Infidelity in couple therapy 1 Running head ...

Infidelity in couple therapy

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Running head: INFIDELITY IN COUPLE THERAPY Outcomes of Couples with Infidelity in a Community-Based Sample of Couple Therapy

David C. Atkins University of Washington Rebeca A. Marín, Tracy T. Y. Lo Fuller Theological Seminary Notker Klann Katholische Arbeitsgemeinschaft für Beratung Kurt Hahlweg Braunschweig Technical University Published as: Atkins, D. C., Marín, R. A., Lo, T. T. Y., Klann, N., & Hahlweg, K. (2010). Outcomes of couples with infidelity in a community-based sample of couple therapy. Journal of Family Psychology, 24, 212-216. PMID: 20438197 DOI: 10.1037/a0018789 Note: Published article is a brief report, and this is the full report on which it is based. This article may not exactly replicate the final version published in the APA journal. It is not the copy of record. Author Note David C. Atkins, Department of Psychiatry and Behavioral Science, University of Washington; Rebeca A. Marín and Tracy T. Y. Lo, Department of Clinical Psychology, Fuller Theological Seminary; Notker Klann, Katholische Arbeitsgemeinschaft für Beratung, Bonn, Germany; Kurt Hahlweg, Braunschweig Technical University, Braunschweig, Germany. Preliminary results were presented at the 42nd annual convention of the Association for Behavioral and Cognitive Therapies, Orlando, FL, in November 2008 by Rebeca Marín. Correspondence concerning this article should be addressed to David Atkins, Center for the Study of Health and Risk Behaviors, 1100 NE 45th Street, Suite 300, Seattle, WA, 98105. Email: [email protected]


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Abstract Infidelity is an often cited problem for couples seeking therapy, but the research literature to date is very limited on couple therapy outcomes when infidelity is a problem. The current study is a secondary analysis of a community-based sample of couple therapy in Germany and Austria. Outcomes for 145 couples who reported infidelity as a problem in their relationship were compared to 385 couples who sought therapy for other reasons. Analyses based on hierarchical linear modeling revealed that infidelity couples were significantly more distressed and reported more depressive symptoms at the start of therapy but continued improving through the end of therapy and to six months post-therapy. At the follow-up assessment infidelity couples were not statistically distinguishable from non-infidelity couples, replicating previous research. Sexual dissatisfaction did not depend on infidelity status. Although there was substantial missing data, sensitivity analyses suggested that the primary findings were not due to missing data. The current findings based on a large community sample replicated previous work from an efficacy trial and show generally optimistic results for couples in which there has been an affair. Keywords: Infidelity, Couple Therapy, Hierarchical Linear Modeling


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Outcomes of Couples with Infidelity in a Community-Based Sample of Couple Therapy Infidelity is a common occurrence in marriages, and lifetime prevalence estimates for extramarital sexual affairs in the United States range from 20% to 40% depending on the age and gender of the individual (e.g., Atkins, Baucom, & Jacobson, 2001). Couple therapists report extramarital affairs as one of the most damaging problems couples face and one of the most difficult problems to treat (Whisman, Dixon, & Johnson, 1997). Furthermore, studies show that infidelity takes a toll on the spouse not involved in the affair with an increase in depression and anxiety (e.g., Gordon, Baucom, & Snyder, 2004). Yet, even with these prevalence rates and potential negative impacts, we know surprisingly little about the effectiveness of couple therapy when there has been an affair. The present study reports outcomes from a large sample of affair couples who participated in a study of community-based couple therapy in Germany and Austria. Thus far, only two studies have empirically examined the efficacy of couple therapy when infidelity is a problem. Gordon et al. (2004) reported outcomes on six couples who had experienced an extramarital affair using a treatment designed specifically for infidelity. The study found that the non-involved spouse initially reported more depressive and anxious (i.e., traumatic) symptoms, as well as greater relationship distress relative to the spouse who had the affair. At the end of treatment and follow up, all participants demonstrated gains in most areas targeted by the treatment – specifically decreases in trauma and depression symptomatology, and increases in forgiveness. However, marital distress remained elevated. In a second study, Atkins et al. (2005) examined the outcomes of 19 couples where at least one partner reported infidelity compared to 115 couples who sought couple therapy for other reasons. All couples participated in a randomized clinical trial of two broad-based behavioral couple therapies. The study found that the infidelity couples began therapy


