Sadly, Anton K. Formann passed away in 2000, before this article was published.
Parameter recovery and model selection in mixed Rasch models
Article first published online: 15 JUN 2011
©2011 The British Psychological Society
British Journal of Mathematical and Statistical Psychology
Volume 65, Issue 2, pages 251–262, May 2012
How to Cite
Preinerstorfer, D. and Formann, A. K. (2012), Parameter recovery and model selection in mixed Rasch models. British Journal of Mathematical and Statistical Psychology, 65: 251–262. doi: 10.1111/j.2044-8317.2011.02020.x
- Issue published online: 16 APR 2012
- Article first published online: 15 JUN 2011
- Received 1 July 2010; revised version received 4 May 2011
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.