Why Small is Too Small a Term: Prevention Science for Health Disparities, Culturally Distinct Groups, and Community-Level Intervention

Prevention Science

Volumn 16, Issue 7, 2015, Pages 1026-1032

  Henry, David ^a   Fok, Carlotta Ching Ting ^b   Allen, James ^c  

^a UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

^b UNIVERSITY OF ALASKA FAIRBANKS   (United States)

^c UNIVERSITY OF MINNESOTA MEDICAL SCHOOL   (United States)

Author keywords

Ethnic minority research; Health disparities; Research methods; Small samples; Statistical methods

Indexed keywords

CULTURAL FACTOR; HEALTH CARE DISPARITY; ORGANIZATION AND MANAGEMENT; PREVENTIVE HEALTH SERVICE; SAMPLE SIZE;

CULTURAL CHARACTERISTICS; HEALTHCARE DISPARITIES; PREVENTIVE HEALTH SERVICES; SAMPLE SIZE;

EID: 84942505917     PISSN: 13894986     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11121-015-0577-4     Document Type: Article

References (29)

1. Baraldi, A. N. (2013). Missing data methods. In T. D. Little (Ed.), The Oxford handbook of quantitative methods (Vol. 2): Statistical analysis (pp. 635–664). New York: Oxford University Press.
2. Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81–105.
3. Creswell, J. W., Klassen, A. C., Plano Clark, V. L., Smith, K. C., & for the Office of Behavioral and Social Sciences Research. (2011). Best practices for mixed methods research in the health sciences. National Institutes of Health. Retrieved February 16, 2015 fromhttp://obssr.od.nih.gov/mixed_methods_research.
4. Cook, T. D. (2008). "Waiting for life to arrive": A history of the regression-discontinuity design in psychology, statistics and economics. Journal of Econometrics, 142, 636–654.
5. Cook, T. D., Shadish, W. R., & Wong, V. C. (2008). Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within-study comparisons. Journal of Policy Analysis and Management, 27, 724–750.
6. Dziak, J. J., 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.
7. Fok, C. C. T., & Henry, D. (2015). Increasing the sensitivity of measures to change. Prevention Science. doi:10.1007/s11121-015-0545-z.
8. Fok, C. C. T., Henry, D., & Allen, J. (2015a). Research designs for intervention research with small samples II: Stepped wedge and interrupted time-series designs. Prevention Science. doi:10.1007/s11121-015-0572-9.
9. Fok, C. C. T., Henry, D., & Allen, J. (2015b). Maybe small is too small a term: Introduction to advancing small sample prevention science. Prevention Science. doi:10.1007/s11121-015-0584-5.
10. Eldridge, S. M., Ashby, D., & Kerry, S. (2006). Sample size for cluster randomized trials: Effect of coefficient of variation of cluster size and analysis method. International Journal of Epidemiology, 35, 1292–1300. doi:10.1093/ije/dyl129.
11. Garcia, E. (2005). Keywords co-occurrence and semantic connectivity. Retrieved fromhttp://www.miislita.com/semantics/c-index-1.html.
12. Hedeker, D. (2015). Methods for multilevel ordinal data in prevention research. Prevention Science. doi:10.1007/s11121-014-0495-x.
13. Hemming, K., Girling, A. J., Stich, A. J., Marsh, J., & Liliford, R. J. (2011). Sample size calculations for cluster randomised controlled trials with a fixed number of clusters. BMC Medical Research Methodology, 11, 102.
14. Henry D., Dymnicki, A. B., Mohatt, N., Kelly, J. G., & Allen, J. (2015). Clustering methods with qualitative data: A mixed methods approach for prevention research with small samples. Prevention Science.
15. Hopkin, C. R., Hoyle, R. H., & Gottfredson, N. C. (2015). Maximizing the yield of small samples in prevention research: A review of general strategies and best practices. Prevention Science. doi:10.1007/s11121-014-0542-7.
16. Hoyle, R. H., & Gottfredson, N. C. (2015). Sample size considerations in prevention research applications of multilevel modeling and structural equation modeling. Prevention Science. doi:10.1007/s11121-014-0489-8.
17. Kadane, J. B. (2015). Bayesian methods for prevention research. Prevention Science. doi:10.1007/s11121-014-0531-x.
18. Kaplan, D. (2014). Bayesian statistics for the social sciences. New York: Guilford.
19. Kraemer, H. C. (2000). Pitfalls of multisite randomized clinical trials of efficacy and effectiveness. Schizophrenia Bulletin, 26, 533–541.
20. Shadish, W. R., Clark, M. H., & Steiner, P. M. (2008). Can nonrandomized experiments yield accurate answers? A randomized experiment comparing random to nonrandom assignment. Journal of the American Statistical Association, 103, 1334–1343.
21. Shadish, W. R., & Cook, T. D. (2009). The renaissance of field experimentation in evaluating interventions. Annual Review of Psychology, 60, 607–629.
22. Steiner, P. M., Cook, T. D., Shadish, W. R., & Clark, M. H. (2010). The importance of covariate selection in controlling for selection bias in observational studies. Psychological Methods, 15, 250–267.
23. The Diabetes Prevention Program Research Group. (2000). The diabetes prevention program: Baseline characteristics of the randomized cohort. Diabetes Care, 23, 1619–1629.
24. Trickett, E. J. (2011). Community-based participatory research as worldview or instrumental strategy: Is it lost in translation(al) research? American Journal of Public Health, 101, 1353–1355.
25. Trickett, E. J., Beehler, S., Deutsch, C., Green, L. W., Hawe, P., McLeroy, K., et al. (2011). Advancing the science of community-level interventions. American Journal of Public Health, 101, 1410–1419.
26. Van Gough, V. (1882). Letter to Theo van Gogh from The Hague (22 October 1882).
27. West, S. G. (2009). Alternatives to randomized experiments. Current Directions in Psychological Science, 18, 299–304.
28. Wyman, P. A., Henry, D., & Knoblauch, S. (2015). Designs for testing group-based interventions with limited number of social units: The dynamic wait-listed and regression point displacement designs. Prevention Science. doi:10.1007/s11121-014-0535-6.
29. Yuan, Y., & MacKinnon, D. P. (2009). Bayesian mediation analysis. Psychological Methods, 14, 301–322. doi:10.1037/a0016972.