Estimated precision for predictions from generalized linear models in sociological research

In this paper I present a general method for constructing confidence intervals for predictions from the generalized linear model in sociological research. I demonstrate that the method used for constructing confidence intervals for predictions in classical linear models is indeed a special case of the method for generalized linear models. I examine four such models - the binary logit, the binary probit, the ordinal logit, and the Poisson regression model - to construct confidence intervals for predicted values in the form of probability, odds, Z score, or event count. The estimated confidence interval for an event prediction, when applied judiciously, can give the researcher useful information and an estimated measure of precision for the prediction so that interpretation of estimates from the generalized linear model becomes easier.