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significantly more distressed than non-infidelity couples but that infidelity couples showed a positive trajectory for relationship satisfaction during therapy. By the end of treatment, infidelity and non-infidelity couples were no longer significantly different in reported marital satisfaction. At present, the sum total of our empirical knowledge about couple therapy for infidelity is based on the 25 couples in these two studies. Both studies offer promising suggestions that couple therapy could be helpful in improving relationship satisfaction and individual symptoms such as depression and trauma. The current study provides data on the trajectory and outcome of a community sample of 145 couples who reported infidelity as a problem in their relationship compared to 385 distressed couples without infidelity. Method The current study is a secondary analysis of data from two, highly similar studies of the effectiveness of couple therapy in the community, based in Germany and Austria (Hahlweg & Klann, 1997; Klann, Hahlweg, Baucom, & Kroeger, in press). Relevant aspects of the participants and procedures are described here; further information can be found in the primary sources. Procedure Counselors from Catholic and Lutheran counseling agencies in Germany and Austria were invited to participate in a study of couple therapy. Counselors who participated were asked to recruit the next three couples they accepted for marital counseling. Following informed consent, partners independently completed self-report instruments prior to treatment, at the end of counseling (for pragmatic reasons set at approximately 6 months after initial assessment, called post treatment) and at the follow-up six months after the post treatment (approximately 12 months after initial assessment) using the same set of questionnaires.


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Participants Clients. A total of 530 heterosexual couples took part in the two studies. The demographic characteristics of the participants were as follows: average age was 37.7 years (SD = 9.2) and 40.2 years (SD = 10.4) for men and women, respectively. Ninety percent of couples were married, and 78% had one or more children. Approximately 44% had some college education, 43% had completed high school. All participants were Caucasian. Infidelity couples were those where at least one partner endorsed infidelity from a list of relationship problems during initial assessment. Counselors. A total of 134 counselors from all over Germany and Austria participated in the study (there are no differences with regard to marital counseling in the two countries). All counselors had university (MA) degrees: 32% psychology, 23% theology, 19% education, 12% social work, and 14% other degrees. Primary therapy orientations included integrative, systemic, psychodynamic, Gestalt, and behavior therapy. The original studies were entirely observational and primarily concerned with estimating an effect-size for marital therapy as it is practiced in the community. Treatment approach and intervention were not controlled by the research team. Measures Marital Satisfaction Inventory (MSI; Klann, Hahlweg, & Hank, 1992). To evaluate change in the marital relationship, the German version of the MSI was used. The MSI is a multidimensional self-report measure containing 11 scales with 280 items. The present study used the GDS and SEX subscales to assess relationship distress and sexual dissatisfaction. Center for Epidemiological Studies Depression Scale (CES-D, Hautzinger & Bailer, 1992). The German version of the CES-D was used to measure depression. It contains 20 items,


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was developed for epidemiological purposes, and has an internal consistency of .90. Raw-scores that were greater than or equal to 23 is an approximate cutoff for clinical depression. Data Analysis Hierarchical linear modeling was used to model the longitudinal and nested data on couples (Atkins, 2005). Because there were only three assessments and the pattern of change across time clearly nonlinear, time was modeled with two dummy-codes: one comparing the post-therapy assessment to pre-treatment, and a second comparing the six month follow-up assessment to pre-treatment. The equation below represents our primary analytic model, using the composite form of the equation: DVtij = β0 + β1Post-tx + β2Follow-up + β3Female + β4Post-tx:Female + β5Follow-up:Female + β6Infidelity + β7Post-tx:Infidelity + β8Follow-up:Infidelity + u00j + u10jPost-tx + u20jFollow-up + r0ij + etij where t indexes time, i indexes individuals, and j indexes couples. Post-tx and Follow-up represent the two dummy-codes for time; Female (Male = 0, Female = 1) and Infidelity (No infidelity reported in relationship = 0, Infidelity reported by one or both partners = 1) were also dummy-coded. For each dependent variable, two models were run: a basic model without infidelity, and a second that included the infidelity terms. Our primary interest focused on whether the three infidelity terms improved the fit of the model, and if so, what the nature of the differences were due to infidelity status. When analyzing CESD and SEX outcomes, we controlled for relationship satisfaction (i.e., GDS) by including it as a time-varying covariate. HLM assumes normality and homoskedasticity of all error terms. Results


