A sequential cognitive diagnosis model for polytomous responses.

This paper proposes a general polytomous cognitive diagnosis model for a special type of graded responses, where item categories are attained in a sequential manner, and associated with some attributes explicitly. To relate categories to attributes, a category-level Q-matrix is used. When the attribute and category association is specified a priori, the proposed model has the flexibility to allow different cognitive processes (e.g., conjunctive, disjunctive) to be modelled at different categories within a single item. This model can be extended for items where categories cannot be explicitly linked to attributes, and for items with unordered categories. The feasibility of the proposed model is examined using simulated data. The proposed model is illustrated using the data from the Trends in International Mathematics and Science Study 2007 assessment.

[1]  J. D. L. Torre,et al.  The Generalized DINA Model Framework. , 2011 .

[2]  Louis V. DiBello,et al.  31A Review of Cognitively Diagnostic Assessment and a Summary of Psychometric Models , 2006 .

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

[4]  Young-sun Lee,et al.  A Cognitive Diagnostic Modeling of Attribute Mastery in Massachusetts, Minnesota, and the U.S. National Sample Using the TIMSS 2007 , 2011 .

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

[6]  E. Maris Estimating multiple classification latent class models , 1999 .

[7]  John T. Willse,et al.  Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables , 2009 .

[8]  Jung Yeon Park,et al.  Examination of gender differences using the multiple groups DINA model , 2013 .

[9]  Chia-Yi Chiu,et al.  Cluster Analysis for Cognitive Diagnosis: Theory and Applications , 2009 .

[10]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

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

[12]  J. Templin,et al.  Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art , 2008 .

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

[14]  K. Tatsuoka,et al.  Open-Ended Versus Multiple-Choice Response Formats—It Does Make a Difference for Diagnostic Purposes , 1987 .

[15]  Menucha Birenbaum,et al.  Effects of Response Format on Diagnostic Assessment of Scholastic Achievement , 1992 .

[16]  R. D. Bock,et al.  Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm , 1981 .

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

[18]  Mark Hansen,et al.  Hierarchical Item Response Models for Cognitive Diagnosis , 2013 .

[19]  Jimmy de la Torre,et al.  An Empirically Based Method of Q‐Matrix Validation for the DINA Model: Development and Applications , 2008 .

[20]  Robert J. Mislevy,et al.  TEST THEORY RECONCEIVED , 1994 .

[21]  L. T. DeCarlo On the Analysis of Fraction Subtraction Data: The DINA Model, Classification, Latent Class Sizes, and the Q-Matrix , 2011 .

[22]  A. Rukhin Bayes and Empirical Bayes Methods for Data Analysis , 1997 .

[23]  Alka Arora,et al.  TIMSS 2011 User Guide for the International Database. , 2013 .

[24]  J. D. L. Torre,et al.  Evaluating the Wald Test for Item‐Level Comparison of Saturated and Reduced Models in Cognitive Diagnosis , 2013 .

[25]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[26]  Fumiko Samejima,et al.  Acceleration model in the heterogeneous case of the general graded response model , 1995 .

[27]  Yu-Lan Su,et al.  Cognitive diagnostic analysis using hierarchically structured skills , 2013 .

[28]  Jimmy de la Torre,et al.  Model Evaluation and Multiple Strategies in Cognitive Diagnosis: An Analysis of Fraction Subtraction Data , 2008 .

[29]  André A. Rupp,et al.  The Impact of Model Misspecification on Parameter Estimation and Item‐Fit Assessment in Log‐Linear Diagnostic Classification Models , 2012 .

[30]  K. Tatsuoka RULE SPACE: AN APPROACH FOR DEALING WITH MISCONCEPTIONS BASED ON ITEM RESPONSE THEORY , 1983 .

[31]  Matthias von Davier,et al.  A general diagnostic model applied to language testing data. , 2008, The British journal of mathematical and statistical psychology.

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

[33]  Bruce Green,et al.  The Impact of Model-Misspecification on Model Based Personalised Dosing , 2016, The AAPS Journal.