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.
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.
Allison, P. D. (2003). Missing data techniques for structural equation modeling. Journal of Abnormal Psychology, 112, 545–557.
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.
Bauer, D. & Curran, P. J. (2004). The integration of continuous and discrete latent variables: Potential problems and promising opportunities. Psychological Methods, 9, 3–29.
Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.
Bollen, K. A. & Curran, P. J. (2007). Latent curve models: A structural equation modeling approach. New York: Wiley.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Multiple correlation/regression for the behavioral sciences (3rd ed.). Mahwah, NJ: Erlbaum.
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.
Embretson, S. E. & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Erlbaum.
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.
Hosmer, D. & Lemeshow, S. (1989). Applied logistic regression. New York: Wiley.
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.
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.
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.
MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. Mahwah, NJ: Erlbaum.
Maxwell, S. E. (2004). The persistence of underpowered studies in psychological research: Causes, consequences, and remedies. Psychological Methods, 9, 147–163.
McCullagh, P. & Nelder, J. (1989). Generalized linear models. London: Chapman and Hall.
McDonald, R. P. & Ho, M. R. (2002). Principles and practices in reporting structural equation modeling analyses. Psychological Methods, 7, 64–82.
Messick, S. (1989). Validity. In R. L. Linn (Ed.), Educational measurement (3rd ed., pp. 13–103). New York: Macmillan.
Muthén, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22, 376–398.
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.
Muthén, B. & Masyn, K. (2004). Discrete-time survival mixture analysis. Journal of Educational and Behavioral Statistics, 30, 27–58.
Muthén, L. K. & Muthén, B. O. (2004). Mplus, statistical analysis with latent variables: User’s guide. Los Angeles, CA: Muthén &Muthén.
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.
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.
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.
Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
Radloff, L. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401.
Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press.
Schafer. J. L. & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177.
Schumacker, R. E. (2002). Latent variable interaction modeling. Structural Equation Modeling, 9, 40–54.
Schumacker, R. E. & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling. Mahwah, NJ: Erlbaum.
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.
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.
Singer, J. D. & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University.
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.
Wirth, R. J. & Edwards, M. C. (2007). Item factor analysis: Current approaches and future directions. Psychological Methods, 12, 58–79.
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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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