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Because of the complexity of some analyses, additional details on analyses are available in supplemental, online material. Figure 1 presents means and bootstrapped confidence intervals for each dependent variable by assessment and infidelity status. Prior to treatment couples who have experienced an affair report notably higher mean relationship distress and depressive symptoms but do not report higher sexual dissatisfaction. Couples without an affair make notable gains from pre-therapy to post-therapy, but there are fewer gains from post-therapy to follow-up. Infidelity couples appear to make fewer gains initially but further gains between post-therapy and follow-up relative to non-infidelity couples. The two groups of couples appear to finish the study with approximately similar outcomes. An additional issue seen in Figure 1 is that the confidence intervals are steadily widening for all groups over time, which indicates that there is significant missing data due to attrition, which is addressed below. Models with and without infidelity predictors were compared via deviance tests and Akaike Information Criterion (AIC; see Atkins, 2005), which weighs increased fit to the data relative to the number of additional parameters. GDS and CESD both revealed significantly better fit with the addition of the infidelity terms on deviance tests (χ2(3)= 23.6, p < .01 and χ2(3)= 33.2, p < .01, respectively) and AIC (-18 and -58, respectively), though the SEX outcome did not (χ2(3)= 6.1, p = .11). Reflecting the patterns seen in Figure 1, the individual infidelity coefficients for GDS and CESD revealed that infidelity couples: a) reported more distress at pretreatment, b) showed greater improvement at post-therapy and follow-up relative to noninfidelity couples, and c) were not statistically different from non-infidelity couples at follow-up. Regression tables with coefficients are included in the supplemental material. We considered three sensitivity analyses to verify that the present results were not biased by: a) model assumptions, b) missing data due to attrition, or c) regression to the mean. Residual


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analysis showed that the present data generally fit the assumptions of HLM, with the exception of the GDS where there was a strong ceiling effect (i.e., notable stack of values at upper limit of GDS). Bootstrapped analyses that relax assumptions regarding normality of errors revealed similar results to those reported above. Thus, the ceiling effect of the GDS does not appear to effect substantive results. Pattern-mixture models for non-ignorable missing data were used to assess the sensitivity of the results to missing data. For GDS and SEX outcomes, these analyses revealed that missing data patterns were not significantly different by infidelity status, nor did missing data appear to influence differential patterns by infidelity status. For the CESD there was some evidence that missing data was related to infidelity status patterns across time, but further exploration revealed that, if anything, the pattern-mixture approach suggested change for infidelity couples would be somewhat greater, accounting for missing data. A final set of sensitivity analyses matched infidelity and non-infidelity couples on pretherapy distress, to examine whether the differential improvement was simply a function of initial distress. Coefficients revealed the same pattern of effects for infidelity moderation at posttherapy and follow-up, though not all coefficients were significant. As part of the matching, the total sample size was reduced by half (as non-matched couples were not included). Thus, these results matching couples on pre-treatment distress appear to broadly match those with the full sample, though not all effects reached conventional levels of significance, attributable in part to the reduced sample size. The supplemental materials have further details on all sensitivity analyses. Discussion The present study represents a significant expansion of the empirical knowledge about how infidelity couples respond to couple therapy. The outcomes of the 145 infidelity couples


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revealed solid improvements in both relationship satisfaction and depressive symptoms. This pattern of results replicates results found by Atkins et al. (2005) where infidelity couples were significnatly more distressed initially but improved at a somewhat more rapid rate over the course of treatment. These two studies are similar in that couples were recruited generally for couple distress and not specifically for infidelity, though the present results come from an uncontrolled community-based sample. It is encouraging that findings from the present, community-based sample mirror those found in a highly controlled efficacy trial. The current results suggest that couples struggling with affairs - who seek therapy - have reasonably good outcomes, approximately similar to couples presenting for couple therapy with non-infidelity relationship problems. At the same time, the mean GDS at six months posttreatment is quite close to the common distress cutoff of 59. Thus, there is certainly risk for future relationship problems for the sample as a whole, though based on the present results it is not clear whether infidelity couples would be at particular risk for future problems. A question that has been raised in the treatment literature on infidelity is whether general couple therapy approaches are sufficient in working with affairs, or whether an infidelity-specific treatment is needed (e.g., Baucom, Gordon, Synder, Atkins, & Christensen, 2006). The current results in tandem with Atkins et al. (2005) might suggest that existing treatment approaches represent a solid base that, perhaps could be improved, but do not seem to argue for an infidelityspecific approach. Further outcome data is certainly warranted, but data on the process of therapy with infidelity couples is also critical. Trust and forgiveness will be central in therapy with couples where there has been an affair, and thus it would be advantageous to have specific information about how therapists and couples navigate this process.


