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18 - Designing and Managing Longitudinal Studies

from Part IV - Developmental Psychopathology and Longitudinal Methods

Published online by Cambridge University Press:  23 March 2020

By
M. Brent Donnellan  and
Deborah A. Kashy
Edited by
Aidan G. C. Wright  and
Michael N. Hallquist
Aidan G. C. Wright
Affiliation:
University of Pittsburgh
Michael N. Hallquist
Affiliation:
Pennsylvania State University
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Summary

This chapter outlines critical design decisions for longitudinal research and provides practical tips for managing such studies. It emphasizes that generative longitudinal studies are driven by conceptual and theoretical insights and describes four foundational design issues including questions about time lags and sample sizes. It then provides advice about how to manage a longitudinal study and reduce attrition. The chapter concludes by considering how the advice offered comports with recent discussions about ways to improve psychological science and providing recommended further reading.

Keywords

longitudinal studiesresearch designsample size planningtime lagsreducing attritionopen sciencetransparency
Type
Chapter
Information
The Cambridge Handbook of Research Methods in Clinical Psychology , pp. 230 - 240
Publisher: Cambridge University Press
Print publication year: 2020

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References

Recommended Reading

Block’s chapter provides an insider perspective on longitudinal studies of personality and offers nine desiderata for studies.

This paper summarizes the thorny issues involved in selecting time lags for longitudinal studies.

Although it is over 25 years old, this piece provides practical advice and tips for running a large-scale longitudinal study. The advice holds for many less expansive designs as well.

This article offers a wealth of advice about reducing attrition beyond what was covered in this chapter.

Transparency and an awareness of researcher degrees of freedom are helpful for all kinds of psychological research. The guidelines in this article seem especially relevant when analyzing data from existing longitudinal studies.

This work provides an overview of the kinds of choices facing researchers and the checklist may increase awareness of p-hacking and improve the rigor of longitudinal analyses.

Block, J. (1993). Studying Personality the Long Way. In Funder, D. C., Parke, R. D., Tomlinson Keasy, C., and Widaman, K. F. (Eds.), Studying Lives through Time: Personality and Development (pp. 9–41). Washington, DC: American Psychological Association.Google Scholar
Dormann, C., & Griffin, M. A. (2015). Optimal Time Lags in Panel Studies. Psychological Methods, 20, 489‒505.Google Scholar
Stouthamer-Loeber, M., van Kammen, W., & Loeber, R. (1992). The Nuts and Bolts of Implementing Large-Scale Longitudinal Studies. Violence and Victims, 7, 63‒78.CrossRefGoogle ScholarPubMed
Ribisl, K. M., Walton, M. A., Mowbray, C. T., Luke, D. A., Davidson, W. S., & Bootsmiller, B. J. (1996). Minimizing Participant Attrition in Panel Studies through the Use of Effective Retention and Tracking Strategies: Review and Recommendations. Evaluation and Program Planning, 19, 1‒25.CrossRefGoogle Scholar
Weston, S. J., Ritchie, S. J., Rohrer, J. M., & Przybylski, A. (2018). Recommendations for Increasing the Transparency of Analysis of Pre-Existing Data Sets. Advances in Methods and Practices in Psychological Science, 2515245919848684.Google Scholar
Wicherts, J. M., Veldkamp, C. L., Augusteijn, H. E., Bakker, M., Van Aert, R., & Van Assen, M. A. (2016). Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking. Frontiers in Psychology, 7, 1832.CrossRefGoogle ScholarPubMed

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