R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates ()
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.
Share and Cite:
Beauducel, A. (2024) R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates.
Open Journal of Statistics,
14, 38-54. doi:
10.4236/ojs.2024.141002.
Cited by
No relevant information.