Validating a Reading Assessment Within the Cognitive Diagnostic Assessment Framework: Q-Matrix Construction and Model Comparisons for Different Primary Grades

Abstract

Cognitive diagnostic assessment (CDA) has been developed rapidly to provide fine-grained diagnostic feedback on students’ subskills and to provide insights on remedial instructions in specific domains. To date, most cognitive diagnostic studies on reading tests have focused on retrofitting a single booklet from a large-scale assessment (e.g., PISA and PIRLS). Critical issues in CDA involve the scarcity of research to develop diagnostic tests and the lack of reliability and validity evidence. This study explored the development and validation of the Diagnostic Chinese Reading Comprehension Assessment (DCRCA) for primary students under the CDA framework. Reading attributes were synthesized based on a literature review, the national curriculum criteria, the results of expert panel judgments, and student think-aloud protocols. Then, the tentative attributes were used to construct three booklets of reading comprehension items for 2–6 graders at three key stages. The assessment was administered to a large population of students (N = 21,466) in grades 2–6 from 20 schools in a district of Changchun City, China. Q-matrices were compared and refined using the model-data fit and an empirical validation procedure, and five representative cognitive diagnostic models (CDMs) were compared for optimal performance. The fit indices suggested that a six-attribute structure and the G-DINA model were best fitted for the reading comprehension assessment. In addition, diagnostic reliability, construct, internal and external validity results were provided, supporting CDM classifications as reliable, accurate, and useful. Such diagnostic information could be utilized by students, teachers, and administrators of reading programs and instructions.