Handbook of Diagnostic Classification Models: Models and Model Extensions, Applications, Software Packages

✍ Scribed by Matthias von Davier, Young-Sun Lee

• Publisher: Springer International Publishing
• Year: 2019
• Tongue: English
• Leaves: 646
• Series: Methodology of Educational Measurement and Assessment
• Edition: 1st ed. 2019
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xiv
Introduction: From Latent Classes to Cognitive Diagnostic Models (Matthias von Davier, Young-Sun Lee)....Pages 1-17
Front Matter ....Pages 19-19
Nonparametric Item Response Theory and Mokken Scale Analysis, with Relations to Latent Class Models and Cognitive Diagnostic Models (L. Andries van der Ark, Gina Rossi, Klaas Sijtsma)....Pages 21-45
The Reparameterized Unified Model System: A Diagnostic Assessment Modeling Approach (William Stout, Robert Henson, Lou DiBello, Benjamin Shear)....Pages 47-79
Bayesian Networks (Russell G. Almond, Juan-Diego Zapata-Rivera)....Pages 81-106
Nonparametric Methods in Cognitively Diagnostic Assessment (Chia-Yi Chiu, Hans-Friedrich Köhn)....Pages 107-132
The General Diagnostic Model (Matthias von Davier)....Pages 133-153
The G-DINA Model Framework (Jimmy de la Torre, Nathan D. Minchen)....Pages 155-169
Loglinear Cognitive Diagnostic Model (LCDM) (Robert Henson, Jonathan L. Templin)....Pages 171-185
Diagnostic Modeling of Skill Hierarchies and Cognitive Processes with MLTM-D (Susan E. Embretson)....Pages 187-205
Explanatory Cognitive Diagnostic Models (Yoon Soo Park, Young-Sun Lee)....Pages 207-222
Insights from Reparameterized DINA and Beyond (Lawrence T. DeCarlo)....Pages 223-243
Front Matter ....Pages 245-245
Q-Matrix Learning via Latent Variable Selection and Identifiability (Jingchen Liu, Hyeon-Ah Kang)....Pages 247-263
Global- and Item-Level Model Fit Indices (Zhuangzhuang Han, Matthew S. Johnson)....Pages 265-285
Exploratory Data Analysis for Cognitive Diagnosis: Stochastic Co-blockmodel and Spectral Co-clustering (Yunxiao Chen, Xiaoou Li)....Pages 287-306
Recent Developments in Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT): A Comprehensive Review (Xiaofeng Yu, Ying Cheng, Hua-Hua Chang)....Pages 307-331
Identifiability and Cognitive Diagnosis Models (Gongjun Xu)....Pages 333-357
Measures of Agreement: Reliability, Classification Accuracy, and Classification Consistency (Sandip Sinharay, Matthew S. Johnson)....Pages 359-377
Differential Item Functioning in Diagnostic Classification Models (Xue-Lan Qiu, Xiaomin Li, Wen-Chung Wang)....Pages 379-393
Bifactor MIRT as an Appealing and Related Alternative to CDMs in the Presence of Skill Attribute Continuity (Daniel M. Bolt)....Pages 395-417
Front Matter ....Pages 419-419
Utilizing Process Data for Cognitive Diagnosis (Hong Jiao, Dandan Liao, Peida Zhan)....Pages 421-436
Application of Cognitive Diagnostic Models to Learning and Assessment Systems (Benjamin Deonovic, Pravin Chopade, Michael Yudelson, Jimmy de la Torre, Alina A. von Davier)....Pages 437-460
CDMs in Vocational Education: Assessment and Usage of Diagnostic Problem-Solving Strategies in Car Mechatronics (Stephan Abele, Matthias von Davier)....Pages 461-488
Applying the General Diagnostic Model to Proficiency Data from a National Skills Survey (Xueli Xu, Matthias von Davier)....Pages 489-501
Reduced Reparameterized Unified Model Applied to Learning Spatial Rotation Skills (Susu Zhang, Jeff Douglas, Shiyu Wang, Steven Andrew Culpepper)....Pages 503-524
How to Conduct a Study with Diagnostic Models (Young-Sun Lee, Diego A. Luna-Bazaldua)....Pages 525-545
Front Matter ....Pages 547-547
The R Package CDM for Diagnostic Modeling (Alexander Robitzsch, Ann Cathrice George)....Pages 549-572
Diagnostic Classification Modeling with flexMIRT (Li Cai, Carrie R. Houts)....Pages 573-579
Using Mplus to Estimate the Log-Linear Cognitive Diagnosis Model (Meghan Fager, Jesse Pace, Jonathan L. Templin)....Pages 581-591
Cognitive Diagnosis Modeling Using the GDINA R Package (Wenchao Ma)....Pages 593-601
GDM Software mdltm Including Parallel EM Algorithm (Lale Khorramdel, Hyo Jeong Shin, Matthias von Davier)....Pages 603-628
Estimating CDMs Using MCMC (Xiang Liu, Matthew S. Johnson)....Pages 629-646
Back Matter ....Pages 647-656

✦ Subjects

Education; Assessment, Testing and Evaluation; Psychometrics; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law; Cognitive Psychology