## ARCHIVED: In Stata, how does .logit handle a non-binary dependent variable?

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Logistic regression models deal with categorical binary dependent variable stata variables. Depending on the number of categories and on whether or not these categories are ordered, different models are available. The first command will produce the model estimates in terms of logit coefficients; the second and third command will yield what some people call "effect coefficients", i.

Here, logit will "translate" the immediately preceding model with effect coefficients into a model with logit coefficients. With Stata binary dependent variable stata mlogityou may estimate the influence of variables on a dependent variable with several categories such as "Brand A", "Brand B", "Brand C", "Brand D".

Note that if these categories are ordered such as in statements like "strongly agree" The option baseoutcome is binary dependent variable stata only if you wish to depart from Stata's default, i. Another option is rrrwhich causes stata to display the odds ratios and the associated confidence intervals instead of the logit coefficients. Actually, Stata offers several possibilities to analyze an ordered dependent variable, say, an attitude towards abortion.

The most common model is based on cumulative logits and goes like this:. Probit models are alternatives to logistic regression models or logit models. The commands for the binary, multinomial and ordered case go like this:. Stata can compute the effects of independent variables on the outcome in terms of probabilities, either literally predicted probabilities or as marginal effects predicted changes of probability.

Margins are particularly important in the case of the multinomial model, as the regression coefficients may be very misleading. They must be obtained separately for each category of the dependent variable. This holds true for the ordinal model as well. To achieve this, you can use all the commands described above, just adding an option indicating the category for which the margins are to be computed.

There are two ways to achieve this which I will describe for the simplest case, a categorical independent variable:. The significance tests on the coefficients based on the z statistic are not considered the best available. A superior test is based on the likelihood ratio statistic. Unfortunately, computation is a bit tedious.

You binary dependent variable stata to save the estimates from binary dependent variable stata model first, then compute a constrained model e. The procedure is as follows:. Of course, you may estimate several models, store the estimates under different names and test any models you like afterwards. Make sure that the models you estimate contain the same number of cases and always binary dependent variable stata model is nested within the other. Another way would be simply to compute the LR test "by hand" or rather by brain using the log-likelihoods from the Stata output.

For many purposes, Stata's output concerning overall model fit is sufficient. Both the model chi-square i. A number of additional statistics are available from the fitstat package by J. Scott Long and Jeremey Freese.

This package may be installed as follows:. Imputation Step Multiple Imputation: Factor Variables Preliminaries II: Performing the LR test; note the dot at the end indicating that the last model estimated is to be tested against model "anyname".