Abstract
Factor analysis (FA) and item response theory (IRT) modeling are compared (a) theoretically by examining the model definitions and considering the most commonly applied estimation methods for the models, (b) empirically by reviewing journal articles on scaling research to show what is actually done in practice, (c) in model estimation performance by means of a Monte Carlo simulation study to investigate the robustness of either approach against violations of distributional assumptions, and (d) in practice by applying the models to empirical data sets.
In a simulation study, the effects of four explanatory variables (latent variable (LV) distribution, item response distribution, scale strength, and sample size) on model estimation are investigated by applying four selected scaling models on samples of generated data: FA of the sample covariance matrix (FAlin), FA of the estimated polychoric correlation matrix (FApoly), the graded response IRT model (IRTgrm), and the nonparametric Mokken IRT model extended to polytomous items (IRTmok).
For normal data it is shown that both FApoly and IRTgrm perform well. Under conditions of a skewnormal LV, the performance of all parametric models deteriorates compared to normal LV conditions, most notably when combined with skewed item variables. IRTgrm performs best in such circumstances. Nonnormal LV and item distributions do not pose any estimation problems for IRTmok.
Guidelines are provided for applied researchers employing scale analysis: Take a large enough sample size, use available substantive knowledge, inspect the sample data, choose a model based on specific characteristics of the data, assess model fit, and use modelestimated LV scores.
In a simulation study, the effects of four explanatory variables (latent variable (LV) distribution, item response distribution, scale strength, and sample size) on model estimation are investigated by applying four selected scaling models on samples of generated data: FA of the sample covariance matrix (FAlin), FA of the estimated polychoric correlation matrix (FApoly), the graded response IRT model (IRTgrm), and the nonparametric Mokken IRT model extended to polytomous items (IRTmok).
For normal data it is shown that both FApoly and IRTgrm perform well. Under conditions of a skewnormal LV, the performance of all parametric models deteriorates compared to normal LV conditions, most notably when combined with skewed item variables. IRTgrm performs best in such circumstances. Nonnormal LV and item distributions do not pose any estimation problems for IRTmok.
Guidelines are provided for applied researchers employing scale analysis: Take a large enough sample size, use available substantive knowledge, inspect the sample data, choose a model based on specific characteristics of the data, assess model fit, and use modelestimated LV scores.
Translated title of the contribution  Een vergelijking tussen factoranalyse en itemresponstheorie in schaalanalyse 

Original language  English 
Qualification  Doctor of Philosophy 
Awarding Institution 

Award date  23Jun2014 
Place of Publication  [S.l.] 
Publisher  
Print ISBNs  9789036770927 
Electronic ISBNs  9789036770910 
Publication status  Published  2014 