1 University of California, Davis, CA, USA.
2 Northwestern University, Evanston, IL, USA.
Pers Soc Psychol Bull. 2019 Feb;45(2):167-181. doi: 10.1177/0146167218780689. Epub 2018 Jun 27.
Many psychological hypotheses require testing whether the similarity between two variables predicts important outcomes. For example, the ideal standards model posits that the match between (A) a participant's ideal partner preferences, and (B) the traits of a current/potential partner, predicts (C) evaluative outcomes (e.g., the decision to date someone, relationship satisfaction, breakup); tests of the predictive validity of ideal-matching require A × B → C analytic strategies. However, recent articles have incorrectly suggested that documenting a positive samplewide correlation between a participant's ideals and a current partner's traits (an A-B correlation) implies that participants pursued, selected, or desired partners with traits that matched their ideals. There are at least six alternative explanations for the emergence of a samplewide A-B correlation; A-B correlations do not provide evidence that ideals guide the selection/evaluation of specific partners. We review appropriately rigorous A × B → C tests that can aid scholars in identifying the circumstances in which ideal-matching exhibits predictive validity.
许多心理学假设都需要检验两个变量之间的相似性是否可以预测重要结果。例如，理想标准模型假设（A）参与者的理想伴侣偏好与（B）当前/潜在伴侣的特征之间的匹配程度可以预测（C）评估结果（例如，约会某人的决定、关系满意度、分手）；理想匹配的预测有效性检验需要 A×B→C 分析策略。然而，最近的一些文章错误地表明，记录参与者的理想与当前伴侣特征之间存在正的样本-wide 相关性（A-B 相关性），这意味着参与者追求、选择或期望与他们的理想相匹配的伴侣。对于样本-wide A-B 相关性的出现，至少有六个替代解释；A-B 相关性并不能证明理想指导了特定伴侣的选择/评估。我们回顾了适当严格的 A×B→C 检验，这些检验可以帮助学者确定在哪些情况下理想匹配具有预测有效性。
