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Volumn 41, Issue 5, 2017, Pages 472-505

Implications of Small Samples for Generalization: Adjustments and Rules of Thumb

Author keywords

content area; education; methodological development

Indexed keywords

ACADEMIC SUCCESS; ADOLESCENT; CHILD; EVALUATION STUDY; HUMAN; INDIANA; MANAGEMENT; PRESCHOOL CHILD; PROPENSITY SCORE; RANDOMIZED CONTROLLED TRIAL (TOPIC); SAMPLE SIZE; STATISTICAL MODEL;

EID: 85036585924     PISSN: 0193841X     EISSN: 15523926     Source Type: Journal    
DOI: 10.1177/0193841X16655665     Document Type: Article
Times cited : (89)

References (29)
  • 2
    • 51749124303 scopus 로고    scopus 로고
    • Constructing inverse probability weights for marginal structural models
    • Cole S. R., Hernán M. A., (2008). Constructing inverse probability weights for marginal structural models. American Journal of Epidemiology, 168, 656–664.
    • (2008) American Journal of Epidemiology , vol.168 , pp. 656-664
    • Cole, S.R.1    Hernán, M.A.2
  • 4
    • 40549104115 scopus 로고    scopus 로고
    • Misunderstandings between experimentalists and observationalists about causal inference
    • Imai K., King G., Stuart E. A., (2008). Misunderstandings between experimentalists and observationalists about causal inference. Journal of the Royal Statistical Society, Series A, 171, 481–502.
    • (2008) Journal of the Royal Statistical Society, Series A , vol.171 , pp. 481-502
    • Imai, K.1    King, G.2    Stuart, E.A.3
  • 8
    • 0035044501 scopus 로고    scopus 로고
    • Validating recommendations for coronary angiography following an acute myocardial infarction in the elderly: A matched analysis using propensity scores
    • Normand S. L. T., Landrum M. B., Guadagnoli E., Ayanian J. Z., Ryan T. J., Cleary P. D., McNeil B. J., (2001). Validating recommendations for coronary angiography following an acute myocardial infarction in the elderly: A matched analysis using propensity scores. Journal of Clinical Epidemiology, 54, 387–398.
    • (2001) Journal of Clinical Epidemiology , vol.54 , pp. 387-398
    • Normand, S.L.T.1    Landrum, M.B.2    Guadagnoli, E.3    Ayanian, J.Z.4    Ryan, T.J.5    Cleary, P.D.6    McNeil, B.J.7
  • 11
    • 84929079701 scopus 로고    scopus 로고
    • 2014 Rossi award lecture: Beyond internal validity
    • Orr L. L., (2015). 2014 Rossi award lecture: Beyond internal validity. Evaluation Review, 39, 167–178. doi:10.1177/0193841X15573659
    • (2015) Evaluation Review , vol.39 , pp. 167-178
    • Orr, L.L.1
  • 12
    • 84914179053 scopus 로고    scopus 로고
    • Vienna, Austria, R Foundation for Statistical Computing, Retrieved from
    • R Core Team. (2014). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org/
    • (2014) R: A language and environment for statistical computing
  • 13
    • 77951622706 scopus 로고
    • The central role of the propensity score in observational studies for causal effects
    • Rosenbaum P. R., Rubin D. B., (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.
    • (1983) Biometrika , vol.70 , pp. 41-55
    • Rosenbaum, P.R.1    Rubin, D.B.2
  • 14
    • 84949193513 scopus 로고
    • Reducing bias in observational studies using subclassification on the propensity score
    • 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, 516–524.
    • (1984) Journal of the American Statistical Association , vol.79 , pp. 516-524
    • Rosenbaum, P.R.1    Rubin, D.B.2
  • 15
    • 58149417330 scopus 로고
    • Estimating causal effects of treatments in randomized and nonrandomized studies
    • Rubin D. B., (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.
    • (1974) Journal of Educational Psychology , vol.66 , pp. 688-701
    • Rubin, D.B.1
  • 16
    • 0002531157 scopus 로고
    • Bayesian inference for causal effects: The role of randomization
    • Rubin D. B., (1978). Bayesian inference for causal effects: The role of randomization. Annals of Statistics, 6, 34–58.
    • (1978) Annals of Statistics , vol.6 , pp. 34-58
    • Rubin, D.B.1
  • 17
    • 0000810054 scopus 로고
    • Discussion of “randomization analysis of experimental data in the Fisher randomization test” by D. Basu
