 (.11) H total.PD - .33 (.10) - .33 (.10) - .16 (.09) - .32 (.09) - .28 (.10) - .20 (.09) - .16 (.09) - .18 (.10) - .12 (.10) - .19 (.09) W paranoid - .17 (.10) - .04 (.10) - .20 (.10) - .25 (.09) - .06 (.11) - .07 (.12) - .00 (.11) - .06 (.11) - .11 (.11) - .14 (.12) H paranoid - .31 (.10) - .34 (.11) - .17 (.09) - .25 (.10) - .30 (.10) - .22 (.10) - .02 (.10) - .20 (.10) - .34 (.10) - .05 (.09) W schizoid .06 (.10) .01 (.10) .05 (.10) .06 (.11) .13 (.11) .09 (.11) .08 (.11) .16 (.11) .02 (.11) .05 (.12) H schizoid - .10 (.09) - .15 (.09) - .02 (.08) - .09 (.09) - .09 (.09) .06 (.08) .07 (.08) .06 (.09) .11 (.09) .01 (.08) W schizotypal - .04 (.11) - .01 (.10) .04 (.10) - .17 (.10) .04 (.12) .12 (.12) .21 (.11) .12 (.11) - .12 (.11) .17 (.12) H schizotypal - .24 (.09) - .23 (.10) - .12 (.08) - .27 (.09) - .18 (.10) - .04 (.08) - .05 (.08) - .02 (.09) .03 (.09) - .07 (.08) W antisocial - .28 (.11) - .14 (.10) - .26 (.11) - .33 (.11) - .19 (.12) .04 (.12) .01 (.12) .01 (.12) .05 (.12) .13 (.13) H antisocial - .28 (.08) - .29 (.09) - .12 (.08) - .22 (.08) - .30 (.08) - .14 (.08) - .05 (.08) - .13 (.09) - .16 (.08) - .09 (.08) W borderline - .13 (.09) - .02 (.09) - .21 (.09) - .14 (.08) - .06 (.10) - .15 (.10) - .09 (.10) - .13 (.10) - .17 (.10) - .11 (.11) H borderline - .29 (.10) - .27 (.11) - .12 (.10) - .31 (.10) - .28 (.11) - .25 (.09) - .19 (.10) - .20 (.11) - .19 (.10) - .24 (.09) W histrionic - .13 (.09) - .09 (.09) - .08 (.09) - .14 (.09) - .12 (.09) - .14 (.10) - .11 (.10) - .17 (.09) - .09 (.09) - .08 (.10) H histrionic - .26 (.10) - .15 (.11) - .14 (.09) - .24 (.10) - .32 (.10) - .15 (.09) - .13 (.09) - .08 (.10) - .14 (.10) - .14 (.08) W narcissistic - .13 (.10) .02 (.10) - .15 (.10) - .20 (.10) - .07 (.11) .03 (.11) .11 (.10) .00 (.10) .06 (.10) .02 (.11) H narcissistic .12 (.10) .02 (.11) .17 (.09) .13 (.10) .10 (.10) - .11 (.09) - .05 (.09) - .07 (.10) - .15 (.10) - .11 (.09) W avoidant .06 (.09) .07 (.09) .06 (.09) .01 (.09) .09 (.09) .14 (.10) .15 (.10) .08 (.10) .08 (.10) .12 (.11) H avoidant - .21 (.11) - .26 (.11) - .12 (.10) - .24 (.10) - .10 (.11) - .02 (.10) - .12 (.10) - .09 (.10) .08 (.10) .05 (.09) W dependent - .09 (.09) - .07 (.08) - .06 (.08) - .10 (.09) - .04 (.09) - .10 (.09) - .10 (.09) - .11 (.09) - .04 (.09) - .06 (.09) H dependent - .18 (.11) - .22 (.12) - .16 (.10) - .11 (.11) - .14 (.12) - .14 (.10) - .13 (.10) - .17 (.11) - .08 (.11) - .11 (.10) W obsess - comp .07 (.09) .06 (.09) .06 (.09) .03 (.09) .09 (.10) - .08 (.10) - .01 (.10) - .07 (.10) - .13 (.10) - .06 (.10) H obsess - comp - .25 (.10) - .19 (.11) - .17 (.09) - .29 (.09) - .17 (.10) - .17 (.09) - .10 (.09) - .17 (.09) - .12 (.09) - .18 (.08) Note: H = Husband, W = Wife. Values represent standardized Beta ( ) estimates from multi - level regression models. Standard Errors ( SE ) of these estimates are in parenthesis. Bold values indicate p < .05. 