Explanatory Cognitive Diagnostic Models

  • Yoon Soo ParkEmail author
  • Young-Sun Lee
Part of the Methodology of Educational Measurement and Assessment book series (MEMA)


Student- and school-level information from large-scale educational data have been shown to explain trends in test taker performance and to inform factors that can enhance the learning environment. This study presents methods to specify and model predictive relationships of latent and observed explanatory variables within a cognitive diagnostic model, referred to as the Explanatory Cognitive Diagnostic Model (ECDM) framework. Explanatory factors can be incorporated simultaneously as observed covariates or latent variables (estimated using item response theory) that can explain patterns of attribute mastery. This chapter is divided into two studies that demonstrate real-world application using large-scale international testing data and simulation studies, which examine parameter recovery and classification for varying sample sizes and number of attributes. Simultaneous estimation of multiple observed and latent (using dichotomous and polytomous items as indicators for the latent construct) predictors show consistency in attribute classification and parameter recovery. Extensions of the ECDM framework are discussed.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Medical Education, College of MedicineUniversity of Illinois at ChicagoChicagoUSA
  2. 2.Teachers CollegeColumbia UniversityNew YorkUSA

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