IV. DEVELOPMENTS IN THE ANALYSIS OF LONGITUDINAL DATA

Kevin Grimm, Pega Davoudzadeh, Nilam Ram

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Longitudinal data analytic techniques include a complex array of statistical techniques from repeated-measures analysis of variance, mixed-effects models, and time-series analysis, to longitudinal latent variable models (e.g., growth models, dynamic factor models) and mixture models (longitudinal latent profile analysis, growth mixture models). In this article, we focus our attention on the rationales of longitudinal research laid out by Baltes and Nesselroade (1979) and discuss the advancements in the analysis of longitudinal data since their landmark paper. We highlight the developments in growth and change analysis and its derivatives because these models best capture the rationales for conducting longitudinal research. We conclude with additional rationales of longitudinal research brought about by the development of new analytic techniques.

Original languageEnglish (US)
Pages (from-to)46-66
Number of pages21
JournalMonographs of the Society for Research in Child Development
Volume82
Issue number2
DOIs
StatePublished - Jun 1 2017

ASJC Scopus subject areas

  • Developmental and Educational Psychology

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