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.
Original language | English (US) |
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Pages (from-to) | 276-312 |
Number of pages | 37 |
Journal | European Journal of Personality |
Volume | 37 |
Issue number | 3 |
DOIs | |
State | Published - May 1 2023 |
Funding
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 NSF Grant BCS-0951571 awarded to Daniel C. Molden and a UC Davis Small Research Grant awarded to Paul W. Eastwick.
Keywords
- attraction
- compatibility
- hookups
- random forests
- romantic relationships
ASJC Scopus subject areas
- Social Psychology
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Predicting romantic interest during early relationship development: A preregistered investigation using machine learning
Eastwick, P. W. (Creator), Joel, S. (Creator), Carswell, K. L. (Creator), Molden, D. C. (Creator), Finkel, E. J. (Creator) & Blozis, S. A. (Creator), SAGE Journals, 2022
DOI: 10.25384/sage.c.6018840.v1, https://sage.figshare.com/collections/Predicting_romantic_interest_during_early_relationship_development_A_preregistered_investigation_using_machine_learning/6018840/1
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