%0 Journal Article
%T R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates
%A Andr¨¦ Beauducel
%J Open Journal of Statistics
%P 38-54
%@ 2161-7198
%D 2024
%I Scientific Research Publishing
%R 10.4236/ojs.2024.141002
%X Effects of performing <span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\">an </span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\">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 </span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\">in</span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\"> the variation of R-factor loading estimates beyond </span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\">the </span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\">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.</span></span>
%K R-Factor Analysis
%K Q-Factor Analysis
%K Loading Bias
%K Model Error
%K Multivariate Kurtosis
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=131145