Abstract
This paper reviews the use of logit and probit models in marketing and focuses on demonstrating the use of ordered probability models. This type of model is appropriate for many applications in marketing and business where the dependent variable of interest is ordinal (e.g., likert scales). A comparison between the properties of the ordinary least squares (OLS) model and ordered logit and probit models is made using consumer satisfaction data on automobiles. This comparison between the two models shows that the use of OLS for ordered categorical data gives misleading results and produces biased estimates, leading to inaccurate hypothesis testing. The paper concludes that ordered probability models, such as the ones illustrated, should be employed in marketing and business research where the dependent variable is ordinal.
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