Best Practices for Testing the Predictive Validity of Ideal Partner Preference-Matching.

Area of Science:

• Psychology
• Social Psychology
• Relationship Science

Background:

• Many psychological theories investigate how similarity between variables predicts outcomes.
• The ideal standards model suggests that matching ideal partner preferences with a partner's traits influences relationship evaluations.
• Testing this model requires analyzing the interaction between ideals and partner traits (A × B → C).

Purpose of the Study:

• To address the misconception that samplewide correlations between ideals and partner traits (A-B correlations) demonstrate that ideals guide partner selection.
• To highlight the limitations of A-B correlations in validating the ideal standards model.
• To review appropriate analytical strategies (A × B → C) for testing the predictive validity of ideal-matching.

Main Methods:

• Critically examining the inferential leap from samplewide correlations (A-B) to evidence of guided partner selection.
• Reviewing and recommending rigorous analytical strategies (A × B → C) for testing ideal-matching hypotheses.
• Discussing alternative explanations for observed samplewide A-B correlations.

Main Results:

• Samplewide correlations between a participant's ideals and their partner's traits (A-B correlations) do not inherently indicate that ideals guided partner selection.
• At least six alternative explanations can account for positive A-B correlations.
• A-B correlations alone are insufficient evidence for the predictive validity of ideal-matching.

Conclusions:

• Scholars must employ appropriate A × B → C analytic strategies to rigorously test the ideal standards model.
• Understanding the conditions under which ideal-matching predicts relationship outcomes requires moving beyond simple correlational evidence.
• Correctly applying advanced statistical methods is crucial for advancing research on partner preferences and relationship science.