Marriages After Infidelity: a Prospective Study Paul S

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2012 Marriages after Infidelity: A Prospective Study Paul S. Stanford

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COLLEGE OF HUMAN SCIENCES

MARRIAGES AFTER INFIDELITY: A PROSPECTIVE STUDY

PAUL S. STANFORD

A Dissertation submitted to the Department of Family and Child Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Degree Awarded: Fall Semester, 2012

Paul S. Stanford defended this dissertation on August 7, 2012.

The members of the supervisory committee were:

B. Kay Pasley

Professor Directing Dissertation

Betsy J. Becker

University Representative

Ming Cui

Committee Member

Robert E. Lee

Committee Member

The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.

I dedicate this to my wonderful family, who always love and support me unconditionally.

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ACKNOWLEDGEMENTS

First and foremost, I would like to thank Dr. Kay Pasley for unending support throughout my doctoral process. She has been instrumental in my development as an academic writer, researcher, and teacher. She has always provided motivation and maintained belief in my abilities, even in times when I lacked both. Her countless hours spent advising me and editing this paper cannot be repaid. I would also like to thank my committee: Dr. Ming Cui, Dr. Robert E. Lee, and Dr. Betsy Becker. Your guidance in this dissertation, in research, in classes, and in life has truly impacted my academic career.

I would also like express my appreciation for my mother for her numerous sacrifices she made on a daily basis to provide for myself and my sisters. You are a living testament to the idea that hard work and perseverance do indeed eventually pay off. Without your strength, love, and support, I would not be where I am today, and I certainly would not have completed this degree. Finally I’d like to thank my sister and best friend, Christy. You have been with me from the day I entered this world, and you remain firmly by my side to this day. To all my family, I love you, and I consider this degree to be “our” achievement.

TABLE OF CONTENTS

List of Tables ...... vii Abstract ...... ix

1. INTRODUCTION ...... 1 Study Purpose ...... 1 Rationale and Justification ...... 2

2. REVIEW OF LITERATURE ...... 4 Theoretical Orientation ...... 4 General Stress Theory ...... 4 Review of the Literature on Infidelity ...... 6 Conceptualization ...... 6 Prevalence ...... 6 Correlates of Infidelity ...... 7 Chronic Strains as Precursors to Infidelity ...... 8 Effects of Infidelity on Individuals ...... 10 Effects of Infidelity on Marriages ...... 12 Hypotheses… ...... 15

3. METHODS ...... 16 Data…...... 16 Sample ...... 16 Measures ...... 17 Data Analysis ...... 21 Hypothesis 1...... 21 Hypothesis 2...... 22 Hypothesis 3 and 4 ...... 22 Missing Data ...... 23

4. RESULTS ...... 24 Preliminary Analyses ...... 24 Testing the Hypotheses ...... 24 Summary of Results ...... 30

5. DISCUSSION ...... 32 Influence of Infidelity on Individual and Relational Outcomes ...... 32 Individual Outcomes ...... 32 Relational Outcomes ...... 33 Limitations ...... 37 Conclusions ...... 39 Implications ...... 39 Future Research ...... 39 Practice ...... 40

6. APPENDIX A. TABLES ...... 42 7. APPENDIX B. MEASURES ...... 64 8. APPENDIX C. HUMAN SUBJECTS APPROVAL MEMORANDUM ...... 70 9. REFERENCES ...... 72 10. BIOGRAPHICAL SKETCH ...... 79

LIST OF TABLES

Table 1. Comparative Information with Demographic Characteristics in 1980 (N = 1,928) 61 Table 2. Comparative Information with Demographic Characteristics in 1980 among Infidelity Respondents (N = 120) 62 Table 3. Correlations and Descriptive Statistics 63 Table 4. Summary of Repeated Measures Logistic Regression Analysis for Measuring Change in Depression (N = 120) 66 Table 5. Summary of Repeated Measures Logistic Regression Analysis for Measuring Change in Domestic Violence (N = 120) 67 Table 6. Summary of t-test Analyses for Measuring Change in Personal Satisfaction, Marital Distress, and Marital Instability Among Those Reporting Infidelity (N = 120) 68 Table 7. Summary of Hierarchical Regression Analyses for Infidelity Predicting Short-term Depression, Controlling for Other Variables (N = 1,847) 69 Table 8. Summary of Hierarchical Regression Analyses for Infidelity Predicting Long-term Depression, Controlling for Other Variables (N = 1,826) 70 Table 9. Summary of Hierarchical Regression Analyses for Infidelity Predicting Short-term Personal Satisfaction, Controlling for Other Variables (N = 1,846) 71 Table 10. Summary of Hierarchical Regression Analyses for Infidelity Predicting Long-term Personal Satisfaction, Controlling for Other Variables (N = 1,826) 72 Table 11. Summary of Hierarchical Regression Analyses for Infidelity Predicting Short- term Marital Distress, Controlling for Other Variables (N = 1,833) 73 Table 12. Summary of Hierarchical Regression Analyses for Infidelity Predicting Long- term Marital Distress, Controlling for Other Variables (N = 1,792) 74 Table 13. Summary of Hierarchical Regression Analyses for Infidelity Predicting Short- term Domestic Violence, Controlling for Other Variables (N = 1,847) 75 Table 14. Summary of Hierarchical Regression Analyses for Infidelity Predicting Long- term Domestic Violence, Controlling for Other Variables (N = 1,805) 76 Table 15. Summary of Hierarchical Regression Analyses for Infidelity Predicting Short- term Marital Instability, Controlling for Other Variables (N = 1,827) 77

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Table 16. Summary of Hierarchical Regression Analyses for Infidelity Predicting Long- term Marital Instability, Controlling for Other Variables (N = 1,807) 78 Table 17. Summary of Logistic Regression Analysis for Infidelity Predicting Between- group Differences in Likelihood of Later Divorce, Controlling for Other Variables (N = 1,817) 79 Table 18. Summary of Logistic Regression Analysis for Variables Predicting Depression, Domestic Violence and Divorce, Controlling for Other Variables (N = 120) 80

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ABSTRACT

The purpose of this study was to examine the trajectory of marriages following reported experiences with infidelity. General Stress Theory was used to conceptualize the effect of infidelity on subsequent marital stress. Using longitudinal data from the Panel Study of Marital Instability Over the Life Course, I explored the consequences of infidelity on short-term and long-term individual outcomes (depression and personal satisfaction) and relational outcomes (marital distress, domestic violence, marital instability, and divorce). I used t tests, logistic regression, and hierarchical regression to test the hypotheses. Results show that infidelity was significantly associated with higher instances of short-term depression and domestic violence, lower levels of personal satisfaction, and higher levels of marital distress and marital instability. Over a longer period, these findings remained true for marital distress, domestic violence, and marital instability, but not for depression or personal satisfaction. Infidelity was not significantly related to short-term divorce, but did significantly impact whether the individual reported ever divorcing. When only a respondent’s spouse committed infidelity, respondents were not more likely to report feelings of depression than were respondents who had committed infidelity themselves. Reports of domestic violence were not significantly affected by the wife committing infidelity compared to instances in which only the husband committed infidelity. Unexpectedly, a wife’s infidelity significantly reduced the probability of subsequent divorce. Limitations, implications for practitioners, and suggestions for future research are discussed.

CHAPTER 1

INTRODUCTION

Research indicates that Americans rate having a healthy marriage as one of the most important life goals (Karney, Garvan, & Thomas, 2003). Even with the divorce rate approaching 50%, over 90% of Americans are, have been, or will be married at some point in their lives (Bergman, 2006), and most still expect marriages to endure (Coontz, 2005). Although Americans intend to marry and do so, research shows that marital happiness diminishes over time, regardless of age at marriage and duration of the relationship (Umberson, Williams, Powers, Chen, & Campbell, 2005; VanLaningham, Johnson, & Amato, 2001). Many factors affect marital happiness, and evidence shows that marital infidelity is a significant factor (Atkins, Baucom, & Jacobson, 2001; Buss & Shackelford, 1997; Prins, Buunk, & VanYperen, 1993; Treas & Gieson, 2000) and increases the odds of divorce (Amato & Rogers, 1997; Betzig, 1989; Cano, Christian-Herman, O’Leary, & Avery-Leaf, 2002; Kelly & Conley, 1987). Further, research shows that infidelity can cause great individual and relational stress (e.g., Atkins, Eldridge, Baucom, & Christensen, 2005; Cano & O’Leary, 2000; Gordon, Baucom, & Snyder, 2004; Sweeny & Horowitz, 2001).Yet, studies have failed to examine the effects of infidelity longitudinally and prospectively after such suggestions were offered (Allen, Atkins, Baucom, Snyder, Gordon, & Glass, 2005; Beach, Jouriles, & O’Leary, 1985)

Study Purpose

The purpose of this study was to examine the trajectory of marriages following reported infidelity. Using general stress theory (Pearlin, Menaghan, Lieberman, & Mullan, 1981) which suggests that infidelity can intensify ongoing marital strains to create stress and, in turn, affect marital outcomes, I proposed two goals. One goal was to explore the nature of subsequent individual and relational changes among spouses who do and do not report infidelity in their marriages. Specifically, I examined individual changes in depression and personal satisfaction and relational changes in marital distress, domestic violence, marital instability, and divorce following reports of infidelity. In addition, I assessed the influence of which spouse was responsible for the infidelity (respondent or spouse of respondent) on these changes. A second goal was to examine differences among individuals who experience infidelity and their

propensity to experience marital instability, divorce or domestic violence over time, as well as the effects of gender of the perpetrator on these outcomes.

Rationale and Justification

Not surprisingly, marital infidelity is linked with divorce, and many people deem infidelity to be the greatest “crime” against a relationship. Results from the Gallup Survey (2010) showed that over 90% of respondents agree that sex with someone other than one’s spouse is immoral and unacceptable. Such beliefs are not new, as 99% of married individuals reported that they expected sexual exclusivity from their partner (Johnson, Stanley, Glenn, Amato, Nock, Markman et al., 2002; Treas & Gieson, 2000). However, this oft-voiced expectation that spouses remain faithful does not deter somewhere between 20-25% of Americans from committing infidelity at some point in their marriage (Atkins, Dimidjian, & Jacobson, 2001). In fact, infidelity is so common that some studies show it to be the most cited reason for divorce (Atwood & Seifer, 1997; Spanier & Margolis, 1983). Infidelity is also reported as one of the most common problems that couples present with in therapy (Atkins, Baucom, & Jacobson, 2001; Geiss & O’Leary, 1981; Whisman, Dixon, & Johnson, 1997).

Much of the research on marital infidelity has focused on precursors. Many fewer studies address the personal and the relational consequences of infidelity – the focus of this study – and those which do focused only on personal distress rather than relational consequences. For example, infidelity is associated with increased personal distress in both partners (Beach et al., 1985; Glass, 2003; Glass & Wright, 1997; Gordon et al., 2004), as well as negative emotional reactions and postmarital distress, such as emotional reactions reported by the victim (Beach et al., 1985; Cano & O’Leary, 2000; Charny & Parnass, 1995; Glass & Wright, 1997; Gordon & Baucom, 1999; Gordon et al., 2004). Gender differences in reactions are also noted (Betzig, 1989; Daly & Wilson, 1988; Jankowiak, Nell, & Buckmaster, 2002), including gender differences in postmarital distress among victims (Sweeney & Horowitz, 2001). Even more alarming are the potential health risks associated with infidelity, such as the infrequent use of condoms by cheating partners, especially men (e.g., Choi, Catania, & Dolcini, 1994; Gerber & Berman, 2009; Hall, Falls-Stewart, & Fincham, 2008; Wardlow, 2007), leading to an increased risk of the non-cheating partner contracting HIV and other sexually transmitted infections (STIs).

In the clinical literature, practicing therapists ranked infidelity high (2nd, 3rd, or 9th) among common couple problems that were most damaging and most difficult to treat (Geiss & O’Leary, 1981; Whisman et al., 1997), suggesting that there are serious relational consequences. For example, other research shows that infidelity is associated with subsequent relationship dissolution (Amato & Rogers, 1997; Betzig, 1989; Blow & Hartnett, 2005b; Cano et al., 2002; Kelly & Conley, 1987), marital distress (Atkins, Eldridge et al., 2005; Charny & Parnass, 1995), and violence (Jankowiak et al.; McCarroll, Castro, Nelson, ZiZhong, Evans, & Rivera, 2008). However, these studies typically used small clinical samples or were retrospective in nature. In fact, most of the research on relational consequences is limited to studies of relationship dissolution rather than other effects (see notable exceptions: Atkins, Eldridge et al.; Gordon et al., 2004; Jankowiak et al., 2002; McCarroll et al.; Olson, Russell, Higgins-Kessler, & Miller, 2002; Sweeney & Horwitz, 2001). Of the studies addressing other effects, the use of small clinical samples or following individuals and couples through the post-divorce process using qualitative methods are common. Only Sweeney and Horwitz used data from the National Survey of Families and Households, a nationally representative sample; however, their findings are suspect because respondents who had divorced were asked whether infidelity had played a role. Therefore, their findings are limited to only those who divorced as a result of infidelity playing a role in the breakup. The current study adds to the extant literature in two key ways. First, I explored prospectively how infidelity affects individuals and their marriages. Specifically, I examined individual (depression, personal satisfaction) and relational (marital distress, marital instability, domestic violence, divorce) consequences of infidelity by following a group of married individuals for several years after reports of infidelity to determine the multiple effects of infidelity. Second, I examined within-group variation among those reporting infidelity to identify the ways in which this experience affects their marriages.

