1,686
Views
15
CrossRef citations to date
0
Altmetric
Measurement, Statistics, and Research Design

Propensity Score Matching for Education Data: Worked Examples

, & ORCID Icon

References

  • Aiken, L. R. (1974). Two scales of attitude toward mathematics. Journal for Research in Mathematics Education, 5, 67–71. doi: 10.2307/748616
  • Aiken, L. S., West, S. G., & Millsap, R. E. (2008). Doctoral training in statistics, measurement, and methodology in psychology: Replication and extension of Aiken, West, Sechrest, and Reno’s (1990) survey of PhD programs in North America. American Psychologist, 63(1), 32–50. doi: 10.1037/0003-066X.63.1.32
  • American College Testing Program (1989). ASSET technical manual. Iowa City, IA: Author.
  • Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21(6), 1086–1120. doi: 10.1016/j.leaqua.2010.10.010
  • Austin, P. C. (2009). Some methods of propensity-score matching had superior performance to others: Results of an empirical investigation and Monte Carlo simulations. Biometrical Journal, 51(1), 171–184. doi: 10.1002/bimj.200810488
  • Austin, P. C. (2011). Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharmaceutical Statistics, 10(2), 150–161. doi: 10.1002/pst.433
  • Austin, P. C. (2014). A comparison of 12 algorithms for matching on the propensity score. Statistics in Medicine, 33(6), 1057–1069. doi: 10.1002/sim.6004
  • Austin, P. C., & Mamdani, M. M. (2006). A comparison of propensity score methods: A case-study estimating the effectiveness of post-AMI statin use. Statistics in Medicine, 25(12), 2084–2106.
  • Austin, P. C., Grootendorst, P., &Anderson, G. M. (2007). A comparison of the ability if different propensity score models to balance measured variables between treated and untreated subjects: A Monte Carlo study. Statistics in Medicine, 26, 734–753.
  • Barratt, W. (2006). The Barratt simplified measure of social status (BSMSS) measuring SES. Retrieved from: http://www.marson-and-associates.com/syllabi/151BSMSS.pdf
  • Beaujean, A. A., Hull, D. M., Sheng, Y., Worrell, F. C., Bolen, J., & Verdisco, A. E. (in press). Psychometric properties of the Shipley Block Design task: A study with Jamaican young adults. The Journal of Psychoeducational Assessment, doi: 10.1177/0734282916643439
  • Burnham, K. P., Anderson, D. R., & Huyvaert, K. P. (2011). AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behavioral Ecology and Sociobiology, 65, 23–35. doi: 10.1007/s00265-010-1029-6
  • Cochran, W. G. (1965). The planning of observational studies of human populations. Journal of the Royal Statistical Society, 128, 134–155.
  • Cochran, W. G. (1968). The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics, 24(2), 295–313.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Mahwah, NJ: Erlbaum.
  • Collis, K., & Romberg, T. (1992). Collis-Romberg mathematical problem solving profiles. Hawthorne Victoria, Australia: Australian Council for Educational Research (ACER).
  • Cox, B. E., McIntosh, K., Reason, R. D., & Terenzini, P. T. (2014). Working with missing data in higher education research: A primer and real-world example. The Review of Higher Education, 37(3), 377–402.
  • D’Agostino, R. B. (1998). Propensity score methods for bias reduction in the comparison of treatment to non-randomized control group. Statistics in Medicine, 17(19), 2265–2281.
  • D’Agostino, R. B. (2004). Propensity score estimation with missing data. In A. Gelman & X. L. Meng (Eds.) Missing data and Bayesian methods in practice: Contributions by Donald Rubin’s statistical family (pp. 163–174). New York, NY: John Wiley & Sons.
  • D’Agostino, R. B. (2007). Propensity scores in cardiovascular research. Circulation, 115(17), 2340–2343.
  • D'Agostino, R. B., & Rubin, D. B. (2000). Estimating and using propensity scores with partially missing data. Journal of the American Statistical Association, 95(451), 749–759.
  • Dehejia, R. H., & Wahba, S. (2002). Propensity score-matching methods for nonexperimental causal studies. The Review of Economics and Statistics, 84(1), 151–161.
  • Foster, E. M. (2010). Causal inference and developmental psychology. Developmental Psychology, 46(6), 1454–1480. doi: 10.1037/a0020204
  • Frölich, M. (2004). Finite-sample properties of propensity-score matching and weighting estimators. Review of Economics and Statistics, 86(1), 77–90.
