Closeness Discrepancies in Couple Relationships: A Dyadic Response Surface Analysis.

Individuals can differ in the degree of closeness they desire in their romantic relationships: Some people may perceive their current level of closeness as just right, whereas others may feel not close enough or too close to their partners (referred to as negative and positive closeness discrepancy, respectively). This study (N = 1,177 individuals from 748 couples) examined the implications of closeness discrepancies for subjective relationship quality (SRQ) using dyadic response surface analysis. The analyses found evidence for linear, but not broad, closeness discrepancy effects: SRQ was lower for individuals reporting more negative closeness discrepancies and, independent of this actor effect, for individuals with partners who reported more negative closeness discrepancies. These results suggest that low levels of closeness paired with a strong desire for closeness can impair both partners' relational well-being.

Motivational Interdependence in Couple Relationships.

This article presents an integrative conceptual model of motivational interdependence in couples, the MIC model. Based on theoretical tenets in motivation psychology, personality psychology, and research on interpersonal perception, the MIC model postulates that two partners' motive dispositions fundamentally interact in shaping their individual motivation and behavior. On a functional level, a partner's motivated behavior is conceptualized as an environmental cue that can contribute to an actor's motive expression and satisfaction. However, the partner's motivated behavior is considered to gain this motivational relevance only via the actor's subjective perception. Multilevel analyses of an extensive experience sampling study on partner-related communal motivation (N = up to 60,803 surveys from 508 individuals nested in 258 couples) supported the MIC model. Participants, particularly those with strong communal motive dispositions, behaved more communally at moments when they perceived their partners to behave more communally. In addition, participants experienced momentary boosts in satisfaction when they behaved more communally and, at the same time, perceived their partners' behavior as similarly communal. Broader implications of the MIC model for research on romantic relationships are discussed.

Measuring motivational relationship processes in experience sampling: A reliability model for moments, days, and persons nested in couples.

The investigation of within-person process models, often done in experience sampling designs, requires a reliable assessment of within-person change. In this paper, we focus on dyadic intensive longitudinal designs where both partners of a couple are assessed multiple times each day across several days. We introduce a statistical model for variance decomposition based on generalizability theory (extending P. E. Shrout & S. P. Lane, 2012), which can estimate the relative proportion of variability on four hierarchical levels: moments within a day, days, persons, and couples. Based on these variance estimates, four reliability coefficients are derived: between-couples, between-persons, within-persons/between-days, and within-persons/between-moments. We apply the model to two dyadic intensive experience sampling studies (n1 = 130 persons, 5 surveys each day for 14 days, ≥ 7508 unique surveys; n2 = 508 persons, 5 surveys each day for 28 days, ≥ 47764 unique surveys). Five different scales in the domain of motivational processes and relationship quality were assessed with 2 to 5 items: State relationship satisfaction, communal motivation, and agentic motivation; the latter consists of two subscales, namely power and independence motivation. Largest variance components were on the level of persons, moments, couples, and days, where within-day variance was generally larger than between-day variance. Reliabilities ranged from .32 to .76 (couple level), .93 to .98 (person level), .61 to .88 (day level), and .28 to .72 (moment level). Scale intercorrelations reveal differential structures between and within persons, which has consequences for theory building and statistical modeling.