CHAPTER 2

REVIEW OF THE LITERATURE

In this chapter I explain the intricacies of general stress theory which provides the foundation for the study, and I offer examples of how this theory is used to inform the study of infidelity. Then I present a critical review of research on infidelity, explicating the hypotheses to be tested.

Theoretical Orientation

General Stress Theory

Several theoretical perspectives have been applied to the study of infidelity, including commitment theory (Drigotas, Safstrom, & Gentilia, 1999), evolutionary psychology (Easton, Schipper, & Shackelford, 2008), and social exchange theory (Baumeister & Vohs, 2004). However, they are used to explain why infidelity occurs rather than providing insight into the aftermath of infidelity – the focus of my study. I use Pearlin et al.’s (1981) general stress theory as a foundation.

According to this theory, life events exacerbate chronic strains which can negatively affect self-esteem and ultimately produce stress. For example, low marital happiness is a fairly common chronic strain in marriages (Umberson et al., 2005; VanLaningham et al., 2001). However, when infidelity is discovered, this life event can result in a more dramatic deterioration of an individual’s marital happiness than would be expected otherwise. Theoretically, lowered marital happiness leads to lowered self-esteem in the victim, and poor self-esteem leads to more stress in the individual directly and the marital relationship indirectly.

Pearlin et al. (1981) identified two primary sources of stress: discrete life events and continuous or chronic problems. Life events (commonly referred to as stressors) are discrete occurrences that require immediate behavioral changes in the short term (e.g., infidelity; Holmes & Rahe, 1967; Thoits, 1995). Chronic problems, often referred to as strains or role strains, are long-term, reoccurring issues that demand extended change (examples relevant to this study include marital dissatisfaction and sexual dissatisfaction). These events and chronic problems interact to produce stress. Applied to this study, I examined how infidelity (stressor) interacts

4 with chronic strains (marital dissatisfaction and sexual dissatisfaction) to produce stress that affects the individuals and their marriages.

Theoretically, the interactions of discrete events and chronic strains occur in two ways: (a) events bring focus back to old problems and lead to stress by negatively altering the perception of ongoing life strains, or (b) events bring about new strains or intensify old strains, once again creating stress. Life events and chronic strains are especially detrimental when they negatively impact one’s self-concept (Pearlin et al., 1981). Applied here, using prospective data with indicators of chronic strains prior to infidelity, I explored how infidelity interacts with chronic strains to affect self-concept via depression and personal satisfaction and ultimately one’s marriage via indicators of later marital distress, domestic violence, marital instability and divorce.

Victims of infidelity often see their partner’s infidelity (stressor) as a reflection of their own self-worth, particularly as a marital partner (Glass & Wright, 1997). The fact that the partner chose to go elsewhere to fulfill a particular need can be perceived as a sign that the current partner is inadequate in fulfilling that need. Theoretically, infidelity (stressor) coupled with low marital happiness and sexual dissatisfaction (chronic strains) and decreased sense of self-worth (e.g., depression and personal satisfaction) likely leads to personal and relational problems (stress).

Scholars argue that the effects of life events, chronic strains, and self-concept can be buffered by coping resources (e.g., social support, personal resources) and coping strategies (Pearlin et al., 1981; Thoits, 1995) before a life event occurs, between the event and the life strain it exacerbates, between the strain and the effect on self-esteem, or before the resulting stress. Applied to marital infidelity, victims who have higher quality marital relationships, have more ample financial resources, or who go to church more might mitigate the stress of an impending divorce following infidelity. At the least, these resources and relationships could increase one’s ability to cope with the subsequent stress following infidelity.

Additionally, events which are both negative and highly disruptive (Thoits, 1995) and of central importance (Perlin et al, 1981) often lead to significant psychological distress. Because other research shows that infidelity is commonly reported as one of the most difficult life events

within marriages (Geiss & O’Leary, 1981; Whisman et al., 1997), infidelity is expected to be both a negative and highly disruptive life event. Further, assuming one’s marriage is highly valued (Gallop, 2010), infidelity can have a significant negative impact both on the individual and his/her relationship.

Review of the Literature on Infidelity

Conceptualization

There is a lack of consistency in the conceptualization of infidelity. For example, infidelity is defined by a myriad of behaviors, such as hugging, kissing, intimacy without coitus, and intimacy with coitus with someone other than his or her spouse (Blow & Hartnett, 2005a). However, this definition has broadened beyond physical contact to include the prevalence and effects of emotional infidelity, a phenomenon that includes intimate talking, flirting, and establishing a bond or relationship without sexual intimacy with someone other than a spouse (Buunk, 1980; Cann, Mangum, & Wells, 2001; Glass, 2003; Glass & Wright, 1985, 1988; Shackelford, LeBlanc, & Drass, 2000). Additionally, as the internet has grown in popularity and functionality, relationships established and maintained over the internet rather than in person with someone other than a spouse are now considered by many to also reflect infidelity (Whitty, 2003). For the purpose of this study, infidelity was defined by problems occurring in the participant’s marriage as a result of either spouse having a sexual relationship with someone else.

Prevalence

Estimates are that between 20-30% individuals will engage in infidelity at some point in their lifetime (Atkins et al., 2001; Wiederman, 1997). However, actual estimates from nationally representative samples show lifetime prevalence ranging from 12-25% (e.g., Burdette, Ellison, Sherkat, & Gore, 2007) with some variation by study. Differences in estimates stem from differing definitions of infidelity, the assessment method, and the reference to time. For example, in studies where infidelity was defined more broadly (e.g., flirting, kissing, and hugging), participants report higher prevalence than do those where infidelity is limited to sexual intercourse (Blow & Hartnett, 2005b). Whisman and Snyder (2007) also found that prevalence differed depending on the assessment method. In their sample of 4,884 women, 6.13% of those using a computer-assisted self-interview reported committing infidelity in the past year

6 compared to only 1.08% of women in face-to-face interviews. Estimates are also inconsistent, because in some studies participants are asked whether they experienced infidelity “in the past 3 months,” whereas others asked “in the past year,” and yet others asked about lifetime experiences (Blow & Hartnett, 2005a).

Correlates of Infidelity

There is no shortage of studies that address the correlates associated with infidelity. Findings show the following are associated with infidelity: suspicion of one’s partner’s infidelity (Whisman, Gordon, & Chatav, 2007), wife’s pregnancy (Whisman, Gordon, & Chatav), childhood sexual abuse (Whisman & Snyder, 2007), having divorced parents (Amato & Rogers, 1997), dishonesty (Atkins, Yi, Baucom, & Christensen, 2005), arguing about trust (Atkins, Yi et al., 2005), a high number of sexual partners (Whisman & Snyder) and more previous sexual partners (Treas & Giesen, 2000), more education (DeMaris, 2009), higher interest in sex (Treas & Giesen), and living in a big city (Treas & Giesen).

Still other studies identified personality traits and gender as correlates of infidelity. For example, high moodiness, high psychoticism, low agreeableness, low conscientiousness, low emotional stability (Buss, 1991; Buss & Shackelford, 1997; Shackelford, Besser, & Goetz, 2008), impulsive antisociality (Witt & Donnellan, 2008), and higher neuroticism (Atkins, Yi et al., 2005; Buss & Shackelford; Whisman, Gordon, & Chatav, 2007) are linked with increased likelihood of infidelity. Also, Lawson and Samson (1988) found that men were more likely than women to report multiple instances of infidelity, and young women were most likely to commit infidelity early in marriage, a finding later supported by Treas and Giesen (2000), who added the role of sexual interest and permissive sexual values. Still other research compared male and female perpetrators and found that men committing infidelity were older, more sexually dissatisfied, and more likely to abuse substances (Atkins, Yi et al.).

Findings are not consistent regarding other factors such as self-esteem, type of job, cohabitation, and duration of marriage. Specifically, lower self-esteem was associated with more infidelity in one study (Whisman, Gordon, & Chatav, 2007), although Eaves and Roberston- Smith (2007) found this association held only for men. Treas and Giesen (2000) found that a job requiring contact with potential sexual partners increased the likelihood of infidelity; however,

results from a prospective study controlling for prior instances of infidelity, did not find support for a correlation between work-related free time and subsequent infidelity (DeMaris, 2009). Regarding cohabitation, some studies found that cohabiting before marriage increased the likelihood of infidelity in their marriages (Amato, & Rodgers, 1997; Treas & Giesen; Whisman & Snyder, 2007), whereas DeMaris failed to produce a significant association between cohabitation and subsequent infidelity. Findings regarding duration of marriage show that relationships of longer duration were associated with greater likelihood of infidelity (Fisher, Corona, Bandini, Mannucci, Lotti et al., 2009; Treas & Giesen). However, DeMaris found that longer marital duration was related to a decrease in future infidelity. Such contradictions could be due to methodology, as Treas and Giesen asked if respondents had ever had sex with anyone other than their spouse, and DeMaris asked still-married individuals if they had ever experienced a problem in their relationship due to infidelity. Additionally, Fisher et al.’s study was specific to males attending a clinic for sexual dysfunction.

In addition, numerous studies have addressed church attendance, income, and racial correlates of infidelity. Specifically, studies use church attendance to demonstrate that highly religious respondents report less engagement in infidelity, and this is explained in terms of moral obligations or values (Amato & Rogers, 1997; Atkins, Baucom, & Jacobson, 2001; Atkins & Kessel, 2008; Burdette et al., 2007; DeMaris, 2009; Liu, 2000; Treas & Giesen, 2000; Whisman et al., 2007; Whisman & Snyder, 2007). Atkins, Baucom, and Jacobson found that income and infidelity were positively related. Also, multiple studies found a link between being African American and increased rates of infidelity (Amato & Rogers, 1997; Treas & Giesen, 2000; Whisman & Snyder).

Relevant to the current study, I included religious service attendance, race, marital duration, and income as control variables. Overall, findings generally show that each of these is commonly associated with infidelity.

Chronic Strains as Precursors to Infidelity

Several potential chronic strains are noted as precursors to infidelity. These include marital happiness, sexual satisfaction, and marital instability.

Marital happiness. A number of studies link low marital happiness with infidelity. For example, Prins et al. (1993) used retrospective data to examine the effects of relationship happiness on the desire for and actual involvement in infidelity in a sample of 214 married (87%) and cohabiting (13%) individuals. They found that both men and women who reported more happiness also reported less desire for infidelity. For women only, relationship happiness decreased the odds of committing infidelity. However, of the 214 individuals, 160 were in a relationship with another participant (80 couples), and the authors did not account for this in the analyses. Similarly, using a sample of 107 couples married less than a year, Buss and Shackelford (1997) examined whether different measures of marital happiness predicted susceptibility to infidelity. Both husbands and wives, but especially wives, reported being more susceptible to infidelity when also reporting low levels of marital happiness. Actual infidelity was not measured.

Several other studies focused primarily on reports of infidelity. For example, Treas and Giesen (2000) used a national probability sample of married and cohabiting individuals to explore whether low relational happiness increased the odds of committing infidelity. Overall, most reported high levels of relational happiness; however, those who were extremely pleased with their relationship were less likely to engage in infidelity compared to those who were very pleased. Similar findings were reported by Atkins, Baucom, and Jacobson (2001) and Whisman et al. (2007), both using responses from nationally, representative samples. They found that lower marital happiness was associated with higher instances of infidelity. Additionally, among other things Whisman et al. found that religiosity moderated the association, such that lower religiosity strengthened this association.

Only one study failed to find a link between marital happiness and infidelity. Using a prospective method and data from the Marital Instability over the Life Course study, DeMaris (2009) employed an event history model and did not find an association between marital happiness and subsequent infidelity. However, taken together, findings typically support this association.

Sexual satisfaction. The findings are mixed from studies linking sexual satisfaction with infidelity, and gender differences are noted. For example, some studies (e.g., Buss & Shackelford, 1997; Liu, 2000) found that those who were sexually dissatisfied reported a greater

9 likelihood of infidelity, and Liu (2000) reported that the association was almost twice as strong for men. Another study (Prins et al., 1993) reported that sexual satisfaction was negatively correlated with the desire for but not the act of infidelity and unlike Liu, this correlation was stronger for women. Compared with couples with no report of infidelity, Allen et al. (2008) found that men who committed marital infidelity reported lower premarital sexual and relationship satisfaction, but women who did so reported higher premarital sexual satisfaction than their non-infidelity counterparts. Fisher et al. (2009) found that men who reported infidelity were more likely to report their own sexual desire being high, whereas their partner experienced low sexual desire. Only DeMaris (2009) did not find a correlation between sexual dissatisfaction and infidelity.

