This handbook provides an overview of major developments around diagnostic classification models (DCMs) with regard to modeling, estimation, model checking, scoring, and applications. It brings together not only the current state of the art, but also the theoretical background and models developed for diagnostic classification. The handbook also offers applications and special topics and practical guidelines how to plan and conduct research studies with the help of DCMs.
Commonly used models in educational measurement and psychometrics typically assume a single latent trait or at best a small number of latent variables that are aimed at describing individual differences in observed behavior. While this allows simple rankings of test takers along one or a few dimensions, it does not provide a detailed picture of strengths and weaknesses when assessing complex cognitive skills.
DCMs, on the other hand, allow the evaluation of test taker performance relative to a potentially large number of skill domains. Most diagnostic models provide a binary mastery/non-mastery classification for each of the assumed test taker attributes representing these skill domains. Attribute profiles can be used for formative decisions as well as for summative purposes, for example in a multiple cut-off procedure that requires mastery on at least a certain subset of skills.
The number of DCMs discussed in the literature and applied to a variety of assessment data has been increasing over the past decades, and their appeal to researchers and practitioners alike continues to grow. These models have been used in English language assessment, international large scale assessments, and for feedback for practice exams in preparation of college admission testing, just to name a few.
Nowadays, technology-based assessments provide increasingly rich data on a multitude of skills and allow collection of data with respect to multiple types of behaviors. Diagnostic models can be understood as an ideal match for these types of data collections to provide more in-depth information about test taker skills and behavioral tendencies.
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1 Introduction: From Latent Classes to Cognitive Diagnostic Models |
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1 | (20) |
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Part I Approaches to Cognitive Diagnosis |
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2 Nonparametric Item Response Theory and Mokken Scale Analysis, with Relations to Latent Class Models and Cognitive Diagnostic Models |
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21 | (26) |
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3 The Reparameterized Unified Model System: A Diagnostic Assessment Modeling Approach |
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47 | (34) |
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81 | (26) |
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5 Nonparametric Methods in Cognitively Diagnostic Assessment |
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107 | (26) |
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6 The General Diagnostic Model |
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133 | (22) |
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7 The G-DINA Model Framework |
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155 | (16) |
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8 Loglinear Cognitive Diagnostic Model (LCDM) |
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171 | (16) |
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9 Diagnostic Modeling of Skill Hierarchies and Cognitive Processes with MLTM-D |
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187 | (20) |
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10 Explanatory Cognitive Diagnostic Models |
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207 | (16) |
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11 Insights from Reparameterized DIN A and Beyond |
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223 | (24) |
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12 Q-Matrix Learning via Latent Variable Selection and Identifiability |
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247 | (18) |
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13 Global -- and Item-Level Model Fit Indices |
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265 | (22) |
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14 Exploratory Data Analysis for Cognitive Diagnosis: Stochastic Co-blockmodel and Spectral Co-clustering |
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287 | (20) |
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15 Recent Developments in Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT): A Comprehensive Review |
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307 | (26) |
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16 Identifiability and Cognitive Diagnosis Models |
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333 | (26) |
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17 Measures of Agreement: Reliability, Classification Accuracy, and Classification Consistency |
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359 | (20) |
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18 Differential Item Functioning in Diagnostic Classification Models |
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379 | (16) |
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19 Bifactor MIRT as an Appealing and Related Alternative to CDMs in the Presence of Skill Attribute Continuity |
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395 | (26) |
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20 Utilizing Process Data for Cognitive Diagnosis |
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421 | (16) |
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21 Application of Cognitive Diagnostic Models to Learning and Assessment Systems |
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437 | (24) |
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22 CDMs in Vocational Education: Assessment and Usage of Diagnostic Problem-Solving Strategies in Car Mechatronics |
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461 | (28) |
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23 Applying the General Diagnostic Model to Proficiency Data from a National Skills Survey |
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489 | (14) |
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24 Reduced Reparameterized Unified Model Applied to Learning Spatial Rotation Skills |
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503 | (22) |
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25 How to Conduct a Study with Diagnostic Models |
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525 | (24) |
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Part IV Software, Data, and Tools |
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26 The R Package CDM for Diagnostic Modeling |
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549 | (24) |
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27 Diagnostic Classification Modeling with flexMIRT |
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573 | (8) |
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28 Using Mplus to Estimate the Log-Linear Cognitive Diagnosis Model |
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581 | (12) |
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29 Cognitive Diagnosis Modeling Using the GDINA R Package |
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593 | (10) |
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30 GDM Software mdltm Including Parallel EM Algorithm |
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603 | (26) |
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31 Estimating CDMs Using MCMC |
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629 | (18) |
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| Index |
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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.
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