Course abstract
The short course introduces cognitive diagnosis modeling as an alternative psychometric framework for developing assessments and analyzing item-response data. The models in this framework are specifically designed to generate diagnostic output to classify individuals into a discrete profile of latent attributes. This output is of particular interest for researchers and practitioners in educational or clinical contexts. In addition to the rationale, bases, and frameworks for cognitive diagnosis modeling, the course covers some of the most recent developments in the area, as well as tools to implement these analyses with the R software.
The primary aim of the course is to provide participants with the necessary background to fully appreciate the use of CDMs in various applied settings. Moreover, it aims to highlight the theoretical underpinnings that are needed to ground the proper implementation of CDMs in practice.
The course will cover the fundamentals of cognitive diagnosis modeling and some of the most recent developments in the area, which include approaches to and models for cognitive diagnosis; model estimation, fit evaluation and comparison; Q-matrix validation; and computerized adaptive testing, among others. Moreover, students will be introduced to a number of CDM-related R packages (i.e., GDINA, cdmTools, and cdcatR). These packages are developed by the instructors and are specifically designed for conducting various CDM analyses. Several step-by-step examples based on various datasets will be provided to illustrate how the different analyses covered in the course can be implemented in R.