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IV. DEVELOPMENTS IN THE ANALYSIS OF LONGITUDINAL DATA
Author(s) -
Grimm Kevin J.,
Davoudzadeh Pega,
Ram Nilam
Publication year - 2017
Publication title -
monographs of the society for research in child development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.618
H-Index - 63
eISSN - 1540-5834
pISSN - 0037-976X
DOI - 10.1111/mono.12298
Subject(s) - longitudinal data , longitudinal study , psychology , latent variable , latent growth modeling , latent variable model , variance (accounting) , econometrics , focus (optics) , statistics , developmental psychology , computer science , artificial intelligence , data mining , mathematics , physics , accounting , optics , business
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.