The WWC Attrition Standard: Sensitivity to Assumptions and Opportunities for Refining and Adapting to New Contexts

Evaluation Review

Volumn 41, Issue 2, 2017, Pages 130-154

  Deke, John ^a   Chiang, Hanley ^a  

^a MATHEMATICA POLICY RESEARCH   (United States)

Author keywords

content area; design; design and evaluation of programs and policies; economic evaluation; methodological development; outcome evaluation (other than economic evaluation); real world dissemination

Indexed keywords

ADOLESCENT; ADOLESCENT PREGNANCY; FEMALE; HUMAN; MEDICAL RESEARCH; METHODOLOGY; PREGNANCY; PREVENTION AND CONTROL; PROGRAM EVALUATION; SEXUAL EDUCATION; STANDARDS;

ADOLESCENT; BIOMEDICAL RESEARCH; FEMALE; HUMANS; PREGNANCY; PREGNANCY IN ADOLESCENCE; PROGRAM EVALUATION; RESEARCH DESIGN; SEX EDUCATION;

EID: 85015314413     PISSN: 0193841X     EISSN: 15523926     Source Type: Journal    
DOI: 10.1177/0193841X16670047     Document Type: Article

References (30)

1. Bloom H., Richburg-Hayes L., Black A., (2007). Using covariates to improve precision for studies that randomize schools to evaluate educational interventions. Educational Evaluation and Policy Analysis, 29, 30–59.
2. Brueton V. C., Tierney J. F., Stenning S., Meredith S., Harding S., Nazareth I., Rait G., (2014). Strategies to improve retention in randomised trials: A Cochrane systematic review and meta-analysis. BMJ Open, 4, e003821. doi:10.1136/bmjopen-2013-003821
3. Cohen J., (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.
4. Constantine J., Player D., Silva T., Hallgren K., Grider M., Deke J., (2009). An evaluation of teachers trained through different routes to certification (Final report, NCEE 2009-4043). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance.
5. Deke J., Puma M., (2013, 5). Coping with missing data in randomized controlled trials. Evaluation Technical Assistance Brief for OAH & ACYF Teenage Pregnancy Prevention Grantees, brief 3. Retrieved October 23, 2015, from http://www.hhs.gov/ash/oah/oah-initiatives/for-grantees/assets/copingwithmissingdata.pdf
6. Dynarski M., Agodini R., Heaviside S., Novak T., Carey N., Campuzano L., …Sussex W., (2007). Effectiveness of reading and mathematics software products: Findings from the first student cohort. Washington, DC: U.S. Department of Education, Institute of Education Sciences.
7. Greenberg D., Barnow B. S., (2014). Flaws in evaluations of social programs: Illustrations from randomized controlled trials. Evaluation Review, 38, 359–387.
8. Groves R., (2006). Nonresponse rates and nonresponse bias in household surveys. Public Opinion Quarterly. 70, 646–675.
9. Groves R., Peytcheva E., (2008). The impact of nonresponse rates on nonresponse bias a meta-analysis. Public Opinion Quarterly, 72, 169–189.
10. Hill C. J., Bloom H., Black A. R., Lipsey M. W., (2008). Empirical benchmarks for interpreting effect sizes in research. Child Development Perspectives, 2, 172–177.
11. James-Burdumy S., Deke J., Lugo-Gil J., Carey N., Hershey A., Gersten R., …Faddis B., (2010). Effectiveness of selected supplemental reading comprehension interventions: Findings from two student cohorts (NCEE 2010-4015). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance.
12. James-Burdumy S., Dynarski M., Moore M., Deke J., Mansfield W., Pistorino C., (2005). When schools stay open late: The national evaluation of the 21st Century Community Learning Centers program—Final report. Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance.
13. Kane T. J., (2015). Frustrated with the pace of progress in education? Invest in better evidence. Washington, DC: The Brookings Institution. Retrieved October 23, 2015, from http://www.brookings.edu/research/papers/2015/03/05-education-evidence-kane
14. Lee D., (2009). Training, wages, and sample selection: Estimating sharp bounds on treatment effects. Review of Economic Studies, 76, 1071–1102.
15. Lipsey M. W., Puzio K., Yun C., Hebert M. A., Steinka-Fry K., Cole M. W., …Busick M. D., (2012). Translating the statistical representation of the effects of education interventions into more readily interpretable forms (NCSER 2013-3000). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Special Education Research.
16. Little R. J. A., Rubin D. B., (2002). Statistical analysis with missing data (2nd ed.). Hoboken, NJ: John Wiley & Sons.
17. Manski C. F., (1990). Nonparametric bounds on treatment effects. American Economic Review, Papers and Proceedings, 80, 319–323.
18. National Center for Education Statistics. (2002). NCES statistical standards (NCES 2003-601). Washington, DC: Author. Retrieved October 23, 2015, from http://nces.ed.gov/pubs2003/2003601.pdf
19. Office of Management and Budget. (2006). Guidance on agency survey and statistical information collections. Washington, DC: Author. Retrieved October 23, 2015, from https://www.whitehouse.gov/sites/default/files/omb/inforeg/pmc_survey_guidance_2006.pdf
20. Puma M. J., Olsen R. B., Bell S. H., Price C., (2009). What to do when data are missing in group randomized controlled trials (NCEE 2009-0049). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance.
21. Rubin D. B., (1976). Inference and missing data. Biometrika, 63, 581–592.
22. Schochet P., (2008). Statistical power for random assignment evaluations of education programs. Journal of Educational and Behavioral Statistics, 33, 62–87.
23. Seftor N., (2016). Raising the bar. Evaluation Review.
24. Stock J., Yogo M., (2005). Testing for weak instruments in linear IV regression. In Andrews D. W. K., (Ed.), Identification and inference for econometric models (pp. 80–108). New York, NY: Cambridge University Press.
25. What Works Clearinghouse. (2006). WWC evidence review protocol for dropout prevention interventions (Version 1.0). Washington, DC: U.S. Department of Education, Institute of Education Sciences. Retrieved October 23, 2015, from http://ies.ed.gov/ncee/wwc/pdf/reference_resources/Dropout_protocol_V1.pdf
26. What Works Clearinghouse. (2007). WWC evidence review protocol for elementary school mathematics interventions (Version 1.0). Washington, DC: U.S. Department of Education, Institute of Education Sciences. Retrieved October 23, 2015, from http://ies.ed.gov/ncee/wwc/pdf/reference_resources/esm_protocol_1.0.pdf
27. What Works Clearinghouse. (2008). WWC procedures and standards handbook (Version 2.0). Washington, DC: U.S. Department of Education, Institute of Education Sciences. Retrieved June 2, 2015, from http://ies.ed.gov/ncee/wwc/pdf/reference_resources/wwc_procedures_v2_standards_handbook.pdf
28. What Works Clearinghouse. (2013). Assessing attrition bias (Version 2.1). Washington, DC: U.S. Department of Education, Institute of Education Sciences. Retrieved October 23, 2015, from http://ies.ed.gov/ncee/wwc/pdf/reference_resources/wwc_attrition_v2.1.pdf
29. What Works Clearinghouse. (2014). Assessing attrition bias—addendum (Version 3.0). Washington, DC: U.S. Department of Education, Institute of Education Sciences. Retrieved October 23, 2015, from http://ies.ed.gov/ncee/wwc/pdf/reference_resources/wwc_attrition_v3.0.pdf
30. Zweben A., Fucito L. M., O’Malley S. S., (2009). Effective strategies for maintaining research participation in clinical trials. Drug Information Journal, 43. doi:10.1177/009286150904300411