A Machine Learning Approach to Predicting Perceived Partner Support From Relational and Individual Variables
Dados Bibliográficos
| ID | 21209024 |
|---|---|
| Autores | Laura M Vowels (0000-0001-5594-2095, University of Southampton, autor correspondente), Matthew J Vowels (0000-0002-8811-1156, University of Surrey), Katherine B Carnelley (0000-0003-4064-8576, University of Southampton), Madoka Kumashiro (Goldsmiths University of London) |
| Ano | 2023 |
| Volume | 14 |
| Fascículo | 5 |
| Páginas | 526-538 |
| Data de publicação | 2023-07-01 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Social Psychological and Personality Science (JOURNAL) |
| Identificadores do periódico | ISSN: 1948-5506 • E-ISSN: 1948-5514 |
| Editora | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/19485506221114982 |
| OpenAlex | W4290976981 |
| Idioma | EN |
| Citações recebidas | 3 |
| Referências citadas | 45 |
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
Developmental psychology · Generalizability theory · Machine learning · Salient · Social support · Variables · Attachment and Relationship Dynamics · Computer Science · Evolutionary Psychology and Human Behavior · Family Dynamics and Relationships · Psychology · Social Psychology · Artificial Intelligence
Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies
Statistical Modeling
Social and emotional support and its implication for health
Interdependence, Interaction, and Relationships
From local explanations to global understanding with explainable AI for trees
Relationship Closeness as Including Other in the Self
A New Look at Social Support
Random Forests
Interpersonal relations
The Taboo Against Explicit Causal Inference in Nonexperimental Psychology
The Michelangelo Phenomenon and Personal Well‐Being
Self‐Respect and Pro‐Relationship Behavior in Marital Relationships
Why do people sacrifice to approach rewards versus to avoid costs? Insights from attachment theory
Steps toward the ripening of relationship science
Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being
Social Support in Couples
Partner support and goal outcomes during Covid‐19
Close partner as sculptor of the ideal self
Romantic relationship development
Regulatory focus and the Michelangelo Phenomenon
Close relationships as including other in the self
Daily goal progress is facilitated by spousal support and promotes psychological, physical, and relational well-being throughout adulthood
A Secure Base
Relationship influences on exploration in adulthood
Benefits of daily support visibility versus invisibility across the adult life span
Creating good relationships
Working Models of Attachment Shape Perceptions of Social Support
The link between self-esteem and social relationships
The association between adolescent well-being and digital technology use
An attachment and behavioral systems perspective on social support
Avoidance of Intimacy
Giving when it costs
Does Michelangelo care about age? An adult life-span perspective on the Michelangelo phenomenon
| Obras citantes distintas | 3 |
|---|---|
| Citações por ano | 3 |
| Intervalo de citações | 2025 - 2026 (2) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 3 |