TITLE:
R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates
AUTHORS:
André Beauducel
KEYWORDS:
R-Factor Analysis, Q-Factor Analysis, Loading Bias, Model Error, Multivariate Kurtosis
JOURNAL NAME:
Open Journal of Statistics,
Vol.14 No.1,
February
9,
2024
ABSTRACT: Effects of performing an R-factor analysis of observed variables based on
population models comprising R- and Q-factors were investigated. Although
R-factor analysis of data based on a population model comprising R- and
Q-factors is possible, this may lead to model error. Accordingly, loading
estimates resulting from R-factor analysis of sample data drawn from a
population based on a combination of R- and Q-factors will be biased. It was
shown in a simulation study that a large amount of Q-factor variance induces an
increase in the variation of R-factor loading estimates beyond the chance level. 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.