Cause and Event: Supporting Causal Claims through Logistic Models

Educational Psychology Review

Volumn 23, Issue 2, 2011, Pages 245-261

  O'Connell, Ann A ^a   Gray, DeLeon L ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

Causality; Logistic regression

Indexed keywords

EID: 79958066775     PISSN: 1040726X     EISSN: 1573336X     Source Type: Journal    
DOI: 10.1007/s10648-011-9158-7     Document Type: Article

References (69)

1. Agresti, A. (2007). An introduction to categorical data analysis (2nd ed.). New York: Wiley.
2. Aldrich, J. H., & Nelson, F. D. (1984). Linear probability, logit, and probit models. Newbury Park: Sage.
3. Amico, K. R. (2008). Percent total attrition: A poor metric for study rigor in hosted intervention sites. American Journal of Public Health, 99(9), 1-8.
4. Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables (with commentary). Journal of the American Statistical Association, 91, 444-472.
5. Begg, M. D., & Lagakos, S. (1992). On the consequences of model misspecification in logistic regression. Environmental Health Perspectives, 87, 69-75.
6. Benson, K., & Hartz, A. J. (2000). A comparison of observational studies and randomized, controlled trials. The New England Journal of Medicine, 342(25), 1878-1886.
7. Bingenheimer, J. B., & Raudenbush, S. W. (2004). Statistical and substantive inferences in public health: Issues in the application of multilevel models. Annual Reviews in Public Health, 25, 53-77.
8. Box, G. E. P. (1979). Robustness in the strategy of scientific model building. In R. L. Lauer & G. N. Wilkinson (Eds.), Robustness in statistics (pp. 201-236). New York: Academic.
9. Breslow, N. E. (1996). Statistics in epidemiology: The case-control study. Journal of the American Statistical Association, 91, 14-28.
10. Cangul, M. Z., Chretien, Y. R., Gutman, R., & Rubin, D. B. (2009). Testing treatment effects in unconfounded studies under model misspecification: Logistic regression, discretization, and their combination. Statistics in Medicine, 28, 2531-2551.
11. Concato, J., Shah, N., & Horwitz, R. I. (2000). Randomized, controlled trials, observational studies, and the hierarchy of research designs. The New England Journal of Medicine, 342(25), 1887-1892.
12. Dannels, S. A. (2011). Research design. In G. R. Hancock & R. O. Mueller (Eds.), The reviewers guide to quantitative methods in the social sciences (pp. 343-356). New York: Routledge.
13. Davies, H. T. O., Crombie, I. K., & Tavakoli, M. (1998). When can odds ratios mislead? British Medical Journal, 316, 989-991.
14. Des Jarlais, D. C., Lyles, C., Crepaz, N., & The TREND Group. (2004). Improving the reporting quality of nonrandomized evaluations of behavioral and public health interventions: The TREND statement. American Journal of Public Health, 94(3)), 361-366.
15. Diez Roux, A. V. (2004). Estimating health effects: The challenges of causal inference in a complex world. Social Science & Medicine, 58, 1953-1960.
16. Donner, A., & Klar, N. (2000). Design and analysis of cluster randomization trials in health research. London: Arnold.
17. Fowler, F. J. (2009). Survey research methods. Thousand Oaks: Sage.
18. Fox, J. (2008). Applied regression analysis and generalized linear models. Thousand Oaks: Sage.
19. Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. NY: Cambridge University Press.
20. Guo, S., & Fraser, M. W. (2010). Propensity score analysis: Statistical methods and applications. Thousand Oaks: Sage.
21. Haddock, C., Rindskopf, D., & Shadish, W. (1998). Using odds ratios as effect sizes for meta-analysis of dichotomous data: A primer on methods and issues. Psychological Methods, 3(3), 339-353.
22. Harrell, F. E., Lee, K. L., & Mark, D. B. (1996). Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine, 15, 361-387.
23. Hill, J. (2008). Discussion of research using propensity-score matching: Comments on 'A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003'. Statistics in Medicine, 27, 2055-2061.
24. Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945-990.
25. Hong, G., & Raudenbush, S. W. (2005). Effects of kindergarten retention policy on children's cognitive growth in reading and mathematics. Educational Evaluation and Policy Analysis, 27(3), 205-224.
26. Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York: Wiley.
27. Hosmer, D. W., Hosmer, T., Le Cessie, S., & Lemeshow, S. (1997). A comparison of goodness-of-fit tests for the logistic regression model. Statistics in Medicine, 16, 965-980.
28. Hser, Y.-I., Shen, H., Chou, C.-P., Messer, S. C., & Anglin, M. D. (2001). Analytic approaches for assessing long-term treatment effects: Examples of empirical applications and findings. Evaluation Review, 25(2), 233-262.
29. Kish, L. (1995). Survey sampling. New York: Wiley (Original work published 1965).
30. Liao, J. G., & McGee, D. (2003). Adjusted coefficients of determination for logistic regression. American Statistician, 57(3), 161-165.
31. Light, R. J., Singer, J. D., & Willet, J. B. (1990). By design: Planning research on higher education. Cambridge: Harvard University Press.
32. Little, R. J., & Rubin, D. B. (2000). Causal effects in clinical and epidemiological studies via potential outcomes: Concepts and analytical approaches. Annual Reviews in Public Health, 21, 121-145.
