When second best is good enough: A comparison between a true experiment and a regression discontinuity quasi-experiment

Journal of Experimental Criminology

Volumn 6, Issue 2, 2010, Pages 191-208

  Berk, Richard ^a   Barnes, Geoffrey ^a   Ahlman, Lindsay ^b   Kurtz, Ellen ^b  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b Philadelphia Adult Probation and Parole Department   (United States)

Author keywords

Quasi experiment; Randomized clinical trial; Regression discontinuity design

Indexed keywords

EID: 77953021886     PISSN: 15733750     EISSN: 15728315     Source Type: Journal    
DOI: 10.1007/s11292-010-9095-3     Document Type: Article

References (40)

1. Ahlman, L. C., & Kurtz, E. M. (2009). The APPD randomized controlled trial in low risk supervision: The effects on low risk supervision on rearrest. Philadelphia: Adult Probation and Parole Department.
2. Berk, R. A. (2008a). Statistical learning from a regression perspective. New York: Springer.
3. Berk, R. A. (2008b) Forecasting methods in crime and justice. In: J. Hagan, K. L. Schepple, T. R. Tyler (Eds.), Annual review of law and social science. Palo Alto: Annual reviews.
4. Berk, R. A. (2010). Recent pertspectives on the regression discontinuity design. In A. Piquero & D. Weisburd (Eds.), Handbook of quantitative criminology. New York: Springer. forthcoming.
5. Berk, R. A., & de Leeuw, J. (1999). An evaluation of California's inmate classification system using a generalized regression discontinuity design. Journal of the American Statistical Association, 94(448), 1045-1052.
6. Berk, R. A., & Rauma, D. (1983). Capitalizing on nonrandom assignment to treatments: a regression discontinuity evaluation of a crime control program. Journal of the American Statistical Association, 78(381), 21-27.
7. Berk, R. A., Brown, L., & Zhao, L. (2009a) Statistical inference after model selection. University of Pennsylvania, Department of Statistics, Working Paper (under review).
8. Berk, R. A., Sherman, L., Barnes, G., Kurtz, E., & Ahlman, L. (2009b). Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning. Journal of the Royal Statistical Society (Series A), 172, 191-211. part 1.
9. Borden, H. G. (1928). Factors predicting parole success. Journal of the American Institute of Criminal Law and Criminology, 19, 328-336.
10. Boruch, R. F., & DeGracie, J. S. (1975) Regression-discontinuity evaluation of the mesa reading program: background and technical report." Evanston, Illinois, Northwestern University, NIE Project on Secondary Analysis, working paper.
11. Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
12. Burgess, E. W. (1928). Factors determining success or failure on parole. In A. A. Bruce, A. J. Harno, E. W. Burgess, & J. Landesco (Eds.), The working of the indeterminant sentence law and the parole system in Illinois (pp. 205-249). Springfield: Illinois, State Board of Parole.
13. Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-experimental designs for research. Chicago: Rand McNally.
14. Chen, M. K., & Shapiro, J. M. (2007). Do harsher prison conditions reduce recidivism? a discontinuity-based approach. American Law and Economics Review, 9(1), 1-29.
15. 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, 727(4), 724-750.
16. Dean, C. W., & Dugan, T. J. (1968). Problems in parole prediction: an historical analysis. Social Problems, 15, 450-459.
17. Farrington, D. P. (1987). Predicting individual crime rates. In D. M. Gottfredson & M. Tonry (Eds.), Prediction and classification. Chicago: University of Chicago Press.
18. Freedman, D. A. (2006). Statistical models for causation: what inferential leverage do they provide? Evaluation Review, 30, 691713.
19. Freedman, D. A. (2008a). On regression adjustments to experimental data. Advances in Applied Mathematics, 40, 180193.
20. Freedman, D. A. (2008b). Randomization does not justify logistic regression. Statistical Science, 23, 237249.
21. Glaser, D. (1987). Classification for Risk. In D. M. Gottfredson & M. Tonry (Eds.), Prediction and classification. Chicago: University of Chicago Press.
22. Gottfredson, S. D., & Moriarty, L. J. (2006). Statistical risk assessment: old problems and new applications. Crime and Delinquency, 52(1), 178-200.
23. Green, P. J., & Silverman, B. W. (1994). Nonparametric regression and generalized linear models. New York: Chapman & Hall.
24. Hastie, T. J., & Tibshirani, R. (1990). Generalized additive models. New York: Chapman and Hall.
25. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). New York: Springer.
26. Hill, J. (2008). Comment. Journal of the American Statistical Association, 103(484), 1346-1350.
27. Hoenig, J. M., & Heisey, D. M. (2001). The abuse of power: the pervasive fallacy of power calculation for data analysis. American Statistician, 55, 19-24.
28. Imbens, G., & Kalyanaraman, K. (2009). Optimal bandwidth choice for the regression discontinuity estimator. Department of Economics: Harvard University. Working Paper.
29. Imbens, G., & Lemieux, T. (2008). Regression discontinuity designs: a guide to practice. Journal of Econometrics, 142, 611-614.
30. Lee, D. S, & Lemieux, T. (2009) Regression Discontinuity Designs in Economics. National Bureau of Economic Research: working paper #14723.
31. Little, R. J., Long, Q., & Lin, X. (2008). Comment. Journal of the American Statistical Association, 103(484), 1344-1346.
32. Ohlin, L. E., & Duncan, O. D. (1949). The efficiency of prediction in criminology. American Journal of Sociology, 54, 441-452.
33. Ohlin, L. E., & Lawrence, R. A. (1952). A comparison of alternative methods of parole prediction. American Sociological Review, 17, 268-274.
34. Rubin, D. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688701.
35. Rubin, D. (2008). Comment: the design and analysis of gold standard randomized experiments. Journal of the American Statistical Association, 103(484), 1350-1353.
36. Shadish, W. R., Clark, M. H., & Steiner, P. M. (2008). Can nonrandomized experiments yield accurate answers? a randomized experiment comparing random and nonrandom assignment. Journal of the American Statistical Association, 103(484), 1334-1356.
37. Thistlewaite, D. L., & Campbell, D. T. (1960). Regression-discontinuity analysis: an alternative to the ex-post facto design. Journal of Educational Psychology, 51, 309-317.
38. Trochim, W. M. K. (2001) Regression discontinuity design. In N. J. Smelser, P. B. Bates (Eds.), International encyclopedia of the social and behavioral sciences, vol. 19. 12940-12945.
39. Wilkins, L. T. (1980). Problems with existing prediction studies and future research needs. The Journal of Criminal Law and Criminology, 71, 98-101.
40. Wood, S. N. (2008). Fast stable direct fitting and smoothness selection for generalized additive models. Journal of the Royal Statistical Society, Series B, 70(3), 495-518.