161
Views
0
CrossRef citations to date
0
Altmetric
Methodological Studies

Asymdystopia: The Threat of Small Biases in Evaluations of Education Interventions That Need to Be Powered to Detect Small Impacts

, & ORCID Icon
Pages 207-240 | Received 04 Dec 2019, Accepted 13 Sep 2020, Published online: 16 Apr 2021

References

  • Agodini, R., & Harris, B. (2010). An experimental evaluation of four elementary school math curricula. Journal of Research on Educational Effectiveness, 3(3), 199–253. https://doi.org/10.1080/19345741003770693
  • Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature, 567(7748), 305–307. https://doi.org/10.1038/d41586-019-00857-9
  • Armstrong, T. B., & Kolesár, M. (2018). Optimal inference in a class of regression models. Econometrica, 86(2), 655–683. https://doi.org/10.3982/ECTA14434
  • Armstrong, T. B., & Kolesár, M. (2020). Simple and honest confidence intervals in nonparametric regression. Quantitative Economics, 11(1), 1–39. https://doi.org/10.3982/QE1199
  • Balu, R., Zhu, P., Doolittle, F., Schiller, E., Jenkins, J., & Gersten, R. (2015). Evaluation of response to intervention practices for elementary school reading. National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.
  • Barreca, A. I., Lindo, J. M., & Waddell, G. R. (2016). Heaping-induced bias in regression discontinuity designs. Economic Inquiry, 54(1), 268–293. https://doi.org/10.1111/ecin.12225
  • Bartalotti, O. (2019). Regression discontinuity and heteroskedasticity robust standard errors: Evidence from a fixed-bandwidth approximation. Journal of Econometric Methods, 8(1), 1–26. https://doi.org/10.1515/jem-2016-0007
  • Bartalotti, O., Calhoun, G., & He, Y. (2017). Bootstrap confidence intervals for sharp regression discontinuity designs. In M. D. Cattaneo & J. C. Escanciano (Eds.), Advances in econometrics: Regression discontinuity designs theory and applications (Vol. 38, pp. 421–453). Emerald Publishing Limited.
  • Bloom, H. S. (1995). Minimum detectable effects: A simple way to report the statistical power of experimental designs. Evaluation Review, 19(5), 547–556. https://doi.org/10.1177/0193841X9501900504
  • Bloom, H. S. (2005). Randomizing groups to evaluate place-based programs. In H. S. Bloom (Ed.), Learning more from social experiments: Evolving analytic approaches (pp. 115–172). Russell Sage Foundation.
  • Bloom, H. S., Richburg-Hayes, L., & Black, A. R. (2007). Using covariates to improve precision for studies that randomize schools to evaluate educational interventions. Educational Evaluation and Policy Analysis, 29(1), 30–59. https://doi.org/10.3102/0162373707299550
  • Branson, Z., Rischard, M., Bornn, L., & Miratrix, L. W. (2019). A nonparametric Bayesian methodology for regression discontinuity designs. Journal of Statistical Planning and Inference, 202, 14–30. https://doi.org/10.1016/j.jspi.2019.01.003
  • Calonico, S., Cattaneo, M. D., & Farrell, M. H. (2020). Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs. The Econometrics Journal, 23(2), 192–210. https://doi.org/10.1093/ectj/utz022
  • Calonico, S., Cattaneo, M., & Titiunik, R. (2014). Robust nonparametric confidence intervals for regression-discontinuity designs. Econometrica, 82(6), 2295–2326. https://doi.org/10.3982/ECTA11757
  • Campuzano, L., Dynarski, M., Agodini, R., & Rall, K. (2009). Effectiveness of reading and mathematics software products: Findings from two student cohorts. National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.
  • Cattaneo, M. D., Frandsen, B. R., & Titiunik, R. (2015). Randomization inference in the regression discontinuity design: An application to party advantages in the U.S. Senate. Journal of Causal Inference, 3(1), 1–24. https://doi.org/10.1515/jci-2013-0010
  • Cattaneo, M. D., Titiunik, R., & Vasquez-Bare, G. (2017). Comparing inference approaches for RD designs: A reexamination of the effect of Head Start on child mortality. Journal of Policy Analysis and Management, 36(3), 643–681. https://doi.org/10.1002/pam.21985
  • Chen, Y., Feng, S., Heckman, J. J., & Kautz, T. (2020). Sensitivity of self-reported noncognitive skills to survey administration conditions. Proceedings of the National Academy of Sciences, 117(2), 931–935. https://doi.org/10.1073/pnas.1910731117
  • Chiang, H., Wellington, A., Hallgren, K., Speroni, C., Herrmann, M., Glazerman, S., & Constantine, J. (2015). Evaluation of the Teacher Incentive Fund: Implementation and impacts of pay-for-performance after two years. National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  • 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. National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.
  • Deke, J., & Dragoset, L. (2012). Statistical power for regression discontinuity designs in education: Empirical estimates of design effects relative to randomized controlled trials. Mathematica Policy Research.