33. Long, J. S. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks: Sage.
34. McCoach, D. B., Black, A. C., & O'Connell, A. A. (2007). Errors of inference in structural equation modeling. Psychology in the Schools, 44(5), 461-470.
35. McCullagh, P., & Nelder, J. A. (1989). Generalized linear models. Monographs on statistics and applied probability, 37. Boca Raton: Chapman & Hall/CRC.
36. Menard, S. (2000). Coefficients of determination for multiple logistic regression analysis. American Statistician, 54(1), 17-24.
37. Menard, S. (2002). Applied logistic regression analysis (2nd ed.). Thousand Oaks: Sage.
38. Menard, S. (2004a). Six approaches to the calculating standardized logistic regression coefficients. American Statistician, 58(3), 218-223.
39. Menard, S. (2004b). Correction. The American Statistician, 58(4), 364.
40. Moher, D., Schulz, K., & Altman, D. (2001). The CONSORT statement: Revised recommendations for improving the quality of reports of parallel group randomized trials. BMC Medical Research Methodology, 1, 2.
41. Murray, D. M. (1998). Design and analysis of group-randomized trials. New York: Oxford University Press.
42. National Research Council. (2002). Scientific research in education. Washington: Author.
43. Nemes, S., Jonasson, J., Genell, A., & Steineck, G. (2009). Bias in odds ratios by logistic regression modelling and sample size. BMC Medical Research Methodology, 9(1), 56.
44. Oakes, J. M. (2004). The (mis)estimation of neighborhood effects: Causal inference for a practicable social epidemiology. Social Science & Medicine, 58, 1929-1952.
45. O'Connell, A. A. (2000). Sampling for evaluation: Issues and strategies for community-based HIV prevention programs. Evaluation & the Health Professions, 23(2), 212-234.
46. O'Connell, A. A. (2006). Logistic regression models for ordinal response variables. Thousand Oaks: Sage.
47. O'Connell, A. A., & Amico, K. R. (2010). Logistic regression. In G. R. Hancock & R. O. Mueller (Eds.), The reviewers guide to quantitative methods in the social sciences (pp. 221-239). New York: Routledge.
48. O'Connell, A. A., Goldstein, J., Rogers, J., & Peng, C. J. (2008). Multilevel logistic models for dichotomous and ordinal data. In A. A. O'Connell & D. B. McCoach (Eds.), Multilevel modeling of educational data. Charlotte: Information Age Publishing.
49. Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996). A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology, 49(12), 1373-1379.
50. Raudenbush, S., & Bryk, A. (2002). Hierarchical linear models (2nd ed.). Newbury Park: Sage.
51. Riehl, C. (2006). Feeling better: A comparison of medical research and education research. Educational Researcher, 35, 24-29.
52. Rosenbaum, P. R. (1989). Optimal matching for observational studies. Journal of the American Statistical Association, 84(408), 1024-2032.
53. Rosenbaum, P. R. (1995). Observational studies. New York: Springer.
54. Rosenbaum, P. R. (2002). Covariance adjustment in randomized experiments and observational studies. Statistical Science, 17, 286-327.
55. 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.
56. Rubin, D. B. (1973). The use of matched sampling and regression adjustment to remove bias in observational studies. Biometrics, 29(1), 185-203.
57. 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.
58. Rubin, D. B. (1980). Bias reduction using Mahalanobis-metric matching. Biometrics, 36(2), 293-298.
59. 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.
60. Rubin, D. B. (2004). Multiple imputation for nonresponse in surveys. New Jersey: Wiley.
61. Rubin, D. B., & Thomas, N. (2000). Combining propensity score matching with additional adjustments for prognostic covariates. Journal of the American Statistical Association, 95, 572-585.
62. Schneider, B., Carnoy, M., Kilpatrick, J., Schmidt, W. H., & Shavelson, R. J. (2007). Estimating causal effects: Using experimental and observational designs. Washington: AERA.
63. Shadish, W. R. (2005). Prudent inquiry: Conceptual complexity versus practical simplicity in knowing what works. In J. S. Carlson & J. R. Levin (Eds.), The no child left behind legislation: Educational research and federal funding (pp. 129-134). Greenwich: Information Age Publishing.
64. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton-Mifflin.
65. Snijders, T., & Bosker, R. (1999). Multilevel analysis. Thousand Oaks: Sage.
66. Steyerberg, E. W., Harrell, F. E., Jr., Borsboom, G. J. J. M., Eijkemans, M. J. C., Vergouwe, Y., & Habbema, D. F. (2001). Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis. Journal of Clinical Epidemiology, 54, 774-781.
67. Tjur, T. (2009). Coefficients of determination in logistic regression models-A new proposal: The coefficient of discrimination. American Statistician, 63(4), 366-372.
68. Towne, L., Wise, L. L., & Winters, T. M. (Eds.). (2004). Advancing scientific research in education. Washington: Committee on Research in Education, National Research Council.
69. Zhang, J., & Yu, K. F. (1998). What's the relative risk?: A method of correcting the odds ratio in cohort studies of common outcomes. Journal of the American Medical Association, 280(19), 1690-1691.