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As noted earlier this study is not without limitations. Although the sample size is far larger than the previous studies on couple therapy and infidelity, there was significant attrition. However, two findings mitigate this concern to some extent: a) the pattern-mixture sensitivity analyses suggested that missing data did not bias the results based on infidelity status, and b) the results are highly similar to Atkins et al. (2005), which was a highly controlled efficacy trial with little missing data. Even given these points, the amount of missing data must temper the conclusions and replication is needed. Also, the present study did not collect information about the nature, status, and secrecy of the infidelity all of which may impact the couples‘ outcome. In addition, we know little about the therapists, so any questions regarding how they addressed the affairs are totally unknown, and relatedly, it was not possible to explore therapist effects or differences due to training or background of therapists in the present data. Moreover, the data come from Germany and Austria, and thus it is not known how well the findings could generalize to other countries and cultures. Given the number of couple prevention and therapy studies done across western Europe, Australia, and the United States, there is reason to believe the current findings would extend to western contexts, but it is not clear how they might generalize to other cultural contexts. A final problem that is particularly challenging with couple therapy with infidelity is the issue of secrecy. The present findings only extend to known, or revealed, affairs whereas past research has shown a not insubstantial percentage of infidelity couples have secret affairs that are either revealed during therapy or not at all (Atkins et al., 2005). These couples represent a fascinating and clinically relevant, but highly challenging, subpopulation of infidelity couples to study in future research. The current investigation considerably extends the empirical knowledge base on couple therapy outcomes for infidelity. Moreover, the results are quite encouraging, showing that


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couples who have experienced an affair and seek treatment show robust gains during and shortly after therapy, though process research is needed to help understand the specific types of interventions and processes that are helpful with these challenging couples.


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References Atkins, D. C. (2005). Using Multilevel Models to Analyze Couple and Family Treatment Data: Basic and Advanced Issues. Journal of Family Psychology, 1, 98-110. Atkins, D. C., Baucom, D. H., & Jacobson, N. S. (2001). Understanding infidelity: Correlates in a national samples. Journal of Family Psychology, 15, 735-749. Atkins, D. C., Eldridge, K. A., Baucom, D. H., and Christensen, A. (2005). Infidelity and behavioral couple therapy: Optimism in the face of betrayal. Journal of Consulting and Clinical Psychology, 73, 144-150. Gordon, K. C., Baucom, D. H., & Snyder, D. K. (2004). An integrative intervention for promoting recovery from extramarital affairs. Journal of Marital and Family Therapy, 30, 213-231. Hahlweg, K. & Klann, N. (1997). The effectiveness of marital counseling in Germany: A contribution to health services research. Journal of Family Psychology, 11, 410-421. Hautzinger, M. & Bailer, M. (1992). Allgemeine Depressions Skala. (CES-D Depression Scale). Weinheim: Beltz Test Gesellschaft. Klann, H., Hahlweg, K. Baucom, D. H., & Kroeger, C. (in press). The effectiveness of couple therapy in Germany: A replication study. Journal of Marital and Family Therapy. Klann, N., Hahlweg, K. und Hank G. (1992). Deutsche Validierung des "Marital Satisfaction Inventory" (MSI) von Snyder (1981). (Validation of Snyder´s `Marital Satisfaction Inventory` in Germany). System Familie, 5, 10 - 21. Whisman, M. A., Dixon, A. E., & Johnson, B. (1997). Therapists’ perspectives of couples problems and treatment issues in the practice of couples therapy. Journal of Family Psychology, 11, 361-366.


