REFERENCES
- Amemiya, T. (1981). Qualitative response models: A survey. Journal of Econometric Literature, 19, 1483–1536.
- Breen, R. & Karlson, K. B. (2013). Counterfactual causal analysis and nonlinear probability models. In S. L. Morgan (Ed.), Handbook of causal analysis for social research (pp. 167–188). New York, NY: Springer.
- Clogg, C. C., Petkova, E. & Haritou, A. (1995). Statistical models for comparing regression coefficients between models. The American Journal of Sociology, 100, 1261–1293.
- Cohen, J. (1969). Statistical power analysis for the behavioral sciences. New York, NY: Academic Press.
- Cramer, J. S. (2007). Robustness of logit analysis: Unobserved heterogeneity and mis-specified disturbances. Oxford Bulletin of Economics and Statistics, 69, 545–555.
- Gail, M. H., Wiand, S. & Piantadosi, S. (1984). Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika, 71, 431–444.
- Greenland, S. (1987). Interpretation and choice of effect measures in epidemiological analysis. American Journal of Epidemiology, 125, 761–768.
- Greenland, S., Robins, J. M. & Pearl, J. (1999). Confounding and collapsibility in causal inference. Statistical Science, 14, 29–46.
- Hauck, W. W., Neuhaus, J. M., Kalbfleish, J. D. & Anderson, S. (1991). A consequence of omitted covariates when estimating odds ratios. Journal of Clinical Epidemiology, 44, 77–81.
- Horowitz, J. L. & Savin, N. E. (2001). Binary response models: Logits, probits and semiparametrics. The Journal of Economic Perspectives, 15, 43–56.
- Karlson, K. B., Holm, A. & Breen, R. (2012). Comparing regression coefficients between models using logit and probit: A new method. Sociological Methodology, 42, 286–313.
- Long, J. S. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks, CA: Sage.
- Long, J. S. & Freese, J. (2006). Regression models for categorical dependent variables using Stata. College Station, TX: StataCorp LP.
- Maddala, G. S. (1983). Limited-dependent and qualitative variables in econometrics. New York, NY: Cambridge University Press.
- McKelvey, R. D. & Zavoina, W. (1975). A statistical model for the analysis of ordinal level dependent variables. Journal of Mathematical Sociology, 4, 103–120.
- Neuhaus, J. M., Kalbfleisch, J. D. & Hauck, W. W. (1991). A comparison of cluster-specific and population-averaged approaches for analyzing correlated binary data. International Statistical Review, 59, 25–35.
- Powers, D. A. & Xie, Y. (2000). Statistical methods for categorical data analysis. San Diego, CA: Academic Press.
- Winship, C. & Mare, R. D. (1983). Structural equations and path analysis for discrete data. The American Journal of Sociology, 89, 54–110.
- Winship, C. & Mare, R. D. (1984). Regression models with ordinal variables. American Sociological Review, 49, 512–525.
- Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press.
- Yatchew, A. & Griliches, Z. (1985). Specification error in probit models. The Review of Economics and Statistics, 67, 134–139.