Marginal Maximum Likelihood Estimation of Item Response Models in R. tem 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.
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References in zbMATH (referenced in 3 articles , 1 standard article )
Showing results 1 to 3 of 3.
- Lee, Yi-Hsuan; Ying, Zhiliang: A mixture cure-rate model for responses and response times in time-limit tests (2015)
- Jan de Leeuw; Patrick Mair: An Introduction to the Special Volume on ”Psychometrics in R” (2007) not zbMATH
- Matthew Johnson: Marginal Maximum Likelihood Estimation of Item Response Models in R (2007) not zbMATH