Literature DB >> 23082893

Measuring change for a multidimensional test using a generalized explanatory longitudinal item response model.

Sun-Joo Cho1, Michele Athay, Kristopher J Preacher.   

Abstract

Even though many educational and psychological tests are known to be multidimensional, little research has been done to address how to measure individual differences in change within an item response theory framework. In this paper, we suggest a generalized explanatory longitudinal item response model to measure individual differences in change. New longitudinal models for multidimensional tests and existing models for unidimensional tests are presented within this framework and implemented with software developed for generalized linear models. In addition to the measurement of change, the longitudinal models we present can also be used to explain individual differences in change scores for person groups (e.g., learning disabled students versus non-learning disabled students) and to model differences in item difficulties across item groups (e.g., number operation, measurement, and representation item groups in a mathematics test). An empirical example illustrates the use of the various models for measuring individual differences in change when there are person groups and multiple skill domains which lead to multidimensionality at a time point.
© 2012 The British Psychological Society.

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Year:  2012        PMID: 23082893     DOI: 10.1111/j.2044-8317.2012.02058.x

Source DB:  PubMed          Journal:  Br J Math Stat Psychol        ISSN: 0007-1102            Impact factor:   3.380


  4 in total

1.  Additive multilevel item structure models with random residuals: item modeling for explanation and item generation.

Authors:  Sun-Joo Cho; Paul De Boeck; Susan Embretson; Sophia Rabe-Hesketh
Journal:  Psychometrika       Date:  2013-12-12       Impact factor: 2.500

Review 2.  Multivariate Hypothesis Testing Methods for Evaluating Significant Individual Change.

Authors:  Chun Wang; David J Weiss
Journal:  Appl Psychol Meas       Date:  2017-10-13

3.  Detecting intervention effects using a multilevel latent transition analysis with a mixture IRT model.

Authors:  Sun-Joo Cho; Allan S Cohen; Brian Bottge
Journal:  Psychometrika       Date:  2013-01-05       Impact factor: 2.500

4.  Multidimensional latent trait linear mixed model: an application in clinical studies with multivariate longitudinal outcomes.

Authors:  Jue Wang; Sheng Luo
Journal:  Stat Med       Date:  2017-06-01       Impact factor: 2.373

  4 in total

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