Parameter recovery and model selection in mixed Rasch models
Journal
British Journal of Mathematical and Statistical Psychology
Type
journal article
Date Issued
2012
Author(s)
Preinerstorfer, David
;
Formann, Anton K.
Abstract
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.
Language
English
HSG Classification
contribution to scientific community
Refereed
Yes
Volume
65
Number
2
Start page
251
End page
262
Subject(s)
Eprints ID
266218