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Figure 1. Means and 95% Bootstrapped Confidence Intervals by Assessment and Infidelity Status Infidelity Pre

Post

Follow-up

GDS

SEX

65

58

20

54

60

56

15 10

Dependent Variable

60

70

25

CESD

No infidelity

Pre

Post

Follow-up

Pre

Assessment Period

Post

Follow-up


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These online, supplemental materials provide further details on data analyses and results, including a descriptive table of means and standard deviations for each dependent variable, tables of regression coefficients for each outcome, and further details on sensitivity analyses. Supplemental Results Table A.1 has descriptive statistics, reflected in Figure 1 in the printed manuscript. GDS Results from HLM analyses of the GDS are reported in Table A.2. Model 1 shows that men began couple therapy with an average GDS of 67.3 and women were significantly more distressed by just over 2 points. On average, men improved about 5 GDS points by the end of treatment and gains are slightly increased at six month follow-up. Women show more rapid improvement at the end of treatment, but are statistically similar to men at follow-up. With the infidelity indicators included, there is a significant improvement in model fit by deviance test (χ2(3)= 23.6, p < .01) and also by the Akaike Information Criterion (AIC). The main effect of infidelity shows that couples in which there was an affair were significantly more distressed at the start of treatment. The two cross-level interactions are not significant by themselves, and thus, it may be that the overall model improvement was driven by the higher distress for infidelity couples at the start of treatment. To examine this, we conducted a deviance test using a model with only the main effect of infidelity compared to Model 2 in Table A.2. Results showed significant improvement in the model with the two time by infidelity contrasts (χ2(2)= 8.1, p = .02) and was preferred using AIC by four points. These two contrasts show that infidelity couples are somewhat more distressed than non-infidelity couples at post-treatment, but make greater gains by the follow-up period. Notably, the greater improvement at follow-up is virtually identical to the initial difference (coefficients of -3.6 and 3.4, respectively), suggesting


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that the two groups are quite similar at the end of follow-up. The implications of missing data for all HLM models are considered below, in Model Assumptions and Missing Data. SEX Similar models with and without infidelity indicators were fit to the SEX subscale of the MSI. Results are shown in Table A.3. As was seen in the descriptive statistics, there is a strong gender main effect, wherein men report significantly higher sexual dissatisfaction prior to and throughout treatment. In addition, there is a strong association between sexual dissatisfaction and general relationship dissatisfaction. The infidelity indicators do not significantly improve the model (χ2(3)= 6.1, p = .11), as seen in the identical AIC statistics. CESD Similar models with and without infidelity indicators were fit to the CESD measure of depressive symptomatology. The CESD was somewhat positively skewed, and a square-root transformation was used to fit model assumptions. (The CESD was also modeled as a Poisson outcome to reflect that it is a count variable; however, results were identical to those reported here, and thus, we report the transformed results in the text for comparability with other outcomes.) Results can be found in Table A.4. Results without infidelity indicators show men beginning treatment at approximately 17 on the CESD (i.e., 4.132 = 17, to convert back to original units from square-root). Women report significantly more depressive symptoms initially ([4.13 + 0.25]2 = 19.2). Both sexes report significant improvements in depressive symptoms over the course of couple therapy, controlling for the strong association with couple distress. The infidelity indicators strongly improve the model (χ2(3)= 33.2, p < .01; change in AIC = -58). Couples in which there has been an affair reported elevated depressive symptoms initially ([4.0 + 0.47]2= 20.0), but significantly greater improvement in depressive symptoms to post-


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treatment and six month follow-up. Interestingly, their improvement by six month follow-up suggests they report fewer depressive symptoms than non-infidelity couples at this time point. However, as noted previously, there is significant missing data that could affect the interpretation of our models. Sensitivity Analyses Model Assumptions HLM assumes that all error terms are normally distributed and that errors are homoskedastic around the regression plane. Residual plots of our models revealed that the present data fit these assumptions, with one exception. The GDS has a maximum score of 80, and many of the couples reported this maximum value, leading to a ceiling effect and somewhat non-normally distributed random-effects for the GDS. We used the bootstrap (Lunneborg, 2000) to examine whether these model violations impacted substantive results. Specifically, 1,000 bootstrap resamples were generated by sampling with replacement from couple's data (i.e., we sampled an entire couple's set of data). Bootstrapping provides nonparametric tests of regression coefficients that do not rely on the normality of the error terms. With the present data, these sensitivity analyses revealed similar conclusions to those reported in the text; thus, earlier results do not appear to be sensitive to this violation. Missing Data A final, significant issue with the present data is missing data. Both groups had significant missing data by the six month follow-up, with only 22 of 145 infidelity couples (15.2%) and 83 of 384 non-infidelity couples (21.6%), reporting data. These rates are not statistically different across the two groups (χ2(1)= 1.49, p = .22). In part, the missing data reflect that the study is a community-based sample, with a trade-off of greater external validity but


