Analyzing Hierarchical Data with the DINA-HC Approach
By: Jianzhou Zhang
Publisher: [publisher not identified]
Published: 2015
Place of publication: [New York, N.Y.?]
Language: eng
Description: Cognitive Diagnostic Models (CDMs) are a class of models developed in order to diagnose the cognitive attributes of examinees. They have received increasing attention in recent years because of the need of more specific attribute and item related information. A particular cognitive diagnostic model, namely, the hierarchical deterministic, input, noisy ‘and’ gate model with convergent attribute hierarchy (DINA-HC) is proposed to handle situations when the attributes have a convergent hierarchy. Su (2013) first introduced the model as the deterministic, input, noisy ‘and’ gate with hierarchy (DINA-H) and retrofitted The Trends in International Mathematics and Science Study (TIMSS) data utilizing this model with linear and unstructured hierarchies. Leighton, Girl, and Hunka (1999) and Kuhn (2001) introduced four forms of hierarchical structures (Linear, Convergent, Divergent, and Unstructured) by assuming the interrelated competencies of the cognitive skills. Specifically, the convergent hierarchy is one of the four hierarchies (Leighton, Gierl & Hunka, 2004) and it was used to describe the attributes that have a convergent structure.

One of the features of this model is that it can incorporate the hierarchical structures of the cognitive skills in the model estimation process (Su, 2013). The advantage of the DINA-HC over the Deterministic, input, noisy ‘and’ gate (DINA) model (Junker & Sijtsma, 2001) is that it will reduce the number of parameters as well as the latent classes by imposing the particular attribute hierarchy. This model follows the specification of the DINA except that it will pre-specify the attribute profiles by utilizing the convergent attribute hierarchies. Only certain possible attribute pattern will be allowed depending on the particular convergent hierarchy. Properties regarding the DINA-HC and DINA are examined and compared through the simulation and empirical study. Specifically, the attribute profile pattern classification accuracy, the model and item fit are compared between the DINA-HC and DINA under different conditions when the attributes have convergent hierarchies. This study indicates that the DINA-HC provides better model fit, less biased parameter estimates and higher attribute profile classification accuracy than the DINA when the attributes have a convergent hierarchy.

The sample size, the number of attributes, and the test length have been shown to have an effect on the parameter estimates. The DINA model has better model fit than the DINA-HC when the attributes are not dependent on each other.
Notes: Department: Measurement and Evaluation.

Thesis advisor: Matthew S. Johnson.

Thesis (Ph.D.)--Columbia University, 2015.