The University of Southampton
University of Southampton Institutional Repository

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

A machine learning approach to predicting perceived partner support from relational and individual variables
A machine learning approach to predicting perceived partner support from relational and individual variables
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.
close relationships, partner support, machine learning, Shapley Values, random forest
1948-5506
Vowels, Laura
c30dc6eb-4a98-4534-b784-499c2d291c5f
Vowels, Matthew
e4ad5650-6b93-4ed8-8789-c7303a8bdbc0
Carnelley, Katherine
02a55020-a0bc-480e-a0ff-c8fe56ee9c36
Kumashiro, Madoka
b02dc8f1-7317-4dfc-94e3-8dcb75080778
Vowels, Laura
c30dc6eb-4a98-4534-b784-499c2d291c5f
Vowels, Matthew
e4ad5650-6b93-4ed8-8789-c7303a8bdbc0
Carnelley, Katherine
02a55020-a0bc-480e-a0ff-c8fe56ee9c36
Kumashiro, Madoka
b02dc8f1-7317-4dfc-94e3-8dcb75080778

Vowels, Laura, Vowels, Matthew, Carnelley, Katherine and Kumashiro, Madoka (2022) A machine learning approach to predicting perceived partner support from relational and individual variables. Social Psychological and Personality Science. (doi:10.1177/19485506221114).

Record type: Article

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.

Text
220624ML_support - Accepted Manuscript
Download (1MB)

More information

Accepted/In Press date: 2 July 2022
e-pub ahead of print date: 12 August 2022
Keywords: close relationships, partner support, machine learning, Shapley Values, random forest

Identifiers

Local EPrints ID: 468413
URI: http://eprints.soton.ac.uk/id/eprint/468413
ISSN: 1948-5506
PURE UUID: d954d3a3-25ee-42ca-aa64-fa0015dbe561
ORCID for Laura Vowels: ORCID iD orcid.org/0000-0001-5594-2095
ORCID for Katherine Carnelley: ORCID iD orcid.org/0000-0003-4064-8576

Catalogue record

Date deposited: 12 Aug 2022 21:38
Last modified: 17 Mar 2024 07:25

Export record

Altmetrics

Contributors

Author: Laura Vowels ORCID iD
Author: Matthew Vowels
Author: Madoka Kumashiro

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×