Multidimensional latent Markov models in a developmental study of inhibitory control and attentional flexibility in early childhood

TL;DR

This paper introduces a multidimensional latent Markov model to analyze developmental data on inhibitory control and attentional flexibility in preschool children, providing insights into their distinctness and developmental trajectories.

Contribution

The paper develops a novel multidimensional latent Markov model with an EM algorithm for developmental psychology data analysis, allowing for testing of dimensionality and transition dynamics.

Findings

Inhibitory control and attentional flexibility are distinct constructs.

The model reveals developmental trajectories of these abilities.

Likelihood ratio tests support the model's dimensionality assumptions.

Abstract

We demonstrate the use of a multidimensional extension of the latent Markov model to analyse data from studies with correlated binary responses in developmental psychology. In particular, we consider an experiment based on a battery of tests which was administered to pre-school children, at three time periods, in order to measure their inhibitory control and attentional flexibility abilities. Our model represents these abilities by two latent traits which are associated to each state of a latent Markov chain. The conditional distribution of the tests outcomes given the latent process depends on these abilities through a multidimensional two-parameter logistic parameterisation. We outline an EM algorithm to conduct likelihood inference on the model parameters; we also focus on likelihood ratio testing of hypotheses on the dimensionality of the model and on the transition matrices of the latent process. Through the approach based on the proposed model, we find evidence that supports that inhibitory control and attentional flexibility can be conceptualised as distinct constructs. Furthermore, we outline developmental aspects of participants' performance on these abilities based on inspection of the estimated transition matrices.