Why use logistic regression rather than ordinary linear regression?
When I was in graduate school, people didn’t use logistic regression with a binary DV. They just used ordinary linear regression instead. Statisticians won the day, however, and now most psychologists use logistic regression with a binary DV for the following reasons: • If you use linear regression, the predicted values will become greater than one and less than zero if you move far enough on the X-axis. Such values are theoretically inadmissible. • One of the assumptions of regression is that the variance of Y is constant across values of X (homoscedasticity). This cannot be the case with a binary variable, because the variance is PQ. When 50 percent of the people are 1s, then the variance is .25, its maximum value. As we move to more extreme values, the variance decreases. When P=.10, the variance is .1*.9 = .09, so as P approaches 1 or zero, the variance approaches zero. • The significance testing of the b weights rest upon the assumption that errors of prediction (Y-Y’) are normall
