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

Vowels, Laura M.; Vowels, Matthew J.; Carnelley, Katherine B. and Kumashiro, Madoka. 2023. A machine learning approach to predicting perceived partner support from relational and individual variables. Social Psychological and Personality Science, 14(5), pp. 526-538. ISSN 1948-5506 [Article]

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Abstract or Description

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. This 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 6 months later. We analyzed data from five dyadic data sets (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, whereas 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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The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the first author’s Jubilee Scholarship, University of Southampton. Funding for the Samples 1 and 2 were obtained from the National Science Foundation (BCS-719780) awarded to Eli J. Finkel, for the Sample 3 from the National Science Foundation (BCS-0132398) awarded to Caryl E. Rusbult, for the Sample 4 from the Fetzer Institute awarded to awarded to Caryl E. Rusbult, and for the Sample 5 from the Templeton Foundation (5158) awarded to Caryl E. Rusbult.

The supplemental material is available in the online version of the article.


close relationships, partner support, machine learning, Shapley values, random forest

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2 July 2022Accepted
12 August 2022Published Online
June 2023Published

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Date Deposited:

27 Sep 2022 08:20

Last Modified:

22 May 2023 10:11

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Yes, this version has been peer-reviewed.



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