Advertisement

Prevention Science

, Volume 15, Issue 4, pp 516–525 | Cite as

Random Assignment of Schools to Groups in the Drug Resistance Strategies Rural Project: Some New Methodological Twists

  • John W. Graham
  • Jonathan Pettigrew
  • Michelle Miller-Day
  • Janice L. Krieger
  • Jiangxiu Zhou
  • Michael L. Hecht
Article

Abstract

Random assignment to groups is the foundation for scientifically rigorous clinical trials. But assignment is challenging in group randomized trials when only a few units (schools) are assigned to each condition. In the DRSR project, we assigned 39 rural Pennsylvania and Ohio schools to three conditions (rural, classic, control). But even with 13 schools per condition, achieving pretest equivalence on important variables is not guaranteed. We collected data on six important school-level variables: rurality, number of grades in the school, enrollment per grade, percent white, percent receiving free/assisted lunch, and test scores. Key to our procedure was the inclusion of school-level drug use data, available for a subset of the schools. Also, key was that we handled the partial data with modern missing data techniques. We chose to create one composite stratifying variable based on the seven school-level variables available. Principal components analysis with the seven variables yielded two factors, which were averaged to form the composite inflate-suppress (CIS) score which was the basis of stratification. The CIS score was broken into three strata within each state; schools were assigned at random to the three program conditions from within each stratum, within each state. Results showed that program group membership was unrelated to the CIS score, the two factors making up the CIS score, and the seven items making up the factors. Program group membership was not significantly related to pretest measures of drug use (alcohol, cigarettes, marijuana, chewing tobacco; smallest p > .15), thus verifying that pretest equivalence was achieved.

Keywords

Random assignment Group randomized trial Principal component analysis Missing data 