  • Gelman, A., Su, Y.-S., Yajima, M., Hill, J., Pittau, M. G., & Kerman, J. (2014). Arm: Data analysis using regression and multilevel/hierarchical models. (Version 1.7-07) [Computer software]. New York, NY. Retrieved from http://cran.r-project.org/web/packages/arm/index.html.
  • Glynn, R. J., Schneeweiss, S., & Sturmer, T. (2006). Indications for propensity scores and review of their use in pharmacoepidemiology. Basic Clinical Pharmacology Toxicology, 98(3), 253–259.
  • Goldberg, L. R., Johnson, J. A., Eber, H. W., Hogan, R., Ashton, M. C., Cloninger, C. R., & Gough, H. G. (2006). The international personality item pool and the future of public-domain personality measures. Journal of Research in Personality, 40(1), 84–96. doi: 10.1016/j.jrp.2005.08.007
  • Harder, V. S., Stuart, E. A., & Anthony, J. C. (2010). Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research. Psychological Methods, 15(3), 234–249. doi: 10.1037/a0019623
  • Henson, R. K., Hull, D. M., & Williams, C. S. (2010). Methodology in our education research culture. Educational Researcher, 39(3), 229–240. doi: 10.3102/0013189x10365102
  • Henson, R. K., & Williams, C. S. (2006). April). Doctoral training in research methodology: A national survey of education-related degrees. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.
  • Hinkelmann, K., & Kempthorne, O. (2007). Design and analysis of experiments, volume 1: Introduction to experimental design (2nd ed.). New York, NY: John Wiley & Sons.
  • Ho, D., Imai, K., King, G., & Stuart, E. A. (2007). Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis, 15(03), 199–236. doi: 10.1093/pan/mp1013.
  • Ho, D., Imai, K., King, G., & Stuart, E. A. (2011). MatchIt: Nonparametric preprocessing for parametric causal inference. Journal of Statistical Software, 42(8), 1–28. Retrieved from http://www.jstatsoft.org/v42/i08/paper
  • Holland, J. L. (1996). Exploring careers with a typology: What we have learned and some new directions. American Psychologist, 51(4), 397–406.
  • Hong, G. (2012). Marginal mean weighting through stratification: A generalized method for evaluating multivalued and multiple treatments with nonexperimental data. Psychological Methods, 17(1), 44–60. doi: 10.1037/a0024918
  • Huppler Hullsiek, K., & Louis, T. A. (2002). Propensity score modeling strategies for the causal analysis of observational data. Biostatistics, 3(2), 179–193.
  • Imai, K., King, G., & Lau, O. (2010). Zelig: Everyone’s statistical software. Retrieved from http://gking.harvard.edu/zelig.
  • Imbens, G. W. (2003). Sensitivity to exogeneity assumptions in program evaluation. American Economic Review, 93(2), 126–132.
  • Institute of Education Sciences (IES). (2006). WWC study design classification. Retrieved from https://ies.ed.gov/ncee/wwc/Docs/referenceresources/wwc_version1_standards.pdf
  • Institute of Education Sciences (IES). (2017). What works clearinghouse standards handbook (Version 4.0). Washington, DC: Author.
  • Joffe, M. M., & Rosenbaum, P. R. (1999). Invited commentary: Propensity scores. American Journal of Epidemiology, 150(4), 327–333.
  • Keller, B., & Tipton, E. (2016). Propensity score analysis in R: A software review. Journal of Educational and Behavioral Statistics, 41(3), 326–348. doi: 10.3102/1076998616631744
  • Leech, N. L., & Goodwin, L. D. (2008). Building a methodological foundation: Doctoral level methods courses in colleges of education. Research in the Schools, 15(1), 1–8.
  • Leite, W. (2017). Practical propensity score methods using R. Los Angeles: Sage Publications Inc.
  • Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). Hoboken, NJ: John Wiley.
  • Luellen, J. K., Shadish, W. R., & Clark, M. H. (2005). Propensity scores: An introduction and experimental test. Evaluation Review, 29(6), 530–558.
  • Murnane, R. J., & Willett, J. B. (2011). Methods matter: Improving causal inference in educational and social science research. New York: Oxford University Press.
  • Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). New York, NY: Cambridge University Press.
  • Perham, H. J. (2010). Quantitative training of doctoral school psychologists: Statistics, measurement, and methodology curriculum. Unpublished master's thesis, Arizona State University, Tempe, AZ.