62 Table 10. Warmth and Dominance and MPQ Positive Emotionality Communion Mean Warmth Mean Dominance Total Best Things Vacation Husband Conflict Wife Conflict Total Best Things Vacation Husband Conflict Wife Conflict H intercept - .04 (.09) - .09 (.08) - .05 (.09) .02 (.09) - .04 (.10) - .01 (.10) - .01 (.10) .03 (.09) - .01 (.09) - .11 (.10) W intercept .04 (.09) - .01 (.09) .10 (.08) .04 (.09) .02 (.09) .04 (.09) .03 (.09) - .02 (.09) .01 (.09) .16 (.08) Actor Effects H PEM - C .16 (.09) .14 (.09) .16 (.09) .11 (.09) .15 (.10) .00 (.10) - .02 (.10) - .06 (.10) .09 (.10) .01 (.10) W PEM - C .32 (.09) .36 (.10) .23 (.09) .26 (.09) .25 (.10) - .01 (.09) - .08 (.09) .08 (.10) - .06 (.10) .03 (.09) H social.close .19 (.09) .20 (.09) .22 (.09) .08 (.09) .16 (.10) .01 (.10) .03 (.10) - .03 (.10) .06 (.09) .00 (.10) W social.close .29 (.09) .29 (.10) .24 (.08) .20 (.09) .26 (.10) .01 (.09) - .04 (.09) .16 (.09) - .09 (.09) .01 (.08) H wellbeing .12 (.09) .07 (.09) .07 (.09) .14 (.09) .10 (.10) - .01 (.10) - .07 (.10) .09 (.10) .11 (.09) .01 (.10) W wellbeing .23 (.09) .30 (.09) .15 (.09) .22 (.09) .16 (.10) .00 (.09) - .07 (.09) .00 (.10) .00 (.09) .07 (.08) Partner Effects W PEM - C .20 (.09) .17 (.09) .13 (.09) .23 (.09) .10 (.10) .02 (.10) .02 (.10) .02 (.10) .05 (.10) - .02 (.11) H PEM - C .15 (.09) .12 (.10) .09 (.09) .14 (.09) .13 (.10) .09 (.09) .10 (.09) .13 (.10) - .03 (.09) .09 (.08) W social.close .16 (.09) .13 (.09) .13 (.09) .17 (.09) .07 (.10) .00 (.10) - .06 (.10) - .05 (.10) .10 (.10) .03 (.11) H social.close .15 (.09) .14 (.10) .13 (.08) .12 (.09) .11 (.09) .12 (.09) .09 (.09) .13 (.09) .01 (.09) .13 (.08) W wellbeing .19 (.09) .18 (.09) .12 (.09) .20 (.09) .12 (.10) .03 (.10) .09 (.10) .07 (.10) .00 (.10) - .05 (.10) H wellbeing .17 (.09) .14 (.10) .08 (.09) .17 (.09) .17 (.09) .03 (.09) .07 (.09) .12 (.10) - .08 (.09) .00 (.08) Note: H = Husband, W = Wife. Values represent standardized Beta ( ) estimates from multi - level regression models. Standard Errors ( SE ) of these estimates are in parenthesis. Bold values indicate p < .05. 63 Table 11. Warmth and Dominance and MP Q Positive Emotionality Agency Mean Warmth Mean Dominance Total Best Things Vacation Husband Conflict Wife Conflict Total Best Things Vacation Husband Conflict Wife Conflict H intercept - .04 (.09) - .09 (.09) - .05 (.09) .03 (.09) - .04 (.10) - .01 (.10) - .01 (.09) .03 (.09) - .01 (.09) - .11 (.10) W intercept .05 (.09) .01 (.10) .10 (.08) .05 .09) .03 (.10) .04 (.08) .02 (.09) - .02 (.09) .01 (.09) .16 (.08) Actor Effects H PEM - A - .13 (.09) - .06 (.09) - .02 (.09) - .13 (.09) - .20 (.10) .13 (.10) .09 (.10) .05 (.09) .12 (.09) .21 (.10) W PEM - A - .06 (.09) .04 (.10) - .06 (.08) - .06 (.09) - .08 (.10) .18 (.08) .22 (.08) .16 (.09) .09 (.09) .15 (.08) H achieve - .03 (.09) - .06 (.09) - .01 (.09) .01 (.09) - .06 (.10) .03 (.10) .01 (.10) .01 (.09) .05 (.09) .05 (.10) W achieve - .05 (.09) .03 (.10) - .14 (.08) - .04 (.09) - .04 (.10) - .02 (.08) .09 (.09) - .07 (.09) - .05 (.09) .00 (.08) H social.potent - .17 (.09) - .06 (.09) - .03 (.09) - .21 (.09) - .25 (.09) .17 (.09) .11 (.09) .08 (.09) .13 (.09) .26 (.09) W social.potent - .05 (.09) .03 (.10) .01 (.08) - .07 (.09) - .09 (.09) .30 (.08) .27 (.08) .30 (.09) .17 (.09) .24 (.08) Partner Effects W PEM - A - .04 (.09) - .08 (.09) - .04 (.09) - .05 (.09) - .04 (.10) - .15 (.10) - .16 (.09) - .03 (.09) - .05 (.09) - .15 (.10) H PEM - A - .12 (.09) - .04 (.10) - .07 (.08) - .13 (.09) - .13 (.10) .02 (.08) .04 (.09) - .02 (.09) - .14 (.09) .08 (.08) W achieve .00 (.09) - .05 (.09) - .04 (.09) - .02 (.09) .09 (.10) .08 (.10) .00 (.10) .21 (.09) .01 (.09) .03 (.09) H achieve - .05 (.10) - .02 (.10) - .07 (.08) - .07 (.09) - .03 (.10) .10 (.09) .08 (.09) .05 (.09) .03 (.09) .15 (.08) W social.potent - .07 (.09) - .08 (.09) - .03 (.09) - .06 (.09) - .05 (.09) - .28 (.09) - .23 (.09) - .22 (.09) - .21 (.09) - .24 (.09) H social.potent - .15 (.09) - .06 (.10) - .06 (.08) - .14 (.09) - .18 (.10) - .05 (.08) .01 (.08) - .06 (.09) - .10 (.09) - .01 (.08) Note: H = Husband, W = Wife. Values represent standardized Beta ( ) estimates from multi - level regression models. Standard Errors ( SE ) of these estimates are in parenthesis. Bold values indicate p < .05. 64 Table 12. APIM Associations b Warmth and Dominance and MPQ Constraint Mean Warmth Mean Dominance Total Best Things Vacation Husband Conflict Wife Conflict Total Best Things Vacation Husband Conflict Wife Conflict H intercept - .04 (.09) - .08 (.09) - .05 (.09) .02 (.09) - .04 (.10) - .01 (.10) - .01 (.09) .04 (.09) .00 (.09) - .11 (.10) W intercept .05 (.09) .01 (.10) .10 (.08) .04 (.09) .02 (.10) .04 (.08) .03 (.09) - .02 (.09) .02 (.09) .16 (.08) Actor Effects H CON - .01 (.09) - .02 (.09) .07 (.09) - .02 (.09) - .05 (.10) .12 (.10) .05 (.10) .13 (.09) .14 (.09) .09 (.10) W CON - .10 (.10) - .16 (.10) - .12 (.09) - .01 (.09) - .13 (.10) - .31 (.08) - .21 (.09) - .34 (.09) - . 23 (.09) - .24 (.08) H control .15 (.09) .11 (.09) .15 (.09) .15 (.09) .08 (.10) .16 (.10) .10 (.10) .17 (.10) .17 (.10) .10 (.10) W control .05 (.10) - .07 (.11) - .03 (.09) .18 (.10) .00 (.10) - .27 (.09) - .15 (.09) - .26 (.09) - .21 (.09) - .25 (.08) H harm.avoid .15 (.09) .10 (.09) .20 (.09) .11 (.09) .07 (.10) .10 (.10) .08 (.10) .06 (.10) .15 (.10) .09 (.10) W harm.avoid - .22 (.10) - .28 (.10) - .07 (.09) - .10 (.10) - .25 (.10) - .09 (.09) - .13 (.09) - .03 (.10) - .03 (.10) - .08 (.09) H traditional .02 (.10) - .03 (.10) .09 (.10) - .01 (.10) .00 (.11) .11 (.11) - .01 (.11) .14 (.11) .08 (.10) .12 (.11) W traditional - .03 (.10) .02 (.11) .04 (.09) - .05 (.10) - .11 (.10) - .22 (.09) - .19 (.10) - .23 (.10) - .20 (.10) - .09 (.09) Partner Effects W CON - .09 (.09) - .12 (.09) - .08 (.09) - .06 (.09) - .06 (.10) . 