Data Analysis and Modeling Longitudinal Processes

This article presents a nontechnical overview of the major data analysis techniques for modeling longitudinal processes, with an explicit focus on their advantages and disadvantages as tools for drawing inferences about different specific aspects of change over time. It is argued that traditional lo...

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Main Author:	CHAN, David
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2003
Subjects:	longitudinal analysis assessment of change latent variable growth modeling Quantitative Psychology
Online Access:	https://ink.library.smu.edu.sg/soss_research/207
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Institution:	Singapore Management University
Language:	English

id	sg-smu-ink.soss_research-1206
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spelling	sg-smu-ink.soss_research-12062010-08-31T09:30:04Z Data Analysis and Modeling Longitudinal Processes CHAN, David This article presents a nontechnical overview of the major data analysis techniques for modeling longitudinal processes, with an explicit focus on their advantages and disadvantages as tools for drawing inferences about different specific aspects of change over time. It is argued that traditional longitudinal analysis techniques offer limited ways of addressing many specific questions about change. Recent advances in latent variable techniques, when adequately driven by theory, design, and measurement, offer a unified and flexible framework for addressing such questions. 2003-01-01T08:00:00Z text https://ink.library.smu.edu.sg/soss_research/207 info:doi/10.1177/1059601102250814 Research Collection School of Social Sciences eng Institutional Knowledge at Singapore Management University longitudinal analysis assessment of change latent variable growth modeling Quantitative Psychology
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	longitudinal analysis assessment of change latent variable growth modeling Quantitative Psychology
spellingShingle	longitudinal analysis assessment of change latent variable growth modeling Quantitative Psychology CHAN, David Data Analysis and Modeling Longitudinal Processes
description	This article presents a nontechnical overview of the major data analysis techniques for modeling longitudinal processes, with an explicit focus on their advantages and disadvantages as tools for drawing inferences about different specific aspects of change over time. It is argued that traditional longitudinal analysis techniques offer limited ways of addressing many specific questions about change. Recent advances in latent variable techniques, when adequately driven by theory, design, and measurement, offer a unified and flexible framework for addressing such questions.
format	text
author	CHAN, David
author_facet	CHAN, David
author_sort	CHAN, David
title	Data Analysis and Modeling Longitudinal Processes
title_short	Data Analysis and Modeling Longitudinal Processes
title_full	Data Analysis and Modeling Longitudinal Processes
title_fullStr	Data Analysis and Modeling Longitudinal Processes
title_full_unstemmed	Data Analysis and Modeling Longitudinal Processes
title_sort	data analysis and modeling longitudinal processes
publisher	Institutional Knowledge at Singapore Management University
publishDate	2003
url	https://ink.library.smu.edu.sg/soss_research/207
_version_	1770568009393373184

Data Analysis and Modeling Longitudinal Processes

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