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Xue Zhang; Chun Wang – Grantee Submission, 2021
Among current state-of-art estimation methods for multilevel IRT models, the two-stage divide-and-conquer strategy has practical advantages, such as clearer definition of factors, convenience for secondary data analysis, convenience for model calibration and fit evaluation, and avoidance of improper solutions. However, various studies have shown…
Descriptors: Error of Measurement, Error Correction, Item Response Theory, Comparative Analysis
Suh, Youngsuk; Cho, Sun-Joo; Bottge, Brian A. – Grantee Submission, 2018
This article presents a multilevel longitudinal nested logit model for analyzing correct response and error types in multilevel longitudinal intervention data collected under a pretest-posttest, cluster randomized trial design. The use of the model is illustrated with a real data analysis, including a model comparison study regarding model…
Descriptors: Hierarchical Linear Modeling, Longitudinal Studies, Error Patterns, Change
Cho, Sun-Joo; Bottge, Brian A. – Grantee Submission, 2015
In a pretest-posttest cluster-randomized trial, one of the methods commonly used to detect an intervention effect involves controlling pre-test scores and other related covariates while estimating an intervention effect at post-test. In many applications in education, the total post-test and pre-test scores that ignores measurement error in the…
Descriptors: Item Response Theory, Hierarchical Linear Modeling, Pretests Posttests, Scores
Cho, Sun-Joo; Cohen, Allan S.; Bottge, Brian – Grantee Submission, 2013
A multilevel latent transition analysis (LTA) with a mixture IRT measurement model (MixIRTM) is described for investigating the effectiveness of an intervention. The addition of a MixIRTM to the multilevel LTA permits consideration of both potential heterogeneity in students' response to instructional intervention as well as a methodology for…
Descriptors: Intervention, Item Response Theory, Statistical Analysis, Models