Li Cai|Ji Seung Yang|Mark Hansen
Generalized full-information item bifactor analysis.
University of California, Los Angeles|California Department of Education|University of California, Los Angeles|California Department of Education|University of California, Los Angeles
fullinformation item bifactor analysis important statistical method psychological educational measurement current methods limited singlegroup analysis inflexible types item response models supported propose flexible multiplegroup item bifactor analysis framework supports variety multidimensional item response theory models arbitrary mixing dichotomous ordinal nominal items extended item bifactor model also enables estimation latent variable means variances data 1 group present generalized userdefined parameter restrictions permitted within across groups derive efficient fullinformation maximum marginal likelihood estimator estimation method achieves substantial computational savings extending gibbons hedekers 1992 bifactor dimension reduction method optimization marginal loglikelihood requires 2dimensional integration regardless dimensionality latent variables use simulation studies demonstrate flexibility accuracy proposed methods apply model study crosscountry differences including differential item functioning using data large international education survey mathematics literacy