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R-factor analysis of data generated by a combination of R- and Q-factors leads to biased loading estimates
- Publication Year :
- 2022
-
Abstract
- Effects of performing R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. It was noted that estimating a model comprising R- and Q-factors has to face loading indeterminacy beyond rotational indeterminacy. Although R-factor analysis of data based on a population model comprising R- and Q-factors is nevertheless possible, this may lead to model error. Accordingly, even in the population, the resulting R-factor loadings are not necessarily close estimates of the original population R-factor loadings. It was shown in a simulation study that large Q-factor variance induces an increase of the variation of R-factor loading estimates beyond chance level. The results indicate that performing R-factor analysis with data based on a population model comprising R- and Q-factors may result in substantial loading bias. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.
- Subjects :
- Statistics - Applications
62H25
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2201.11973
- Document Type :
- Working Paper