A Longitudinal Nonparametric Classification Method for Assessing Change
in Latent Attributes Over Time
Abstract
This study proposed a longitudinal Hamming distance discrimination
(Long-HDD) method to improve the application of longitudinal cognitive
diagnosis in practical teaching by introducing a simple computation and
less time-consuming nonparametric classification method—HDD—into
longitudinal diagnostic data processing. Compared with the HDD, the
proposed method represents correlation or dependence between adjacent
time points of the same student using Hamming distance in anticipation
of using information from the previous time point to improve the
classification accuracy at the current time point. A simulation study
was conducted to explore the performance of the proposed method in
longitudinal diagnostic data analysis and to compare the performance of
the proposed method with the HDD and a parametric longitudinal
diagnostic classification model. The findings suggest that (1) the
Long-HDD can provide high classification accuracy in longitudinal
diagnostic data analysis; (2) compared with the parametric model, the
Long-HDD is almost unaffected by sample size and performs better than
the parametric model in small sample sizes; and (3) the Long-HDD
consumes much less computing time than the parametric model. Overall,
the Long-HDD is well suited to analyzing longitudinal diagnostic data
and can provide speedy diagnostic feedback due to its convenient
computation, which is especially significant in small-scale assessments
at the classroom and school levels.