In:
Educational and Psychological Measurement, SAGE Publications, Vol. 74, No. 3 ( 2014-06), p. 495-515
Abstract:
In the social sciences, latent traits often have a hierarchical structure, and data can be sampled from multiple levels. Both hierarchical latent traits and multilevel data can occur simultaneously. In this study, we developed a general class of item response theory models to accommodate both hierarchical latent traits and multilevel data. The freeware WinBUGS was used for parameter estimation. A series of simulations were conducted to evaluate the parameter recovery and the consequence of ignoring the multilevel structure. The results indicated that the parameters were recovered fairly well; ignoring multilevel structures led to poor parameter estimation, overestimation of test reliability for the second-order latent trait, and underestimation of test reliability for the first-order latent traits. The Bayesian deviance information criterion and posterior predictive model checking were helpful for model comparison and model-data fit assessment. Two empirical examples that involve an ability test and a teaching effectiveness assessment are provided.
Type of Medium:
Online Resource
ISSN:
0013-1644
,
1552-3888
DOI:
10.1177/0013164413509628
Language:
English
Publisher:
SAGE Publications
Publication Date:
2014
detail.hit.zdb_id:
206630-0
detail.hit.zdb_id:
1500101-5
SSG:
5,2
SSG:
5,3
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