Achen, C. H. (1977). Measuring representation: Perils of the correlation coefficient. American Journal of Political Science, 21, 805–821.
Allison, P. D. (1999). Comparing logit and probit coefficients across groups. Sociological Methods & Research, 28, 186–208.
Amemiya, T. (1975). Qualitative response models. Annals of Economic and Social Measurement, 4, 363–388.
Angrist, J. D., & Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist’s companion. Princeton: Princeton University Press.
Blalock, H. M. (1967a). Path coefficients versus regression coefficients. The American Journal of Sociology, 72, 675–676.
Blalock, H. M. (1967b). Causal inference, closed populations, and measures of association. American Political Science Review, 61, 130–136.
Blundell, R., Dearden, L., & Sianesi, B. (2005). Evaluating the effect of education on earnings: Models, methods and results from the National Child Development Survey. Journal of the Royal Statistical Society, Series A, 168, 473–512.
Breen, R., Karlson, K. B., & Holm, A. (2012). Correlations and non-linear probability models. Unpublished paper.
Cameron, S. V., & Heckman, J. J. (1998). Life cycle schooling and dynamic selection bias: Models and evidence for five cohorts of American males. Journal of Political Economy, 106, 262–333.
Cohen, J. (1969). Statistical power analysis for the behavioral sciences. New York: Academic.
Cox, D. R. (1958). Planning of experiments. New York: Wiley.
Cramer, J. S. (2007). Robustness of logit analysis: Unobserved heterogeneity and mis-specified disturbances. Oxford Bulletin of Economics and Statistics, 69, 545–555.
Fienberg, S. E. (1977). The analysis of cross-classified categorical data. Cambridge, MA: MIT Press.
Fisher, R. A. (1932). Statistical methods for research workers. Edinburgh: Oliver and Boyd.
Gail, M. H. (1986). Adjusting for covariates that have the same distribution in exposed and unexposed cohorts. In S. H. Moolgavkar & R. L. Prentice (Eds.), Modern statistical methods in chronic disease epidemiology (pp. 3–18). New York: Wiley.
Gail, M. H., Wieand, S., & Piantdosi, S. (1984). Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika, 71, 431–444.
Gangl, M. (2010). Causal inference in sociological research. Annual Review of Sociology, 36, 21–48.
Hauck, W. W., Neuhaus, J. M., Kalbfleisch, J. D., & Anderson, S. (1991). A consequence of omitted covariates when estimating odds ratios. Journal of Clinical Epidemiology, 44, 77–81.
Heckman, J. J. (1979). Sample selection bias as specification error. Econometrica, 47, 153–161.
Heckman, J. J., Ichimura, H., Smith, J., & Todd, P. (1998). Characterizing selection bias using experimental data. Econometrica, 66, 1017–1098.
Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica, 62, 467–475.
Imbens, G. W., & Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47, 5–86.
Karlson, K. B., Holm, A., & Breen, R. (2012). Comparing regression coefficients between same sample nested models using logit and probit: A new method. Sociological Methodology, 42(1), 286–313.
Kim, J.-O., & Mueller, C. W. (1976). Standardized and unstandardized coefficients in causal analysis: An expository note. Sociological Methods & Research, 4, 423–438.
Mare, R. D. (2006). Response: Statistical models of educational stratification – Hauser and Andrew’s models for school transitions. Sociological Methodology, 36, 27–37.
McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 105–142). New York: Academic.
McKelvey, R. D., & Zavoina, W. (1975). A statistical model for the analysis of ordinal level dependent variables. Journal of Mathematical Sociology, 4, 103–120.
Mood, C. (2010). Logistic regression: Why we cannot do what we think we can do, and what we can do about it. European Sociological Review, 26, 67–82.
Morgan, S. L., & Winship, C. (2007). Counterfactuals and causal inference: Methods and principles for social research. New York: Cambridge University Press.
Olsen, R. J. (1982). Independence from irrelevant alternatives and attrition bias: Their relation to one another in the evaluation of experimental programs. Southern Economic Journal, 49, 521–535.
Pearl, J. (1995). Causal diagrams for empirical research. Biometrika, 82, 669–710.
Pearl, J. (2006). Causality: Models, reasoning and inference. Cambridge: Cambridge University Press.
Robins, J. M. (1999). Association, causation, and marginal structural models. Synthese, 121, 151–179.
Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11, 550–560.
Robinson, L. D., & Jewell, N. P. (1991). Some surprising results about covariate adjustment in logistic regression models. International Statistical Review, 58, 227–240.
Swait, J., & Louviere, J. (1993). The role of the scale parameter in the estimation and comparison of multinomial logit models. Journal of Marketing Research, 30, 305–314.
Train, K. (2009). Discrete choice methods with simulation. Cambridge: Cambridge University Press.
Vytlacil, E. (2002). Independence, monotonicity, and latent index models: An equivalence result. Econometrica, 70, 331–441.
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.
Xie, Y. (2011). Values and limitations of statistical models. Research in Social Stratification and Mobility, 29, 343–349.
Yatchew, A., & Griliches, Z. (1985). Specification error in probit models. The Review of Economics and Statistics, 67, 134–139.