The selection of cognitive diagnostic models for a reading comprehension test

Hongli Li, C. Vincent Hunter, Pui Wa Lei

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


Cognitive diagnostic models (CDMs) have great promise for providing diagnostic information to aid learning and instruction, and a large number of CDMs have been proposed. However, the assumptions and performances of different CDMs and their applications in regard to reading comprehension tests are not fully understood. In the present study, we compared the performance of a saturated model (G-DINA), two compensatory models (DINO, ACDM), and two non-compensatory models (DINA, RRUM) with the Michigan English Language Assessment Battery (MELAB) reading test. Compared to the saturated G-DINA model, the ACDM showed comparable model fit and similar skill classification results. The RRUM was slightly worse than the ACDM and G-DINA in terms of model fit and classification results, whereas the more restrictive DINA and DINO performed much worse than the other three models. The findings of this study highlighted the process and considerations pertinent to model selection in applications of CDMs with reading tests.

Original languageEnglish (US)
Pages (from-to)391-409
Number of pages19
JournalLanguage Testing
Issue number3
StatePublished - Jul 1 2016

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Social Sciences (miscellaneous)
  • Linguistics and Language


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