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BRIEF REPORT: HIGHLY CORRELATED PREDICTOR VARIABLES IN MULTIPLE REGRESSION MODELS
Multivariate Behavioral Research
|January 9, 2016
Summary
Including highly correlated predictor variables in multiple regression models is often discouraged. However, this approach is justifiable when theoretical or empirical evidence supports their inclusion, especially with squared variables.
Area of Science:
- Statistics
- Econometrics
- Social Sciences
Background:
- Multiple regression analysis commonly excludes highly correlated predictor variables.
- The exclusion is based on the argument that these variables account for redundant variance.
Purpose of the Study:
- This study defends the inclusion of highly correlated predictor variables in specific circumstances.
- It focuses on the use of squared elements of original variables as predictors.
Main Methods:
- The study discusses scenarios where including correlated predictors is appropriate.
- It examines the justification based on group membership vectors and theoretical/empirical support.
Main Results:
- The inclusion of highly correlated variables is defended under specific conditions.
- Theoretical or empirical justification is a key factor for their inclusion.
Conclusions:
- The practice of including highly correlated predictor variables, particularly squared terms, can be statistically valid.
- Researchers should consider theoretical and empirical justifications when deciding on variable inclusion in regression models.
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