Identifying the strongest self-report predictors of sexual satisfaction using machine learning

Datos Bibliográficos

ID2933730
AutoresLaura M Vowels (0000-0001-5594-2095, University of Lausanne, autor de correspondencia), Matthew J Vowels (0000-0002-8811-1156, Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN, USA), Kristen P Mark (0000-0002-0524-3357, Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN, USA)
Año2022
Volumen39
Número5
Páginas1191-1212
Fecha de publicación2022-05-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaJournal of Social and Personal Relationships (JOURNAL)
Identificadores de la revistaISSN: 0265-4075 • E-ISSN: 1460-3608
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1177/02654075211047004
OpenAlexW4205527822
IdiomaEN
Citas recibidas7
Referencias citadas48

Sexual satisfaction has been robustly associated with relationship and individual well-being. Previous studies have found several individual (e.g., gender, self-esteem, and attachment) and relational (e.g., relationship satisfaction, relationship length, and sexual desire) factors that predict sexual satisfaction. The aim of the present study was to identify which variables are the strongest, and the least strong, predictors of sexual satisfaction using modern machine learning. Previous research has relied primarily on traditional statistical models which are limited in their ability to estimate a large number of predictors, non-linear associations, and complex interactions. Through a machine learning algorithm, random forest (a potentially more flexible extension of decision trees), we predicted sexual satisfaction across two samples (total N = 1846; includes 754 individuals forming 377 couples). We also used a game theoretic interpretation technique, Shapley values, which allowed us to estimate the size and direction of the effect of each predictor variable on the model outcome. Findings showed that sexual satisfaction is highly predictable (48-62% of variance explained) with relationship variables (relationship satisfaction, importance of sex in relationship, romantic love, and dyadic desire) explaining the most variance in sexual satisfaction. The study highlighted important factors to focus on in future research and interventions

Developmental psychology · Explained variation · Psychological intervention · Romance · Statistics · Variables · Variance (accounting · Attachment and Relationship Dynamics · Evolutionary Psychology and Human Behavior · Mathematics · Psychology · Sexual function and dysfunction studies · Social Psychology