Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

This paper uses log-linear models with latent variables (Hagenaars, in Loglinear Models with Latent Variables, 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for cognitive diagnosis, new alternatives to modeling the functional relationship between attribute mastery and the probability of a correct response are discussed.

[1]  H. Akaike A new look at the statistical model identification , 1974 .

[2]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[3]  B. Junker,et al.  Cognitive Assessment Models with Few Assumptions, and Connections with Nonparametric Item Response Theory , 2001 .

[4]  Jonathan Templin,et al.  Robustness of Hierarchical Modeling of Skill Association in Cognitive Diagnosis Models , 2008 .

[5]  Jeffrey A Douglas,et al.  Higher-order latent trait models for cognitive diagnosis , 2004 .

[6]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[7]  Gerhard H. Fischer,et al.  Some Applications of Logistic Latent Trait Models with Linear Constraints on the Parameters , 1982 .

[8]  A. Agresti,et al.  Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.

[9]  Sarah M. Hartz,et al.  A Bayesian framework for the unified model for assessing cognitive abilities: Blending theory with practicality. , 2002 .

[10]  Rianne Janssen,et al.  Psychometric Modeling of Componentially Designed Synonym Tasks , 1997 .

[11]  J. Templin,et al.  Measurement of psychological disorders using cognitive diagnosis models. , 2006, Psychological methods.

[12]  David Rindskopf A general framework for using latent class analysis to test hierarchical and nonhierarchical learning models , 1983 .

[13]  S. Haberman Log-Linear Models for Frequency Tables Derived by Indirect Observation: Maximum Likelihood Equations , 1974 .

[14]  J. Hagenaars Loglinear Models with Latent Variables , 1993 .

[15]  Eunice Eunhee Jang,et al.  A *Validity Narrative: Effects of Reading Skills Diagnosis on Teaching and Learning in the Context of NG TOEFL , 2005 .

[16]  Matthias von Davier,et al.  A GENERAL DIAGNOSTIC MODEL APPLIED TO LANGUAGE TESTING DATA , 2005 .

[17]  Edward H. Haertel Using restricted latent class models to map the skill structure of achievement items , 1989 .

[18]  Scott M. Lynch,et al.  Bayesian Posterior Predictive Checks for Complex Models , 2004 .

[19]  C. Mitchell Dayton,et al.  The Use of Probabilistic Models in the Assessment of Mastery , 1977 .

[20]  Jonathan L. Templin Generalized Linear Mixed Proficiency Models for Cognitive Diagnosis , 2004 .

[21]  K. Tatsuoka Toward an Integration of Item-Response Theory and Cognitive Error Diagnosis. , 1987 .

[22]  Xiao-Li Meng,et al.  POSTERIOR PREDICTIVE ASSESSMENT OF MODEL FITNESS VIA REALIZED DISCREPANCIES , 1996 .