The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Full Matching in an Observational Study of Coaching for the SAT
Abstract
Among matching techniques for observational studies, full matching is in principle the best, in the sense that its alignment of comparable treated and control subjects is as good as that of any alternate method, and potentially much better. This article evaluates the practical performance of full matching for the first time, modifying it in order to minimize variance as well as bias and then using it to compare coached and uncoached takers of the SAT. In this new version, with restrictions on the ratio of treated subjects to controls within matched sets, full matching makes use of many more observations than does pair matching, but achieves far closer matches than does matching with k≥ 2 controls. Prior to matching, the coached and uncoached groups are separated on the propensity score by 1.1 SDs. Full matching reduces this separation to 1% or 2% of an SD. In older literature comparing matching and regression, Cochran expressed doubts that any method of adjustment could substantially reduce observed bias of this magnitude.To accommodate missing data, regression-based analyses by ETS researchers rejected a subset of the available sample that differed significantly from the subsample they analyzed. Full matching on the propensity score handles the same problem simply and without rejecting observations. In addition, it eases the detection and handling of nonconstancy of treatment effects, which the regression-based analyses had obscured, and it makes fuller use of covariate information. It estimates a somewhat larger effect of coaching on the math score than did ETS's methods.
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Citations
Cites
Comparison of Multivariate Matching Methods: Structures, Distances, and Algorithms
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“Rosenbaum (1991) introduced full matching, Gu and Rosenbaum (1993) did a simulation study of it, and Marcus (2000) made use of it to assess the Head Start compensatory education program.”
Method
Cited by
Austin, P. C. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.568786 · Cited by 12,242
Matching Methods for Causal Inference: A Review and a Look Forward
Stuart, E. A. (2010) · Statistical Science · doi:10.1214/09-sts313 · Cited by 5,478
MatchIt : Nonparametric Preprocessing for Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i08 · Cited by 4,385
Why We (Usually) Don't Have to Worry About Multiple Comparisons
Gelman, A., Hill, J., & Yajima, M. (2012) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2011.618213 · Cited by 1,274
Harder, V. S., Stuart, E. A., & Anthony, J. C. (2010) · Psychological Methods · doi:10.1037/a0019623 · Cited by 802
Optimal Full Matching and Related Designs via Network Flows
Hansen, B. B., & Klopfer, S. O. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x137047 · Cited by 703
Self-citationA Systematic Review of Propensity Score Methods in the Social Sciences
Thoemmes, F. J., & Kim, E. S. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.540475 · Cited by 577
Adding Bricks to Clicks: Predicting the Patterns of Cross-Channel Elasticities over Time
Avery, J., Steenburgh, T. J., Deighton, J., & Caravella, M. (2012) · Journal of Marketing · doi:10.1509/jm.09.0081 · Cited by 533
The Use of Propensity Scores to Assess the Generalizability of Results from Randomized Trials
Stuart, E. A., Cole, S. R., Bradshaw, C. P., & Leaf, P. J. (2011) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2010.00673.x · Cited by 524
Matching Methods for Causal Inference with Time‐Series Cross‐Sectional Data
Imai, K., Kim, I. S., & Wang, E. H. (2023) · American Journal of Political Science · doi:10.1111/ajps.12685 · Cited by 349
Ali, M. S., Prieto-Alhambra, D., Lopes, L. C., Ramos, D., Bispo, N., Ichihara, M. Y., Pescarini, J. M., Williamson, E., Fiaccone, R. L., Barreto, M. L., et al. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00973 · Cited by 200
Touchton, M. (2015) · Journal of Political Science Education · doi:10.1080/15512169.2014.985105 · Cited by 137
Quasi-experimental designs for causal inference
Kim, Y., & Steiner, P. (2016) · Educational Psychologist · doi:10.1080/00461520.2016.1207177 · Cited by 135
“An alternative matching strategy that allows for estimating ATE is full matching, which stratifies all subjects into the maximum number of strata, where each stratum contains at least one treatment and one control subject (Hansen, 2004).”
BackgroundAt your service on the table: Impact of tabletop technology on restaurant performance
Tan, T. F., & Netessine, S. (2020) · Management Science · doi:10.1287/mnsc.2019.3430 · Cited by 111
O’Connor, M., Cloney, D., Kvalsvig, A., & Goldfeld, S. (2019) · Educational Researcher · doi:10.3102/0013189x19848724 · Cited by 93
Domitrovich, C. E., Li, Y., Mathis, E. T., & Greenberg, M. T. (2019) · Journal of School Psychology · doi:10.1016/j.jsp.2019.07.015 · Cited by 80
Harder, V. S., Stuart, E. A., & Anthony, J. C. (2008) · American Journal of Epidemiology · doi:10.1093/aje/kwn184 · Cited by 76
(2013) · MDRC · Cited by 45
Ward, J. T., Krohn, M. D., & Gibson, C. L. (2014) · Journal of Interpersonal Violence · doi:10.1177/0886260513505219 · Cited by 42
Rana, P., & Miller, D. C. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/aafa8f · Cited by 41
Crossett, A., Kent, B. P., Klei, L., Ringquist, S., Trucco, M., Roeder, K., & Devlin, B. (2010) · Statistics in Medicine · doi:10.1002/sim.4057 · Cited by 15
Bottigliengo, D., Lorenzoni, G., Ocagli, H., Martinato, M., Berchialla, P., & Gregori, D. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18136694 · Cited by 13
Patterns of Effects and Sensitivity Analysis for Differences-in-Differences
Keele, L. J., Small, D. S., Hsu, J. Y., & Fogarty, C. B. (2019) · arXiv · doi:10.48550/arxiv.1901.01869 · Cited by 10
Metadata
- Authors
- Hansen, B. B.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2004-09
- Volume
- 99
- Issue
- 467
- Pages
- 609-618
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2002595054
- Cited by
- 605
- References
- 1
- FWCI
- 5.25
- Type
- journal article
- Language
- en
- Open access
- closed
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