The Impact of Sample Attrition on Longitudinal Learning Diagnosis: A Prolog

Abstract

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Missing data are hard to avoid, or even inevitable, in longitudinal learning diagnosis and other longitudinal studies. Sample attrition is one of the most common missing patterns in practice, which refers to students dropping out before the end of the study and not returning. This brief research aims to examine the impact of a common type of sample attrition, namely, individual-level random attrition, on longitudinal learning diagnosis through a simulation study. The results indicate that (1) the recovery of all model parameters decreases with the increase of attrition rate; (2) comparatively speaking, the attrition rate has the greatest influence on diagnostic accuracy, and the least influence on general ability; and (3) a sufficient number of items is one of the necessary conditions to counteract the negative impact of sample attrition.

Keywords

• cognitive diagnosis
• longitudinal learning diagnosis
• missing data
• sample attrition
• Long-DINA model