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Perils of Partialing: Can Scholars Predict Residualized Variables' Nomological Nets?
Leigha Rose1, Donald R Lynam2, Joshua D Miller1
1University of Georgia, Athens, Georgia, USA.
Journal of Personality
|December 4, 2025
Summary
Psychological researchers struggle to accurately predict how partialing affects variable relationships. This statistical technique, used to isolate unique construct properties, often leads to misinterpretations of residualized correlations.
Area of Science:
- Psychology
- Statistics
Background:
- Partialing is a statistical method to remove shared variance between constructs, aiming to isolate unique properties.
- Its interpretation is challenging, particularly with highly correlated original variables, leading to criticism.
Purpose of the Study:
- To assess the accuracy of psychological researchers in estimating the nomological networks of partialed variables.
- Investigate the impact of intercorrelations and partialing magnitude on prediction accuracy.
Main Methods:
- Used variables with varying intercorrelations (e.g., anxiety-depression, personality disorders).
- Tested experts' ability to estimate partialed variables' nomological networks against personality trait profiles.
Main Results:
- Experts demonstrated poor accuracy in predicting residualized correlations.
- High intercorrelations and significant changes in nomological nets after partialing affected accuracy.
Conclusions:
- The study highlights difficulties in interpreting partialed variables in psychological research.
- Researchers' predictions of residualized correlations were inaccurate, questioning the practice's utility.
Keywords:
construct validitymultiple regressionquestionable measurement practicesresidualized variablessemipartial effectsMore Related Videos
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