19 (.10) .12 (.10) .21 (.09) .11 (.09) .17 (.10) H CON - .13 (.09) - .11 (.10) - .09 (.08) - .15 (.09) - .12 (.10) .03 (.08) .06 (.09) - .11 (.09) .07 (.09) .07 (.08) W control .10 (.09) .02 (.09) .09 (.09) .12 (.09) .09 (.10) .12 (.10) .05 (.10) .17 (.09) .10 (.10) .04 (.10) H control .08 (.10) .08 (.10) .07 (.09) .06 (.10) .04 (.10) - .02 (.08) .06 (.09) - .10 (.09) - .03 (.09) .02 (.08) W harm.avoid - .12 (.10) - .13 (.09) - .13 (.09) - .01 (.10) - .15 (.10) .03 (.10) .07 (.10) .02 (.10) .00 (.10) - .04 (.11) H harm.avoid .06 (.10) - .04 (.10) - .04 (.09) - .12 (.10) - .04 (.10) - .03 (.09) - .03 (.09) - .11 (.09) .03 (.09) - .03 (.08) traditional - .15 (.10) - .09 (.10) - .11 (.10) - .12 (.10) - .15 (.10) .10 (.11) .12 (.10) .04 (.10) .03 (.10) .14 (.11) traditional - .05 (.11) - .13 (.11) - .10 (.10) .02 (.11) .02 (.11) .04 (.09) .03 (.10) - .05 (.10) .10 (.10) .04 (.09) Note: H = Husband, W = Wife. Values represent standardized Beta ( ) estimates from multi - level regression models. Standard Errors ( SE ) of these estimates are in parenthesis. Bold values indicate p < .05. 65 APPENDIX B: FIGURES Figure 1. The Vulnerability Stress Adaptation (VSA) M odel of Marriage Note: This model was initially proposed by Karney and Bradbury (1995, p. 23). They hypothesized that all paths shown in this model relate as predicted. In my study, I will be examining the paths represented by the bold (non - dashed) lines. These include the associations between adaptive processes and 1) marital quality, and 2) enduring vulnerabilities. 66 Figure 2. The Interpersonal Circumplex (IPC) 67 Figure 3. Negative R eciprocity and D emand - W ithdraw Patterns Depicted o n the IPC 68 Figure 4. An Integrative Model of Adaptive Processes rooted in VSA and IPC. 11 69 Figure 5. Hypothesis 1.1: APIM Relating Husbands and Wives Warmth and Marital Satisfaction Note: Paths represent standardized effects from multi - level regression models. Analyses testing Hypothesis 2.1 and 2.2 were also tested using this model, excepting that the dependent variables were husband and wife mean warmth, and the independent variables were husband and wife individual difference characteristic s (i.e., personality traits, problems, and disorders). 70 Figure 6. Hypothesis 1.2: APIM Relating Dyadic Correspondence and Marital Satisfaction Note: Paths represent standardized effects from multi - level regression models. 71 Figure 7. Hypothesis 1.3: A PIM Relating Marital Satisfaction with Dyadic Correspondence, Individual Warmth, and their Interaction Note: Paths represent standardized effects from multi - level regression models. 