A comparison of subset selection and analysis of covariance for the adjustment of confounders

Psychological Methods

Volumn 5, Issue 4, 2000, Pages 459-476

  Little, Roderick J ^a   An, Hyonggin ^b   Johanns, Jewel ^b   Giordani, Bruno ^b  

^a UNIVERSITY OF MICHIGAN   (United States)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS OF VARIANCE; ARTICLE; HUMAN; NEUROPSYCHOLOGICAL TEST; PSYCHOMETRY; REPRODUCIBILITY; STATISTICAL MODEL; STATISTICS;

ANALYSIS OF VARIANCE; HUMANS; MODELS, STATISTICAL; NEUROPSYCHOLOGICAL TESTS; PSYCHOMETRICS; REPRODUCIBILITY OF RESULTS;

EID: 0034576526     PISSN: 1082989X     EISSN: None     Source Type: Journal    
DOI: 10.1037/1082-989X.5.4.459     Document Type: Review

References (40)

1. Adams, K. M., Brown, G. C., &. Grant, I. (1985). Analysis of covariance as a remedy for demographic mismatch of research subjects groups: Some sobering simulations. Journal of Clinical and Experimental Neuropsychology, 7, 445-462.
2. Armitage, P., & Berry, G. (1987). Statistical methods in medical research (2nd ed.). Boston: Blackwell.
3. Bentler, P. M. (1995). EQS structural equations program manual. Encino, CA: Multivariate Software.
4. Berent, S., Giordani, B., Gilman, S., Junck, L., Lehtinen, S., Markel, D. S., Boivin, M. J., Kluin, K., Parks, R., & Koeppe, R. A. (1990). Neuropsychological changes in olivopontocerebellar atrophy. Archives of Neurology, 47, 997-1001.
5. Berman, N. C., & Greenhouse, S. W. (1992). Comment: Adjusting for demographic covariates by the analysis of covariance [with rejoinder]. Journal of Clinical and Experimental Neuropsychology, 14, 981-985.
6. Campbell, D. T., & Boruch, R. F. (1975). Making the case for randomized assignment to treatments by considering the alternatives: Six ways in which quasi-experimental evaluations tend to underestimate the effects. In C. A. Bennet & A. A. Lumsdaine (Eds.), Evaluation and experiment: Some critical issues in assessing social programs (pp. 195-296). New York: Academic Press.
7. Carroll, R. J., & Ruppert, D. (1988). Transformation and weighting in regression. London: Chapman & Hall.
8. Cochran, W. G., & Rubin, D. B. (1973). Controlling bias in observational studies; a review. Sankhya, A, 35, 417-446.
9. Conover, W. J. (1980). Practical nonparametric statistics (2nd ed.). New York: Wiley.
10. Cook, R. D., & Weisberg, S. (1999). Applied regression including computing and graphics. New York: Wiley.
11. Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Boston: Houghton Mifflin.
12. Elashoff, J. D. (1969). Analysis of covariance: A delicate instrument. American Educational Research Journal, 6, 383-401.
13. Gilman, S., Sima, A. A. F., Junck, L., Kluin, K. J., Koeppe, R. A., Lohman, M. E., & Little, R. J. A. (1996). Spino-cerebellar ataxia Type 1 with multiple system degeneration and glial cytoplasmic inclusions. Annals of Neurology, 39, 241-254.
14. Green, P. J., & Silverman, B. W. (1994). Nonparametric regression and generalized linear models. London: Chapman & Hall.
15. Hastie, T., & Tibshirani, R. (1990). Generalised additive models. London: Chapman & Hall.
16. Huitema, B. E. (1980). The analysis of covariance and alternatives. New York: Wiley.
17. Jöreskog, K. G., & Sörbom, D. (1993). Lisrel 8: User's reference guide. Chicago: Scientific Software.
18. Little, R. J. A., & Rubin, D. B. (2000). Causal effects in clinical and epidemiological studies via potential outcomes: Concepts and analytical approaches. Annual Review of Public Health, 21, 121-145.
19. Maxwell, S. E., O'Callaghan, M. F., & Delaney, H. D. (1993). Analysis of covariance. In L.K. Edwards (Ed.), Applied analysis of variance in behavioral science (pp. 63-104). New York: Dekker.
20. Porter, A. C., & Raudenbush, S. W. (1987). Analysis of covariance: Its model and use in psychological research. Journal of Counseling Psychology, 34, 383-392.
21. Rogosa, D. (1980). Comparing nonparallel regression lines. Psychological Bulletin, 88, 307-321.
22. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55.
23. 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.
24. Rosenbaum P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling incorporating the propensity score. The American Statistician, 39, 33-38.
25. Ross, D. (1988). Group matching: Is this a research technique to be avoided? Educational and Psychological Measurement, 48, 281-295.
26. Rubin, D. B. (1976a). Multivariate matching methods that are equal percent bias reducing: I. Some examples. Biometrics, 32, 109-120, and printer's correction note, 955.
27. Rubin, D. B. (1976b). Multivariate matching methods that are equal percent bias reducing: II. Maximums on bias reduction for fixed sample sizes. Biometrics, 32, 121-132, and printer's correction note, 955.
28. 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.
29. Rubin D. B. (1984). William G. Cochran's contributions to the design, analysis, and evaluation of observational studies. In J. N. K. Rao & J. Sedransk (Eds.), W. G. Cochran's impact on statistics (pp. 37-69). New York: Wiley.
30. Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine, 127, 757-763.
31. Rubin, D.B., & Thomas, N. (1992). Affinely invariant matching methods with ellipsoidal distributions. Annals of Statistics, 20, 1079-1093.
32. Rubin, D. B., & Thomas, N. (2000). Combining propensity score matching with additional adjustments for prognostic covariates. Journal of the American Statistical Association, 95, 573-585.
33. SAS. (1990). SAS/STAT user's guide, Version 6 (Vol. 2, 4th ed.). Cary, NC: SAS Institute.
34. Scheffé, H. (1970). Practical solutions of the Behrens-Fisher problem. Journal of the American Statistical Association, 65, 1501-1515.
35. Smith, H. L. (1997). Matching with multiple controls to estimate treatment effects in observational studies. Sociological Methodology, 27, 325-353.
36. Venables, W. N., & Ripley, B. D. (1994). Modern applied statistics with S-plus. New York: Springer.
37. Wechsler, D. (1981). WAIS-R manual. New York: Psychological Corporation.
38. West, S. G., Biesanz, J. C., & Pitts, S. C. (2000). Causal inference and generalization in field settings: Experimental and quasi-experimental designs. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social psychology (pp. 40-84). New York: Cambridge University Press.
39. Whitehurst, G. J., Zevenbergen, A. A., Crone, D. A., Schultz, M. D., Velting, O. N., & Fischel, J. E. (1999). Outcomes of an emergent literacy intervention from Head Start through second grade. Journal of Educational Psychology, 91, 261-272.
40. Wilcox, R. R. (1996). Statistics for the social sciences. New York: Academic Press.