Modeling Longitudinal Data with Application to Educational and Psychological Measurement

8 Pages Posted: 7 Dec 2012

See all articles by Francesco Bartolucci

Francesco Bartolucci

Università di Perugia - Finanza e Statistica - Dipartimento di Economia

Date Written: December 5, 2012

Abstract

I review a class of models for longitudinal data, showing how it may be applied in a meaningful way for the analysis of data collected by the administration of a series of items finalized to the educational or psychological measurement. In this class of models, the unobserved individual characteristics of interest are represented by a sequence of discrete latent variables, which follows a Markov chain. Inferential problems involved in the application of these models are discussed considering, in particular, maximum likelihood estimation based on the Expectation-Maximization algorithm, model selection, and hypothesis testing. Most of these problems are common to hidden Markov models for time-series data. The approach is illustrated by different applications in education and psychology.

Keywords: backward and forward recursions, Expectation-Maximization algorithm, hidden Markov models, latent Markov models, Rasch model

Suggested Citation

Bartolucci, Francesco, Modeling Longitudinal Data with Application to Educational and Psychological Measurement (December 5, 2012). Available at SSRN: https://ssrn.com/abstract=2185392 or http://dx.doi.org/10.2139/ssrn.2185392

Francesco Bartolucci (Contact Author)

Università di Perugia - Finanza e Statistica - Dipartimento di Economia ( email )

06123

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