This article reviews the problems associated with using item response theory (IRT)-based latent variable scores for analytical modeling, discusses the connection between IRT and structural equation modeling (SEM)-based latent regression modeling for discrete data, and compares regression parameter estimates obtained using predicted IRT scores and standardized number-right scores in Ordinary Least Squares (OLS) regression with regression estimates obtained using the combined IRT-SEM approach. The Monte Carlo results show the expected a posteriori (EA approach is insensitive to sample size as expected but leads to appreciable attenuation in regression parameter estimates. Standardized number-right estimates and EAP regression estimates were found to be highly comparable. On the other hand, the IRT-SEM method produced smaller finite sample bias, and as expected, generated consistent regression estimates for suitably large sample sizes. Copyright
Structural Equation Modeling
Carleton University

Lu, I, Thomas, D.R. (D. Roland), & Zumbo, B.D. (Bruno D.). (2005). Embedding IRT in structural equation models: A comparison with regression based on IRT scores. Structural Equation Modeling (Vol. 12, pp. 263–277). doi:10.1207/s15328007sem1202_5