Mixed Effects Models for Item Response Data
Feb 3, 2014, 10:15am-11:45am
A special selection of item response theory (IRT) models can be understood as generalized mixed-effects models (GLMM), and as such can be estimated using existent software packages such as lme4 in R or PROC NLMIXED in SAS. The benefits of estimating IRT models using GLMM methodology is the ability to include additional fixed and random effect variables to help explain the rich properties a test may posses. However, although a GLMM approach can be used for some IRT models, it is not flexible enough to include more common models seen in educational and psychological testing literature.
This talk will explore a newer estimation framework designed to be flexible to user specifications, accurate in the presence of multiple random effect covariates and allow a much larger number of useful IRT models to be used in item analysis work. The GLMM approach to modelling IRT data will be contrasted with the proposed estimation framework, and analysis of simulated and empirical data will be presented.
Suggested readings: De Boeck, P. D., et al. (2011). The Estimation of Item Response Models with the lmer Function from the lme4 Package in R. Journal of Statistical Software, 39, 1-28.
|Location:||Norm Endler Room, 164 Behavioural Science Building|
|Posted by:||Jolynn Pek|