    • Rubin D. B., (1980). Discussion of “randomization analysis of experimental data in the Fisher randomization test” by D. Basu. Journal of the American Statistical Association, 74, 318–328.
    • (1980) Journal of the American Statistical Association , vol.74 , pp. 318-328
    • Rubin, D.B.1
  • 18
    • 84972506931 scopus 로고
    • Neyman (1923) and causal inference in experiments and observational studies
    • Rubin D. B., (1990). Neyman (1923) and causal inference in experiments and observational studies. Statistical Science, 5, 472–480.
    • (1990) Statistical Science , vol.5 , pp. 472-480
    • Rubin, D.B.1
  • 19
    • 0035761763 scopus 로고    scopus 로고
    • Using propensity scores to help design observational studies: Application to the tobacco litigation
    • Rubin D. B., (2001). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services & Outcomes Research Methodology, 2, 169–188.
    • (2001) Health Services & Outcomes Research Methodology , vol.2 , pp. 169-188
    • Rubin, D.B.1
  • 20
    • 84921343508 scopus 로고    scopus 로고
    • NCEE 2014–4017, Washington, DC, U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Analytic Technical Assistance and Development, Retrieved from
    • Schochet P. Z., Puma M., Deke J., (2014). Understanding variation in treatment effects in education impact evaluations: An overview of quantitative methods (NCEE 2014–4017). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Analytic Technical Assistance and Development. Retrieved from http://ies.ed.gov/ncee/edlabs
    • (2014) Understanding variation in treatment effects in education impact evaluations: An overview of quantitative methods
    • Schochet, P.Z.1    Puma, M.2    Deke, J.3
  • 22
    • 77956791074 scopus 로고    scopus 로고
    • The importance of covariate selection in controlling for selection bias in observational studies
    • 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, 250–267.
    • (2010) Psychological Methods , vol.15 , pp. 250-267
    • Steiner, P.M.1    Cook, T.D.2    Shadish, W.R.3    Clark, M.H.4
  • 23
    • 77957806232 scopus 로고    scopus 로고
    • Matching methods for causal inference: A review and a look forward
    • Stuart E. A., (2010). Matching methods for causal inference: A review and a look forward. Statistical Science, 25, 1–21.
    • (2010) Statistical Science , vol.25 , pp. 1-21
    • Stuart, E.A.1
  • 24
    • 85036525110 scopus 로고    scopus 로고
    • Assessing the generalizability of randomized trial results to target populations
    • Stuart E. A., Bradshaw C. P., Leaf P. J., (2014). Assessing the generalizability of randomized trial results to target populations. Prevention Science, 16, 1–11.
    • (2014) Prevention Science , vol.16 , pp. 1-11
    • Stuart, E.A.1    Bradshaw, C.P.2    Leaf, P.J.3
  • 26
    • 84877984187 scopus 로고    scopus 로고
    • Improving generalizations from experiments using propensity score subclassification: Assumptions, properties, and contexts
    • Tipton E., (2013). Improving generalizations from experiments using propensity score subclassification: Assumptions, properties, and contexts. Journal of Educational and Behavioral Statistics, 38, 239–266.
    • (2013) Journal of Educational and Behavioral Statistics , vol.38 , pp. 239-266
    • Tipton, E.1
  • 27
    • 84919426986 scopus 로고    scopus 로고
    • How generalizable is your experiment? Comparing a sample and population through a generalizability index
    • a)
    • Tipton E., (2014a). How generalizable is your experiment? Comparing a sample and population through a generalizability index. Journal of Educational and Behavioral Statistics, 39, 478–501.
    • (2014) Journal of Educational and Behavioral Statistics , vol.39 , pp. 478-501
    • Tipton, E.1
  • 28
    • 84898869520 scopus 로고    scopus 로고
    • Stratified sampling using cluster analysis: A sample selection strategy for improved generalizations from experiments
    • b)
    • Tipton E., (2014b). Stratified sampling using cluster analysis: A sample selection strategy for improved generalizations from experiments. Evaluation Review, 37, 109–139.
    • (2014) Evaluation Review , vol.37 , pp. 109-139
    • Tipton, E.1


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