Applied to the current study, the results from most of the studies reported here demonstrate a link between infidelity and marital happiness and sexual satisfaction. However, due to the nature of the study designs (e.g., cross-sectional), it is impossible to determine whether low marital happiness leads to infidelity or infidelity leads to low marital happiness, as an example. Due to the fairly consistent correlations between these constructs and infidelity, earlier lack of marital happiness and sexual satisfaction are identified as chronic strains and were included as control variables in the current study.

Effects of Infidelity on Individuals

Because there is a dearth of literature regarding the aftermath of infidelity, the present study sought to fill this void and focus on the ways in which infidelity affects individual spouses and their marriages over time. Several individual outcomes are notable, including depression and personal satisfaction, and differences between victims and perpetrators are noted. Also, several relational outcomes are examined, including marital distress, marital instability, domestic violence, and divorce.

Depression. In one of the few studies following couples post-infidelity, Beach et al. (1985) used therapeutic intake interviews to examine depression levels in 120 couples presenting with problems pertaining to infidelity. Couples experiencing infidelity were more likely to have at least one spouse report mild depression compared with those not experiencing infidelity (73% to 43%, respectively). Both husbands (89%) and wives (82%) who committed infidelity were

10 significantly more likely to be depressed than were husbands (46%) and wives (48%) who did not do so. Interestingly, for victims of infidelity (both husbands and wives), there was no difference in their depression levels compared with those who did not experience infidelity.

Other research also using clinical samples found similar results. For example, Cano and O’Leary (2000) investigated whether certain humiliating marital events resulted in subsequent major depressive episodes. Their sample included 25 women who had experienced a humiliating marital event in the past six months and a control group of 25 women who had no such experience but who reported similar levels of marital discord. The group that experienced an event (i.e., either a husband’s infidelity or a husband making divorce threats) was six times more likely to be diagnosed with a major depressive episode. These same women also had higher levels of nonspecific depression and nonspecific anxiety. Relatedly, another study (Gordon et al., 2004) used data from a small sample of six of couples who had experienced infidelity in the past year to explore the efficacy of a treatment modality. In the pretreatment assessment, they found that the victim had elevated levels of both depression and posttraumatic stress disorder symptoms.

Only one study was found that did not use a clinical sample. Using the first two waves of the National Survey of Families and Households, Sweeney and Horowitz (2001) examined how divorce, depression, and infidelity are interrelated in individuals who had recently divorced and stated that infidelity had occurred just before their marriage ended. Findings failed to show that infidelity was linked with increased depression in the victims. However, findings did show that when both spouses committed infidelity and initiated divorce, respondents were more depressed than in all other combinations of infidelity and divorce initiation (e.g., respondent commits infidelity and spouse initiates divorce). When there was no reported spousal infidelity, and the respondent initiated divorce, they were less depressed than in all other combinations.

Clearly, these findings are from studies that commonly used clinical samples and retrospective methods. Given that the goal of this study was to examine the effects of infidelity on individual and marital outcomes prospectively, I hypothesized that after experiencing infidelity in their marriage, participants will be more likely to report feelings of depression compared to their pre-infidelity reports (H1) and compared to individuals not experiencing infidelity (H2). I also hypothesized that individuals who report that only their spouse committed

infidelity will be more likely to report feelings of depression than those who report only committing infidelity themselves (H3).

Personal satisfaction. No studies were found that examined the effects of infidelity on personal satisfaction. In fact, Allen and colleagues (2005) pointed to the overall lack of research on the effects of infidelity on the individual. However, based on the findings that link infidelity with personal guilt, depression, lower self-esteem, lower trust, and negative feelings about self, I hypothesized that individuals who report infidelity will also report less personal satisfaction afterwards compared with their pre-infidelity levels of personal satisfaction (H1) and compared to those not experiencing infidelity (H2).

Effects of Infidelity on Marriages

According to a recent review (Allen et al., 2005), the most commonly noted marital consequences following infidelity include marital distress, domestic violence, and divorce. This is not surprising considering Amato and Previti’s (2003) finding that infidelity was the most cited reason for divorce, and early on Daly and Wilson (1988) reported that infidelity was commonly linked with subsequent domestic abuse. Assuming that not all couples end their marriages, I included the effects of infidelity on marital instability as a seldom examined yet important additional relational outcome.

Marital distress. Overall, the research links infidelity with marital distress. For example, Charny and Parnass (1995) explored whether marriages benefited or suffered after infidelity by asking 62 practicing therapists about a specific case of infidelity with which they were familiar. Of the couples considered by the therapists, 44% remained married but were still perceived to be in great distress, according to the therapists’ reports. When asking individuals, infidelity is associated with higher levels of stress (e.g., Atkins, Eldridge et al., 2005; Gordon et al., 2004) and lower dyadic adjustment. Based on these limited findings, I hypothesized that individuals experiencing infidelity will report higher subsequent marital distress compared to their pre-infidelity levels (H1) and compared to individuals not experiencing infidelity (H2).

Domestic violence. Using the Standard Cross-cultural Sample from Murdock and White (1969), supplemented with more recent ethnographic data from 66 cultures, Jankowiac, Nell, and Buckmaster (2002) explored men’s and women’s responses to infidelity. Men were more likely

than women to resort to physical violence (88% to 64%, respectively), but both were highly likely to show violence. Other research about the murder of wives by husbands in Ghana (Adinkrah, 2008) showed that the suspicion of infidelity was the most commonly cited reason (31.7%). Similar research of 1,681 abuse incidents at a U.S. Army base (McCarroll et al., 2008) found that 15.5% were precipitated by infidelity. Although findings are limited, these studies show that infidelity has the potential to engender post-infidelity violence. Thus, I hypothesized that individuals who experience infidelity are more likely to report occurrences of domestic violence afterwards than beforehand (H1) and compared to individuals who do not experience infidelity (H2). I also hypothesized that cases in which the wife commits infidelity will be more susceptible to such reports than those in which only the husband commits infidelity (H3).

Divorce and marital instability. Several studies found a link between infidelity and divorce. For example, using a sample of 300 engaged couples in the 1930s, Kelly and Conley (1987) explored the precursors to divorce and satisfaction, following the couples over 5 decades and 3 data collections. They found that infidelity was cited as a major reason for divorce in 16 (32%) of the 50 cases. Across all of the societies in his study, Betzig (1989) also found that infidelity was a more common cause of divorce than every other cause except sterility, a finding that was true of Americans in a recent study (Amato & Previti, 2003). In fact, about 1/3 of the societies allowed divorce following the wife’s infidelity, only 2 allowed divorce following only husband infidelity, and 25 allowed divorce by either partner. Further, Amato and Rogers (1997) found that if either the husband or wife reported problems due to infidelity in 1980, their odds of divorce by 1992 increased significantly. Similarly, Cano et al. (2002) found an association between marital stressors, one of which was infidelity, and marital dissolution.

Veroff, Douvan, and Hatchett (1995) used a random sample of 317 White and 377 Black couples applying for marriage licenses to determine factors affecting marital instability in the first years of marriage. They found that a wife’s infidelity significantly predicted marital instability for both groups of couples. Although the relationship was not as strong, a husband’s infidelity was related to marital instability for White couples, but did not affect Black couples.

Although the literature examining marital instability (divorce proneness) following infidelity is limited, links between instability and divorce have been reported (Booth, Johnson, White, & Edwards, 1985). Applied to the current study, I hypothesized that individuals

13 experiencing infidelity will report higher levels of marital instability than before infidelity (H1). Additionally I hypothesized that individuals experiencing infidelity will report higher levels of marital instability than those not experiencing infidelity (H2), and more frequent divorce than those not experiencing infidelity (H2), even after controlling for earlier marital happiness, sexual satisfaction, religious service attendance, race, marital duration, and income.

Still other studies looked at the link between gender, infidelity, and divorce. As early as 1953, Kinsey, Pomeroy, Martin, and Gebhard examined data from a study of 263 divorced participants whose former spouses committed infidelity. They found that 51% of males reported that their spouse’s infidelity was a major factor in the decision to dissolve the marriage. Comparatively, only 27% of females reported that their ex-husband’s infidelity was a major factor. Later, Buunk (1987) used a matched sample to compare individuals whose relationships dissolved after infidelity with individuals whose relationships remained intact. He found that men were three times more likely to attribute their breakup to their partner’s infidelity rather than their own infidelity. Although women showed the same trend, the difference was much smaller. Lawson (1988) also reported that men’s infidelity, specifically the number and timing of affairs, did not affect their chances of divorce. However, if a woman had even one infidelity incident, she was more likely to experience marital dissolution. Given these findings, I hypothesized that those individuals whose marriages remain intact post-infidelity are less likely to have experienced a wife’s infidelity compared to those who divorce post-infidelity (H3).

Interestingly, there is little research on couples who remain together following infidelity, and none could be found that examines the similarities and differences among couples who experience infidelity and either remain together or divorce. Such research can inform those who work with couples therapeutically following such reports. Studies using clinical samples show that full disclosure of the infidelity may increase the odds of staying together (Glass, 2002; Gordon et al., 2004) because of unanticipated positive outcomes (e.g., a closer marital relationship, becoming more assertive, realizing the importance of good marital communication; Olson et al., 2002). Further, Charny and Parnass (1995) found that 66% of couples who experienced infidelity remained married throughout their time in therapy, but only 15% actually continued their marriage “in a context of growth.” Given this dearth of information on the aftermath of infidelity experiences, I explored individual and relational consequences among

14 those reporting infidelity and compared those who dissolve their marriage with those who do not, controlling for known factors (e.g., marital happiness, sexual satisfaction, religious service attendance, race, marital duration, and income) associated with both infidelity and divorce. For example, African Americans are more likely to divorce than are non-Hispanic Whites and Hispanics, and having less income is also linked with a higher probability of divorce (Amato, 2010). Similarly, relationships characterized by short marital duration (Amato) are also more prone to divorce.

Hypotheses

H1) Individuals experiencing infidelity will be more likely to report feelings of depression, lower personal satisfaction, higher marital distress, increased occurrences of domestic violence, and higher marital instability compared to their pre-infidelity reports. H2) Individuals experiencing infidelity will be more likely to report feelings of depression, lower personal satisfaction, higher marital distress, increased occurrences of domestic violence, higher marital instability, and be more likely to divorce compared to individuals not experiencing infidelity, controlling for earlier marital happiness, sexual satisfaction, religious service attendance, race, marital duration, and income. H3) Among the infidelity group, individuals who report that only their spouse committed infidelity will be more likely to report feelings of depression, controlling for marital happiness, sexual satisfaction, religious service attendance, race, marital duration, and income. H4) Cases in which the wife commits infidelity will be more susceptible to domestic violence and divorce than those in which only the husband commits infidelity, controlling for earlier marital happiness, sexual satisfaction, religious service attendance, race, marital duration, and income.

CHAPTER 3

METHODS

Data

The data were drawn from the Panel Study of Marital Instability over the Life Course (Booth, Johnson, White, & Edwards, 1981). The study covers 20 years and involves six waves of data collection (1980, 1983, 1988, 1992, 1997, and 2000). Targeting all married individuals under the age of 55 in households in the U.S. in which both spouses were present with a telephone, random digit dialing was used to obtain the original sample. Of those contacted, 78% completed interviews for a final sample of 2,033 married individuals in 1980. Compared to the U.S. Census, the sample was representative in age, race, household size, children, and region of the country (Booth, Amato, & Johnson, 2001).

Sample

The study sample included all available individuals minus 105 who reported an infidelity experience at Wave 1 (N = 1,928). Because the purpose of this study was to examine the effects of marital infidelity over time, these 105 respondents were removed because they had no prior assessment providing the needed baseline information. A subsample of 120 was used in most analyses and is comprised of individuals who reported an infidelity experience after Wave 1 (they had baseline information at Wave 1), leaving a comparison (non-infidelity) group of 1,808 with the same baseline information. To determine the needed sample size for the analyses, Cohen’s (1992) standards were applied. With a medium effect size, an alpha level of .05, power of .95, and 7 independent variables (one predictor variable and six control variables in each analysis), a sample of 102 participants is required for regression. Therefore, in the analyses using the full sample (N = 1,928), non-infidelity subsample (n = 1,808) and the infidelity subsample (n = 120), all samples are adequately large to have sufficient statistical power in predicting effects. Demographic characteristics for the total sample (see Table 1) and within the infidelity group (see Table 2) are presented in Appendix A.

Measures

Several measures were available in the data to assess the various factors of interest in the current study. (See a summary of measures presented in Appendix B.) The dependent variables represent individual (depression and personal satisfaction) and relational outcomes (marital distress, domestic violence, marital instability, and divorce) following reports of infidelity. Participants were measured at two periods: short term (reported during same wave when infidelity experience was noted) and long term (reported one wave later than that reported in the short term). Infidelity was the key independent variable. Theoretically, stress resulting from infidelity can be exacerbated by several chronic strains (Pearlin et al., 1981); therefore, I included the following as control variables representing chronic strain: prior lack of marital happiness and sexual satisfaction. For respondents reporting infidelity, these were taken from the wave prior to reported infidelity. For those not reporting infidelity, marital happiness and sexual satisfaction were measured as an average of their responses across all six waves. Also included were several demographic variables known to be correlated with infidelity (i.e., religious service attendance, race, and income from Wave 1; marital duration prior to reported infidelity for H1, H3, and H4, and at Wave 1 for all respondents in H2).

