Abstract
Optimal design has been an under-utilized methodology. However, it has significant real-world applications, particularly in mixed methods implementation research. We review the concept and demonstrate how it can be used to assess the sensitivity of design decisions and balance competing needs. For observational studies, this methodology enables selection of the most informative study units. For experimental studies, it entails selecting and assigning study units to intervention conditions in the most informative manner. We blend optimal design methods with purposeful sampling to show how these two concepts balance competing needs when there are multiple study aims, a common situation in implementation research.
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References
Bellhouse, D. R. (1984). A review of optimal designs in survey sampling. The Canadian Journal of Statistics., 12, 53–65.
Berger, M. P. F., & Wong, W.-K. (Eds.). (2005). Applied optimal designs. New York: Wiley.
Bhaumik, D. K. (1993). On optimal block designs in the presence of a linear trend. Sankhya Series B, 55(1), 91–102.
Bhaumik, D. K. (1995). Majorization and D-optimality under the nearest neighbor correlation model. Journal of the Royal Statistical Society Series B, 57(1), 139–143.
Bhaumik, D. K., & Whittinghill, D. C. (1991). Optimality and robustness to the unavailability of blocks in block designs. Journal of the Royal Statistical Society B, 53(2), 399–407.
Goos, P., & Jones, B. (2011). Optimal design of experiments: A case study approach. New York: Wiley.
Groves, R. M., & Lyberg, L. (2010). Total survey error: Past, present, and future. Public Opinion Quarterly, 74(5), 849–879.
Heiberger, R. M., Bhaumik, D. K., & Holland, B. (1993). Optimal data augmentation strategies for additive models. Journal of the American Statistical Association, 88(423), 926–938.
Kiefer, J. (1959). Optimum experimental designs (with discussions). Journal of the Royal Statistical Society, Series B (Methodology), 21, 272–319.
Liski, E. P., Mandal, N. K., Shah, K. R., & Sinha, B. K. (2002). Topics in optimal design. New York: Springer.
Murray, D. M., Varnell, S. P., & Blitstein, J. L. (2004). Design, analysis of group-randomized trials: A review of recent methodological developments. American Journal of Public Health, 94(3), 423–432.
Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2013). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health. doi:10.1007/s10488-013-0528-y.
Patton, M. Q. (2002). Qualitative research and evaluation methods (3rd ed.). Thousand Oaks: Sage.
Raudenbush, S. W. (1997). Statistical analysis and optimal design for cluster-randomized trials. Psychological Methods, 2, 173–185.
Raudenbush, S. W., & Liu, X. (2000). Statistical power and optimal design for multisite randomized trials. Psychological Methods, 5(2), 199–213.
Raudenbush, S. W., Spybrook, J., Congdon, R., Liu, X., Martinez, A., Bloom, H., & Hill, C. (2011). Optimal design plus empirical evidence (Version 3.0). http://wtgrantfoundation.org/FocusAreas#tools-for-group-randomized-trials. Accessed 25 Oct 2014
Shah, K. R., & Sinha, B. K. (1989). Theory of optimal designs. New York: Springer.
Shirakura, T., & Tong, W.-P. (1996). Weighted A-optimality for fractional 2 m factorial designs of resolution V. Journal of Statistical Planning and inference, 56, 243–256.
Spybrook, J., Bloom, H., Congdon, R., Hill, C., Martinez, A., & Raudenbush, S. W. (2011). Optimal design plus empirical evidence: Documentation for the “Optimal Design” software Version 3.0. http://wtgrantfoundation.org/FocusAreas#tools-for-group-randomized-trials. Accessed 25 Oct 2014
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Drs. Duan, Hoagwood, and Palinkas were funded through a Grant from the National Institute of Mental Health (P30-MH090322:K. Hoagwood, PI). The authors appreciate helpful comments from two anonymous reviewers.
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Duan, N., Bhaumik, D.K., Palinkas, L.A. et al. Optimal Design and Purposeful Sampling: Complementary Methodologies for Implementation Research. Adm Policy Ment Health 42, 524–532 (2015). https://doi.org/10.1007/s10488-014-0596-7
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DOI: https://doi.org/10.1007/s10488-014-0596-7