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Marginal Maximum Likelihood Estimation of Item Response Models in R

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  • Johnson, Matthew S.

Abstract

Item response theory (IRT) models are a class of statistical models used by researchers to describe the response behaviors of individuals to a set of categorically scored items. The most common IRT models can be classified as generalized linear fixed- and/or mixed-effect models. Although IRT models appear most often in the psychological testing literature, researchers in other fields have successfully utilized IRT-like models in a wide variety of applications. This paper discusses the three major methods of estimation in IRT and develops R functions utilizing the built-in capabilities of the R environment to find the marginal maximum likelihood estimates of the generalized partial credit model. The currently available R packages ltm is also discussed.

Suggested Citation

  • Johnson, Matthew S., 2007. "Marginal Maximum Likelihood Estimation of Item Response Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 20(i10).
    • Handle: RePEc:jss:jstsof:v:020:i10
    DOI: http://hdl.handle.net/10.18637/jss.v020.i10
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    Cited by:

    1. repec:jss:jstsof:20:i01 is not listed on IDEAS
    2. Sandip Sinharay, 2015. "Assessment of Person Fit for Mixed-Format Tests," Journal of Educational and Behavioral Statistics, , vol. 40(4), pages 343-365, August.
    3. repec:jss:jstsof:39:i12 is not listed on IDEAS
    4. repec:jss:jstsof:34:i03 is not listed on IDEAS
    5. Ödegaard, Fredrik & Roos, Pontus, 2012. "Measuring workers' health and psychosocial work-environment on firm productivity," Working Paper Series 2012:17, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    6. repec:jss:jstsof:36:c01 is not listed on IDEAS
    7. Yi-Hsuan Lee & Zhiliang Ying, 2015. "A Mixture Cure-Rate Model for Responses and Response Times in Time-Limit Tests," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 748-775, September.
    8. Andreas Oranje & Andrew Kolstad, 2019. "Research on Psychometric Modeling, Analysis, and Reporting of the National Assessment of Educational Progress," Journal of Educational and Behavioral Statistics, , vol. 44(6), pages 648-670, December.

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