Evaluating the Fit of Sequential G-DINA Model Using Limited-Information Measures

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Limited-information fit measures appear to be promising in assessing the goodness-of-fit of dichotomous response cognitive diagnosis models (CDMs), but their performance has not been examined for polytomous response CDMs. This study investigates the performance of the M-ord statistic and standardized root mean square residual (SRMSR) for an ordinal response CDM-the sequential generalized deterministic inputs, noisy "and" gate model. Simulation studies showed that the M-ord statistic had well-calibrated Type I error rates, but the correct detection rates were influenced by various factors such as item quality, sample size, and the number of response categories. In addition, the SRMSR was also influenced by many factors and the common practice of comparing the SRMSR against a prespecified cut-off (e.g., .05) may not be appropriate. A set of real data was analyzed as well to illustrate the use of M-ord statistic and SRMSR in practice.

cognitive diagnosis, ordinal response, model-data fit, goodness-of-fit, limited information, sequential G-DINA, DIAGNOSTIC CLASSIFICATION MODELS, SELECTION, Social Sciences, Mathematical Methods, Psychology, Mathematical
Ma, W. (2019). Evaluating the Fit of Sequential G-DINA Model Using Limited-Information Measures. In Applied Psychological Measurement (Vol. 44, Issue 3, pp. 167–181). SAGE Publications. https://doi.org/10.1177/0146621619843829