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Parameter recovery and model selection in mixed Rasch models
Author(s) -
Preinerstorfer David,
Formann Anton K.
Publication year - 2012
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/j.2044-8317.2011.02020.x
Subject(s) - rasch model , selection (genetic algorithm) , polytomous rasch model , statistics , model selection , econometrics , mixed model , mathematics , computer science , item response theory , machine learning , psychometrics
This study examines the precision of conditional maximum likelihood estimates and the quality of model selection methods based on information criteria (AIC and BIC) in mixed Rasch models. The design of the Monte Carlo simulation study included four test lengths (10, 15, 25, 40), three sample sizes (500, 1000, 2500), two simulated mixture conditions (one and two groups), and population homogeneity (equally sized subgroups) or heterogeneity (one subgroup three times larger than the other). The results show that both increasing sample size and increasing number of items lead to higher accuracy; medium‐range parameters were estimated more precisely than extreme ones; and the accuracy was higher in homogeneous populations. The minimum‐BIC method leads to almost perfect results and is more reliable than AIC‐based model selection. The results are compared to findings by Li, Cohen, Kim, and Cho (2009) and practical guidelines are provided.