Because infidelity could occur at any time over the course of the study, pre- and post- infidelity measures of the outcome variables were created. For instance, Wave 3 depression (post) was compared to Wave 2 depression (pre) for a respondent reporting infidelity for the first time at Wave 3. To test the long-term effects of infidelity, post-assessments were taken from the next wave beyond that in which infidelity was reported. In the above example, the long-term measure of depression would be at Wave 4. This established a pre-infidelity score and post- infidelity scores for each individual, allowing the presence of depression before infidelity to be compared with the effects of infidelity on the outcome variables of interest at two time points: shortly after infidelity is reported and next data collection point. The difference between points varied from 3 to 5 years. Therefore, for the hypotheses predicting change in any outcome following reported infidelity (H1, H3, and H4), each of the 120 respondents have a pre- and two post-infidelity score for all outcome variables, specific to when they first reported infidelity. Importantly, this represents individuals who reported infidelity at different points in their marriages. The lone exception was divorce, because the study design did not allow respondents

to report infidelity and divorce at the same wave. Therefore, short-term divorce was measured by examining whether a respondent reported divorce one wave after reporting infidelity (Wave 4 in the above example). Long-term divorce was measured by examining whether a respondent ever divorced at any time after reporting infidelity. In the above example, this would capture a divorce by Waves 5 or 6 for someone who had not divorced by Wave 4.

For the hypothesis suggesting that infidelity predicts significant group differences in the outcomes (H2), the outcome variables for respondents reporting infidelity were measured the same as in H1, H3, and H4. For those not reporting infidelity, each outcome variable was represented as an average of their individual response across the six waves, except for divorce, which was measured by whether a respondent ever divorced across the six waves.

Independent variable. Infidelity was the only stressor assessed. At each wave, participants were asked, “Have you had a problem in your marriage because one of you has had a sexual relationship with someone else?” The answers were no; yes, spouse; yes, self; or both. Responses were then recoded to no (0) and yes (1). For Hypothesis #3, spouse infidelity was coded as yes, self or both (0) and spouse only (1). For Hypothesis #4, wife infidelity was coded as husband only (0) and wife only or both (1).

Dependent variables. Two measures were used to assess the individual factors of interest. A single-item measure of depression was available at each wave. Participants were asked whether he or she had experienced feeling extremely unhappy, nervous, irritable, or depressed since the last survey. Responses were yes or no. Similarly, to measure personal satisfaction, at each wave a single item was available asking, “Taking all things together, how would you say you are these days? Would you say you are very happy, pretty happy, or not too happy?” Possible responses ranged from very happy (1) to not too happy (3). Personal satisfaction was reverse coded for ease of interpretation, such that higher scores reflect more personal satisfaction.

Four relational outcomes were assessed. Marital distress was measured using a composite score of responses to 12 items. Respondents were asked, “Have you had a problem in your marriage because one of you gets angry easily, has feelings that are easily hurt, and is jealous, as examples. Responses were yes (1) or no (0), and a count was made of the yes

18 responses with a possible range of 0-12; higher scores indicate greater marital distress. This scale had a Chronbach’s alpha of .71 for the study sample, similar to that reported for the original study (α = .73; Booth, Johnson, Amato, & Rogers, 2001).

Again at each wave, using a single item participants were asked about incidences of domestic violence: “In many households bad feelings and arguments occur from time to time. In many cases people get so angry that they slap, hit, push, kick, or throw things at one another. Has this ever happened between you and your (husband/wife)?” Respondents answered either yes (1) or no (0).

Marital instability was assessed with 13 items asking about the frequency and timing of thoughts and behaviors that reflect proneness to divorce developed by Booth, Johnson, and Edwards (1983). At each wave, respondents were asked to answer yes (1) or no (0) to items, such as: “Have you thought your marriage might be in trouble within the last 3 years?” “As far as you know, has your spouse ever thought your marriage was in trouble?” “Have you talked with family members, friends, clergy, counselors, or social workers about problems in your marriage within the last 3 years?” “Have you or your (husband/wife) filed for a divorce or separation petition?” Responses were summed, and higher scores indicated greater marital instability. For the current study, this scale had a Chronbach’s α of .89, which is comparable to that reported by the original authors of the study (α = .91; Booth et al., 2001). To demonstrate construct validity, Edwards, Johnson, and Booth (1987) used a panel of experts to rank the items in order of seriousness with regard to the possibility of subsequent divorce. Results showed a .80 correlation between the rankings and those who scored highly on the marital instability index. By correlating with known predictors of divorce in the same way actual divorce does, the index also showed convergent validity (Edwards et al.). Finally, researchers demonstrated the index has predictive validity by comparing participants’ 1980 scores with the 1983 dissolution rates. Of those who reported no signs of marital instability in 1980, only 3% had divorced by 1983. In contrast, those who scored high on the 1980 instability index had a divorce rate of 27% (Edwards et al.).

Divorce was measured at each wave by asking participants if they had divorced or separated permanently since the previous interview. Responses were no (0) and yes (1). In addition, divorce was measured using a pre-constructed composite item indicating whether a

respondent had ever divorced over the course of the study; this included those who had divorced at any time after reporting experiencing infidelity.

Control variables. Several variables identified as chronic strains were included here as control variables. Marital happiness was measured using a composite score of responses to 10 items assessing participants’ happiness with different aspects of their marriage. Respondents were asked to indicate whether they were not too happy (1), pretty happy (2), or very happy (3) on five items: extent of understanding received from spouse, amount of love received, extent of agreement about things, spouse as someone who takes care of things around the house, spouse as someone to do things with, and spouse's faithfulness. Next, respondents were asked, “Taking all things together, how would you describe your marriage? Would you say that your marriage is very happy (3), pretty happy (2), or not too happy (1)?” Another item asked, “Compared to other marriages you know about, do you think your marriage is better than most (3), about the same as most (2), or not as good as most (1)?” Respondents were also asked, “Comparing your marriage to three years ago, is your marriages getting better (3), staying the same (2), or getting worse (1)?” Finally, respondents were asked, “Would you say the feelings of love you have for your spouse are extremely strong, very strong, pretty strong, not too strong, or not strong at all?” (recoded by combining the lower two and higher two scores, resulting in three categories: not strong (1), pretty strong (2), and very strong (3). Possible scores from this combination of items range from 10 to 30, with higher scores indicating greater marital happiness. For the between groups comparisons (H2), marital happiness was measured by all respondents’ Wave 1 responses. For the within groups comparisons (H3 and H4), marital happiness was measured at one wave prior to the report of infidelity for each respondent. For this study sample, this scale had a Chronbach’s α of .84, similar to that of the original authors (α = .87; Booth et al., 2001) Sexual satisfaction was measured using one item that asked, “How happy are you with your sexual relationship?” Available responses were not too happy (1), pretty happy (2), and very happy (3). As with marital happiness, for between groups comparisons (H2), Wave 1 sexual satisfaction was used for all respondents. For within group comparisons (H3 and H4), sexual satisfaction was measured at one wave prior to the report of infidelity for each respondent. Religious service attendance was measured using one item at Wave 1 that asked, “How often do you and your spouse attend church together?” Possible answers were weekly or more (1); once a month or more, but less than weekly (2); once a year or more, but less than monthly

(3); or less than once a year or never (4). This item was reverse coded for ease of interpretation, such that higher scores reflect more frequent religious attendance.

Marital duration of the respondent’s current marriage was taken from the marital history information in Wave 1 asking the age of respondent when he/she got married. This was subtracted from the respondent’s age at Wave 1 to calculate years married.

The measure of total family income was also taken from Wave 1 when respondents were told, “I am going to mention a number of income categories. When I mention the category which describes your total family income in 1979, please stop me.” Categories ranged in increments of $5,000 from Under $5,000 to $60,000 or more. The originators of the study developed a constructed variable that represents the midpoint of each interval reported, and $65,000 was used for the final interval. This constructed variable was used in the current study. Respondents reported their race as White, non-Hispanic; White, Hispanic; Black; and Other. Due to the consistent finding that being African American is associated with more frequent infidelity compared with all other races (Amato & Rogers, 1997; Treas & Giesen, 2000; Whisman & Snyder, 2007), this variable was coded as Black = 1 and Else = 0.

Data Analysis

Hypothesis 1

To test the within group effects (H1) of infidelity on the outcome variables, I used repeated measures logistic regression for the categorical level dependent variables (depression and domestic violence) and paired t tests for interval level dependent variables (personal satisfaction, marital distress, and marital instability). This allowed me to assess changes in the outcome from pre-infidelity levels to post-infidelity (both short-term and long-term) levels. For the repeated measures logistic regression, I report the betas (β), standard errors of the beta (SE), Wald’s chi-square values (χ2), and significance (p). The beta shows the direction and strength of the association between the independent and dependent variables, and the Wald’s chi-square determines whether this association is significant. For the paired t tests, I report the t values, means, standard deviations, and significance (p).

Hypothesis 2

To test Hypothesis 2, the effects of infidelity on the outcome variables (short-term and long-term) that are interval level, I used hierarchical multiple regression. This allowed me to assess the unique effects of infidelity, entering the control variables first (marital happiness, sexual satisfaction, religious service attendance, race, marital duration, and income) and then adding the independent variable (infidelity). Additionally, because depression and domestic violence were computed as a composite average of scores across all six waves among the non- infidelity group, these variables were treated as interval level variables with a possible range from 0 to 1 in these analyses.

Because divorce is a dichotomous variable, I conducted a binary logistic regression, entering the control variables first (marital happiness, sexual satisfaction, religious service attendance, race, marital duration, and income). Then I added the independent variable (infidelity).

Hypotheses 3 and 4

Similarly, to examine whether spousal infidelity predicts future depression (H3) and whether wife’s infidelity predicts future domestic violence and divorce (H4), I conducted six binary logistic regressions using both short-term and long-term indicators for each outcome variable. In each analysis, the control variables were entered first, followed by the independent variable. I report the unstandardized logistic coefficients (B), standard error (SE B), and the odds ratio (eB). Although the coefficients reveal the strength and significance of the relationship between the two variables, they are difficult to interpret (Crosnoe, Mistry, & Elder, 2002). Therefore, the odds ratio is included to more easily interpret the relationship. A significant odds ratio with a value below 1 indicates that the independent variable predicts a reduction in the likelihood of the dependent variable having a value of 1. A significant odds ratio above 1 indicates an increase. Subtracting 1 from the odds ratio and multiplying it by 100 gives the percentage chance that the dependent variable will have a value of 1. For example, if the odds ratio for divorce regressed on infidelity is 2.50, those who report infidelity would be reported a as being 150% more likely to divorce than those not reporting infidelity. If the odds ratio was 0.75, those reporting infidelity would be 25% less likely to divorce.

Missing Data

Missing data, whether due to non-responsiveness or attrition, is typical in national longitudinal data sets (Kazdin, 2003). However, due to the recoding of variables, for the infidelity subsample (n = 120) there were fewer than five missing cases for each variable, including the pre- and post-infidelity measures. Therefore, listwise deletion was used.

CHAPTER 4

RESULTS

In this chapter, results from all preliminary analyses and hypothesis testing are presented. The statistical results are available in table form in Appendix A.

Preliminary Analyses

Preliminary analyses were performed prior to conducting the regression analyses to address potential issues of multicollinearity, skewness, and kurtosis. A bivariate correlation matrix of all variables in the study was examined for highly correlated variables as an indication of issues of multicollinearity (see Table 3). Although several variables were significantly correlated, none the correlations approached the cutoff of .80 which would raise concerns regarding multicollinarity (Berry & Feldman, 1985; Bryman & Cramer, 1994).

I then examined the skewness and kurtosis values. Typically skewness values in the ±2 range and kurtosis values in the ±7 range are considered acceptable (Pedhazur, 1997). Only one variable was outside these ranges. Infidelity had a skewness of 3.63 and a kurtosis of 11.17. However, because (a) the 105 participants who reported experiencing infidelity at Wave 1 were omitted to provide prospective data, and (b) infidelity is a rare-occurring event, this variable was expected to be skewed. Therefore, it was determined that the variables in this study are assumed to be normally distributed.

Testing the Hypotheses

Hypothesis 1. For the categorical outcome variables in Hypothesis #1 (Individuals experiencing infidelity will more likely to report feelings of depression, lower personal satisfaction, higher marital distress, increased occurrences of domestic violence, and higher marital instability compared to their pre-infidelity reports), repeated measures logistic regressions were used to determine whether the presence of infidelity was associated with a higher probability of being depressed or reporting domestic violence. For the continuous outcome variables (personal satisfaction, marital distress, and marital instability), paired-samples t tests were used to detect significant differences in the pre- and post-infidelity scores.

