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Multilevel IRT using dichotomous and polytomous response data.
Br J Math Stat Psychol. 2005 May; 58(Pt 1):145-72.BJ

Abstract

A structural multilevel model is presented where some of the variables cannot be observed directly but are measured using tests or questionnaires. Observed dichotomous or ordinal polytomous response data serve to measure the latent variables using an item response theory model. The latent variables can be defined at any level of the multilevel model. A Bayesian procedure Markov chain Monte Carlo (MCMC), to estimate all parameters simultaneously is presented. It is shown that certain model checks and model comparisons can be done using the MCMC output. The techniques are illustrated using a simulation study and an application involving students' achievements on a mathematics test and test results regarding management characteristics of teachers and principles.

Authors+Show Affiliations

Department of Research Methodology, Measurement and Data Analysis, University of Twente, The Netherlands. fox@edte.utwente.nl

Pub Type(s)

Comparative Study
Journal Article

Language

eng

PubMed ID

15969844

Citation

Fox, J-P. "Multilevel IRT Using Dichotomous and Polytomous Response Data." The British Journal of Mathematical and Statistical Psychology, vol. 58, no. Pt 1, 2005, pp. 145-72.
Fox JP. Multilevel IRT using dichotomous and polytomous response data. Br J Math Stat Psychol. 2005;58(Pt 1):145-72.
Fox, J. P. (2005). Multilevel IRT using dichotomous and polytomous response data. The British Journal of Mathematical and Statistical Psychology, 58(Pt 1), 145-72.
Fox JP. Multilevel IRT Using Dichotomous and Polytomous Response Data. Br J Math Stat Psychol. 2005;58(Pt 1):145-72. PubMed PMID: 15969844.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Multilevel IRT using dichotomous and polytomous response data. A1 - Fox,J-P, PY - 2005/6/23/pubmed PY - 2006/8/3/medline PY - 2005/6/23/entrez SP - 145 EP - 72 JF - The British journal of mathematical and statistical psychology JO - Br J Math Stat Psychol VL - 58 IS - Pt 1 N2 - A structural multilevel model is presented where some of the variables cannot be observed directly but are measured using tests or questionnaires. Observed dichotomous or ordinal polytomous response data serve to measure the latent variables using an item response theory model. The latent variables can be defined at any level of the multilevel model. A Bayesian procedure Markov chain Monte Carlo (MCMC), to estimate all parameters simultaneously is presented. It is shown that certain model checks and model comparisons can be done using the MCMC output. The techniques are illustrated using a simulation study and an application involving students' achievements on a mathematics test and test results regarding management characteristics of teachers and principles. SN - 0007-1102 UR - https://www.unboundmedicine.com/medline/citation/15969844/Multilevel_IRT_using_dichotomous_and_polytomous_response_data_ L2 - https://doi.org/10.1348/000711005X38951 DB - PRIME DP - Unbound Medicine ER -