A machine learning approach to predicting perceived partner support from relational and individual variables

Abstract

Perceiving one’s partner as supportive is considered essential for relationships, but we know little about which factors are central to predicting perceived partner support. Traditional statistical techniques are ill-equipped to compare a large number of potential predictor variables and cannot answer this question. The current research used machine learning analysis (random forest with Shapley values) to identify the most salient self-report predictors of perceived partner support cross-sectionally and six months later. We analyzed data from five dyadic datasets (N = 550 couples) enabling us to have greater confidence in the findings and ensure generalizability. Our novel results advance the literature by showing that relationship variables and attachment avoidance are central to perceived partner support while partner similarity, other individual differences, individual well-being, and demographics explain little variance in perceiving partners as supportive. The findings are crucial in constraining and further developing our theories on perceived partner support.

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Identifiers

ORCID for Laura Vowels: orcid.org/0000-0001-5594-2095

ORCID for Katherine Carnelley: orcid.org/0000-0003-4064-8576

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