Overfactoring in rating scale data: A comparison between factor analysis and item response theory.

ABSTRACT: Educational and psychological measurement is typically based on dichotomous variables or rating scales comprising a few ordered categories. When the mean of the observed responses approaches the upper or the lower bound of the scale, the distribution of the data becomes skewed and, if a categorical factor model holds in the population, the Pearson correlation between variables is attenuated. The consequence of this correlation attenuation is that the traditional linear factor model renders an excessive number of factors. This article presents the results of a simulation study investigating the problem of overfactoring and some solutions. We compare five widely known approaches: (1) The maximum-likelihood factor analysis (FA) model for normal data, (2) the categorical factor analysis (FAC)

SUBMITTER: Revuelta J 

PROVIDER: S-EPMC9750161 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature