This research examines the logit regression procedure. The initial discussion in following this introduction both defines logit regression and explains the logit regression procedure. Following the definition and explanation of the logit regression concept and procedure, the more appropriate applications for the logit regression procedure are reviewed.
Logit Regression: What Is Logit Regression, and How Does The Procedure Work
For accuracy and power, regression analysis depends upon a linear and additive relationship between independent and dependent variables (Mallios, 1989, pp. 108-110). Problems arise with respect to regression analysis, however, when a dependent variable is dichotomous (Dwyer, 1983, p. 447). A dichotomous variable is one wherein the classifications are mutually exclusive, such as the female and males classifications of gender as a variable.
The first problem that arises in relation to regression analysis with respect to dichotomous dependent variables is that the relationship between the independent and dependent variables is non-linear (Kim and Kohout, 1975, pp. 368-369). With a dichotomous variable such as gender, there is no way to transform the values to attain a linear character. In such instances, a special regression equation employing a dummy variable may be used or non-parametric statistical procedures may be used (Pfaffenberger and Patterson, 1991, pp. 779-780; Summers, Peters, and Armstrong, 1993, pp. 512-532). If, however, the underlying bivariate relationship of a dichotomous variable can be expected to take a known form, it is frequently possible to restate the values of the variable to cause them to become linear (Kim and Kohout, 1975, p. 369). The most frequently used method used to make such a transformation of variable values is the log transformation (Kim and Kohout, 1975, p. 369).
Log transformations are based on the concept of the logistic cur...