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Effects of performing R-factor analysis of observed variables based onpopulation models comprising R- and Q-factors were investigated. It was notedthat estimating a model comprising R- and Q-factors has to face loadingindeterminacy beyond rotational indeterminacy. Although R-factor analysis ofdata based on a population model comprising R- and Q-factors is neverthelesspossible, this may lead to model error. Accordingly, even in the population,the resulting R-factor loadings are not necessarily close estimates of theoriginal population R-factor loadings. It was shown in a simulation study thatlarge Q-factor variance induces an increase of the variation of R-factorloading estimates beyond chance level. The results indicate that performingR-factor analysis with data based on a population model comprising R- andQ-factors may result in substantial loading bias. Tests of the multivariatekurtosis of observed variables are proposed as an indicator of possibleQ-factor variance in observed variables as a prerequisite for R-factoranalysis.

R-factor analysis of data generated by a combination of R- and Q-factors leads to biased loading estimates