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ERIC Number: EJ935330
Record Type: Journal
Publication Date: 2011
Pages: 22
Abstractor: As Provided
Reference Count: 49
ISSN: ISSN-0027-3171
Analyzing Longitudinal Item Response Data via the Pairwise Fitting Method
Fu, Zhi-Hui; Tao, Jian; Shi, Ning-Zhong; Zhang, Ming; Lin, Nan
Multivariate Behavioral Research, v46 n4 p669-690 2011
Multidimensional item response theory (MIRT) models can be applied to longitudinal educational surveys where a group of individuals are administered different tests over time with some common items. However, computational problems typically arise as the dimension of the latent variables increases. This is especially true when the latent variable distribution cannot be integrated out analytically, as with MIRT models for binary data. In this article, based on the pseudolikelihood theory, we propose a pairwise modeling strategy to estimate item and population parameters in longitudinal studies. Our pairwise method effectively reduces the dimensionality of the problem and hence is applicable to longitudinal IRT data with high-dimensional latent variables, which are challenging for classical methods. And in the low-dimensional case, our simulation study shows that it performs comparably with the classical methods. We further illustrate the implementation of the pairwise method using a development study of mathematics levels of junior high school students in which the response data are collected from 65 classes of 8 schools from 4 different school districts in China. (Contains 4 tables and 1 figure.)
Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: Junior High Schools
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: China