Model Selection for Cogitative Diagnostic Analysis of the Reading Comprehension Test - Liu, Hui; Bian, Yufang (Frontiers in Psychology, 2021)
Reading subskills are generally regarded as continuous variables, while most models used in previous reading diagnoses have the hypothesis that the latent variables are dichotomous. Considering that the multidimensional item response theory (MIRT) model has continuous latent variables and can be used for diagnostic purposes, this study compared the performances of MIRT with two representatives of traditionally widely used models in reading diagnoses (R-RUM and G-DINA). The comparison was carried out with both empirical and simulated data. First, model-data fit indices were used to evaluate whether MIRT was more appropriate than R-RUM and G-DINA with real data. Then with simulated data, relations between the estimated scores from MIRT, R-RUM, and G-DINA and the true scores were compared to examine the whether the true abilities were well represented, correct classification rates under different research conditions for MIRT, R-RUM, and G-DINA were calculated to examine the person parameter recovery, and the frequency distributions of subskill mastery probability were also compared to show the deviation of the estimated subskill mastery probabilities from the true values in the general value distribution. MIRT obtained better model-data fit, gained estimated scores being more reasonable representation for the true abilities, had an advantage on correct classification rates, and showed less deviation from the true values in frequency distributions of subskill mastery probabilities, which means it can produce more accurate diagnostic information about test-takers’ reading abilities. Considering that more accurate diagnostic information has greater guiding value for the remedial teaching and learning, and in reading diagnoses the score interpretation will be more reasonable with the MIRT model, this study recommended MIRT as a new methodology for future reading diagnostic analyses.