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How to Improve the Regression Factor Score Predictor When Individuals Have Different Factor Loadings.
André Beauducel1, Norbert Hilger1, Anneke C Weide1
1University of Bonn, Bonn, Germany.
Educational and Psychological Measurement
|August 19, 2025
Summary
This study introduces a new heterogeneous regression factor score predictor (HRFS) that improves prediction accuracy by accounting for individual differences in factor loadings, outperforming conventional methods when heterogeneity is substantial.
Area of Science:
- Psychometrics
- Statistical Modeling
Background:
- Conventional factor models assume uniform factor loadings across individuals.
- Ignoring individual differences in factor loadings can decrease the accuracy of factor score predictors.
Purpose of the Study:
- To propose a heterogeneous regression factor score predictor (HRFS) that offers greater determinacy than the conventional regression factor score predictor (RFS).
- To introduce a method for estimating individual loadings and a binomial test for assessing loading heterogeneity.
Main Methods:
- Estimation of individual factor loadings.
- Computation of the HRFS using individual loading estimates.
- A binomial test to determine if HRFS is appropriate based on loading heterogeneity.
Main Results:
- Simulation studies demonstrate that HRFS yields higher determinacy than RFS in populations with significant loading heterogeneity.
- An empirical example showed improved determinacy for the emotional stability factor using HRFS.
Conclusions:
- HRFS provides a more determinate factor score prediction when individual factor loadings vary.
- The proposed method and test offer a practical approach to enhance factor analysis outcomes.
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