Prevention Science

, Volume 14, Issue 3, pp 267–278 | Cite as

Toward Rigorous Idiographic Research in Prevention Science: Comparison Between Three Analytic Strategies for Testing Preventive Intervention in Very Small Samples

  • Ty A. Ridenour
  • Thomas Z. Pineo
  • Mildred M. Maldonado Molina
  • Kristen Hassmiller Lich
Article

Abstract

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.

Keywords

ARIMA Diabetes Blood glucose Indicated prevention Mixed model trajectory analysis P-technique Selective prevention Sliding scale 

References

  1. 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
  2. Biglan, A., Ary, D., Koehn, V., & Levings, D. (1996). Mobilizing positive reinforcement in communities to reduce youth access to tobacco. American Journal of Community Psychology, 24, 625–638.PubMedCrossRefGoogle Scholar
  3. Boker, S. M., Molenaar, P. C. M., & Nesselroade, J. R. (2009). Issues in intraindividual variability: Individual differences in equilibria and dynamics over multiple time series. Psychology and Aging, 24, 858–862.PubMedCrossRefGoogle Scholar
  4. Bolderman, K. M. (2002). Putting your patients on the pump (pp. 39–52). Alexandria, VA: American Diabetes Association, Inc.Google Scholar
  5. Borkenau, P., & Ostendorf, F. (1998). The Big Five as states: How useful is the five-factor model to describe intraindividual variations over time? Journal of Research in Personality, 32, 202–221.CrossRefGoogle Scholar
  6. 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
  7. Chatfield, C. (2004). The analysis of time series: an introduction (6th ed.). Boca Raton, FL: Chapman and Hall/CRC Press.Google Scholar
  8. Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Chicago: Rand McNallyGoogle Scholar
  9. De Los Reyes, A., & Kazdin, A. E. (2005). Informant discrepancies in the assessment of childhood psychopathology: A critical review, theoretical framework, and recommendations for further study. Psychological Bulletin, 131, 483–509.PubMedCrossRefGoogle Scholar
  10. Ennett, S. T., Ringwalt, C. L., Thorne, J., Rohrbach, L. A., Vincus, A., Simons-Rudolph, A., & Jones, S. (2003). A comparison of current practice in school-based substance use prevention programs with meta-analysis findings. Prevention Science, 4, 1–14.PubMedCrossRefGoogle Scholar
  11. Gaynor, A. K. (1998). Analyzing problems in schools and school systems: a theoretical approach. Mahwah, N.J., L.: Erlbaum Associates.Google Scholar
  12. 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.
  13. Hedeker, D., & Gibbons, R. D. (2006). Longitudinal data analysis. Hoboken: Wiley.Google Scholar
  14. 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
  15. Howe, G. W., Beach, S. R. H., & Brody, G. H. (2010). Microtrial methods for translation gene- environment dynamics into preventive interventions. Prevention Science, 11, 343–354.PubMedCrossRefGoogle Scholar
  16. Hoyle, R. H. (1999). Statistical strategies for small sample research. Thousand Oaks, CA: Sage.Google Scholar
  17. Kazdin, A. E., & Blase, S. L. (2011). Rebooting psychotherapy research and practice to reduce the burden of mental illness. Perspective on Psychological Science, 6, 21–37.CrossRefGoogle Scholar
  18. 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
  19. 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/.
  20. 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
  21. 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
  22. 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
  23. Molenaar, P. C. M., & Nesselroade, J. R. (2009). The recoverability of P-technique factor analysis. Multivariate Behavioral Research, 44, 130–141.CrossRefGoogle Scholar
  24. 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
  25. Pickup, J., & Keen, H. (2002). Continuous subcutaneous insulin infusion at 25 years: Evidence base for the expanding use of insulin pump therapy in type 1 diabetes. Diabetes Care, 25, 593–598.PubMedCrossRefGoogle Scholar
  26. Ridenour, T. A., Hall, D. L., & Bost, J. E. (2009). A small sample randomized clinical trial methodology using N-of-1 designs and mixed model analysis. American Journal of Drug and Alcohol Abuse, 35, 260–266.PubMedCrossRefGoogle Scholar
  27. Russell, R. L., Jones, M. E., & Miller, S. A. (2007). Core process components in psychotherapy: A synthetic review of P-technique studies. Psychotherapy Research, 17, 273–291.CrossRefGoogle Scholar
  28. Sivo, S., Fan, X., & Witta, W. (2005). The biasing effects of unmodeled ARMA time series processes on latent growth curve model estimates. Structural Equation Modeling, 12, 215–231.CrossRefGoogle Scholar
  29. 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

Copyright information

© Society for Prevention Research 2013

Authors and Affiliations

  • Ty A. Ridenour
    • 1
  • Thomas Z. Pineo
    • 2
  • Mildred M. Maldonado Molina
    • 3
  • Kristen Hassmiller Lich
    • 4
  1. 1.Department of Pharmaceutical Sciences, Center for Education and Drug Abuse ResearchUniversity of PittsburghPittsburghUSA
  2. 2.University of Pittsburgh Medical CenterPittsburghUSA
  3. 3.University of FloridaGainesvilleUSA
  4. 4.The University of North Carolina at Chapel HillChapel HillUSA

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