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The Dyadic Nature of Relationships: Relationship Satisfaction among Married and Cohabiting Couples

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Abstract

Using data from the 2010 Married and Cohabiting Couples survey, this study tests the correlates of relationship satisfaction among 752 married couples and 323 cohabiting couples, using the social exchange framework and the Actor-Partner Interdependence Model (APIM). Specifically, it considers how conflict in work-family balance, fairness in the division of paid and unpaid work at home, and prior relationship history are associated with relationship satisfaction. Dyadic data analysis with structural equation modeling is used to determine the respective contributions of each respondent’s predictors (i.e., actor effects) and his/her spouse’s or partner’s predictors (i.e., partner effects). No gender differences in actor and partner effects emerge, yet the actor and partner effects remain distinct for both groups. Fairness in the division of paid and unpaid work at home, as well as less conflict in balancing work and family responsibilities, act as rewards for both married and cohabiting couples. Prior relationship history does not have any significant actor effects for either group, but the partner effects are significant for cohabiting couples. Some of these results suggest that relationship dynamics differ between married and cohabiting couples. Specifically, the negative partner effects of both prior relationship history and conflict in work-family balance on relationship satisfaction are significantly stronger among cohabiting couples than among married couples.

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Notes

  1. This study also uses several other variables that predict relationship satisfaction, including relationship duration, presence of preschool children living in the household, cohabiting with one’s spouse before marriage (for married couples), and plans to get married in the future (for cohabiting couples) (Brown 2003; Brown and Booth 1996; Dush et al. 2008; Nock 1995; Previti and Amato 2003; Skinner et al. 2002; Stevens et al. 2001; Teachman et al. 1999).

  2. The indicators load significantly with the latent construct, and a high percentage of the variance in these indicators is explained by the latent construct (wives: R2 = 33–68%, CFI = 0.99, RMSEA = 0.02, Chi-square: 4.38 (df = 5), p > 0.10; husbands: R2 = 23–66%, CFI = 0.98, RMSEA = 0.08, Chi-square: 37.06 (df = 5), p < 0.001).

  3. The indicators load significantly with the latent construct, and a high percentage of the variance in these indicators is explained by the latent construct (female partners: R2 = 33–69%, CFI = 0.98, RMSEA = 0.05, Chi-square: 8.36 (df = 5), p > 0.10; male partners: R2 = 18–66%, CFI = 0.98, RMSEA = 0.07, Chi-square: 12.36 (df = 5), p < 0.05).

  4. Specifically, this study compares a model where both partners’ reports of relationship satisfaction are combined into one latent construct (CFI = 0.82, RMSEA = 0.15, p < 0.001) to a model where both partners’ reports of relationship satisfaction are modeled separately (CFI = 0.99, RMSEA = 0.05, p < 0.01). The χ2 difference test (χ2 = 222.81, df = 3, p < 0.001) indicates that combining both partners’ reports of relationship satisfaction offers a worse fit. Thus, the two latent constructs are modeled separately in the analyses.

  5. One limitation of this data is that married couples are only asked about previous marriages (but not about prior cohabiting relationships), and cohabiting couples are only asked about their prior cohabiting relationships (but not about prior marriages).

  6. In this study, the mixed independent variables are conflict between work and family responsibilities, fairness in the division of paid and unpaid work, and prior relationship history.

  7. Among cohabiting couples, around 98% of the sample agreed on their likelihood of marriage in the future. Thus, only the female partner’s report for this question was used.

  8. There are likely reciprocal relationships between the independent and outcome variables. Moreover, due to the cross-sectional design of the data, causation is difficult to establish. Thus, these results should be classified as “correlates” and not true “causes” of relationship satisfaction. Among married couples, 4% of husbands (N = 32) and 5% of wives (N = 40) had some missing data. Among cohabiting couples, around 8% of male partners (N = 26) and 7% of female partners (N = 24) had some missing data. Maximum likelihood estimation was used to impute the missing scores, making it possible to analyze all 752 married and 323 cohabiting couples. This approach yields less biased results than listwise deletion (Allison 2002; Little 1992).

  9. Multi-group analyses are only performed for variables that are the same for both married and cohabiting couples. The control variables “cohabitation with spouse before marriage” for married couples and “plans to marry the current partner in the future” for cohabiting couples were therefore not used for multi-group analysis.

  10. One limitation of this data is that married and cohabiting couples are only asked details about their current relationship, but not about their relationship(s) with previous spouses (for married couples) or partners (for cohabiting couples).

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    Deniz Yucel

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Yucel, D. The Dyadic Nature of Relationships: Relationship Satisfaction among Married and Cohabiting Couples. Applied Research Quality Life 13, 37–58 (2018). https://doi.org/10.1007/s11482-017-9505-z

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