Logistic regression is used for modeling outcomes that are binary (1/0) variables. A linear probability model has a number of shortcomings in estimating binary dependent variables (Judge et al 1985, Cox and Snell, 1989). Adult outcomes that are binary in our report are alcohol abuse or dependence, past-month drug use, ever being in jail by the age of 33, ever being under poverty at ages 25-29, and ever being on welfare at ages 21-33.
Coefficient estimates from logistic regression do not allow an easy interpretation. Instead, odds ratios, an alternative and preferred measure, are used to present estimated results. Odds ratios measure the relative probability of the estimated outcome among one group relative to the reference group. For example, in estimating the probability of alcohol abuse and dependence, we obtained an odds ratio of 2.25 for males relative to females. Then the interpretation is that male respondents in the sample were 2.25 times as likely to develop alcohol abuse and dependence as female respondents in the sample. If an odds ratio for a group is greater than 1, then this group is more likely to end up with the outcome compared with the reference group (whose odds ratio is 1); if an odds ratio is less than 1, then this group is less likely to end up with the outcome compared with the reference group.