A Longitudinal Higher-Order Diagnostic Classification Model

TL;DR

This paper introduces a longitudinal higher-order diagnostic classification model that measures growth over time by capturing correlations among multiple attributes and accounting for local item dependence, with demonstrated effectiveness through simulations and real data.

Contribution

It presents a novel modeling approach that integrates longitudinal analysis with higher-order diagnostic classification, addressing correlations and local dependence among repeated items.

Findings

Model effectively captures growth trajectories.

Simulation results validate estimation procedures.

Empirical analysis demonstrates practical advantages.

Abstract

Providing diagnostic feedback about growth is crucial to formative decisions such as targeted remedial instructions or interventions. This paper proposed a longitudinal higher-order diagnostic classification modeling approach for measuring growth. The new modeling approach is able to provide quantitative values of overall and individual growth by constructing a multidimensional higher-order latent structure to take into account the correlations among multiple latent attributes that are examined across different occasions. In addition, potential local item dependence among anchor (or repeated) items can also be taken into account. Model parameter estimation is explored in a simulation study. An empirical example is analyzed to illustrate the applications and advantages of the proposed modeling approach.