Scale Alignment in Multidimensional Rasch Family Models [Online]

Abstract

Scores estimated from multidimensional item response theory (IRT) models are not necessarily comparable across dimensions. In this article, the concept of aligned dimensions is formalized in the context of Rasch models, and two methods are described—delta dimensional alignment (DDA) and logistic regression alignment (LRA)—to transform estimated item parameters so that dimensions are aligned. Both the DDA and LRA methods are applied to real and simulated data, and it is demonstrated that both methods are broadly effective for achieving aligned scales. The routine use of scale alignment methods is recommended prior to comparing scores across dimensions.

Leah Feuerstahle is currently assistant professor at the Department of Psychology, Fordham University. Her research interests include theoretical and applied issues in item response theory, psychometric model specification and interpretation, latent variable modeling, nonparametric methods, and bayesian statistics. Her work appears in peer-reviewed journals such as Multivariate Behavioral Research, Applied Psychological Measurement, and Journal of Educational Measurement. Leah received her Ph.D. in Quantitative Psychology from the University of Minnesota in 2016 and a Master's degree in Statistics from the same university in 2015.

Date: 
Tuesday, October 27, 2020 - 2:00pm
Building: 
Online session
Room: 
Zoom
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