Cognitive diagnostic models (CDMs; DiBello, Roussos, & Stout, 2007) have received increasing attention in educational measurement for the purpose of diagnosing strengths and weaknesses of examinees’ latent attributes. And yet, despite the current popularity of a number of diagnostic models, research seeking to assess model-data fit has been limited. The current study applied one of the Bayesian model checking methods, namely the posterior predictive model check method (PPMC; Rubin, 1984), to its investigation of model misfit. We employed the technique in order to assess the model-data misfit from various diagnostic models, using real data and conducting two simulation studies. An important issue when it comes to the application of PPMC is choice of discrepancy measure. This study examines the performance of three discrepancy measures utilized to assess different aspects of model misfit: observed total-scores distribution, association of item pairs, and correlation between attribute pairs as adequate measures of the diagnostic models.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8SQ8ZV7 |
| Date | January 2015 |
| Creators | Park, Jung Yeon |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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