A sequential cognitive diagnosis model for polytomous responses

Wenchao Ma · Jimmy DE LA TORRE

134 citations0/0 files storedBritish Journal of Mathematical and Statistical Psychology

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Psychometric Methodologies and Testing Educational Assessment and Pedagogy Educational Technology and Assessment

Abstract

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.

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References

1981Marginal Maximum Likelihood Estimation of Item Parameters: Application of an EM Algorithm 1972Estimating Item Parameters and Latent Ability when Responses are Scored in Two or More Nominal Categories 1983RULE SPACE: AN APPROACH FOR DEALING WITH MISCONCEPTIONS BASED ON ITEM RESPONSE THEORY 2011The Generalized DINA Model Framework 2006Measurement of psychological disorders using cognitive diagnosis models.2008Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables 2007A general diagnostic model applied to language testing data 1989Using Restricted Latent Class Models to Map the Skill Structure of Achievement Items

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2017Cognitive diagnosis modelling incorporating item response times 2019Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial 2020Partial identifiability of restricted latent class models 2019An empirical Q‐matrix validation method for the sequential generalized DINA model 2016GDINA: The Generalized DINA Model Framework

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