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  • 标题:Marginal Maximum Likelihood Estimation of Item Response Models in R
  • 本地全文:下载
  • 作者:Matthew S. Johnson
  • 期刊名称:Journal of Statistical Software
  • 印刷版ISSN:1548-7660
  • 电子版ISSN:1548-7660
  • 出版年度:2007
  • 卷号:20
  • 期号:1
  • 页码:1-24
  • 语种:English
  • 出版社:University of California, Los Angeles
  • 摘要: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.
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