Fitting the Reduced RUM with Mplus: A Tutorial

• Published in: International journal of testing Vol. 16; no. 4; pp. 331 - 351
• Main Authors: Chiu, Chia-Yi, Köhn, Hans-Friedrich, Wu, Huey-Min
• Format: Journal Article
• Language: English
• Published: Philadelphia Routledge 01.10.2016; Taylor & Francis Ltd
• Subjects: Algorithms; Bayesian Statistics; Classification; cognitive diagnosis; Computation; Computer Software; diagnostic models; Economic models; Educational Assessment; Educational Diagnosis; Equations (Mathematics); Fitting; general cognitive; Item Response Theory; Latent class analysis; LCDM; Mathematics; Maximum likelihood method; Maximum Likelihood Statistics; MCMC; Mplus; Multivariate Analysis; Parameters; Psychometrics; reduced RUM EM; Tutorial Programs
• ISSN: 1530-5058; 1532-7574
• DOI: 10.1080/15305058.2016.1148038

The Reduced Reparameterized Unified Model (Reduced RUM) is a diagnostic classification model for educational assessment that has received considerable attention among psychometricians. However, the computational options for researchers and practitioners who wish to use the Reduced RUM in their work, but do not feel comfortable writing their own code, are still rather limited. One option is to use a commercial software package that offers an implementation of the expectation maximization (EM) algorithm for fitting (constrained) latent class models like Latent GOLD or Mplus. But using a latent class analysis routine as a vehicle for fitting the Reduced RUM requires that it be re-expressed as a logit model, with constraints imposed on the parameters of the logistic function. This tutorial demonstrates how to implement marginal maximum likelihood estimation using the EM algorithm in Mplus for fitting the Reduced RUM.