The Reduced RUM as a Logit Model: Parameterization and Constraints

Cognitive diagnosis models (CDMs) for educational assessment are constrained latent class models. Examinees are assigned to classes of intellectual proficiency defined in terms of cognitive skills called attributes, which an examinee may or may not have mastered. The Reduced Reparameterized Unified Model (Reduced RUM) has received considerable attention among psychometricians. Markov Chain Monte Carlo (MCMC) or Expectation Maximization (EM) are typically used for estimating the Reduced RUM. Commercial implementations of the EM algorithm are available in the latent class analysis (LCA) routines of Latent GOLD and Mplus, for example. Fitting the Reduced RUM with an LCA routine requires that it be reparameterized as a logit model, with constraints imposed on the parameters. For models involving two attributes, these have been worked out. However, for models involving more than two attributes, the parameterization and the constraints are nontrivial and currently unknown. In this article, the general parameterization of the Reduced RUM as a logit model involving any number of attributes and the associated parameter constraints are derived. As a practical illustration, the LCA routine in Mplus is used for fitting the Reduced RUM to two synthetic data sets and to a real-world data set; for comparison, the results obtained by using the MCMC implementation in OpenBUGS are also provided.     

Cognitive Diagnosis for Small Educational Programs: The General Nonparametric Classification Method

The focus of cognitive diagnosis (CD) is on evaluating an examinee’s strengths and weaknesses in terms of cognitive skills learned and skills that need study. Current methods for fitting CD models (CDMs) work well for large-scale assessments, where the data of hundreds or thousands of examinees are available. However, the development of CD-based assessment tools that can be used in small-scale test settings, say, for monitoring the instruction and learning process at the classroom level has not kept up with the rapid pace at which research and development proceeded for large-scale assessments. The main reason is that the sample sizes of the small-scale test settings are simply too small to guarantee the reliable estimation of item parameters and examinees’ proficiency class membership. In this article, a general nonparametric classification (GNPC) method that allows for assigning examinees to the correct proficiency classes with a high rate when sample sizes are at the classroom level is proposed as an extension of the nonparametric classification (NPC) method (Chiu and Douglas in J Classif 30:225–250, 2013). The proposed method remedies the shortcomings of the NPC method and can accommodate any CDM. The theoretical justification and the empirical studies are presented based on the saturated general CDMs, supporting the legitimacy of using the GNPC method with any CDM. The results from the simulation studies and real data analysis show that the GNPC method outperforms the general CDMs when samples are small.