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Predicting romantic interest during early relationship development: A preregistered investigation using machine learning

Eastwick, Paul W; Joel, Samantha; Carswell, Kathleen L; Molden, Daniel C; Finkel, Eli J; Blozis, Shelley A

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Authors

Paul W Eastwick

Samantha Joel

Daniel C Molden

Eli J Finkel

Shelley A Blozis



Abstract

There are massive literatures on initial attraction and established relationships. But few studies capture early relationship development: the interstitial period in which people experience rising and falling romantic interest for partners who could—but often do not—become sexual or dating partners. In this study, 208 single participants reported on 1,065 potential romantic partners across 7,179 data points over 7 months. In stage 1, we used random forests (a type of machine learning) to estimate how well different classes of variables (e.g., individual differences vs. target-specific constructs) predicted participants’ romantic interest in these potential partners. We also tested (and found only modest support for) the perceiver × target moderation account of compatibility: the meta-theoretical perspective that some types of perceivers experience greater romantic interest for some types of targets. In stage 2, we used multilevel modeling to depict predictors retained by the random-forests models; robust (positive) main effects emerged for many variables, including sociosexuality, sex drive, perceptions of the partner’s positive attributes (e.g., attractive and exciting), attachment features (e.g., proximity seeking), and perceived interest. Finally, we found no support for ideal partner preference-matching effects on romantic interest. The discussion highlights the need for new models to explain the origin of romantic compatibility.

Citation

Eastwick, P. W., Joel, S., Carswell, K. L., Molden, D. C., Finkel, E. J., & Blozis, S. A. (2023). Predicting romantic interest during early relationship development: A preregistered investigation using machine learning. European Journal of Personality, 37(3), 276-312. https://doi.org/10.1177/08902070221085877

Journal Article Type Article
Acceptance Date Feb 18, 2022
Online Publication Date May 28, 2022
Publication Date 2023-05
Deposit Date Jul 25, 2022
Publicly Available Date May 18, 2023
Journal European Journal of Personality
Print ISSN 0890-2070
Electronic ISSN 1099-0984
Publisher SAGE Publications
Peer Reviewed Peer Reviewed
Volume 37
Issue 3
Pages 276-312
DOI https://doi.org/10.1177/08902070221085877

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