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TDCM: An R Package for Estimating Longitudinal Diagnostic Classification Models.

Matthew J Madison1, Minjeong Jeon2, Michael Cotterell3

  • 1Assistant Professor, Quantitative Methodology, Department of Educational Psychology, University of Georgia, Athens, GA, USA.

Multivariate Behavioral Research
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Summary
This summary is machine-generated.

This study introduces a new R package for estimating longitudinal diagnostic classification models (DCMs), addressing limitations in current software for tracking changes in examinee attribute mastery over time.

Keywords:
Diagnostic classification modelcognitive diagnosis modelestimationgrowthlongitudinalpackagesoftwaretransition

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Diagnostic Classification Models (DCMs) classify individuals based on latent attributes.
  • Longitudinal DCMs extend this to model attribute changes over time.
  • Existing software for longitudinal DCMs is often limited, costly, or difficult to use.

Purpose of the Study:

  • To introduce and demonstrate a new R package for estimating general longitudinal DCMs.
  • To provide an accessible and versatile tool for applied researchers.
  • To facilitate the analysis of changes in examinee proficiency over time.

Main Methods:

  • Development of a new R package implementing the transition diagnostic classification model.
  • Demonstration of the package's functionality for estimating longitudinal DCMs.
  • Focus on a general framework for modeling attribute mastery dynamics.

Main Results:

  • The developed R package provides a functional and generalizable tool for longitudinal DCM estimation.
  • The package simplifies the process for applied researchers compared to existing software.
  • Successful demonstration of the transition diagnostic classification model's estimation capabilities.

Conclusions:

  • The new R package offers a valuable resource for researchers studying changes in latent attributes over time.
  • It enhances the accessibility and practicality of longitudinal DCM analysis.
  • This tool supports more robust modeling of examinee proficiency development.