References

  1. Caldwell, L. L, Smith, E. A., Collins, L. M., Graham, J. W., Lai, M., Wegner, L., … Jacobs, J. (2012). Translational research in South Africa: Evaluating implementation quality using a factorial design. Children and Youth Care Forum, 41, 119–136. doi: 10.1007/s10566-011-9164-4.
  2. Colby, M., Hecht, M. L., Miller-Day, M., Krieger, J. R., Syvertsen, A. K., Graham, J. W., & Pettigrew, J. (2013). Adapting school-based substance use prevention curriculum through cultural grounding: A review and exemplar of adaptation processes for rural schools. American Journal of Community Psychology, 51, 190–205. doi: 10.1007/s10464-012-9524-8.PubMedCentralCrossRefPubMedGoogle Scholar
  3. Dent, C. W., Sussman, S., & Flay, B. R. (1993). The use of archival data to select and assign schools in a drug prevention trial. Evaluation Review, 17, 159–181. doi: 10.1177/0193841X9301700203.CrossRefGoogle Scholar
  4. Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576. doi: 10.1146/annurev.psych.58.110405.085530.CrossRefPubMedGoogle Scholar
  5. Graham, J. W. (2012). Missing data: Analysis and design. New York: Springer.CrossRefGoogle Scholar
  6. Graham, J. W., Cumsille, P. E., & Elek-Fisk, E. (2003). Methods for handling missing data. In J. A. Schinka & W. F. Velicer (Eds.), Research methods in psychology (pp. 87–114). New York: Wiley.Google Scholar
  7. Graham, J. W., Cumsille, P. E., & Shevock, A. E. (2012). Methods for handling missing data. In J. A. Schinka & W. F. Velicer (Eds.), Research methods in psychology (2nd ed., pp. 109–141). New York: Wiley.Google Scholar
  8. Graham, J. W., Flay, B. R., Johnson, C. A., Hansen, W. B., & Collins, L. M. (1984). Group comparability: A multiattribute utility measurement approach to the use of random assignment with small numbers of aggregated units. Evaluation Review, 8, 247–260. doi: 10.1177/0193841X8400800206.CrossRefGoogle Scholar
  9. Graham, J. W., & Schafer, J. L. (1999). On the performance of multiple imputation for multivariate data with small sample size. In R. Hoyle (Ed.), Statistical strategies for small sample research (pp. 1–29). Thousand Oaks: Sage.Google Scholar
  10. Hansen, W. B., & Graham, J. W. (1991). Preventing alcohol, marijuana, and cigarette use among adolescents: Peer pressure resistance training versus establishing conservative norms. Preventive Medicine, 20, 414–430. doi: 10.1016/0091-7435(91)90039-7.CrossRefPubMedGoogle Scholar
  11. Harthun, M. L., Dustman, P. A., Reeves, L. J., Hecht, M. L., & Marsiglia, F. F. (2008). Culture in the classroom: Developing teacher proficiency in delivering a culturally-grounded prevention curriculum. The Journal of Primary Prevention, 29, 435–454. doi: 10.1007/s10935-008-0150-z.PubMedCentralCrossRefPubMedGoogle Scholar
  12. Hecht, M. L., & Krieger, J. K. (2006). The principle of cultural grounding in school-based substance use prevention: The Drug Resistance Strategies Project. Journal of Language and Social Psychology, 25, 301–319. doi: 10.1177/0261927X06289476.CrossRefGoogle Scholar
  13. Hecht, M. L., Marsiglia, F. F., Elek, E., Wagstaff, D. A., Kulis, S., Dustman, P., & Miller-Day, M. (2003). Culturally grounded substance abuse prevention: An evaluation of the keepin’ it REAL curriculum. Prevention Science, 4, 233–248. doi: 10.1023/A:1026016131401.CrossRefPubMedGoogle Scholar
  14. Hecht, M. L., & Miller-Day, M. (2007). The drug resistance strategies project as translational research. Journal of Applied Communication Research, 35, 343–349. doi: 10.1080/00909880701611086.CrossRefGoogle Scholar
  15. Kahan, B. C., & Morris, T. P. (2012). Improper analysis of trials randomised using stratified blocks or minimisation. Statistics in Medicine, 31, 328–340. doi: 10.1002/sim.4431.CrossRefPubMedGoogle Scholar
  16. Little, R. J. A. (1994). A class of pattern-mixture models for normal incomplete data. Biometrika, 81, 471–483. doi: 10.1093/biomet/81.3.471.CrossRefGoogle Scholar
  17. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). Hoboken: Wiley.Google Scholar
  18. Matts, J. P., & Lachin, J. M. (1988). Properties of permuted-block randomization in clinical trials. Controlled Clinical Trials, 9, 327–344. doi: 10.1016/0197-2456(88)90047-5.CrossRefPubMedGoogle Scholar
  19. Miller-Day, M., Pettigrew, J., Hecht, M. L., M., Shin, Y., Graham, J., & Krieger, J. (2013). How prevention curricula are taught under real-world conditions: Types of and reasons for teacher adaptations. Health Education, 113(4), in press.Google Scholar
  20. Ringwalt, C. L., Vincus, A., Ennett, S., Johnson, R., & Rohrbach, L. A. (2004). Reasons for teachers’ adaptation of substance use prevention curricula in schools with non-white student populations. Prevention Science, 5, 61–67. doi: 10.1023/B:PREV.0000013983.87069.a0.CrossRefPubMedGoogle Scholar
  21. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177. doi: 10.1037/1082-989X.7.2.147.CrossRefPubMedGoogle Scholar
  22. Schafer, J. L. (1997). Analysis of incomplete multivariate data. New York: Chapman and Hall.CrossRefGoogle Scholar
  23. Xu, Z., & Kalbfleisch, J. D. (2010). Propensity score matching in randomized clinical trials. Biometrics, 66, 813–823. doi: 10.1111/j.1541-0420.2009.01364.x.PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Society for Prevention Research 2013

Authors and Affiliations

  • John W. Graham
    • 1
    • 2
    • 7
  • Jonathan Pettigrew
    • 3
    • 4
  • Michelle Miller-Day
    • 3
    • 5
  • Janice L. Krieger
    • 6
  • Jiangxiu Zhou
    • 1
  • Michael L. Hecht
    • 2
    • 3
  1. 1.Department of Biobehavioral HealthThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Prevention Research CenterThe Pennsylvania State UniversityUniversity ParkUSA
  3. 3.Department of Communication Arts and SciencesThe Pennsylvania State UniversityUniversity ParkUSA
  4. 4.School of Communication StudiesUniversity of TennesseeKnoxvilleUSA
  5. 5.Department of Communication StudiesChapman UniversityOrangeUSA
  6. 6.School of CommunicationThe Ohio State UniversityColumbusUSA
  7. 7.Department of Biobehavioral HealthThe Pennsylvania State UniversityUniversity ParkUSA

Personalised recommendations