72 Figure 8. Total Score Simple Slope Interactions between Marital Satisfaction , Mean Warmth, and Momentary Dominance Complementarity . Total Note: Dom Comp indicates momentary dominance complementarity. High and low values were represents predicted satisfaction across levels of complementarity when both partners are is relatively low. This format also applies to Figures 8 - 12. - 1.500 - 1.000 - 0.500 0.000 0.500 1.000 Lo Dom Comp Hi Dom Comp Husbands ' Marital Satisfaction Panel A Hi Hus, Hi Wife Hi Hus, Lo Wife Lo Hus, Hi Wife Lo Hus, Lo Wife Mean Warmth - 1.500 - 1.000 - 0.500 0.000 0.500 1.000 Lo Dom Comp Hi Dom Comp Wives ' Marital Satisfaction Panel B Hi Wife, Hi Hus Hi Wife, Lo Hus Lo Wife, Hi Hus Lo Wife, Lo Hus Mean Warmth 73 Fig ure 9. Husband Conflict Simple Slope Interactions between Marital Satisfaction , Mean Warmth, and Momentary Dominance Complementarity . Husband Conflict - 1.000 - 0.500 0.000 0.500 1.000 Lo Dom Comp Hi Dom Comp Husbands ' Marital Satisfaction Panel A Hi Hus, Hi Wife Hi Hus, Lo Wife Lo Hus, Hi Wife Lo Hus, Lo Wife Mean Warmth - 1.000 - 0.500 0.000 0.500 1.000 Lo Dom Comp Hi Dom Comp Wives ' Marital Satisfaction Panel B Hi Wife, Hi Hus Hi Wife, Lo Hus Lo Wife, Hi Hus Lo Wife, Lo Hus Mean Warmth 74 Figure 10. Wife Conflict Simple Slope Interactions between Marital Satisfaction , Mean Warmth, a nd Momentary Dominance Complementarity Wife Conflict - 1.500 - 1.000 - 0.500 0.000 0.500 1.000 Lo Dom Comp Hi Dom Comp Husbands ' Marital Satisfaction Panel A Hi Hus, Hi Wife Hi Hus, Lo Wife Lo Hus, Hi Wife Lo Hus, Lo Wife Mean Warmth - 1.500 - 1.000 - 0.500 0.000 0.500 1.000 Lo Dom Comp Hi Dom Comp Wives ' Marital Satisfaction Panel B Hi Wife, Hi Hus Hi Wife, Lo Hus Lo Wife, Hi Hus Lo Wife, Lo Hus Mean Warmth 75 Figure 11. Best T hings Simple Slope Interactions between Marital Satisfaction , Mean Warmth, and Momentary Dominance Complementarity . Best Things - 1.000 - 0.500 0.000 0.500 1.000 Lo Dom Comp Hi Dom Comp Husbands' Marital Satisfaction Panel A Hi Hus, Hi Wife Hi Hus, Lo Wife Lo Hus, Hi Wife Lo Hus, Lo Wife Mean Warmth - 1.000 - 0.500 0.000 0.500 1.000 Lo Dom Comp Hi Dom Comp Wives ' Marital Satisfaction Panel B Hi Wife, Hi Hus Hi Wife, Lo Hus Lo Wife, Hi Hus Lo Wife, Lo Hus Mean Warmth 76 Figure 12. Vacation Simple Slope Interactions between Marital Satisfaction , Mean Warmth, and Momentary Dominance Complementarity . Vacation - 1.000 - 0.500 0.000 0.500 1.000 Lo Dom Comp Hi Dom Comp Husbands ' Marital Satisfaction Panel A Hi Hus, Hi Wife Hi Hus, Lo Wife Lo Hus, Hi Wife Lo Hus, Lo Wife Mean Warmth - 1.000 - 0.500 0.000 0.500 1.000 Lo Dom Comp Hi Dom Comp Wives ' Marital Satisfaction Panel B Hi Wife, Hi Hus Hi Wife, Lo Hus Lo Wife, Hi Hus Lo Wife, Lo Hus Mean Warmth 77 Figure 13. 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