Data Analysis and Modeling Longitudinal Processes
Publication Type
Journal Article
Publication Date
1-2003
Abstract
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.
Keywords
longitudinal analysis, assessment of change, latent variable, growth modeling
Discipline
Quantitative Psychology
Research Areas
Psychology
Publication
Group and Organization Management
Volume
28
Issue
3
First Page
341
Last Page
365
ISSN
1059-6011
Identifier
10.1177/1059601102250814
Publisher
SAGE
Citation
CHAN, David.(2003). Data Analysis and Modeling Longitudinal Processes. Group and Organization Management, 28(3), 341-365.
Available at: https://ink.library.smu.edu.sg/soss_research/207
Additional URL
https://doi.org/10.1177/1059601102250814