Skip to main content

The “Quick Start Guide” for Conducting and Publishing Longitudinal Research


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.

This is a preview of subscription content, access via your institution.

Fig. 1


  • Allison, P. D. (2001). Missing data. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Anseel, F., Lievens, F., Schollaert, E., & Choragwicka, B. (2010). Response rates in organizational science, 1995–2008. A meta-analytic review and guidelines for survey researchers. Journal of Business and Psychology, 25, 335–349.

    Article  Google Scholar 

  • Antonakis, J., Bendahan, S., Jacquart, H., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21, 1086–1120.

    Article  Google Scholar 

  • Biesanz, J. C., Deeb-Sossa, N., Papadakis, A. A., Bollen, K. A., & Curran, P. J. (2004). The role of coding time in estimating and interpreting growth curve models. Psychological Methods, 9, 30–52.

    PubMed  Article  Google Scholar 

  • Bliese, P. D., & Ployhart, R. E. (2002). Growth modeling using random coefficient models: Model building, testing, and illustrations. Organizational Research Methods, 5, 362–387.

    Article  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.

  • Conway, J. M., & Lance, C. E. (2010). What reviewers should expect from authors regarding common method bias in organizational research. Journal of Business and Psychology, 25, 325–334.

    Article  Google Scholar 

  • Duncan, S. C., Duncan, T. E., & Hops, H. (1996). Analysis of longitudinal data within accelerated longitudinal designs. Psychological Methods, 1, 236–248.

    Article  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 

  • Graham, J. W., Hofer, S. M., & MacKinnon, D. P. (1996). Maximizing the usefulness of data obtained with planned missing value patterns: An application of maximum likelihood procedures. Multivariate Behavioral Research, 31, 197–218.

    Article  Google Scholar 

  • Graham, J. W., Taylor, B. J., & Cumsille, P. E. (2001). Planned missing data designs in analysis of change. In L. Collins & M. Sayers (Eds.), New methods for the analysis of change (pp. 335–353). Washington, DC: American Psychological Association.

    Chapter  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 

  • Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12, 23–44.

    PubMed  Article  Google Scholar 

  • Mehta, P. D., & West, S. G. (2000). Putting the individual back into individual growth curves. Psychological Methods, 5, 23–43.

    PubMed  Article  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 

  • Newman, D. A. (2003). Longitudinal modeling with randomly and systematically missing data: A simulation of ad hoc, maximum likelihood, and multiple imputation techniques. Organizational Research Methods, 6, 328–362.

    Article  Google Scholar 

  • Palmer, R. F., & Royall, D. R. (2010). Missing data? Plan on it!. Journal of the American Geriatrics Society, 58, 343–348.

    Article  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.

  • Ployhart, R. E., & Vandenberg, R. J. (2010). Longitudinal research: The theory, design, and analysis of change. Journal of Management, 36, 94–120.

    Article  Google Scholar 

  • Ployhart, R. E., Weekley, J. A., & Ramsey, J. (2009). The consequences of human resource stocks and flows: A longitudinal examination of unit service orientation and unit effectiveness. Academy of Management Journal, 52, 996–1015.

    Article  Google Scholar 

  • Raudenbush, S. W., & Liu, X. (2001). Effects of study duration, frequency of observation, and sample size on power in studies of group differences in polynomial change. Psychological Methods, 6, 387–401.

    PubMed  Article  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 

  • Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis. New York, NY: Oxford University Press.

    Book  Google Scholar 

  • Willett, J. B. (1989). Some results on reliability for the longitudinal measurement of change: Implications for the design of studies of individual growth. Educational and Psychological Measurement, 49, 587–602.

    Article  Google Scholar 

Download references


We would like to thank Steven Rogelberg and Scott Tonidandel for their helpful and constructive comments on this article.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Robert E. Ployhart.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Ployhart, R.E., Ward, AK. The “Quick Start Guide” for Conducting and Publishing Longitudinal Research. J Bus Psychol 26, 413–422 (2011).

Download citation

  • Published:

  • Issue Date:

  • DOI:


  • Longitudinal research
  • Study design
  • Research methods
  • Time
  • Modeling change