The “Quick Start Guide” for Conducting and Publishing Longitudinal Research
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Consideration of temporal issues adds precision and insight to our theories, yet most organizational and applied psychological research is based on cross-sectional designs. Calls for longitudinal research have become common in leading journals, but the existing literature provides little prescriptive guidance to overcome the many challenges of this type of research. This article provides a concise summary of challenges to address when theorizing, designing, conducting, and publishing longitudinal research. We structure the article around 12 judgment calls that typically confront researchers when conducting longitudinal studies. We respond to these judgment calls using theory and findings from the relevant literatures, as well as our own experience in designing and conducting longitudinal research across many scholarly domains. Included in these judgment calls is an emphasis on presenting and framing one’s study for publication. We challenge readers to develop theory that addresses the when, why, and duration of change, and to test the theory with the appropriate longitudinal methods. This “quick start guide” is intended to serve as a useful reference for authors and reviewers at any level of methodological expertise.
KeywordsLongitudinal research Study design Research methods Time Modeling change
We would like to thank Steven Rogelberg and Scott Tonidandel for their helpful and constructive comments on this article.
- Allison, P. D. (2001). Missing data. Thousand Oaks, CA: Sage.Google Scholar
- Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective. Hoboken, NJ: John Wiley & Sons.Google Scholar
- Chen, G., Ployhart, R. E., Cooper-Thomas, H. D., Anderson, N., & Bliese, P. D. (2011). The power of momentum: A new model of dynamic relationships between job satisfaction change and turnover intentions. Academy of Management Journal, in press.Google Scholar
- George, J. M., & Jones, G. R. (2000). The role of time in theory and theory building. Journal of Management, 26, 657–684.Google Scholar
- Keppel, G. (1991). Design and analysis: A researcher’s handbook. Upper Saddle River, NJ: Prentice Hall.Google Scholar
- Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). New York, NY: Wiley.Google Scholar
- Mitchell, T. R., & James, L. R. (2001). Building better theory: Time and the specification of when things happen. Academy of Management Review, 26, 530–547.Google Scholar
- Ployhart, R. E., & Kim, Y. (in press). Dynamic longitudinal growth modeling. Dynamic longitudinal growth models. In J. Cortina & R. Landis (Eds.), Frontiers of methodology in organizational research. New York, NY: Routledge.Google Scholar
- Rogosa, D. R. (1995). Myths and methods: “myths about longitudinal research” plus supplemental questions. In J. M. Gottman (Ed.), The analysis of change (pp. 3–66). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar