Estimated Precision for Predictions from Generalized Linear Models in Sociological Research

Estimated Precision for Predictions from Generalized Linear Models in Sociological Research In this paper I present a general method forconstructing confidence intervals for predictionsfrom the generalized linear model in sociologicalresearch. I demonstrate that the method used forconstructing confidence intervals for predictions inclassical linear models is indeed a special case ofthe method for generalized linear models. I examinefour such models – the binary logit, the binaryprobit, the ordinal logit, and the Poissonregression model – to construct confidence intervalsfor predicted values in the form of probability,odds, Z score, or event count. The estimatedconfidence interval for an event prediction, whenapplied judiciously, can give the researcher usefulinformation and an estimated measure of precisionfor the prediction so that interpretation ofestimates from the generalized linear model becomeseasier. Quality & Quantity Springer Journals

Estimated Precision for Predictions from Generalized Linear Models in Sociological Research

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Kluwer Academic Publishers
Copyright © 2000 by Kluwer Academic Publishers
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
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