Making the Most of What We Have: A Practical Application of Multidimensional Item Response Theory in Test Scoring

This article proposes a practical method that capitalizes on the availability of information from multiple tests measuring correlated abilities given in a single test administration. By simultaneously estimating different abilities with the use of a hierarchical Bayesian framework, more precise esti...

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Bibliographic Details
Published inJournal of educational and behavioral statistics Vol. 30; no. 3; pp. 295 - 311
Main Authors de la Torre, Jimmy, Patz, Richard J.
Format Journal Article
LanguageEnglish
Published Los Angeles, CA American Educational Research Association and American Statistical Association 01.09.2005
SAGE Publications
American Educational Research Association
Subjects
A posteriori knowledge
Achievement tests
Bayes estimators
Bayesian analysis
Bayesian Statistics
Computation
Correlations
Covariance matrices
Estimate reliability
Estimating techniques
Estimation methods
Item Response Theory
Markov analysis
Markov chains
Markov Processes
Measurement Techniques
Monte Carlo Methods
Monte Carlo simulation
Multidimensional Item Response Theory
Parametric models
Scoring
Tests
Bayesian estimation
ability estimation
item response theory
Markov chain Monte Carlo
multidimensionality
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Summary:This article proposes a practical method that capitalizes on the availability of information from multiple tests measuring correlated abilities given in a single test administration. By simultaneously estimating different abilities with the use of a hierarchical Bayesian framework, more precise estimates for each ability dimension are obtained. The efficiency of the proposed method is most pronounced when highly correlated abilities are estimated from multiple short tests. Employing Markov chain Monte Carlo techniques allows for straightforward estimation of model parameters.
ISSN:1076-9986
1935-1054
DOI:10.3102/10769986030003295