Longitudinal Research

by

Mo Wang, Zhefan Huang

• LAST REVIEWED: 11 January 2024
• LAST MODIFIED: 11 January 2024
• DOI: 10.1093/obo/9780199846740-0028

Introduction

Longitudinal research refers to a family of research methods that involves obtaining repeated measures of variables from the same group of individuals over an extended period of time. Data are first collected at the outset of the study and may then be gathered repeatedly throughout the length of the study. When testing causal theories, social scientists typically prefer longitudinal research design over cross-sectional research design because longitudinal research can help establish evidences both for covariation between variables and for the temporal order of variables, and thus can enhance causal inference. Nevertheless, longitudinal research design cannot eliminate competing explanations (e.g., third-variable effects) and, as a result, does not absolutely establish a causal relationship or allow causal claims. Longitudinal research is also useful for studying changes in same variables as well as in associations between variables over time.

Overviews of Research Methods

Given that longitudinal research considers a broad scope of issues related to research design, measurements, and analysis, few books provide a comprehensive coverage of all these issues. One exception is the Handbook of Longitudinal Research (Menard 2007). The editor of this work also authored a very brief entry-level book on longitudinal research (Menard 2002). Each of the remaining books listed here offers a good overview of a subarea of longitudinal research. Specifically, Cook and Campbell 1979 lays out the fundamental principles for conducting longitudinal research from a measurement and research design perspective. Singer and Willett 2003 (cited under Methods) discusses how multilevel analysis and survival analysis should be applied to analyze longitudinal data. Duncan, et al. 2006 offers a good overview regarding how to use latent growth curve modeling in longitudinal research to understand changes over time. Allison 2010 (cited under Survival Analysis) provides a data-based introduction for applying survival analysis. Box, et al. 2008 (cited under Time Series Analysis) focuses on time series analysis and explores its applications. Finally, Saldaña 2003 offers a rare overview regarding how to conduct longitudinal qualitative research.

• Cook, Thomas D., and Donald T. Campbell. Quasi-experimentation: Design and Analysis for Field Settings. Boston: Houghton Mifflin, 1979.

  Although this book covers topics that go beyond just longitudinal research, it highlights the important elements of longitudinal research and offers comprehensive considerations about how longitudinal research should be conducted to address threats to validity.

• Duncan, Terry E., Susan C. Duncan, and Lisa A. Strycker. An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications. Mahwah, NJ: Lawrence Erlbaum, 2006.

  This book provides a comprehensive introduction to latent variable growth curve modeling for analyzing repeated measures. It presents the statistical basis for latent variable growth curve modeling and its various methodological extensions, including a number of practical examples of its use.

• Menard, Scott. Longitudinal Research. 2d ed. Thousand Oaks, CA: SAGE, 2002.

  DOI: 10.4135/9781412984867

  Written in nontechnical language, this brief book offers a practical overview on longitudinal research design strategies, methods of data collection, and how longitudinal research and cross-sectional research compare in terms of consistency and accuracy of results.

• Menard, Scott, ed. Handbook of Longitudinal Research: Design, Measurements and Analysis. San Diego, CA: Academic Press, 2007.

  This handbook provides a set of chapters that treat the topic of longitudinal research in an informative and comprehensive fashion. This collection is useful for both novices to longitudinal research and seasoned researchers, covering topics ranging from basic methodological issues to advanced analytical concerns.

• Saldaña, Johnny. Longitudinal Qualitative Research: Analyzing Change through Time. Walnut Creek, CA: AltaMira, 2003.

  This book outlines the basic elements of longitudinal qualitative research, focusing on exploring qualitative approaches that study microlevels of change observed within individual cases and groups of participants. It not only makes the case for studying process but also gives clear strategies for how to bring process into analysis.