What Is Logit Regression?
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 curve (Pfaffenberger and Patterson, 1991, pp. 864-865). The logistic curve is based on the exponential of a function, and is shaped like the letter "s." This transformation, referred to as "logit transformation," is, in effect, a transformation of the conditional probabilities of a dichotomous variable (Dwyer, 1983, p. 447).

The second 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 not additive (Dwyer, 1983, p. 447). A multiplicative model is more appropriate for use with such variables (Dwyer, 1983, p. 447). Although the multiplicative model is not linear in character, the character can be made to be linear through logit transformation (Dwyer, 1983, p. 447).

Dwyer (1983, pp. 447-453) provided an illustration of the type of problem that is susceptible to solution through logit transformation. For this illustration, assume th

 
 
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    Pfaffenberger Patterson | Summary Conclusion | Logit Regression | Cunningham Capone | Caudill Gropper | Regression Procedure | Kim Kohout | Fraser Furtado | United Woodward | EXAMINATION Introduction | logit regression | logit transformation | dependent variables | conditional probabilities | logit regression procedure | educational attainment | regression procedure | regression analysis | formal educational | formal educational attainment | dwyer 1983 | logit regression model | regression model | independent dependent variables | relationship independent dependent |  
   
 
 
 
   
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