# A Longitudinal Higher-Order Diagnostic Classification Model

arXiv: https://arxiv.org/abs/1709.03431 · HTML: https://mlsanity.com/paper/1709.03431/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2017.

## Built on by (most cited first, 1 of 1)

- [Dynamical Non-compensatory Multidimensional IRT Model Using Variational Approximation](https://mlsanity.com/paper/2609.10028/) (2026)

## Abstract (arXiv)

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.