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has been cited by the following article:

• 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.