Introduction to The Special Issue: Advances in Methods and Measurement in Family Psychology

This special issue presents a collection of reports that highlight recent advances in methods and measurement and also shed light on the complexity of family psychology. The importance of theory in guiding solid family science is evident throughout these reports. The reports include guides for researchers who incorporate direct observation into their research protocols and the ever-expanding field of tele-health interventions. Advanced analytic approaches are offered in the areas of grid sequence analysis, latent fixed-effects models, and the Factors of Curves Model (FOCUS). These sophisticated analytic approaches may be applied to advance systemic thinking in family psychology. The last set of articles illustrate how complex and innovative methodologies are applied to address important societal issues. Work experiences and marital relationships in African American couples address the importance of spillover effects in contemporary families. The creation of biobehavioral plasticity index has the potential to inform gene x environment contributions to family functioning. Finally, the unique methodological issues that are particularly germane to the diverse nature of stepfamilies and nonresident fathers are addressed. We hope that readers of this special issue will return to these reports as resources and examples of theory-driven methods and measurements

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner's ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person's own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

The Effectiveness of the Within Our Reach Relationship Education Program for Couples: Findings from a Federal Randomized Trial

This study examined the effectiveness of a couple‐based relationship education program, Within Our Reach. Secondary data (n = 3,609) were analyzed from the federal Supporting Healthy Marriage project. Couples were randomly assigned to receive Within Our Reach and associated services or to a no‐treatment (treatment‐as‐usual) control group. Those assigned to Within Our Reach reported better couple and individual outcomes on 8 of 12 outcomes measured (M ES = .15) at the 12‐month follow‐up and 6 of 10 outcomes measured at the 30‐month follow‐up (M ES = .14), including higher relationship happiness, more warmth and support, more positive communication, less negative behavior and emotion, less psychological abuse, less physical assault (for men), lower psychological distress (for women), and less infidelity. They were also less likely to report that their marriage was in trouble. These effects were generally small in size and many were replicated across the two follow‐ups. There were no significant differences between those assigned to Within Our Reach versus control on cooperative parenting, severe psychological assault, or percent married. Implications for future research, programming, and policy are discussed

Recent Advances in the Understanding of Relationship Communication During Military Deployment.

n recent decades, there has been a dramatic increase in the ability of service members and their intimate partners to communicate while the service member is deployed to a combat zone. Communication among partners is a crucial aspect of intimate relationships that has been demonstrated to be highly associated with couples’ satisfaction. In addition, it is often cited by unhappy partners as a primary relationship problem. This special section of the Journal of Family Psychology presents five articles investigating deployment communication among service members and their intimate partners. The studies address the content and goals of deployment communication, the relations of communication to relationship satisfaction, as well as a new measure of deployment communication for potential use in future studies. A greater understanding of communication among partners of military couples during a combat deployment could likely benefit our understanding of relationship communication in a broader range of couples