Applied Regression Models in the Social Sciences

Logistic regression is not limited to the modeling of binary dependent variables. It may be extended to the modeling of dependent variables with three or more categories that are either ordered or are unordered. In this chapter we discuss logistic regression of a multi-categorical dependent variable with ordered categories. An ordinal variable is one that is multi-categorical, and its categories are ordered. For example, one’s quality of life might be classified as “excellent,” “very good,” “good,” “fair,” or “poor.” Although these categories might be coded consecutively, 1, 2, 3, 4, and so forth, the dependent variable is not continuous. The responses may be coded from 1 = “poor” to 5 = “excellent.” But we do not know that the distances between each contiguous pair of responses is the same. Even though the responses might be coded as 1 to 5, we should not use an OLS regression model to predict a dependent variable such as the person’s categorical response to a quality of life question. We should use a statistical model that does not assume that the distances between any pair of categories is not the same. This chapter focuses on ordinal logistic regression.