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somewhat lower internal validity, and therapists and couples were participating without any incentive, which may have lowered response rate. In addition, the ethics board of the clinics felt that it could be intrusive for the research team to contact couples who failed to return questionnaires, which again, likely contributed to missing data, though presumably these hypothesized mechanisms should affect infidelity and non-infidelity couples similarly. Statistically, the missing data raise the issue of whether the models may produce biased results, relative to what would have been found with complete data. Substantively, we are specifically concerned with whether the observed differences due to infidelity status may be impacted by missing data. HLM will provide unbiased results in the presence of missing data as long as covariates related to missing data are part of the model, including earlier time points of the outcome that may predict missing data (Hedeker & Gibbons, 1997). When these conditions are met (i.e., covariates related to missing data are included in the model), missing data are considered missing at random, or more generally, ignorable. If missing data are systematically related to a covariate that is not included in the analysis or to unobserved values of the outcome, then data are non-ignorably missing (Schafer & Graham, 2002). Unfortunately, there is no test or method to determine whether data are ignorable. Statisticians often recommend sensitivity analyses in these cases, focused on whether the pattern of results with the total data appear to be sensitive to the presence of missing data. For the present data, we utilized the pattern-mixture approach to missing data as applied to HLM (see Atkins, 2005; Hedeker & Gibbons, 2006). All participants had complete data at pre-therapy, and thus individuals were categorized into two groups: a) those with complete data at all time points (n = 200), and b) those with any missing data at either time points two or three (n = 878). A missing data dummy-variable was then entered into our HLM analyses along with interactions


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with all other terms. These interaction terms represent sensitivity analyses of our earlier results: Are the earlier coefficients sensitive to the presence of missing data? With the GDS and SEX outcomes, inclusion of the missing data indicators worsened the fit of the models notably based on AIC and likelihood ratio chi-square tests (AIC = -8, χ2(10)= 10.5, p = .32; AIC = -9, χ2(10) = 8.9, p = .44, respectively). Thus, the earlier findings for GDS and SEX do not appear sensitive to the presence of missing data. However, the CESD missing data analyses revealed a significant improvement to the model with the inclusion of the missing data indicator variables (AIC = 3.5, χ2(10) = 21.4, p = .01). Moreover, in examining the individual coefficients from this model, there were significant interactions between the dummy variable for follow-up and the missingness indicator, as well as a significant three-way interaction between the follow-up, infidelity, and missingness indicators. These findings indicate that the differential change across time due to infidelity status is affected by the presence of missing data. In these scenarios the pattern-mixture approach provides formula to create a weighted average of the effects for complete cases and those with missing data, along with standard errors calculated via the delta method (see Atkins, 2005; Hedeker & Gibbons, 1997; 2006 for details). When applied to the CESD output, the only appreciable differences in coefficients relate to time three, which follows from the significant interaction with missingness. The most relevant effect for the current study is that, if anything, the pattern-mixture approach suggests that the decrease at follow-up for infidelity couples is larger when missing data is accounted for (B = -2.53, SE = 0.60, p < .01, 95% CI = [-3.71, -1.34]). Finally, how does the sheer amount of missing data affect the results? There is no simple, statistical answer to this, as it depends on the total sample size, number of repeated measures, amount of missing data, and other elements of the model. However, Hedeker and