According to the results presented in Table 4, infidelity was associated with a higher probability of respondents reporting feelings of depression shortly afterwards compared to their pre-infidelity reports (B = .74, Wald χ2 = 10.50, p < .01). However, infidelity was not associated with respondent’s depression in the longer term (B = -.03, Wald χ2 = .02, p > .05). These results partially support the hypothesis that individuals reporting infidelity will be more likely to report feelings of depression compared to their pre-infidelity reports. Although infidelity had little to no effect on longer-term reports of depression, it did significantly increase the odds of reporting feelings of depression more immediately (in the short term).

According to the results presented in Table 5, infidelity was associated with a higher probability of respondents reporting occurrences of domestic violence shortly afterwards compared to their pre-infidelity reports (B = .62, Wald χ2 = 8.73, p < .01). However, infidelity was not associated with respondents’ reported domestic violence in the longer term (B = -.19, Wald χ2 = .44, p > .05). These results partially support the hypothesis that individuals reporting infidelity will be more likely to report the occurrence of domestic violence compared to their pre-infidelity reports. Although infidelity had little to no effect on the long-term occurrence of domestic violence, it did significantly increase the odds of reporting an occurrence of domestic violence in the short term.

Results for personal satisfaction, marital distress, and marital instability are shown in Table 6. Regarding personal satisfaction, results indicate a significant difference between pre- infidelity (M = 2.31, SD = .56) and more immediate post-infidelity (M = 2.15, SD = .67), t (118) = 2.33, p < .05, showing a decrease in short-term personal satisfaction. For marital distress, results also indicate a significant difference between pre-infidelity (M = 3.59, SD = .2.91) and immediate post-infidelity (M = 6.09, SD = 2.90), t (104) = -8.30, p < .001, showing an increase in short-term marital distress. For marital instability, results also indicate a significant difference between pre-infidelity (M = .48, SD = .41) and immediate post-infidelity (M = .78, SD = .44), t (111) = -7.40, p < .001, again showing an increase in short-term marital instability. Interestingly, however, when comparing pre-infidelity levels of personal satisfaction, marital distress, and marital instability with their longer-term post-infidelity levels, none of the t tests were significant. Thus, these results provide partial support for the hypothesis, as individuals

experiencing infidelity reported significantly higher levels of personal satisfaction, marital distress, and marital instability in the short term, but not in the longer term.

In summary, all five dependent variables significantly differed from their pre-infidelity levels in the expected direction in the short term. Individuals experiencing infidelity were more likely to report feelings of depression, lower personal satisfaction, higher marital distress, increased occurrences of domestic violence, and higher marital instability. Such was not the case when these outcomes were examined over a longer period.

Hypothesis 2. For Hypothesis #2 (Individuals experiencing infidelity will be more likely to report feelings of depression, lower personal satisfaction, higher marital distress, increased occurrences of domestic violence, higher marital instability, and be more likely to divorce compared to individuals not experiencing infidelity, controlling for marital happiness, sexual satisfaction, religious service attendance, race, marital duration, and income), hierarchical multiple regression and binary logistic regression were used, depending on the nature of the outcome variable of interest.

The results in Table 7 show that 9% of the variance (R2) in short-term depression was explained by the various control variables in the model, and an additional 1% was explain by reported infidelity experience, F-change (1, 1840) = 27.94, p < .001. As predicted, infidelity was significantly associated with short-term depression, β = .12 (p < .001), second only to marital happiness (β = -.21) and slightly stronger than marital duration (β = -.09) in its explanatory power. Taken together, these results indicate that infidelity does explain higher levels of short- term depression following infidelity, even when controlling for other variables. However, the R2 values for both models were small, indicating that most of the variance in short-term depression was left unexplained. Importantly, marital happiness and marital duration serve as significant constraints for short-term depression, with marital happiness being the most significant indicator. The results in Table 8 show that 8% of the variance (R2) in long-term depression was explained, and the addition of infidelity experience contributed less than 1%, F-change (1, 1826) = .31, p > .05. Infidelity was also not significantly associated with long-term depression (β = - .01). As with short-term depression, marital happiness (β = -.24) remained the strongest link in the model with marital duration and religious service attendance also being significant. The results in Table 9 show that 28% of the variance (R2) in short-term personal satisfaction was

26 explained, with the addition of infidelity accounting for less than 1% , F-change (1, 1839) = 14.65, p < .001. Yet, as predicted, infidelity was significantly associated with immediate post- infidelity personal satisfaction, β = -.08, p < .001, second only to marital happiness (β = .45) in its predictive power. Sexual satisfaction, religious service attendance, marital duration, and income all were significantly related to short-term personal satisfaction. Marital happiness appears to be a stronger statistical indicator of personal satisfaction than infidelity, with infidelity, sexual satisfaction, religious service attendance, marital duration, and income each adding far less to the variance explained. Taken together, these results indicate that the experience of infidelity is not significantly related to a decrease in personal satisfaction, although it accounts for a small amount of the variance explained. The results reported in Table 10 indicate that 28% of the variance (R2) of long-term personal satisfaction was explained, but the addition of infidelity did not significantly add to the equation, F-change (1, 1819) = 2.30, p > .05. As with short-term personal satisfaction, marital happiness (β = .46) remained the most significant variable in the model, with sexual satisfaction, religious service attendance, race, marital duration, and income also being significant. According to these results, infidelity is not significantly related to personal satisfaction in the long term when controlling for the other variables. The results in Table 11 show that 24% of the variance (R2) in short-term marital distress was explained by the control variables, and infidelity accounted for an additional 12%, F-change (1, 1826) = 343.66, p < .001. As predicted, infidelity was significantly associated with higher levels of marital distress, β = .35, p < .001, second only to marital happiness (β = -.41). In addition, marital duration (β = -.14), religious service attendance (β = -.08), and race (β = .05) were all significant contributors to the model. Together, these results indicate that infidelity is significantly associated with increases in marital distress soon after infidelity is experienced, but marital happiness retained a strong impact on short-term marital distress. The findings reported in Table 12 indicate that 26% of the variance (R2) of marital distress over a longer period was explained by the control variables, and infidelity only added 1% to the variance explained, F-change (1, 1785) = 25.70, p < .001. Although infidelity explained little of the variance, it was statistically significant (β = .11, p < .001). As with short- term marital distress, marital happiness (β = -.46) remained the strongest variable in the model, and marital duration (β = -.14) was a slightly stronger variable than infidelity as well, with

religious service attendance and race also being significant. According to these results, infidelity was significantly associated with more marital distress over a longer period; however, it does not add much explanatory power. The results in Table 13 show that 9% of the variance (R2) in short-term domestic violence was explained, with infidelity accounting for an additional 2%, F-change (1, 1840) = 54.99, p < .001. As predicted, infidelity was significantly linked with more frequent domestic violence, β = .15, p < .001, second only to earlier marital happiness (β = -.22). Race (β = .08), and marital duration (β = -.05) and income (β = -.05) were also significant variables in the final model. These results indicate that infidelity is significantly related to an increase in domestic violence, even after controlling for the other variables. However, the overall variance explained was small, suggesting that other variables not included here likely explain most of the variance in short-term domestic violence. Similarly, the results in Table 14 show that 9% of the variance (R2) in long-term domestic violence was explained, with infidelity adding less than 1%, F-change (1, 1798) = 7.90, p < .01. As predicted, infidelity was significantly associated with domestic violence over a longer period, β = .06, p < .01, but earlier marital happiness (β = -.23), race (β = .07), marital duration (β = - .05), and income (β = -.05) made larger or similar contributions to the final model. These results indicate that infidelity is statistically related to an increase in long-term domestic violence, even after controlling for other variables. However, the variance explained by infidelity was extremely small, as was the overall variance explained, again suggesting that other variables not included in the model likely account for long-term domestic violence. Thus, in practical terms infidelity exerts little effect on long-term domestic violence. The results in Table 15 show that 37% of the variance (R2) in short-term marital instability was explained by the control variables, and that infidelity accounted for an additional 10%, F-change (1, 1820) = 340.13, p < .01. As predicted, infidelity was significantly associated with marital instability, β = .32, p < .001, second only to earlier marital happiness (β = -.51). Marital duration (β = -.26), religious service attendance (β = -.07), and income (β = .04) were also significant variables. Although marital happiness appears to have the strongest impact, these results indicate that infidelity makes a difference in explaining short-term marital instability, even when controlling for other variables.

The results in Table 16 show that 38% of the variance (R2) in long-term marital instability was also explained, but infidelity added only 1%, F-change (1, 1800) = 25.70, p < .001. As predicted, infidelity was significantly linked with marital instability, β = .09, p < .001, but earlier marital happiness (β = -.55), marital duration (β = -.26), religious service attendance (β = -.08), and income (β = .05) made larger or similar contributions to the final model. These results indicate that although infidelity is significantly related to an increase in marital instability over time, even after controlling for other variables, it is of limited practical importance, as the effects of infidelity were small and explained mostly by other variables in the model. The results in Table 17 indicate that infidelity (eB = 1.33) was not significantly associated with an increased propensity for divorce among those who reported earlier infidelity, and only during the more immediate post-infidelity assessment (divorced at the wave when earlier infidelity was reported). However, when assessing whether respondents had ever divorced, the results indicate that infidelity (eB = 2.18, p < .01) was significantly linked with an increased the risk for divorce. That is, those who reported infidelity were 118% more likely to divorce sometime later compared to those who did not report infidelity. This indicates that those who experienced infidelity (recall that they are individuals who remained married for at least one wave post-infidelity) are not more likely to divorce in the short term, but they are more likely to divorce over time. In summary, similar to the within group analyses in Hypothesis #1, five of the six short- term dependent variables were significantly associated with infidelity experiences. Infidelity was associated with short-term outcomes in the expected ways: an increase in depression, distress, domestic violence, and marital instability, and a decrease in personal satisfaction. The effects on the outcomes longer term were less clear. Infidelity was not associated with long-term depression or personal satisfaction. Although infidelity was significantly linked with indicators of long-term marital distress, domestic violence, and marital instability, the strength of these associations was weaker than their short-term counterparts, and infidelity added little to the variance explained. In fact, infidelity was not related to more immediate divorce, but it was related to whether respondents would ever divorce. Therefore, this hypothesis was partially supported. Hypothesis 3. For Hypothesis #3 (Among the infidelity group, individuals who report that only their spouse committed infidelity will be more likely to report feelings of depression, controlling for marital happiness, sexual satisfaction, religious service attendance, race, marital

duration, and income), binary logistic regression was used. Contrary to expectations, the results shown in Table 18 indicate that spousal infidelity (eB = .88) does not significantly associated with a higher propensity to experience depression in depression, either in the short term or longer term (Results not shown in table format, because findings were similarly non-significant.) Therefore, the hypothesis was not supported by the results.

Hypothesis 4.The results of testing Hypothesis #4 (Cases in which the wife commits infidelity will be more susceptible to domestic violence and divorce than those in which only the husband commits infidelity, controlling for marital happiness, sexual satisfaction, religious service attendance, marital duration, income, and race) appear in Table 18. These results indicate that wife infidelity was not significantly associated with an increase in the probability of future reports of domestic violence or divorce in the short term (eB = .88 and .33, respectively). Similarly, wife infidelity was not linked with a higher probability of reports of domestic violence over time. (Results not shown in table format, because findings were similarly non-significant.) However, wife infidelity was associated with whether participants ever divorced (eB = .38, see Table 18), but in the opposite direction than expected. That is, marriages in which the wife committed infidelity (she or both) were 62% less likely to ever divorce compared to marriages in which only the husband committed infidelity. Because the significant results were limited and not in the hypothesized direction, this hypothesis was not supported by the findings.

Summary of Results

In summary, Hypothesis 1 was partially supported by the findings. Individuals experiencing infidelity reported significantly higher likelihood of more immediate depression, marital distress, domestic violence, and marital instability after experiencing infidelity than reported prior to infidelity. They also reported significantly lower levels of personal satisfaction compared to their pre-infidelity levels, again only in the short term. However, over the longer term individuals experiencing infidelity did not report significantly higher levels of these outcomes compared to their pre-infidelity reports. Hypothesis 2 was also supported by the results. Here, infidelity made a significant contribution to explaining short-term future depression, personal satisfaction, marital distress, domestic violence, and marital instability, above that explained by the control variables. Regarding divorce, infidelity was not linked with more immediate divorce, but it was

significantly linked with whether the couple would ever divorce. However, the little of the variance was explained in the final models for depression and domestic violence, suggesting lower confidence in the short-term impact of infidelity on these outcomes. No support was found for either Hypothesis 3 or 4. Spousal infidelity did not significantly predict future depression either in the short-term or longer-term, and wife infidelity did not predict future either short- or long-term domestic violence or divorce.

