Abstract
In this chapter, balanced and unbalanced reduced factorial designs for use in optimization of multicomponent behavioral, biobehavioral, and biomedical interventions are discussed. These designs, which include a carefully selected subset of the experimental conditions in a corresponding complete factorial, can be more efficient and economical than complete factorials when implementation of experimental conditions is expensive or logistically challenging. However, there are important trade-offs that the investigator must make in exchange for this efficiency and economy. Reduced factorial designs are not for every situation, but when used appropriately and strategically, they can make excellent use of limited research resources. Readers should be familiar with the material in all previous chapters, particularly Chaps. 3 and 4.
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References
Box, G. E. P., & Meyer, R. D. (1986). An analysis for unreplicated fractional factorials. Technometrics, 28, 11–18.
Chakraborty, B., Collins, L. M., Strecher, V., & Murphy, S. A. (2009). Developing multicomponent interventions using fractional factorial designs. Statistics in Medicine, 28, 2687–2708.
Collins, L. M., Baker, T. B., Mermelstein, R. J., Piper, M. E., Jorenby, D. E., Smith, S. S., … Fiore, M. C. (2011). The multiphase optimization strategy for engineering effective tobacco use interventions. Annals of Behavioral Medicine, 41, 208–226.
Collins, L. M., Dziak, J. R., & Li, R. (2009). Design of experiments with multiple independent variables: A resource management perspective on complete and reduced factorial designs. Psychological Methods, 14, 202–224.
Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B. (2014). Factorial experiments: Efficient tools for evaluation of intervention components. American Journal of Preventive Medicine, 47, 498–504.
Collins, L. M., & Kugler, K. C. (Eds.) (2018). Optimization of multicomponent behavioral, biobehavioral, and biomedical interventions: Advanced topics. New York, NY: Springer.
Cook, J. W., Collins, L. M., Fiore, M. C., Smith, S. S., Fraser, D., Bolt, D. M., … Mermelstein, R. (2016). Comparative effectiveness of motivation phase intervention components for use with smokers unwilling to quit: A factorial screening experiment. Addiction, 111, 117–128.
Dziak, J. D., Nahum-Shani, I., & Collins, L. M. (2012). Multilevel factorial experiments for developing behavioral interventions: Power, sample size, and resource considerations. Psychological Methods, 17, 153–175.
Fisher, R. A. (1971). The design of experiments. New York, NY: Hafner Publishing.
Montgomery, D. C. (2009). Design and analysis of experiments (7th ed.). Hoboken, NJ: Wiley.
Nahum-Shani, I., & Dziak, J. J. (2018). Multilevel factorial designs in intervention development. In L. M. Collins & K. C. Kugler (Eds.), Optimization of multicomponent behavioral, biobehavioral, and biomedical interventions: Advanced topics (forthcoming). New York, NY: Springer.
Nahum-Shani, I., Dziak, J. J., & Collins, L. M. (2017). Multi-level factorial designs with experimentation-induced clustering. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000128
Piper, M. E., Fiore, M. C., Smith, S. S., Fraser, D., Bolt, D. M., Collins, L. M., … Baker, T. B. (2016). Identifying effective intervention components for smoking cessation: A factorial screening experiment. Addiction, 111, 129–141.
West, S. G., & Aiken, L. S. (1997). Toward understanding individual effects in multicomponent prevention programs: Design and analysis strategies. In K. J. Bryant, M. Windle, & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 167–209). Washington, DC: American Psychological Association.
West, S. G., Aiken, L. S., & Todd, M. (1993). Probing the effects of individual components in multiple component prevention programs. American Journal of Community Psychology, 21, 571–605.
Wu, C. F. J., & Chen, Y. (1992). A graph-aided method for planning two-level experiments when certain interactions are important. Technometrics, 34, 162–175.
Wu, C. F. J., & Hamada, M. S. (2009). Experiments: Planning, analysis, and optimization (2nd ed.). Hoboken, NJ: Wiley.
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Collins, L.M. (2018). Balanced and Unbalanced Reduced Factorial Designs. In: Optimization of Behavioral, Biobehavioral, and Biomedical Interventions. Statistics for Social and Behavioral Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-72206-1_5
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DOI: https://doi.org/10.1007/978-3-319-72206-1_5
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