Handbook of Diagnostic Classification Models

Models and Model Extensions, Applications, Software Packages

  • Matthias von Davier
  • Young-Sun Lee

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Matthias von Davier, Young-Sun Lee
    Pages 1-17
  3. Approaches to Cognitive Diagnosis

    1. Front Matter
      Pages 19-19
    2. William Stout, Robert Henson, Lou DiBello, Benjamin Shear
      Pages 47-79
    3. Russell G. Almond, Juan-Diego Zapata-Rivera
      Pages 81-106
    4. Chia-Yi Chiu, Hans-Friedrich Köhn
      Pages 107-132
    5. Matthias von Davier
      Pages 133-153
    6. Jimmy de la Torre, Nathan D. Minchen
      Pages 155-169
    7. Robert Henson, Jonathan L. Templin
      Pages 171-185
    8. Yoon Soo Park, Young-Sun Lee
      Pages 207-222
    9. Lawrence T. DeCarlo
      Pages 223-243
  4. Special Topics

  5. Applications

    1. Front Matter
      Pages 419-419
    2. Hong Jiao, Dandan Liao, Peida Zhan
      Pages 421-436
    3. Benjamin Deonovic, Pravin Chopade, Michael Yudelson, Jimmy de la Torre, Alina A. von Davier
      Pages 437-460
    4. Susu Zhang, Jeff Douglas, Shiyu Wang, Steven Andrew Culpepper
      Pages 503-524
    5. Young-Sun Lee, Diego A. Luna-Bazaldua
      Pages 525-545
  6. Software, Data, and Tools

    1. Front Matter
      Pages 547-547
    2. Alexander Robitzsch, Ann Cathrice George
      Pages 549-572
    3. Li Cai, Carrie R. Houts
      Pages 573-579
    4. Meghan Fager, Jesse Pace, Jonathan L. Templin
      Pages 581-591
    5. Lale Khorramdel, Hyo Jeong Shin, Matthias von Davier
      Pages 603-628
    6. Xiang Liu, Matthew S. Johnson
      Pages 629-646
  7. Back Matter
    Pages 647-656

About this book


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.


Educational measurement LCDM G-DINA Psychometrics Diagnostic Classification Models GDM Adaptive testing Cognitive Diagnostic Models DINA Identifiability Process data R Packages Dimensionality Q-Matrix Software

Editors and affiliations

  • Matthias von Davier
    • 1
  • Young-Sun Lee
    • 2
  1. 1.National Board of Medical Examiners (NBME)PhiladelphiaUSA
  2. 2.Teachers CollegeColumbia UniversityNew YorkUSA

Bibliographic information

  • DOI
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Education Education (R0)
  • Print ISBN 978-3-030-05583-7
  • Online ISBN 978-3-030-05584-4
  • Series Print ISSN 2367-170X
  • Series Online ISSN 2367-1718
  • Buy this book on publisher's site