Journal of Marketing Research · 1993 · 485 citations · 0 references
Social SciencesBusiness EconomicsEconomicsEconometricsDiscrete Choice ModelsEconomies Of ScaleGeneralized Linear ModelsInferential StatisticsLog-linear ModelsLogistic Regression ApproachesMultinomial Log-linear ModelsMultinomial Regression
Multinomial logit models are widely used in marketing research, but the utility scale unit is inversely related to error variance, so identical utility specifications estimated from different data sources with unequal variances produce different parameter magnitudes, and no existing tests for joint or separate scale and parameter equality are available in the marketing literature. The study aims to establish a proper procedure for comparing MNL parameters across different datasets and to propose a simple relative scaling test implementable with standard MNL software. The authors develop a relative scaling test that can be applied using standard MNL estimation software. Illustrative examples demonstrate the application of the proposed test.
Multinomial logit (MNL) models are widely used in marketing research to analyze choice data, but it is not generally recognized that the unit of the utility scale in a MNL model is inversely related to the error variance. This means that, for instance, parameters of two identical utility specifications estimated from different data sources with unequal variances will necessarily differ in magnitude, even if the true model parameters that generated the utilities are identical in both sets. Despite a growing number of papers that compare MNL coefficients, no examples of appropriate tests of the joint and separate hypotheses of scale and parameter equality in MNL models exist in the marketing literature. The purpose of this paper is to address the proper procedure for MNL parameter comparisons between different data sets and to propose a simple relative scaling test that can be implemented with standard MNL estimation software. Several examples are given to illustrate the approach.