{"title":"IV. DEVELOPMENTS IN THE ANALYSIS OF LONGITUDINAL DATA.","authors":"Kevin J Grimm, Pega Davoudzadeh, Nilam Ram","doi":"10.1111/mono.12298","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":55972,"journal":{"name":"Monographs of the Society for Research in Child Development","volume":"82 2","pages":"46-66"},"PeriodicalIF":9.4000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/mono.12298","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Monographs of the Society for Research in Child Development","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1111/mono.12298","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, DEVELOPMENTAL","Score":null,"Total":0}
引用次数: 13
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.
期刊介绍：
Since 1935, Monographs of the Society for Research in Child Development has been a platform for presenting in-depth research studies and significant findings in child development and related disciplines. Each issue features a single study or a collection of papers on a unified theme, often complemented by commentary and discussion. In alignment with all Society for Research in Child Development (SRCD) publications, the Monographs facilitate the exchange of data, techniques, research methods, and conclusions among development specialists across diverse disciplines. Subscribing to the Monographs series also includes a full subscription (6 issues) to Child Development, the flagship journal of the SRCD, and Child Development Perspectives, the newest journal from the SRCD.
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