Toward Rigorous Idiographic Research in Prevention Science: Comparison Between Three Analytic Strategies for Testing Preventive Intervention in Very Small Samples
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Psychosocial prevention research lacks evidence from intensive within-person lines of research to understand idiographic processes related to development and response to intervention. Such data could be used to fill gaps in the literature and expand the study design options for prevention researchers, including lower-cost yet rigorous studies (e.g., for program evaluations), pilot studies, designs to test programs for low prevalence outcomes, selective/indicated/adaptive intervention research, and understanding of differential response to programs. This study compared three competing analytic strategies designed for this type of research: autoregressive moving average, mixed model trajectory analysis, and P-technique. Illustrative time series data were from a pilot study of an intervention for nursing home residents with diabetes (N = 4) designed to improve control of blood glucose. A within-person, intermittent baseline design was used. Intervention effects were detected using each strategy for the aggregated sample and for individual patients. The P-technique model most closely replicated observed glucose levels. ARIMA and P-technique models were most similar in terms of estimated intervention effects and modeled glucose levels. However, ARIMA and P-technique also were more sensitive to missing data, outliers and number of observations. Statistical testing suggested that results generalize both to other persons as well as to idiographic, longitudinal processes. This study demonstrated the potential contributions of idiographic research in prevention science as well as the need for simulation studies to delineate the research circumstances when each analytic approach is optimal for deriving the correct parameter estimates.
KeywordsARIMA Diabetes Blood glucose Indicated prevention Mixed model trajectory analysis P-technique Selective prevention Sliding scale
The authors gratefully acknowledge the assistance of Peter Molenaar, Hendicks Brown and George Howe for refining this study and the manuscript.
- Biglan, A., Ary, D. V., & Wagenaar, A. C. (2000) The value of interrupted time-series experiments for community intervention research. Prevention Science, 1, 31–49.Google Scholar
- Bolderman, K. M. (2002). Putting your patients on the pump (pp. 39–52). Alexandria, VA: American Diabetes Association, Inc.Google Scholar
- Burns, M. K., Christ, T. J., Boice, C. H., & Szadokierski, I. (2010). Special education in an RTI model: Addressing unique learning needs. In T. A. Glover & S. Vaughn (Eds.), The promise of response to intervention (pp. 267–285). New York: Guilford.Google Scholar
- Chatfield, C. (2004). The analysis of time series: an introduction (6th ed.). Boca Raton, FL: Chapman and Hall/CRC Press.Google Scholar
- Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Chicago: Rand McNallyGoogle Scholar
- Gaynor, A. K. (1998). Analyzing problems in schools and school systems: a theoretical approach. Mahwah, N.J., L.: Erlbaum Associates.Google Scholar
- Hassmiller Lich, K., Ginexi, L., Osgood, N., & Mabry, P. (2012). A call to address complexity in prevention science research. doi: 10.1007/s11121-012-0285-2.
- Hedeker, D., & Gibbons, R. D. (2006). Longitudinal data analysis. Hoboken: Wiley.Google Scholar
- Hintze, J. M., & Marotte, A. M. (2010). Student assessment and data-based decision making. In T. A. Glover & S. Vaughn (Eds.), The promise of response to intervention (pp. 57–77). New York: Guilford.Google Scholar
- Hoyle, R. H. (1999). Statistical strategies for small sample research. Thousand Oaks, CA: Sage.Google Scholar
- Kwok, O., West, S. G., & Green, S. B. (2007). The impact of misspecifying the within-subject covariance structure in multiwave longitudinal multilevel models: A Monte Carlo study. Multivariate Behavioral Research, 42, 557–592.Google Scholar
- Lehmann, E. D., Tarín, C., Bondia, J., Teufel, E., & Deutsch, T. (2011). Development of AIDA v4.3b diabetes simulator technical upgrade to support incorporation of lispro, as part and glargine insulin analogues. Journal of Electrical and Computer Engineering, 2011, 1–17. http://www.hindawi.com/journals/jece/2011/427196/.
- Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D., & Schabenberger, O. (2006). SAS for mixed models (2nd ed.). Cary, NC: SAS Press.Google Scholar
- Maldonado Molina, M. M., & Wagenaar, A. C. (2010). Effects of alcohol taxes on alcohol-related mortality in Florida: Time-series analyses from 1969 to 2004. Alcoholism Clininical and Experimental Research, 34, 1915–1921.Google Scholar
- Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Brining the person back into scientific psychology, this time forever. Measurement, 2, 201–218.Google Scholar
- Murphy, S. A., Lynch, K. G., Oslin, D., McKay, J. R., & Tenhave, T. (2007) Developing adaptive treatment strategies in substance abuse research. Drug and Alcohol Dependence, 88(Suppl 2), S24–S39.Google Scholar
- Velicer, W. F., & Colby, S. M. (1997). Time series analysis for prevention and treatment research. In K. J. Bryant, M. Windle, & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 211–249). Washington, DC.: American Psychological Association.Google Scholar