Selected References for WSCs

Aiken, L.S., West, S.G., Schwalm, D.E., Carroll, J., Hsuing, S. (1998). Comparison of a randomized experiment and two quasi-experimental designs in a single outcome evaluation Efficacy of a university –level remedial writing program. Evalaution Review, 22, 207-244.

Black, D., Galdo, J., & Smith, J. C. (2005). Evaluating the regression discontinuity design using experimental data. Working paper from pdf.

Bloom, H. S., Michalopoulos, C., Hill, C. J., & Lei, Y. (2002). Can nonexperimental comparison group methods match the findings from a random assignment evaluation of mandatory welfare-to-work programs? Washington, DC: Manpower Demonstration Research Corporation.

Cook, T. D., Shadish, W. J., & Wong, V. C. (2008). Three conditions under which observational studies produce the same results as experiments. Journal of Policy Analysis and Management, 27, 4, 724-750.

Dehejia, R., & Wahba, S. (1999). Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. Journal of the American Statistical Association, 94, 1053–1062.

Diaz, J.J. & Handa, S. (2006). As assessment of propensity score matching as a nonexperimental impact estimator: Evidence from Mexico’s PROGRESA program. The Journal of Human Resources, XLI, 2, pp. 319-345.

Fraker, T. & Maynard, R. (1987). The adequacy of comparison group designs for evaluations of employment-related programs. Journal of Human Resources, 22, 2, 194-227.

Glazerman, S., Levy, D., & Myers, D. (2003). Nonexperimental versus experimental estimates of earnings impacts. The Annals of the American Academy , 589, 63-91.

Heckman, J.J., Lalonde, R.J., & Smith, J.A. (1999). The economics and econometrics of active labor market programs. In O. Ashenfelter & D. Card (Eds.), Handbook of Labor Economics, 3, 1865-2097.

Hill, J. L., Reiter, J. P., & Zanutto, E. L. (2004). A comparison of experimental and observational data analyses. In A. Gelman & X. L. Meng (Eds.), Applied Bayesian and causal inference from an incomplete data perspective (pp. 49–60). New York: Wiley.

LaLonde, R. (1986). Evaluating the econometric evalautions of training programs with experiemental data. Annual Economic Review , 76, 604-20.

Pohl, S., Steiner, P. M., Eisermann, J., Soellner, R., & Cook, T. D. (2009). Unbiased causal inference from an observational study: Results of a within-study comparison. Educational Evaluation and Policy Analysis, 31(4), 463-479.

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.

Shadish, W.R., Galindo, R., Wong, V.C., Steiner, P.M., Cook, T.D. (2011). A randomized experiment comparing random to cutoff-based assignment. Psychological Methods, 16, 2, 179-191.

Shadish, W.R., Hedges, L.V., Pustejovsky, J., & Rindskopf, D.M. (2012, March). A d-estimator for single-case designs. Society for Research on Effective Education, Washington, D.C.

Shadish, W.R. & Rindskopf, D.M. (in preparation). A Comparison of Results from Single-Case Designs to a Randomized Experiment.

Smith, J., & Todd, P. (2005). Does matching overcome LaLonde's critique of nonexperimental estimators? Journal of Econometrics , 305-353.

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(3), 250-267.

Wilde, E.T. & Hollister, R. (2007). How close is close enough? Evaluating propensity score matching using data from a class size reduction experiment, 26, 3, 455-477.