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Gibbons (2006) present a missing data simulation of a two-group, longitudinal treatment study, not dis-similar from the present data (though, the present data are couples as opposed to individuals). Those authors show the MLMs accurately recover the true, underlying parameters even when 87.5% of data are missing at the final assessment (pp. 285-289). In summary, there is notable missing data, characteristics associated with the original community-based sample likely contributed to this. However, rates of missing data across infidelity and non-infidelity couples were similar, and pattern-mixture analyses suggest missing data are not driving the substantive effects. Thus, these results provide some evidence that earlier reported moderating effects of infidelity are not caused by missing data. Initial Distress and Regression to the Mean A final concern that was addressed in sensitivity analyses is whether the observed effects could be due to regression to the mean. That is, for the GDS and CESD, infidelity couples are notably more distressed initially. Thus, the change over time could be attributed to regression to the mean, which in theory would be stronger, the farther an individual is from the mean initially. To address this concern, infidelity and non-infidelity couples were matched using a propensity score matching technique (Gelman & Hill, 2007). Pre-therapy GDS, CESD, and SEX scores for each spouse were entered into a logistic regression, with infidelity status as the outcome. The predicted probability of infidelity status based on the logistic regression represents the propensity score (i.e., the “propensity” to be in a couple with infidelity). Couples are then matched using this propensity score, which effectively matches (in a multivariate manner) couples on pre-treatment distress. Using this matching process, 138 infidelity couples were matched to 138 non-infidelity couples (i.e., the matching procedure used a 1:1 matching).


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Figure A.1 shows means and 95% bootstrapped confidence intervals using the matched data for GDS and CESD outcomes (as SEX did not show moderation by infidelity status in the full sample, there is no reason to consider it here). The pre-therapy means across groups are virtually identical, showing that the matching procedure was efficacious in equating the groups on initial distress. Across time, the change is somewhat less dramatic than in the full sample, but the same pattern of means by infidelity status is similar: CESD shows greater improvement for infidelity couples by 6 months post-therapy, and GDS for infidelity couples shows less change at post-therapy but then greater change at 6 months post-therapy. HLM analyses identical to those reported with the full sample were applied to the reduced, matched sample. For the CESD, there was significant moderation by infidelity status at 6 months post-therapy (B = -0.46, SE = 0.22, Z = 2.09, p = .03). The GDS analyses were somewhat more equivocal, with the overall test for improvement in the model with adding the infidelity interactions with post-therapy and 6 months post-therapy a bit above the p < .05 cutoff (χ2(2) = 4.15, p = .12). The individual coefficients show the same pattern as the full sample (as reflected in Figure A.1) but do not reach significance. The challenge here is that the matching procedure reduced the total sample size (of observations) by almost half, from 1,706 to 832. Thus, the lack of significance is certainly affected by the reduction in sample size. Overall, these analyses would suggest that the findings with the full sample are not primarily driven by regression to the mean. At the same time, the impact is somewhat difficult to gauge with the GDS given the reduction in sample size.


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Additional References

Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. New York: Cambridge. Hedeker, D. & Gibbons, R. D. (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods, 1, 64-78. Hedeker, D. & Gibbons, R. D. (2006). Longitudinal data analysis. New York: Wiley Lunneborg, C. E. (2000). Data analysis by resampling: Concepts and applications. California: Duxbury Press. Schafer, J. L. & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 2, 147-177.


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Table A.1 Descriptive Statistics for Outcomes by Time, Gender, and Infidelity Status

Infidelity

Husband Time

n

M

No Infidelity

Wife SD

n

M

Husband SD

n

Wife

M

SD

n

M

SD

GDST Pre

145

69.9

9.1

145

71.7

7.9

384

66.3

11.1

387

68.6

10.8

Post

52

65.6

12.7

55

66.8

12.5

167

60.2

12.4

170

61.0

13.1

Follow-up

22

57.9

13.5

21

61.7

13.1

83

59.1

13.5

78

60.7

12.5

(table continues)


Infidelity

Husband Time

n

M

No Infidelity

Wife SD

n

23

M

Husband SD

n

Wife

M

SD

n

M

SD

CESD Pre

147

22.9

11.5

146

26.8

12.3

391

17.7

10.1

391

20.7

10.6

Post

52

14.3

9.2

55

17.4

10.6

179

12.8

8.1

172

14.4

9.0

Follow-up

22

11.7

11.3

23

11.8

8.3

84

13.7

9.3

84

14.2

8.7

(table continues)


Infidelity

Husband Time

n

M

No Infidelity

Wife SD

n

M

Husband SD

n

Wife

M

SD

n

M

SD

SEX Pre

143

64.5

13.4

143

55.8

10.0

388

64.5

12.5

386

56.0

9.7

Post

52

61.0

14.6

54

55.5

10.8

166

62.2

12.9

168

53.6

10.7

Follow-up

21

59.4

12.5

21

53.4

10.7

82

61.0

13.7

77

52.1

10.6

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Table A.2 HLM Results for GDS Models With and Without Infidelity Indicator.

