  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.
