Introduction to diagnostic classification modeling

With R and Stan

W. Jake Thompson, Ph.D.

Welcome!

Who am I?

W. Jake Thompson, Ph.D.

Assistant Director of Psychometrics
- ATLAS | University of Kansas
Research: Applications of diagnostic psychometric models
- Lead psychometrician and Co-PI for the Dynamic Learnings Maps assessments
- PI for an IES-funded project to develop software for diagnostic models

Acknowledgements

The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grants R305D210045 and R305D240032 to the University of Kansas Center for Research, Inc., ATLAS. The opinions expressed are those of the authors and do not represent the views of the the Institute or the U.S. Department of Education.

Logo for the Institute of Education Sciences.

Schedule

Part 1: Foundations

Brief introduction to DCMs

Theoretical underpinnings

Part 2: Applications

Model estimation and evaluation

Materials

All materials are available on the workshop website:

https://learn.r-dcm.org

Installation

Required
- R (≥ 4.5.0)
- rstan (≥ 2.32.7)
- measr (≥ 2.0.1)
Recommended
- RStudio (≥ 2026.01.1-403)
- cmdstanr (≥ 0.9.0)
- CmdStan (≥ 2.38.0)

Copy and run

install.packages(c("measr", "tidyverse", "fs", "usethis"))


# Optional
install.packages(
  c("rstan", "StanHeaders", "cmdstanr"),
  repos = c("https://stan-dev.r-universe.dev", getOption("repos"))
)

## check toolchain
cmdstanr::check_cmdstan_toolchain()
cmdstanr::install_cmdstan(cores = 2)

For help installing RStan, CmdStanR, or configuring the toolchain, see the prework page.