Modeling Skipped Items in Cognitive Diagnostic Assessments

In cognitive diagnosis assessments, examinees frequently skip some items due to various reasons, such as time constraints, lack of confidence, or the perception of item difficulty. These skipped items are usually due to lacking specific cognitive attributes or knowledge structures. However, many research studies conventionally rely on complete-case analysis, potentially compromising the validity of results by ignoring skipped items. This omission can lead to invalid inferences about the attributes profiles of examinees and introduce bias into item parameters. This study aims to develop statistical models for effectively handling missing data resulting from skipped items. Specifically, we employ an item response theory model for missing indicators and utilize the deterministic inputs, noisy “and†gate (DINA) model to describe cognitive item responses. Furthermore, we introduce a higher-order structure to describe the correlation between the higher-order ability parameters and skipping propensity parameters and the correlation of the item parameters. The proposed new model fills the gaps in missing data handling, providing a more precise evaluation for the examinees who skip items and yielding more accurate research results. This enhances our understanding of cognitive processes and strengthens support for educational policies, aligning them better with student needs and optimizing decision-making.