Handbook of Diagnostic Classification Models

Matthias von Davier is Distinguished Research Scientist at the National Board of Medical Examiners (NBME), in Philadelphia, Pennsylvania. Until 2016, he was a senior research director in the Research & Development Division at Educational Testing Service (ETS), and co-director of the center for Global Assessment at ETS, leading psychometric research and operations of the center. He earned his Ph.D. at the University of Kiel, Germany, in 1996, specializing in psychometrics. In the Center for Advanced Assessment at NBME, he works on psychometric methodologies for analyzing data from technology-based high-stakes assessments. He is one of the editors of the Springer journal Large Scale Assessments in Education, which is jointly published by the International Association for the Evaluation of Educational Achievement (IEA) and ETS. He is also editor-in-chief of the British Journal of Mathematical and Statistical Psychology (BJMSP), and co-editor of the Springer book series Methodology of Educational Measurement and Assessment. Dr. von Davier received the 2006 ETS Research Scientist award and the 2012 NCME Brad Hanson Award for contributions to educational measurement. His areas of expertise include topics such as item response theory, latent class analysis, diagnostic classification models, and, more broadly, classification and mixture distribution models, computational statistics, person-fit, item-fit, and model checking, hierarchical extension of models for categorical data analysis, and the analytical methodologies used in large scale educational surveys. Dr. Lee is an Associate Professor in the program of Measurement, Statistics & Evaluation, in the Department of Human Development at Teachers College, Columbia University. She received her Ph.D. in Quantitative Methods at the University of Wisconsin-Madison, with a minor in Statistics. Her research interests are primarily on psychometric approaches to solving practical problems in educational and psychological testing. Her areas of expertise include topics such as development and applications of diagnostic classification models, item response theory, latent class models, and analytical methodologies used in large scale assessments. In addition to her own research, Dr. Lee collaborates on various projects on the use of latent variable models for purposes of scale development/test construction and for validity studies.

• 1. Introduction: From Latent Class Analysis to DINA and Beyond.- PART 1: Approaches to Cognitive Diagnosis.- 2. Nonparametric Item Response Theory and Mokken Scale Analysis, with Relations to Latent Class Models and Cognitive Diagnostic Models.- 3. The Reparameterized Unified Model System: A Diagnostic Assessment Modeling Approach.- 4.Bayesian Networks.- 5. Nonparametric Classification Models.- 6. General Diagnostic Model (GDM).- 7. Generalized Deterministic Inputs, Noisy “and” Gate Model (G-DINA).- 8. Loglinear Cognitive Diagnostic Model (LCDM).- 9. Diagnostic Modeling of Skill Hierarchies and Cognitive Process with MLTM-D.- 10. Explanatory Diagnostic Models.- 11. Insights from Reparametrized DINA and Beyond.- PART 2:  Special Topics.- 12. Q Matrix Learning via Latent Variable Selection and Identifiability.- 13. Global Model and Item-level Fit Indices.- 14.Exploratory Data Analysis and Cognitive Diagnostic Model.- 15. CDM-CAT.- 16. Identifiability and Cognitive Diagnostic Model.- 17. Classification Consistency and Reliability.- 18.Differential Item Functioning in CDM.- 19. Parameter Invariance and Skill Attribute Continuity in DCMs: Bifactor MIRT as an Appealing and Related Alternative.- PART 3: Applications.- 20.Application of CDMs to Process Data Analysis.- 21. Application of CDMs to Learning Systems.- 22. CDMs in Vocational Education.- 23. Analyzing Large Scale Assessment Data with Diagnostic Models.- 24. Reduced Reparameterized Unified Model Applied to Learning Spatial Reasoning Skills.- 25. How to Conduct a Study with Diagnostic Models.- PART 4: Software, Data, and Tools.- 26. The R package CDM for Diagnostic Modeling.- 27. Diagnostic Classification Modeling with flexMIRT.- 28. Using Mplus to Estimate the Log-Linear Cognitive Diagnosis Model.- 29. The GDINA R-package.- 30. GDM software mdltm including Parallel EM algorithm.-31. Estimating CDMs using MCMC.