  • Gelman, A., & Imbens, G. (2019). Why high-order polynomials should not be used in regression discontinuity designs. Journal of Business & Economic Statistics, 37(3), 447–456.
  • Greenberg, D., & Barnow, B. S. (2014). Flaws in evaluations of social programs: Illustrations from randomized controlled trials. Evaluation Review, 38(5), 359–387. https://doi.org/10.1177/0193841X14545782
  • He, Y., & Bartalotti, O. (2020). Wild bootstrap for fuzzy regression discontinuity designs: Obtaining robust bias-corrected confidence intervals. The Econometrics Journal, 23(2), 211–231. https://doi.org/10.1093/ectj/utaa002
  • Hedges, L. V., & Hedberg, E. C. (2007). Intraclass correlation values for planning group-randomized trials in education. Educational Evaluation and Policy Analysis, 29(1), 60–87. https://doi.org/10.3102/0162373707299706
  • Hill, C. J., Bloom, H. S., Black, A. R., & Lipsey, M. W. (2008). Empirical benchmarks for interpreting effect sizes in research. Child Development Perspectives, 2(3), 172–177. https://doi.org/10.1111/j.1750-8606.2008.00061.x
  • Imbens, G. W., & Kalyanaraman, K. (2012). Optimal bandwidth choice for the regression discontinuity estimator. The Review of Economic Studies, 79(3), 933–959. https://doi.org/10.1093/restud/rdr043
  • Imbens, G., & Wager, S. (2019). Optimized regression discontinuity designs. The Review of Economics and Statistics, 101(2), 264–278. https://doi.org/10.1162/rest_a_00793
  • James-Burdumy, S., Deke, J., Gersten, R., Lugo-Gil, J., Newman-Gonchar, R., Dimino, J., Haymond, K., & Liu, A. Y.-H. (2012). Effectiveness of four supplemental reading comprehension interventions. Journal of Research on Educational Effectiveness, 5(4), 345–383. https://doi.org/10.1080/19345747.2012.698374
  • 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. National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.
  • James-Burdumy, S., Dynarski, M., & Deke, J. (2008). After-school program effects on behavior: Results from the 21st Century Community Learning Centers program national evaluation. Economic Inquiry, 46(1), 13–18. https://doi.org/10.1111/j.1465-7295.2007.00074.x
  • Kane, T. J. (2015). Frustrated with the pace of progress in education? Invest in better evidence. The Brookings Institution.
  • Kolesár, M., & Rothe, C. (2018). Inference in regression discontinuity designs with a discrete running variable. American Economic Review, 108(8), 2277–2304. https://doi.org/10.1257/aer.20160945
  • Leamer, E. (2010). Tantalus on the road to asymptopia. Journal of Economic Perspectives, 24(2), 31–46. https://doi.org/10.1257/jep.24.2.31
  • Lee, D. S., & Card, D. (2008). Regression discontinuity inference with specification error. Journal of Econometrics, 142(2), 655–674. https://doi.org/10.1016/j.jeconom.2007.05.003
  • Lipsey, M. W., Puzio, K., Yun, C., Hebert, M. A., Steinka-Fry, K., Cole, M. W., Roberts, M., Anthony, K. S., & Busick, M. D. (2012). Translating the statistical representation of the effects of education interventions into more readily interpretable forms. National Center for Special Education Research, Institute of Education Sciences, U.S. Department of Education.
  • Murray, D. M. (1998). Design and analysis of group-randomized trials. Oxford University Press.
  • Noack, C., & Rothe, C. (2020). Bias-aware inference in fuzzy regression discontinuity designs. Working paper.
  • Pei, Z., Lee, D. S., Card, D., & Weber, A. (2020). Local polynomial order in regression discontinuity designs. NBER working paper 27424.
  • Puma, M. J., Olsen, R. B., Bell, S. H., & Price, C. (2009). What to do when data are missing in group randomized controlled trials. National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.
  • Sales, A. C., & Hansen, B. B. (2019). Limitless regression discontinuity. Journal of Educational and Behavioral Statistics, 20(10), 1–32.
  • Schochet, P. Z. (2008a). Statistical power for random assignment evaluations of education programs. Journal of Educational and Behavioral Statistics, 33(1), 62–87. https://doi.org/10.3102/1076998607302714
  • Schochet, P. Z. (2008b). Technical methods report: Statistical power for regression discontinuity designs in education evaluations. National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.
  • Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133. https://doi.org/10.1080/00031305.2016.1154108
  • What Works Clearinghouse. (2008). WWC procedures and standards handbook (version 2.0). Institute of Education Sciences, U.S. Department of Education.
  • What Works Clearinghouse. (2013). Assessing attrition bias (version 2.1). Institute of Education Sciences, U.S. Department of Education.
  • What Works Clearinghouse. (2014). Assessing attrition bias—addendum (version 3.0). Institute of Education Sciences, U.S. Department of Education.
  • What Works Clearinghouse. (2020). WWC standards handbook (version 4.1). Institute of Education Sciences, U.S. Department of Education.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.