  • Peugh, J. L., & Enders, C. K. (2004). Missing data in educational research: A review of reporting practices and suggestions for improvement. Review of Educational Research, 74, 525–556.
  • R Development Core Team. (2017). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
  • Rosenbaum, P. R. (1995). Quantiles in nonrandom samples and observational studies. Journal of the American Statistical Association, 90(432), 1424–1431.
  • Rosenbaum, P. R. (2002). Observational studies (2nd ed.). New York: Springer.
  • 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.
  • Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79(387), 516–524.
  • Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701.
  • Rubin, D. B. (1986). Comment: Which ifs have causal answers? Journal of American Statistical Association, 81, 961–962.
  • Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine, 127(8_Part_2), 757–763.
  • 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.
  • Schneider, B., Carnoy, M., Kilpatrick, J., Schmidt, W. H., & Shavelson, R. J. (2007). Estimating causal effects: Using experimental and observational designs. Washington, DC: American Educational Research Association.
  • Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.
  • Shipley, W. C., Gruber, C., Martin, T., & Klein, A. M. (2010). Shipley institute of living scale (2nd ed.). Los Angeles, CA: Western Psychological Services.
  • Storer, B., & Kim, C. (1990). Exact properties of some exact test statistics for comparing two binomial proportions.. Journal of the American Statistical Association, 55, 146–155.
  • Stouffer, S. A. (1950). Some observations on study design. American Journal of Sociology, 55(4), 355–361.
  • Stuart, E. A. (2007). Estimating causal effects using school-level data sets. Educational Researcher, 36(4), 187–198.
  • Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science: A Review Journal of the Institute of Mathematical Statistics, 25(1), 1–21.
  • Stuart, E. A., & Rubin, D. B. (2007). Best practices in quasi-experimental designs: Matching methods for causal inference. In J. Osborne (Ed.), Best practices in quantitative social science (pp. 155–176). Thousand Oaks, CA: Sage Publications.
  • Stürmer, T., Joshi, M., Glynn, R. J., Avorn, J., Rothman, K. J., & Schneeweiss, S. (2006). A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. Journal of Clinical Epidemiology, 59, 437–447.
  • Thoemmes, F. J., & Kim, E. S. (2011). A systematic review of propensity score methods in the social sciences. Multivariate Behavioral Research, 46(1), 90–118. doi: 10.1080/00273171.2011.540475
  • Thoemmes, F. J., & West, S. G. (2011). The use of propensity scores for nonrandomized designs with clustered data. Multivariate Behavioral Research, 46(3), 514–543. doi: 10.1080/00273171.2011.569395
  • U.S. Department of Education. (2016). Non-regulatory guidance: Using evidence to strengthen education investments. Retrieved from https://www2.ed.gov/policy/elsec/leg/essa/guidanceuseseinvestment.pdf
  • Welch, B. L. (1947). The generalization of "Student's" problem when several different population variances are involved. Biometrika, 34(1-2), 28–35. doi: 10.1093/biomet/34.1-2.28
  • West, S., Biesanz, J. C., & Pitts, S. C. (2000). Causal inference and generalization in field settings: Experimental and quasi-experimental designs. In C. M. Reis, H. T. Judd (Ed.), Handbook of research methods in social and personality psychology (pp. 40–84). Cambridge, UK: Cambridge University Press.
  • West, S. G., Cham, H., Thoemmes, F., Renneberg, B., Schulze, J., & Weiler, M. (2014). Propensity scores as a basis for equating groups: Basic principles and application in clinical treatment outcome research. Journal of Consulting and Clinical Psychology, 82(5), 906–919. doi: 10.1037/a0036387
  • What Works Clearinghouse (WWC). (n.d.). About us. Retrieved from http://ies.ed.gov/ncee/wwc/aboutus/.
  • Wilcox, R. R. (2003). Applying contemporary statistical techniques San Diego, CA: Academic Press.
  • Winship, C., & Morgan, S. L. (2014). Counterfactuals and causal inference: Methods and principles for social research (2nd ed.). New York: Cambridge University.

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.