Infidelity

Variable

B

Intercept

67.30 66.41, 68.19

66.36

65.36, 67.37

Post-tx

-4.91 -6.29, -3.54

-5.35

-6.89, -3.81

Follow-up

-6.55 -8.46, -4.64

-5.75

-7.83, -3.67

1.39, 2.96

2.17

1.39, 2.96

-1.51 -2.66,-0.36

-1.52

-2.67,-0.37

Follow-up:Female -0.80 -2.41, 0.81

-0.82

-2.43, 0.79

Infidelity

3.42

1.65, 5.19

Post-tx:Infidelity

1.80

-1.08, 4.68

-3.58

-7.72, 0.55

Female Post-tx:Female

95% CI

No Infidelity

2.17

Folow-up:Infidelity AIC

12394.00

B

95% CI

12376.44

Note: 95% CI that do not include 0 are significant at the p < .05 level. AIC = Akaike Information Criterion. Coding for female (Male = 0, Female = 1), Post-tx (Pre-therapy assessment = 0, Post-therapy assessment = 1), Follow-up (Pre-therapy assessment = 0, 6 month follow-up assessment = 1), and infidelity (No infidelity = 0, infidelity = 1)


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Table A.3 HLM Results for SEX Models With and Without Infidelity Indicator.

Infidelity

No Infidelity

Variable

B

95% CI

Intercept

64.11

63.21, 65.00

64.51

63.50, 65.51

Post-tx

-0.61

-1.72, 0.51

-0.53

-1.76, 0.69

Follow-up

-0.43

-2.01, 1.15

-0.92

-2.63, 0.78

Female

-9.59

-10.53, -8.65

-9.61

-10.55, -8.67

GDS

0.42

0.38, 0.47

0.43

0.39, 0.48

Post-tx:Female

1.28

-0.01, 2.57

1.30

0.00, 2.59

Follow-up:Female 0.81

-1.00, 2.62

0.81

-0.99, 2.62

Infidelity

-1.52

-3.23, 0.19

Post-tx:Infidelity

-0.20

-2.25, 1.84

2.59

-0.52, 5.69

Follow-up:Infidelity AIC

12159.12

B

95% CI

12158.99

Note: 95% CI that do not include 0 are significant at the p < .05 level. AIC = Akaike Information Criterion. Coding for gender (Male = 0, Female = 1), Post-tx (Pre-therapy assessment = 0, Post-therapy assessment = 1), Follow-up (Pre-therapy assessment = 0, 6 month follow-up assessment = 1), and infidelity (No infidelity = 0, infidelity = 1)

Infidelity in couple therapy Table A.4 HLM Results for CESD Models With and Without Infidelity Indicator.

Infidelity

Variable

B

Intercept

4.13

4.03, 4.23

4.00

3.89, 4.11

Post-tx

-0.47,

-0.63, -0.32

-0.37

-0.53, -0.20

Follow-up

-0.43

-0.66, -0.21

-0.23

-0.47, 0.01

Female

0.25

0.13, 0.38

0.26

0.13, 0.38

GDS

0.04

0.04, 0.05

0.04

0.04, 0.05

-0.05

-0.24, 0.14

-0.06

-0.25, 0.13

Follow-up:Female -0.22

-0.48, 0.04

-0.22

-0.49, 0.04

0.47

0.39, 0.64

Post-tx:Infidelity

-0.43

-0.69, -0.18

Follow-up:Infidelity

-0.84

-1.27, -0.42

Post-tx:Female

95% CI

No Infidelity

Infidelity

AIC

5241.28

B

95% CI

5183.29

Note: 95% CI that do not include 0 are significant at the p < .05 level. AIC = Akaike Information Criterion. Coding for gender (Male = 0, Female = 1), Post-tx (Pre-therapy

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28

assessment = 0, Post-therapy assessment = 1), Follow-up (Pre-therapy assessment = 0, 6 month follow-up assessment = 1), and infidelity (No infidelity = 0, infidelity = 1) Figure Caption

Figure A.1: Means and 95% Bootstrapped Confidence Intervals Across Time by Couple’s Infidelity Status


Infidelity

No infidelity Pre

GDS

65

20

60

15 10

Dependent Variable

70

25

CESD

Pre

Post

Post

Follow-up

Assessment Period

Follow-up

29