CHAPTER 5 DISCUSSION

The purpose of this study was to examine the trajectory of marriages following reported infidelity using general stress theory (Pearlin et al., 1981). Longitudinal data from 1980 to 2000 were used to investigate this trajectory across a nationally representative sample of married individuals. In this chapter, I discuss the findings of this study in light of previous research and the theoretical orientation used, addressing the effects of infidelity on individual and relational outcomes and their associated hypotheses. I also discuss the notable limitations, clinical implications, and provide suggestions for future research.

Influence of Infidelity on Individual and Relational Outcomes

Individual Outcomes

Depression. Three sets of findings are relevant to the individual outcome of depression. I found that those who experienced infidelity were more likely to report depression shortly thereafter. In addition, controlling for certain other variables, depression was more common among those experiencing infidelity than those not reporting infidelity. These findings support previous research suggesting that individuals experiencing infidelity are significantly more likely to experience some form of depression (Beach et al., 1985; Cano & O’Leary, 2000; Gordon et al., 2004). Because the earlier studies were cross-sectional and lacked information on depression prior to infidelity, they could not suggest with confidence that a source of depression was reported infidelity. Also, they included individuals who reported experiencing infidelity within the past year. In the current study, infidelity could have occurred up to five years prior to the post-infidelity assessment of depression, suggesting that the individuals included here might have experienced depression over a longer period. Regarding potential longer-term effects of infidelity on depression and similar to the findings from the only prospective study of infidelity and depression (Sweeney & Horwitz, 2001), findings from the current study did not show that infidelity was linked with long-term reports of depression (6 – 10 years later). Lastly, little variance in depression was explained by infidelity experience, suggesting that other variables not included may play an important contributory role.

Finally, individuals who reported that the spouse committed infidelity were not significantly more likely to report feelings of depression. Because the hypothesis regarding those who reported only their spouses committed infidelity was based on a study that looked at the interactional effects of infidelity and initiation of divorce (Sweeney & Horwitz, 2001), the results might indicate that divorce initiation plays a larger role in depression for spouses betrayed by their partner. Because this sample excluded those who divorced immediately after infidelity, the relationship between spousal infidelity and depression may be underestimated here. Additionally, because these analyses were limited to 60 individuals, the statistical power necessary to detect effects may be lacking.

Personal satisfaction. Similar to the findings for depression, the results indicate that individuals who experienced infidelity reported significantly lower personal satisfaction afterwards and lower personal satisfaction than those who did not report infidelity. Also similar to depression, infidelity was not associated with personal satisfaction over a longer period. Again, marital happiness exerted more impact on both short- and long-term personal satisfaction than did infidelity experience, and sexual satisfaction, religious service attendance, marital duration, and income also affected this outcome. These findings suggest that infidelity is related to subsequent personal satisfaction, which is a previously unaddressed question in the literature.

The findings regarding both individual outcomes can be explained theoretically. General stress theory (Pearlin et al., 1981) suggests that discrete life events, or stressors (infidelity), lead to a decreased sense of self-worth (e.g., more depression and less personal satisfaction). However, stressors typically require short-term behavioral change. Because the results show that depression and personal satisfaction are influenced by infidelity, the assumption is that the resulting negative effect on individuals be more immediate than long term. Further, as individuals employ coping strategies, using resources within or outside of a relationship, these negative effects are likely to diminish and potentially return to levels similar to those beforehand, as suggested here.

Relational Outcomes

Marital distress. The findings from this study show that infidelity was significantly associated with subsequent short-term marital distress compared to pre-infidelity distress and

33 compared to non-infidelity respondents. Individuals reporting infidelity had higher levels of marital distress shortly afterwards. In fact, ¼ of the 48% of the variance in short-term marital distress was explained by infidelity alone. Overall, these results are in agreement with findings from previous cross-sectional research indicating that high levels of distress and lower levels of dyadic adjustment follow reports of infidelity (Atkins, Eldridge et al., 2005; Charny & Parnass, 1995; Gordon et al., 2004). Marital happiness also impacted marital distress, such that less happiness was linked with more distress.

The effects of infidelity on marital distress diminished over time. In fact, infidelity failed to show differences in pre- and long-term post- reports of distress, and infidelity contributed little to explaining the variance in comparison with the non-infidelity group. Instead, marital happiness was the strongest statistical predictor of later marital distress, by a wide margin.

Because infidelity is one of the most difficult life events within marriages (Geiss & O’Leary, 1981; Whisman et al., 1997), greater marital distress was an expected consequence. However, these results indicate that, for those who remain married, the relationship between infidelity and marital distress was reduced over time. Marital happiness remained important to explaining marital distress, suggesting that individuals who manage to maintain some degree of marital happiness in spite of infidelity may be more able to decrease their marital distress in the future, thus negating the long-term impact of infidelity. Theoretically, the less effect infidelity has on the chronic strain of low marital happiness, the less relational stress is caused.

Domestic violence. Individuals who experienced infidelity were more likely to report domestic violence afterwards and compared with those who did not report infidelity, but marital happiness rather than infidelity made the largest impact on domestic violence. The findings are consistent with previous research, indicating that infidelity can result in physical and sometimes deadly violence (Adinkrah, 2008; Jankowiac et al., 2002; McCarroll et al., 2008). The extant literature stems from retrospective reports of infidelity or causes of violence. The results of this study lend support for this association because of the prospective nature of the study.

Similar to marital distress, the longer term effects of infidelity on domestic violence diminished over time and explained only 1%. Importantly, no study could be found which examined the longer term effect of infidelity on domestic violence, so this finding is new to the

literature. However, because these findings do not tell us about the frequency or extent of domestic violence, inferences regarding the full effect of infidelity are difficult to make. Also, other evidence shows that domestic violence predicts subsequent divorce (Amato, 2010). Applied to these results, those experiencing episodes of domestic violence may have been excluded from the infidelity sample, because they divorced between the infidelity experience and the next data collection point, contributing to a possible underestimation of the effect.

Finally, whether the wife did or did not committed infidelity failed to predict either short- or long-term instances of domestic violence. These findings contradict those of previous research, showing that men are more likely resort to violence following infidelity (Adinkrah, 2008; Jankowiac et al., 2002). Jankowiac et al. also found that both husbands and wives were likely to show violence in response to infidelity. Because the measure of domestic violence used in the current study taps only presence of violence rather than frequency or extent, it may be less able to differentiate by sex. This sample also excluded individuals who divorced immediately following infidelity, which is potentially even more likely to happen if infidelity is associated with subsequent domestic violence.

Marital instability. Infidelity was linked with subsequent increases in marital instability in the short term, both when compared to pre-infidelity marital instability and to non-infidelity respondents; infidelity accounted for 10% of the 47% of variance explained. These results are in agreement with previous research, indicating higher levels of marital instability tend to follow infidelity experiences (Booth et al., 1985; Veroff et al., 1995). Interestingly, marital happiness and marital duration had similarly large effects on marital instability. Additional results show that this relationship diminished over time, adding little to the variance explained. Marital happiness and marital duration continued to be strong predictors of subsequent marital instability over the longer period. Taken together, these results indicate that, although infidelity was significantly linked with between group differences in long-term marital instability, the impact of earlier infidelity on marital instability lessens over time. Again chronic strains, such as marital happiness, appear to have a greater long-term impact, with discrete stressors (infidelity) affecting more immediate outcomes. Because marital instability is highly correlated with divorce (Amato, 2010), it stands to reason that those with higher initial marital instability are more likely to

35 dissolve their marriages and be excluded from these analyses, leaving a longer-term sample of individuals with lower marital instability.

Divorce. Unlike the previous outcomes, infidelity was not linked with the likelihood of subsequent divorce in the short term. However, as evident in previous research (Amato & Previti, 2003; Amato & Rogers, 1997; Betzig, 1989; Cano et al., 2002; Kelly & Conley, 1987), infidelity was associated with a dramatic increase in the likelihood of ever divorcing. Marital happiness, religious service attendance, and marital duration were significant related to the probability of divorce in both the short term and the long term. Because this sample only included those who remained married for some period following infidelity, they may possess individual or relational characteristics that make them less susceptible to divorce, at least in the short term. However, the fact that infidelity was related to an increase in the probability of divorce over the duration of the study, above and beyond the chronic strains included here, suggests the devastating effect of infidelity on marriage.

Interestingly, marriages in which the wife committed infidelity were no more likely to divorce at any time. Contrary to the literature (Buunk, 1987; Lawson, 1988; Kinsey et al., 1953), these marriages were less likely to ever divorce compared to marriages in which only the husband committed infidelity. However, the hypotheses tested here were derived from the results of two studies (Buunk; Kindsey et al.) that retrospectively asked divorced men and women what or how much they attributed their divorce to their spouse’s infidelity. These findings suggest that retrospective blame is not indicative of a statistical association between which spouse committed infidelity and subsequent divorce. The third study (Lawson) took the number and timing of affairs into account, suggesting that the measure of infidelity in the current study may have been inadequate for finding the association between a wife’s infidelity and divorce. It may also be that those who experienced infidelity and divorced before the next wave of data collection were omitted from the analysis, and they would not be asked the infidelity questions. As such, couples experiencing a wife’s infidelity might be likely to divorce more quickly, ultimately excluding them from this analysis.

Limitations

The findings of this study should be considered with caution based on several limitations. First, infidelity was assessed by asking only still-married respondents whether they experienced a problem due to either spouse having sex with someone outside their marriage. Thus, any respondent who experienced this form of infidelity, but divorced before the next interview when the primary question was asked, was omitted from the analysis. This undoubtedly reduced the number of those reporting infidelity and resulted in an infidelity group of those who elected to remain together. Finally, this omission changes the implications of this study from individuals experiencing infidelity to individuals experiencing infidelity and remaining married, at least in the short term. Thus, they might look quite different from those who divorce fairly immediately, because they may be better equipped individually and/or relationally to weather the impact of infidelity. Unfortunately there are no data to test this speculation.

Second, there was a good amount of missing data in the long-term outcome measures among those who experienced infidelity. This is likely due to three factors: respondents’ marriages ended so they were not asked the long-term relationship questions (e.g., marital distress, domestic violence, and marital instability); respondents dropped out before long-term measures were assessed; or respondents reported infidelity in the final wave, so a long-term assessment was not possible. The resulting reduced sample compromised the statistical power which might limit the ability to detect significant effects.

A third limitation is the variability in years between waves of data collection and the lack of information regarding the timing of infidelity. For example, respondents who experienced infidelity in 1981 had to remain married for two years to report infidelity and short-term effects in 1983. They had to remain married for an additional five years to report the long-term effects in 1988. Conversely, respondents experiencing infidelity in 1993 had to remain married for four years to report infidelity and short-term effects in 1997, and then stay married for an additional three years to report long-term effects in 2000. Also, because respondents were not asked when the infidelity occurred, those who experienced infidelity immediately after the 1983 survey and those who experienced infidelity immediately before the 1988 survey have their “short-term” outcome variables assessed in 1988. Theoretically, these individuals could have remained married for as long as 5 years and as few as 1 year after infidelity. Thus, there is a significant

37 amount of variation in the meaning of short or long term with some short-term measures being further removed from their infidelity experience than the long-term measures of others. Given the apparent diminishing effect of infidelity on outcomes over time, these variations may result in an inaccurate assessment of short- and long-term effects.

A fourth limitation is the way infidelity was assessed. Respondents were only asked if there was a problem due to a sexual relationship with someone else. This does not allow a more comprehensive definition of infidelity to be assessed based on the frequency of sexual encounters, physical infidelity that did not include coitus, or the occurrence of emotional infidelity.

Similarly, a fifth limitation is due to the use of secondary data that does not allow researchers to measure exactly what they intend. Many of the variables in this study, including the independent and 4 of the 6 outcome variables, were represented by single items. These single-item measures may be less reliable compared to established measures that have been repeatedly tested for validity and reliability. Because three outcomes (depression, personal satisfaction, and domestic violence) were either dichotomous or interval level with a limited range (1-3), these data did not capture the potential variation in experiences.

Lastly, several limitations stem from the sample. Sample attrition was more common among male, relatively young, and non-White respondents. These characteristics are also associated with higher than average rates of infidelity and divorce (Amato & Rogers, 1997); this may have resulted in weaker associations between infidelity and the outcome variables, especially divorce. Also, due to the large sample size, even small associations between variables were statistically significant. In several instances infidelity was a significant statistical predictor, when it added little to the variance explained in the outcome of interest. The sample also represents highly satisfied marriages with few divorces overall. Coupled with the removal of 105 participants who reported experiencing infidelity at Wave 1, the resulting sample had a particularly low rate of infidelity (6%) compared to estimates by others (Atkins et al., 2001; Burdette et al., 2007; Wiederman, 1997).

Conclusions

The findings from this study suggest that the impact of infidelity on individuals who remained married in the short term is substantial. Experiencing infidelity was linked to increased individual depression, decreased personal satisfaction, and increased marital distress, domestic violence and marital instability. However, for these same individuals, long-term reports of depression, personal satisfaction, marital distress, domestic violence, and marital instability revert to pre-infidelity levels or diminish over time. These findings contribute to the body of literature by examining consequences of infidelity beyond the two typical outcomes – marital unhappiness and divorce – and doing so prospectively using a large, nationally-representative sample. Further, the findings show that among this unique group of couples who remain together after experiencing infidelity, divorce remains common over time. As such, the results are important for researchers and clinicians.

