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Writing the Data Analysis Plan

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How to Write a Successful Research Grant Application

Abstract

You and your project statistician have one major goal for your data analysis plan: You need to convince all the reviewers reading your proposal that you would know what to do with your data once your project is funded and your data are in hand. The data analytic plan is a signal to the reviewers about your ability to score, describe, and thoughtfully synthesize a large number of variables into appropriately-selected quantitative models once the data are collected. Reviewers respond very well to plans with a clear elucidation of the data analysis steps – in an appropriate order, with an appropriate level of detail and reference to relevant literatures, and with statistical models and methods for that map well into your proposed aims. A successful data analysis plan produces reviews that either include no comments about the data analysis plan or better yet, compliments it for being comprehensive and logical given your aims. This chapter offers practical advice about developing and writing a compelling, “bullet-proof” data analytic plan for your grant application.

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References

  • Aiken, L. S. & West, S. G. (1991). Multiple regression: testing and interpreting interactions. Newbury Park, CA: Sage.

    Google Scholar 

  • Aiken, L. S., West, S. G., & Millsap, R. E. (2008). Doctoral training in statistics, measurement, and methodology in psychology: Replication and extension of Aiken, West, Sechrest and Reno’s (1990) survey of PhD programs in North America. American Psychologist, 63, 32–50.

    Article  PubMed  Google Scholar 

  • Allison, P. D. (2003). Missing data techniques for structural equation modeling. Journal of Abnormal Psychology, 112, 545–557.

    Article  PubMed  Google Scholar 

  • American Psychological Association (APA) Task Force to Increase the Quantitative Pipeline (2009). Report of the task force to increase the quantitative pipeline. Washington, DC: American Psychological Association.

    Google Scholar 

  • Bauer, D. & Curran, P. J. (2004). The integration of continuous and discrete latent variables: Potential problems and promising opportunities. Psychological Methods, 9, 3–29.

    Article  PubMed  Google Scholar 

  • Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.

    Google Scholar 

  • Bollen, K. A. & Curran, P. J. (2007). Latent curve models: A structural equation modeling approach. New York: Wiley.

    Google Scholar 

  • Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Multiple correlation/regression for the behavioral sciences (3rd ed.). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Curran, P. J., Bauer, D. J., & Willoughby, M. T. (2004). Testing main effects and interactions in hierarchical linear growth models. Psychological Methods, 9, 220–237.

    Article  PubMed  Google Scholar 

  • Embretson, S. E. & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Enders, C. K. (2006). Analyzing structural equation models with missing data. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 313–342). Greenwich, CT: Information Age.

    Google Scholar 

  • Hosmer, D. & Lemeshow, S. (1989). Applied logistic regression. New York: Wiley.

    Google Scholar 

  • Hoyle, R. H. & Panter, A. T. (1995). Writing about structural equation models. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 158–176). Thousand Oaks: Sage.

    Google Scholar 

  • Kaplan, D. & Elliott, P. R. (1997). A didactic example of multilevel structural equation modeling applicable to the study of organizations. Structural Equation Modeling, 4, 1–23.

    Article  Google Scholar 

  • Lanza, S. T., Collins, L. M., Schafer, J. L., & Flaherty, B. P. (2005). Using data augmentation to obtain standard errors and conduct hypothesis tests in latent class and latent transition analysis. Psychological Methods, 10, 84–100.

    Article  PubMed  Google Scholar 

  • MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Maxwell, S. E. (2004). The persistence of underpowered studies in psychological research: Causes, consequences, and remedies. Psychological Methods, 9, 147–163.

    Article  PubMed  Google Scholar 

  • McCullagh, P. & Nelder, J. (1989). Generalized linear models. London: Chapman and Hall.

    Google Scholar 

  • McDonald, R. P. & Ho, M. R. (2002). Principles and practices in reporting structural equation modeling analyses. Psychological Methods, 7, 64–82.

    Article  PubMed  Google Scholar 

  • Messick, S. (1989). Validity. In R. L. Linn (Ed.), Educational measurement (3rd ed., pp. 13–103). New York: Macmillan.

    Google Scholar 

  • Muthén, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22, 376–398.

    Article  Google Scholar 

  • Muthén, B. (2008). Latent variable hybrids: overview of old and new models. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 1–24). Charlotte, NC: Information Age.

    Google Scholar 

  • Muthén, B. & Masyn, K. (2004). Discrete-time survival mixture analysis. Journal of Educational and Behavioral Statistics, 30, 27–58.

    Article  Google Scholar 

  • Muthén, L. K. & Muthén, B. O. (2004). Mplus, statistical analysis with latent variables: User’s guide. Los Angeles, CA: Muthén &Muthén.

    Google Scholar 

  • Peugh, J. L. & Enders, C. K. (2004). Missing data in educational research: a review of reporting practices and suggestions for improvement. Review of Educational Research, 74, 525–556.

    Article  Google Scholar 

  • Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for probing interaction effects in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31, 437–448.

    Article  Google Scholar 

  • Preacher, K. J., Curran, P. J., & Bauer, D. J. (2003, September). Probing interactions in multiple linear regression, latent curve analysis, and hierarchical linear modeling: Interactive calculation tools for establishing simple intercepts, simple slopes, and regions of significance [Computer software]. Available from http://www.quantpsy.org.

  • Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185–227.

    Article  Google Scholar 

  • Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Radloff, L. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401.

    Article  Google Scholar 

  • Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Schafer. J. L. & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177.

    Article  PubMed  Google Scholar 

  • Schumacker, R. E. (2002). Latent variable interaction modeling. Structural Equation Modeling, 9, 40–54.

    Article  Google Scholar 

  • Schumacker, R. E. & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Selig, J. P. & Preacher, K. J. (2008, June). Monte Carlo method for assessing mediation: An interactive tool for creating confidence intervals for indirect effects [Computer software]. Available from http://www.quantpsy.org.

  • Singer, J. D. & Willett, J. B. (1991). Modeling the days of our lives: Using survival analysis when designing and analyzing longitudinal studies of duration and the timing of events. Psychological Bulletin, 110, 268–290.

    Article  Google Scholar 

  • Singer, J. D. & Willett, J. B. (1993). It’s about time: Using discrete-time survival analysis to study duration and the timing of events. Journal of Educational Statistics, 18, 155–195.

    Article  Google Scholar 

  • Singer, J. D. & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University.

    Book  Google Scholar 

  • Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4–69.

    Article  Google Scholar 

  • Wirth, R. J. & Edwards, M. C. (2007). Item factor analysis: Current approaches and future directions. Psychological Methods, 12, 58–79.

    Article  PubMed  CAS  Google Scholar 

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Panter, A.T. (2010). Writing the Data Analysis Plan. In: Pequegnat, W., Stover, E., Boyce, C. (eds) How to Write a Successful Research Grant Application. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1454-5_22

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  • DOI: https://doi.org/10.1007/978-1-4419-1454-5_22

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