000			01787nam a22002657a 4500
003			ZW-GwMSU
005			20221214112656.0
008			221214b |||||||| |||| 00| 0 eng d
040			_aMSU _cMSU _erda
100			_aKadengye, Damazo T. _eauthor
245			_aDirect likelihood analysis and multiple imputation for missing item scores in multilevel cross-classification educational data _ccreated by Damazo T. Kadengye, Eva Ceulemans, Wim Van den Noortgate
264			_aBelgium : _bSage; _c2013
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_vVolume , number ,
520			_aMultiple imputation (MI) has become a highly useful technique for handling missing values in many settings. In this article, the authors compare the performance of a MI model based on empirical Bayes techniques to a direct maximum likelihood analysis approach that is known to be robust in the presence of missing observations. Specifically, they focus on handling of missing item scores in multilevel cross-classification item response data structures that may require more complex imputation techniques, and for situations where an imputation model can be more general than the analysis model. Through a simulation study and an empirical example, the authors show that MI is more effective in estimating missing item scores and produces unbiased parameter estimates of explanatory item response theory models formulated as cross-classified mixed models.
650			_aExplanatory item response models
650			_aMissing data
650			_aultiple imputation
700			_aCeulemans, Eva _eauthor
700			_aNoortgate, Wim Van den _eauthor
856			_u https://doi/10.1177/0146621613491138
942			_2lcc _cJA
999			_c160773 _d160773