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IRT–ZIP Modeling for Multivariate Zero-Inflated Count Data

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  • Lijuan Wang

Abstract

This study introduces an item response theory–zero-inflated Poisson (IRT–ZIP) model to investigate psychometric properties of multiple items and predict individuals' latent trait scores for multivariate zero-inflated count data. In the model, two link functions are used to capture two processes of the zero-inflated count data. Item parameters are included to investigate item performance from both propensity and level perspectives. The application of the model was illustrated by analyzing the substance use data from the National Longitudinal Study of Youth. A simulation study based on the empirical data analysis scenario showed that the item parameters can be recovered accurately and precisely with adequate sample sizes. Limitations and future directions are discussed.

Suggested Citation

  • Lijuan Wang, 2010. "IRT–ZIP Modeling for Multivariate Zero-Inflated Count Data," Journal of Educational and Behavioral Statistics, , vol. 35(6), pages 671-692, December.
    • Handle: RePEc:sae:jedbes:v:35:y:2010:i:6:p:671-692
    DOI: 10.3102/1076998610375838
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    References listed on IDEAS

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    1. David Thissen & Howard Wainer, 1982. "Some standard errors in item response theory," Psychometrika, Springer;The Psychometric Society, vol. 47(4), pages 397-412, December.
    2. Daniel B. Hall, 2000. "Zero-Inflated Poisson and Binomial Regression with Random Effects: A Case Study," Biometrics, The International Biometric Society, vol. 56(4), pages 1030-1039, December.
    3. Yoshio Takane & Jan Leeuw, 1987. "On the relationship between item response theory and factor analysis of discretized variables," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 393-408, September.
    4. Sophia Rabe-Hesketh & Anders Skrondal, 2007. "Multilevel and Latent Variable Modeling with Composite Links and Exploded Likelihoods," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 123-140, June.
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