Rasch measurement is preferable to factor analysis for reducing complex data matrices to unidimensional variables because factor analysis can mistake ordinally labeled stochastic observations for linear measures, and it does not construct linear measurement. Guidelines and instructions on how to use Rasch measurement to replace factor analysis are…

We consider a general type of model for analyzing ordinal variables with covariate effects and 2 approaches for analyzing data for such models, the item response theory (IRT) approach and the PRELIS-LISREL (PLA) approach. We compare these 2 approaches on the basis of 2 examples, 1 involving only covariate effects directly on the ordinal variables…

The Rasch item fit approach for detecting multidimensionality in response data is compared with principal component analysis without rotation using simulated data. Results indicate that both approaches work in a variety of multidimensional data structures, but the Rasch item fit is better under some circumstances, as discussed. (SLD)

Investigated the factorial invariance of scores from a seventh grade state reading assessment across general education students and selected groups of students with disabilities. Assessed the fit of a two-factor model and five levels of constraint. Results generally support the score comparability of the reading assessment, but more research is…