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Time-Frequency Filtering for Seismic Waves Clustering Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Modeling Longitudinal Data by Latent Markov Models with Application to Educational and Psychological Measurement

Author : Francesco Bartolucci

Published in: Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Publisher: Springer International Publishing

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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 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.

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previous chapter Time-Frequency Filtering for Seismic Waves Clustering
next chapter Clustering of Stratified Aggregated Data Using the Aggregate Association Index: Analysis of New Zealand Voter Turnout (1893–1919)
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Metadata
Title
Modeling Longitudinal Data by Latent Markov Models with Application to Educational and Psychological Measurement
Author
Francesco Bartolucci
Copyright Year
2014
Book
Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Print ISBN: 978-3-319-06691-2

Electronic ISBN: 978-3-319-06692-9

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-06692-9_2

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