Implications

Future Research

Although this study adds to the extant literature, future research should focus on using more comprehensive measures of infidelity. Inclusion of assessments of emotional and physical infidelity are needed, as are measures of physical infidelity not including coitus, cyber infidelity, and frequency and duration of infidelity experiences. In longitudinal studies, all participants should be included in questions about infidelity, regardless of whether their relationship has dissolved. Including these measures of infidelity would provide a broader and more complete picture of the impact of infidelity on individuals and relationships.

Further, researchers should continue to explore the short- and long-term consequences of infidelity, both on marriages that survive and those that dissolve, as well as the individual spouses in either case. As stated, infidelity researchers have primarily focused on predictors of infidelity or simply whether infidelity led to divorce. Research is needed to examine how infidelity affects individuals and couples longitudinally, if clinicians are to better prepare for clients with infidelity as the presenting problem.

To increase the confidence in the findings from the current study, these hypotheses should be tested with a larger sample of respondents experiencing infidelity. Given that the sample size of those experiencing infidelity barely met the standard for a medium effect size, a larger sample would provide more statistical power to detect effects. Additionally, in conjunction with the suggestion of asking divorced respondents if the dissolved marriage included infidelity, a larger sample would provide better estimates of the effects of infidelity on long-term outcomes.

Lastly, future research should continue to examine marriages that stay intact after infidelity to determine which individual and relational factors increase resilience to the negative effects of infidelity. This would assist clinicians in knowing how to approach couples desiring to overcome infidelity and remain married.

Practice

Clinical publications about infidelity are plentiful. However, what is lacking are clinical recommendations based on solid empirical evidence. The findings here have implication for clinicians who attempt to treat couples experiencing the aftermath of infidelity. First, the findings can be used to instill hope in clients, as there were 120 examples of relationships that survived infidelity, at least in the short term. Also, they can communicate with confidence that over time the negative effects, in terms of increased depression, decreased personal satisfaction, increased marital distress, increased domestic violence, and increased marital instability, diminish. Clinicians should also be prepared to address feelings of depression and decreased personal satisfaction in response to infidelity. Addressing these issues can improve the quality of life for individuals, and possibly improve the chances of the relationship surviving.

Given the diminishing effects of infidelity, clinicians should first focus on addressing short-term consequences in the aftermath. These findings lend credence to the Olson et al. (2002) study that identified a three-stage process following the disclosure of infidelity: (a) individuals experience an emotional roller coaster marked by intense emotions including anger, embarrassment, and sometimes violence amidst other negative emotions; (b) if the offended partner progresses and move to a stage of moratorium in which they try to make meaning of the betrayal; and (c) if they are able to progress to moratorium, they eventually begin rebuilding trust with their partner. Combined with the findings from this study, clinicians would be wise to

40 encourage clients to express their negative feelings toward their partner in a controlled environment that allows for the processing of such strong emotions. Clinicians might also want to wait until these emotions begin to diminish before trying to help clients make meaning of the infidelity and eventually helping them to rebuild their relationship. Also, clinicians should be aware of the increased likelihood of domestic violence following infidelity, be more diligent about requiring no-harm contracts from such clients, and be willing to see each partner individually to assess for the likelihood of domestic violence and develop a safety plan. Finally, for couples expressing a desire to remain married despite infidelity, clinicians may wish to prepare them for an initial spike in marital distress and feelings of marital instability that potentially remains for some years. Normalizing these feelings and helping clients understand that these can diminish over time, even a longer time than is desirable, may enhance the therapeutic process and improve the odds of the marriage surviving infidelity.

APPENDIX A

TABLES

Table 1

Comparative Descriptive Information with Demographic Characteristics in 1980 (N = 1,928)

Infidelity (n = 120) Non-infidelity (n = 1,808) Variables M or N (%) SD Range M or N (%) SD Range

Age (in years) 33.21 8.16 20-53 35.63 9.37 16-55 Gender Male 54 (45%) 729 (40.3%) Female 66 (55%) 1,080 (59.7%) Ethnicity Non-Hispanic White 106 (88.3%) 1,602 (88.7%) African American 6 (5%) 79 (4.4%) Hispanic 8 (6.7%) 90 (5%) Other 0 35 (1.9%) Household Income (in dollars) 28,844 12,730 12.5K-65K 27,333 13,126 12.5K-65K Years Married 10.29 8.71 0-34 12.7 9.27 0-38 # of Children Under 18 in Home 1.38 1.23 0-5 1.39 1.21 0-7 # of Marriages 1.16 .41 1-3 1.17 .42 1-3

Table 2

Comparative Descriptive Information with Demographic Characteristics in 1980 among Infidelity Respondents (N = 120)

Infidelity Stay Married Short Term (n = 81) Infidelity Stay Married Long Term (n = 24) Variables M or N SD Range M or N SD Range

Age 29.62 4.49 23-39 33.94 8.66 20-53 Gender Male 6 (25%) 43 (53.1%) Female 18 (75%) 38 (46.9%) Ethnicity Non-Hispanic White 22 (91.7%) 71 (87.7%) African American 1 (4.2%) 3 (3.7%) Hispanic 1 (4.2%) 7 (8.6%) Other Household Income 31354.17 13472.58 12.5K-65K 28468.75 12984.25 12.5K-65K Years Married 6.00 5.00 1-16 11.25 8.90 0-33 # of Children Under 18 in Home 1.37 1.25 0-5 1.42 1.27 0-5 # of Marriages 1.29 .55 1-3 1.14 .38 1-3

Table 3

Correlations and Descriptive Statistics (N = 1928)

Variables 1 2 3 4 5 6 7 8 9

1. Infidelitya 

2. Depressionb .05* 

3. Personal Satisfaction .01 -.25*** 

4. Divorcec .13*** .09*** -.08*** 

5. Marital Distress .11*** .36*** -.29*** .15*** 

6. Domestic Violenced .06** .18*** -.12*** .08** .34*** 

7. Marital Instability .13*** .34*** -.31*** .25*** .56*** .30*** 

8. Marital Happiness -.07** -.22*** .48*** -.17*** -.43*** -.21*** -.51*** 

9. Sexual Satisfaction -.03 -.16*** .08*** -.08*** -.28*** -.17*** -.33*** .62*** 

10. Religious Service -.08** -.10*** .13*** -.13*** -.17*** -.05* -.22*** .18*** .13***

11. Marital Duration -.06** -.08** .00 -.19*** -.11*** -.01 -.21*** -.02 -.06*

Table 3

Continued

Variables 1 2 3 4 5 6 7 8 9

12. Total Family Income .03 -.06* .09*** -.03 -.05* -.04 -.03 .06* 0

13. Racee .01 .02 -.10*** -.02 .07** .10*** .04 -.07** .01

M .06 .52 2.42 .14 2.49 .18 .29 26.19 2.54

SD .24 .50 .56 .35 2.41 .38 .36 3.39 .59

Range 0-1 0-1 1-3 0-1 0-11 0-1 0-1.36 11-30 1-3

α .71 .89 .84

Table 3

Continued

Variables 10 11 12 13

10. Religious Service 

11. Marital Duration .13*** 

12. Total Family Income .01 .21*** 

13. Race .03 -.01 -.04 

M 2.59 12.55 27436 .04

SD 1.21 9.25 13102 .21

Range 1-4 0-38 25k-65k 0-1

α aInfidelity: 0 = no, 1 = yes. bDepression: 0 = no, 1 = yes. cDivorce: 0 = no, 1 = yes. dDomestic Violence: 0 = no, 1 = yes. eRace: 0 = else, 1 = Black. *p < .05. **p < .01. ***p < .001.

Table 4

Summary of Repeated Measures Logistic Regression Analysis for Measuring Change in Depression (N = 120)

Short-term Depression Long-term Depression

Variable B SE B Wald χ2 B SE B Wald χ2

Infidelity .74 .23 10.50** -.03 .23 .02

**p <.01

Table 5

Summary of Repeated Measures Logistic Regression Analysis for Measuring Change in Domestic Violence (N = 120)

Short-term Domestic Violence Long-term Domestic Violence

Variable B SE B Wald χ2 B SE B Wald χ2

Infidelity .62 .21 8.73** -.19 .29 .44

**p <.01

Table 6

Summary of t-test Analyses for Measuring Change in Personal Satisfaction, Marital Distress, and Marital Instability Among Those Reporting Infidelity (N = 120)

Pre-infidelity Post-infidelity Pre-infidelity Post-infidelity

Short Term Long Term

M M t df M M t df

Personal Satisfaction 2.31 2.15 2.33* 118 2.26 2.22 .43 97

(.56) (.67) (.54) (.65)

Marital Distress 3.59 6.09 -8.30*** 104 3.34 3.69 -1.06 67

(2.91) (2.90) (2.74) (2.93)

Marital Instability .48 .78 -7.40*** 111 .45 .47 -2.32 76

(.41) (.44) (.41) (.41)

Note: Standard deviations appear in parentheses below means. *p < .05. ***p < .001.

Table 7

Summary of Hierarchical Regression Analyses for Infidelity Predicting Short-term Depression, Controlling for Other Variables (N = 1,847)

Model 1 Model 2

Variable B SE B β B SE B β

Marital Happiness -.03 .00 -.26*** -.03 .00 -.25***

Sexual Satisfaction .00 .02 .00 .00 .02 .00

Religious Service Attendance -.02 .01 .05* -.02 .01 -.05*

Race -.01 .04 .00 -.01 .04 -.01

Marital Duration .00 .00 -.10*** .00 .00 -.09***

Income .00 .00 -.01 .00 .00 -.02

Infidelity .19 .04 .12***

R2 .09 .10

F for change in R2 29.39*** 27.94***

*p < .05. ***p < .001.

Table 8

Summary of Hierarchical Regression Analyses for Infidelity Predicting Long-term Depression, Controlling for Other Variables (N = 1,826)

Model 1 Model 2

Variable B SE B β B SE B β

Marital Happiness -.03 .00 -.24*** -.03 .00 -.24***

Sexual Satisfaction .00 .02 -.01 .00 .02 -.01

Religious Service Attendance -.02 .01 -.05* -.02 .01 -.05*

Race -.03 .04 -.02 -.03 .04 -.02

Marital Duration .00 .00 -.10*** .00 .00 -.10***

Income .00 .00 -.02 .00 .00 -.02

Infidelity .02 .04 .01

R2 .08 .08

2 F for change in R 24.80*** .31

***p < .001.

Table 9

Summary of Hierarchical Regression Analyses for Infidelity Predicting Short-term Personal Satisfaction, Controlling for Other Variables (N = 1,846)

Model 1 Model 2

Variable B SE B β B SE B β

Marital Happiness .06 .00 .45 .06 .00 .45***

Sexual Satisfaction .06 .02 .08** .06 .02 .08**

Religious Service Attendance .03 .01 .07** .02 .01 .06**

Race -.06 .04 -.03 -.06 .04 -.03

Marital Duration .00 .00 .06** .00 .00 .06**

Income .00 .00 .04* .00 .00 .04*

Infidelity -.14 .04 -.08***

R2 .28 .28

F for change in R2 118.11* 14.65**

*p < .05. **p < .01. ***p < .001.

Table 10

Summary of Hierarchical Regression Analyses for Infidelity Predicting Long-term Personal Satisfaction, Controlling for Other Variables (N = 1,826)

Model 1 Model 2

Variable B SE B β B SE B β

Marital Happiness .06 .00 .46*** .06 .00 .46***

Sexual Satisfaction .06 .02 .08*** .06 .02 .08**

Religious Service Attendance .02 .01 .06** .02 .01 .06**

Race -.09 .04 -.04* -.09 .04 -.04*

Marital Duration .00 .00 .04* .00 .00 .06**

Income .00 .00 .04* .00 .00 .05*

Infidelity -.06 .04 -.03

R2 .28 .28

F for change in R2 119.80* 2.30

*p < .05. **p < .01. ***p < .001.

Table 11

Summary of Hierarchical Regression Analyses for Infidelity Predicting Short-term Marital Distress, Controlling for Other Variables (N = 1,833)

Model 1 Model 2

Variable B SE B β B SE B β

Marital Happiness -.31 .02 -.43*** -.30 .02 -.41***

Sexual Satisfaction -.03 .10 -.01 .01 .09 .00

Religious Service Attendance -.18 .04 -.09*** -.14 .04 -.07***

Race .55 .23 .05* .56 .21 .05**

Marital Duration -.04 .01 -.16*** -.03 .01 -.13***

Income .00 .00 .00 .00 .00 -.01

Infidelity 3.37 .18 .35***

R2 .24 .36

F for change in R2 97.70*** 343.66***

*p < .05. **p < .01. ***p < .001.

