Bias Reduction in Quasi-Experiments With Little Selection Theory but Many Covariates

REFERENCES

• Arceneaux, K., Gerber, A.S., & Green, D.P. (2010). A cautionary note on the use of matching to estimate causal effects: An empirical example comparing matching estimates to an experimental benchmark. Sociological Methods & Research, 39(2), 256–282.
   Web of Science ®Google Scholar
• Beck, A.T., & Beck, R.W. (1972). Screening depressed patients in family practice: A rapid technic. Postgraduate Medicine, 51, 81–85.
   Google Scholar
• Clark, M.H. (2000). A laboratory experiment comparing assignment methods using propensity scores (Unpublished master's thesis). . University of Memphis, Memphis, TN.
   Google Scholar
• Cochran, W.G., & Rubin, D.B. (1973). Controlling bias in observational studies: A review. Sankhya, A, 35, 417–446.
   Google Scholar
• Collingwood, R. (1940). An essay on metaphysics. Oxford, UK: Clarendon Press.
   Google Scholar
• Cook, T.D. (2008). “Waiting for life to arrive”: A history of the regression-discontinuity design in psychology, statistics and economics. Journal of Econometrics, 142(2), 636–654.
   Web of Science ®Google Scholar
• Cook, T.D., & Steiner, P.M. (2010). Case matching and the reduction of selection bias in quasi-experiments: The relative importance of the pretest as a covariate, unreliable measurement and mode of data analysis. Psychological Methods, 15(1), 56–68.
   PubMed Web of Science ®Google Scholar
• Diaz, J.J., & Handa, S. (2006). An assessment of propensity score matching as a nonexperimental impact estimator. The Journal of Human Resources, XLI(2), 319–345.
   Web of Science ®Google Scholar
• Educational Testing Service. (1962). Vocabulary Test II (V-2). Kit of factor referenced cognitive tests. Princeton, NJ: Author.
   Google Scholar
• Educational Testing Service. (1993). Arithmetic Aptitude Test (RG-1). Kit of factor referenced cognitive tests. Princeton, NJ: Author.
   Google Scholar
• Faust, M.W., Ashcraft, M.H., & Fleck, D.E. (1996). Mathematics anxiety effects in simple and complex addition. Mathematical Cognition, 2, 25–62.
   Google Scholar
• Glazerman, S., Levy, D.M., & Myers, D. (2003). Nonexperimental versus experimental estimates of earnings impacts. The Annals of the American Academy, 589, 63–93.
   Web of Science ®Google Scholar
• Goldberg, L.R. (1992). The development of markers for the Big-Five factor structure. Psychological Assessment, 4, 26–42.
   Google Scholar
• Goldberger, A.S. (1972). Selection bias in evaluating treatment effects: Some formal illustrations (Discussion Paper #123). Madison, WI: Institute for Research on Poverty, University of Wisconsin.
   Google Scholar
• Greenland, S. (2003). Quantifying biases in causal models: Classical confounding versus collider-stratification bias. Epidemiology, 14, 300–306.
   PubMed Web of Science ®Google Scholar
• Hallberg, K. (2013). Identifying conditions that support causal inference in observational studies in education: Empirical evidence from within study comparisons (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses. (#3563726), http://gradworks.umi.com/35/63/3563726.html
   Google Scholar
• Holland, P.W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945–970.
   Web of Science ®Google Scholar
• Hong, G., & Raudenbush, S.W. (2006). Evaluating kindergarten retention policy: A case study of causal inference for multilevel observational data. Journal of the American Statistical Association, 101, 901–910.
   Web of Science ®Google Scholar
• Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics, 86(1), 4–29.
   Web of Science ®Google Scholar
• Li, K.H. (1988). Imputation using Markov Chains. Journal of Statistical Computation and Simulation, 30, 57–79.
   Google Scholar
• Lohr, S.L. (1999). Sampling: Design and analysis. Pacific Grove, CA: Duxbury Press.
   Google Scholar
• Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge, UK: Cambridge University Press.
   Google Scholar
• Pearl, J. (2010). On a class of bias-amplifying variables that endanger effect estimates. Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI, 2010), 425–432. Retrieved from http://event.cwi.nl/uai2010/papers/UAI2010_0120.pdf
   Google Scholar
• Peikes, D.N., Moreno, L., & Orzol, S.M. (2008). Propensity score matching: A note of caution for evaluators of social programs. The American Statistician, 62, 222–231.
   Web of Science ®Google Scholar
• Rosenbaum, P.R. (1984). From association to causation in observational studies: The role of tests of strongly ignorable treatment assignment. Journal of the American Statistical Association, 79, 41–48.
   Web of Science ®Google Scholar
• Rosenbaum, P.R. (2002). Observational studies (2nd ed.). New York, NY: Springer-Verlag.
   Google Scholar
• Rosenbaum, P.R., & Rubin, D.B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
   Web of Science ®Google Scholar
• Rubin, D.B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.
   Web of Science ®Google Scholar
• Rubin, D.B. (1979). Using multivariate matched sampling and regression adjustment to control bias in observational studies. Journal of the American Statistical Association, 74, 318–328.
   Web of Science ®Google Scholar
• Rubin, D.B. (2001). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services and Outcomes Research Methodology, 2, 169–188.
   Google Scholar
• Rubin, D.B. (2006). Matched sampling for causal effects. Cambridge, UK: Cambridge University Press.
   Google Scholar
• Schafer, J.L., & Kang, J. (2008). Average causal effects from non-randomized studies: A practical guide and simulated example. Psychological Methods, 13(4), 279–313.
   PubMed Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Shadish, W.R., Cook, T.D., & Campbell, D.T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton-Mifflin.
   Google Scholar
• Shadish, W.R., Steiner, P.M., & Cook, T.D. (2012). A case study about why it can be difficult to test whether propensity score analysis works in field experiments. Journal of Methods and Measurement in the Social Sciences, 3(2), 1–12.
   Google Scholar
• Steiner, P.M. (2012). Using design elements for increasing the severity of causal mediation tests. Commentary on Hong, G., & Nomi, T. (2012). Weighting methods for assessing policy effects mediated by peer change. Journal of Research on Educational Effectiveness, 5, 296–298.
   Web of Science ®Google Scholar
• Steiner, P.M., & Cook, D.L. (2013). Matching and propensity scores. In T.D. Little (Ed.), The Oxford handbook of quantitative methods (Vol. 8, pp. 237–259). New York, NY: Oxford University Press.
   Google Scholar
• Steiner, P.M., Cook, T.D., & Shadish, W.R. (2011). On the importance of reliable covariate measurement in selection bias adjustments using propensity scores. Journal of Educational and Behavioral Statistics, 36(2), 213–236.
   Web of Science ®Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Thistlewaite, D.L., & Campbell, D.T. (1960). Regression-discontinuity analysis: An alternative to the ex post facto experiment. Journal of Educational Psychology, 51, 309–317.
   Web of Science ®Google Scholar
• Wooldridge, J.M. (2005). Violating ignorability of treatment by controlling for too many factors. Econometric Theory, 21, 1026–1028.
   Web of Science ®Google Scholar