R. J. de Ayala

人物简介:

R. J. de Ayala, PhD, is Professor of Quantitative, Qualitative, and Psychometric Methods and Director of the Institutional Research Master's Program in the College of Educational and Human Sciences at the University of Nebraska–Lincoln (UNL). His research interests include psychometrics, item response theory, computerized adaptive testing, applied statistics, and multilevel models. His work has appeared in Applied Psychological Measurement, Applied Measurement in Education, the British Journal of Mathematical and Statistical Psychology, Educational and Psychological Measurement, the Journal of Applied Measurement, and the Journal of Educational Measurement. He is a Fellow of the American Psychological Association’s Division 5: Evaluation, Measurement, and Statistics and of the American Educational Research Association. He is a recipient of a Big 12 Faculty Fellowship and holds a Gallup Research Professorship at UNL.

The Theory and Practice of Item Response Theory书籍相关信息

  • ISBN:9781462547753
  • 作者:R. J. de Ayala
  • 出版社:暂无出版社
  • 出版时间:2022-4-29
  • 页数:643
  • 价格:USD 75.00
  • 纸张:暂无纸张
  • 装帧:Hardcover
  • 开本:暂无开本
  • 语言:暂无语言
  • 适合人群:Academics in education and psychology, Psychometricians, Educators, Test developers, Researchers in educational statistics, and anyone interested in understanding the mathematical and statistical principles behind test and assessment design
  • TAG:statistics / Mathematical Modeling / Psychometrics / Item Response Theory / Educational Measurement / Test Theory
  • 豆瓣评分:暂无豆瓣评分
  • 更新时间:2025-05-08 02:38:16

内容简介:

Noted for addressing both the “hows” and “whys” of item response theory (IRT), this text has been revised and updated with the latest techniques (multilevel models, mixed models, and more) and software packages. Simple to more complex models are covered in consistently formatted chapters that build sequentially. The book takes the reader from model development through the fit analysis and interpretation phases that would be performed in practice. To facilitate understanding, common datasets are used across chapters, with the examples worked through for increasingly complex models. Exemplary model applications include free (BIGSTEPS, NOHARM, Facets, R packages) and commercial (BILOG-MG, flexMIRT, SAS, WINMIRA, SPSS, SYSTAT) software packages.