Table 12

Summary of Hierarchical Regression Analyses for Infidelity Predicting Long-term Marital Distress, Controlling for Other Variables (N = 1,792)

Model 1 Model 2

Variable B SE B β B SE B β

Marital Happiness -.31 .02 -.46*** -.31 .02 -.46***

Sexual Satisfaction .02 .09 -.01 .03 .09 .01

Religious Service Attendance -.15 .04 -.08*** -.14 .04 .08***

Race .56 .21 .05** .56 .21 .05**

Marital Duration -.03 .01 -.14*** -.03 .01 -.14***

Income .00 .00 -.01 .00 .00 -.01

Infidelity 1.11 .22 .11***

R2 .26 .27

F for change in R2 102.80*** 25.70***

**p < .01. ***p < .001.

Table 13

Summary of Hierarchical Regression Analyses for Infidelity Predicting Short-term Domestic Violence, Controlling for Other Variables (N = 1,847)

Model 1 Model 2

Variable B SE B β B SE B β

Marital Happiness -.02 .00 -.23*** -.02 .00 -.22***

Sexual Satisfaction -.02 .02 -.04 -.02 .02 -.03

Religious Service Attendance -.01 .01 -.03 -.01 .01 -.02

Race .13 .04 .08*** .13 .04 .08***

Marital Duration .00 .00 -.06** .00 .00 -.05*

Income .00 .00 -.05* .00 .00 -.05*

Infidelity .21 .03 .15***

R2 .09 .11

F for change in R2 29.31* 47.27***

*p < .05. **p < .01. ***p < .001.

Table 14

Summary of Hierarchical Regression Analyses for Infidelity Predicting Long-term Domestic Violence, Controlling for Other Variables (N = 1,805)

Model 1 Model 2

Variable B SE B β B SE B β

Marital Happiness -.02 .00 -.24*** -.02 .00 -.23***

Sexual Satisfaction -.03 .02 -.05 -.03 .02 -.05

Religious Service Attendance -.01 .01 -.02 -.01 .01 -.02

Race .11 .04 .07** .11 .04 .07**

Marital Duration .00 .00 -.05* .00 .00 -.05*

Income .00 .00 -.05* .00 .00 -.05*

Infidelity .10 .04 .06**

R2 .09 .09

F for change in R2 28.91* 7.90**

*p < .05. **p < .01. ***p < .001.

Table 15

Summary of Hierarchical Regression Analyses for Infidelity Predicting Short-term Marital Instability, Controlling for Other Variables (N = 1,827)

Model 1 Model 2

Variable B SE B β B SE B β

Marital Happiness -.05 .00 -.53*** -.05 .00 -.51***

Sexual Satisfaction .01 .01 .01 .01 .01 .02

Religious Service Attendance -.02 .01 -.08*** -.02 .01 -.07***

Race .01 .03 .00 .00 .03 .00

Marital Duration -.01 .00 -.28*** -.01 .00 -.26***

Income .00 .00 .06** .00 .00 .04*

Infidelity .43 .02 .32***

R2 .37 .47

F for change in R2 182.28* 340.13**

*p < .05. **p < .01. ***p < .001.

Table 16

Summary of Hierarchical Regression Analyses for Infidelity Predicting Long-term Marital Instability, Controlling for Other Variables (N = 1,807)

Model 1 Model 2

Variable B SE B β B SE B β

Marital Happiness -.05 .00 -.55*** -.05 .00 -.55***

Sexual Satisfaction .02 .01 .04 .02 .01 .04

Religious Service Attendance -.02 .01 -.09*** -.02 .01 -.08***

Race .01 .03 .01 .02 .03 .01

Marital Duration -.01 .00 -.27*** -.01 .00 -.26***

Income .00 .00 .05* .00 .00 .05*

Infidelity .14 .03 .09***

R2 .37 .38

F for change in R2 179.61* 25.70***

*p < .05. ***p < .001.

Table 17

Summary of Logistic Regression Analysis for Infidelity Predicting Between-group Differences in Likelihood of Later Divorce, Controlling for Other Variables (N = 1,817)

Short-term Divorce Ever Divorce

Variable B SE B eB B SE B eB

Marital Happiness -.15 .02 .86*** -.15 .02 .86***

Sexual Satisfaction -.01 .14 .99 .06 .14 1.07

Religious Service Attendance -.20 .06 .82** -.20 .06 .82**

Race -.55 .38 .58 -.48 .37 .62

Marital Duration -.08 .01 .93*** -.07 .01 .93***

Income .00 .00 1.00 .00 .00 1.00

Infidelity .29 .26 1.33 .78 .23 2.18**

2(df = 7) 150.53 163.74

Note: eB = exponentiated B.

**p < .01. ***p < .001.

Table 18

Summary of Logistic Regression Analysis for Variables Predicting Depression, Domestic Violence and Divorce, Controlling for Other Variables (N = 120)

Depression Domestic Violence Short-term Divorce

Predictor B SE B eB B SE B eB B SE B eB

Marital Happiness -.16 .09 .85 -.17 .07 .85* .02 .09 1.02

Sexual Satisfaction .52 .53 1.68 .82 .44 2.26 -.34 .51 .71

Religious Service Attendance -.04 .21 .96 -.02 .17 .98 -.30 .24 .74

Race .63 1.19 1.89 1.01 .92 2.73 .14 1.24 1.15

Marital Duration -.08 .03 .92** -.01 .02 .99 -.10 .04 .90**

Income .00 .00 1.00 .00 .00 1.00 .00 .00 1.00 Spouse only infidelitya -.13 .48 .88

Wife infidelityb -.13 .42 .88 -1.10 .61 .33c

Constant

2(df = 7) 18.60 10.53 14.26

Table 18

Continued

Ever Divorced

Predictor B SE B eB Marital Happiness -.05 .07 .95

Sexual Satisfaction .44 .46 1.56

Religious Service Attendance -.29 .20 .75

Race -.33 .97 1.39

Marital Duration -.07 .03 .93**

Income .00 .00 1.00 Spouse only infidelitya Wife infidelityb -.97 .49 .38* Constant 2(df = 7) 16.50 Note: eB = exponentiated B. aSpouse only infidelity: 0 = self or both, 1 = spouse only. bWife infidelity: 0 = husband only, 1 = wife only or both. cp=.068. *p < .05. **p < .01.

APPENDIX B

MEASURES

Construct Questions Responses Scoring Depression “In the last 3 years, were there ever times when you were No (0) extremely unhappy, nervous, irritable, or depressed?” Yes (1)

Personal Satisfaction* “Taking all things together, how would you say you are these Very happy (1) Range = 1-3 days?” Pretty happy (2) Not too happy (3) Divorce “Have you divorced or separated permanently since the No (0) previous interview?” Yes (1)

Domestic Violence “In many households bad feelings and arguments occur from No (0) time to time. In many cases people get so angry that they slap, hit, push, kick, or throw things at one another. Has this Yes (1) ever happened between you and your (husband/wife)?”

Marital Distress “Have you had a problem in your marriage because one of No (0) Range = 0-12 you:” 1. Gets angry easily? Yes (1) 2. Has feelings that are easily hurt?

3. Is jealous? 4. Is domineering? 5. Is critical? 6. Is moody? 7. Won’t talk to the other? 8. Has irritating habits? 9. Is not at home enough? 10. Spends money foolishly? 11. Drinks or uses drugs? 12. Has been in trouble with the law?

Construct Questions Responses Scoring Infidelity “Have you had a problem in your marriage because one of No Dummy coded you has had a sexual relationship with someone else?” Yes, spouse – no = 0, else = Yes, self 1 Both

Marital happiness “I am going to mention some different aspects of married Items 1-7: Range = 10-30, life. For each one, I would like you to tell me whether you higher score = are very happy, pretty happy, or not too happy with this Not too happy (1) more aspect of your marriage.” Pretty happy (2) happiness 1. extent of understanding received from spouse Very happy(3) 2. amount of love received Item 8: 3. extent of agreement about things 4. spouse as someone who takes care of things around the Not as good as house most(1) 5. spouse as someone to do things with About the same as 6. spouse's faithfulness most(2) 7. Taking all things together, how would you describe your Better than most(3) marriage? Would you say that your marriage is very happy, pretty happy, or not too happy? Item 9: 8. Compared to other marriages you know about, do you think your marriage is better than most, about the same as Getting worse(1) most, or not as good as most? Staying the same(2) 9. Comparing your marriage to three years ago, is your Getting better(3) marriage getting better, staying the same, or getting worse? Item 10(recoded): 10. Would you say the feelings of love you have for your spouse are extremely strong, very strong, pretty strong, not Not too strong or not too strong, or not strong at all? strong at all(1) Pretty strong(2) Very strong or extremely strong(3)

Construct Questions Responses Scoring Sexual Satisfaction* “How happy are you with your sexual relationship?” Very happy (1) Range = 1-3 Pretty happy (2) Not too happy(3) Marital Instability 1. “Have you thought your marriage might be in trouble No (0) Range = 1-13 within the last 3 years?” 2. “As far as you know, has your spouse ever thought your Yes (1) Higher marriage was in trouble?” score=more 3. “Have you talked with family members, friends, clergy, instability counselors, or social workers about problems in your marriage within the last 3 years?” 4. “As far as you know, has your (husband/wife) talked with relatives, friends, or a counselor about problems either of you were having with your marriage?” 5. “Has the thought of getting a divorce or separation crossed your mind in the last 3 years?” 6. “As far as you know, has the thought of divorce or separation crossed your (husband's/wife's) mind in the last 3 years?” 7. “Have you or your spouse seriously suggested the idea of divorce in the last 3 years?” 8. “Have you talked about dividing up the property?” 9. “Have you talked about consulting an attorney?” 10. “Have you or your spouse consulted an attorney about a divorce or separation?” 11. “Because of problems people are having with their marriage, they sometimes leave home either for a short time or as a trial separation. Has this happened in your marriage within the last 3 years?” 12. “Have you talked with your spouse about filing for divorce or separation?” 13. “Have you or your (husband/wife) filed for a divorce or separation petition?”

Construct Questions Responses Scoring Religious service “How often do you and your spouse attend church together?” Weekly or more (1) Range = 1-4 attendance* Once a month or more, but less than weekly (2) once a year or more, but less than monthly (3) less than once a year or never (4)

Marital Duration Taken from the Marital History chart that asks respondents Computed variable Range = 0-38 their age when they married and whether the marriage is still in years intact.

Income “I am going to mention a number of income categories. Under $5,000 The midpoint When I mention the category which describes your total $5,000-9,999 of each family income in 1979, please stop me.” $10,000-14,999 interval listed .. in the question $50,000-59,000 was used to $60,000 or more assign a dollar value. For the last interval, $65,000 was used. Range = $2,500- $65,000

Construct Questions Responses Scoring Race “What race do you consider yourself?” White, non-Hispanic Dummy Coded: Black White, Hispanic = 1 Black Else = 0 Other Recoded for the purposes of this study and the literature * = Reverse coded for ease of interpretation

APPENDIX C

HUMAN SUBJECTS APPROVAL MEMORANDUM

Office of the Vice President For Research Human Subjects Committee Tallahassee, Florida 32306-2742 (850) 644-8673 · FAX (850) 644-4392

RE-APPROVAL MEMORANDUM

Date: 3/23/2011

To: Paul Stanford

Address: 1490 Dept.: FAMILY & CHILD SCIENCE

From: Thomas L. Jacobson, Chair

Re: Re-approval of Use of Human subjects in Research Life after infidelity: Exploring the marital processes evident in couples following an affair

Your request to continue the research project listed above involving human subjects has been approved by the Human Subjects Committee. If your project has not been completed by 3/21/2012, you must request a renewal of approval for continuation of the project. As a courtesy, a renewal notice will be sent to you prior to your expiration date; however, it is your responsibility as the Principal Investigator to timely request renewal of your approval from the committee.

If you submitted a proposed consent form with your renewal request, the approved stamped consent form is attached to this re-approval notice. Only the stamped version of the consent form may be used in recruiting of research subjects. You are reminded that any change in protocol for this project must be reviewed and approved by the Committee prior to implementation of the proposed change in the protocol. A protocol change/amendment form is required to be submitted for approval by the Committee. In addition, federal regulations require that the Principal Investigator promptly report in writing, any unanticipated problems or adverse events involving risks to research subjects or others.

By copy of this memorandum, the Chair of your department and/or your major professor are reminded of their responsibility for being informed concerning research projects involving human subjects in their department. They are advised to review the protocols as often as necessary to insure that the project is being conducted in compliance with our institution and with DHHS regulations.

Cc: Beatrice Pasley, Advisor HSC No. 2011.5975

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BIOGRAPHICAL SKETCH

Paul S. Stanford is originally from Nederland, Texas. In the spring of 2003, he received his Bachelor’s degree in Psychology from Texas A&M University. Paul then attended Nova Southeastern University and received his Master’s degree in Marriage and Family Therapy in 2006. In the fall of 2006 he enrolled in the doctoral program in Marriage and Family Therapy at The Florida State University. Under the guidance of Dr. Kay Pasley, Paul’s program of research focuses on the impact of infidelity on individuals and their relationships.