When Forecasting Mutually Supportive Matches Will Be Practically Impossible. (May 2021)

• Record Type: Journal Article
• Authors: Lakey, Brian; Brummans, Jessica; Obreiter, Amy; Hubbard, Sultan Ali; Vander Molen, Randy J.; Fles, Elizabeth H.; Andrews, Justin; Woods, William C.; Hesse, Calvin; Gildner, Brianna; Forster, Kevin; Lutz, Rachel; Maley, Morgan
• Is Part Of: Psychological science. Volume 32:Number 5(2021)
• Page Start: 780
• Page End: 788
• Publication Date: 2021-05
• Subjects: perceived support -- relational-regulation theory -- social-relations model -- affect -- online dating -- open data; Psychology -- Periodicals; 150.5
• DOI: 10.1177/0956797620984460
• Languages: English
• ISSNs: 0956-7976

Abstract

Forecasting which dyads will develop mutually supportive relationships is an important applied and basic research question. Applying psychometric theory to the design of forecasting studies shows that agreement between dyad members about their relationship (relational reciprocity) sets an upper limit for forecasting accuracy by determining the reliability of measurement. To test this, we estimated relational reciprocity in Study 1. Participants in seven samples (six student and one military; N = 504; N dyads = 766) rated each other on support-related constructs in round-robin designs. Relational reciprocity was very low, undermining reliability. Formulas from psychometric theory predicted that forecasting supportive dyads would be practically impossible. To test this, we had participants in Study 2 complete a measure for matching dyads derived from recent theory. As predicted, supportive matches could not be forecast with acceptable precision. Theoretically, this falsifies some predictions of recent social-support theory. Practically, it remains unclear how to translate basic social-support research into effective interventions.