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Generalized Linear Models: Loglinear Modeling in Managerial Research

Louisiana Tech University, white{at}cab.laTech.edu

Richard Tansey

University of Wollongong

Mark Smith

University of Southwest Louisiana

Rebecca G. Long

Louisiana Tech University

In the past three decades we have seen the emergence of the generalized linear model (GLM) techniques for analyzing discrete multivariate data when the independent and dependent variables are categorical, ordinal, or mixed. The primary statistical techniques are loglinear modeling, probit, and logistic regression. The purpose of this article is to (a) briefly describe the emergence of these discrete multivariate techniques in the medical and social sciences, (b) disclose their relationship to one another, and (c) demonstrate the utility of hierarchical loglinear modeling in managerial research

Organizational Research Methods, Vol. 1, No. 2, 223-247 (1998)
DOI: 10.1177/109442819812004


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