Computerized Adaptive and Multistage Testing with R (eBook, PDF)

Using Packages catR and mstR

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Provides exhaustive descriptions of CAT and MST processes in an R environment

Guides users to simulate and implement CAT and MST using R for their applications

Summarizes the latest developments and challenges of packages catR and mstR

Provides R packages catR and mstR and illustrates to users how to do CAT and MST simulations and implementations using R

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David Magis, PhD, is Research Associate of the "Fonds de la Recherche Scientifique - FNRS" at the Department of Psychology, University of Liège, Belgium. His specialization is statistical methods in psychometrics, with special interest in item response theory, differential item functioning and computerized adaptive testing. His research interests include both theoretical and methodological development as well as open source implementation and dissemination in R. He is the main developer and maintainer of the packages catR and mstR, among others.

Duanli Yan, PhD, is Manager of Data Analysis and Computational Research for Automated Scoring group in the Research and Development division at the Educational Testing Service (ETS). She is also an Adjunct Professor at Rutgers University. At ETS, Dr. Yan's responsibilities include the EXADEP(TM) test, the TOEIC® Institutional programs, and automated scoring engines upgrade and scoring. She has been a statistical coordinator and a Psychometrician for several operational programs and a Development Scientist for innovative research applications. Dr. Yan received many awards including the 2011 ETS Presidential Award, the 2013 NCME Brenda Lyod award, the 2015 IACAT Early Career Award, and 2016 AERA Significant Contribution to Educational Measurement and Research Methodology Award. She is a co-author for Bayesian Networks in Educational Assessment and a co-editor for Computerized Multistage Testing: Theory and Applications.

Alina A. von Davier, PhD, is Vice-President at ACTNext and an Adjunct Professor at Fordham University. She was also Senior Research Director of the Computational Psychometrics Research Center at Educational Testing Service (ETS), where she was responsible for developing a team of experts and a psychometric research agenda in support of next generation assessments. Computational psychometrics, which include machine learning and data mining techniques, Bayesian inference methods, stochastic processes and psychometric models are the main set of tools employed in her current work. She also works with psychometric models applied to educational testing: test score equating methods, item response theory models, and adaptive testing.