statalogis - Logistic Regression in Stata Here's a...

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1 Logistic Regression in Stata * Here's a description of how to do logistic regression, as well as ordinal & multinomial logit regression, in Stata. The examples use the UCLA-ATS data set hsb2.dta . For logistic regression, for the outcome variable use the dummy variable hsci (science achievement score>60); if necessary create it yourself. For ordinal logistic regression you’ll create the outcome variable sci3 , as described later on. For the multinomial logistic example we'll use race as the outcome variable. We'll begin with logistic regression, then we’ll do brief examples of ordinal & multinomial logit. Rick Tardanico, March 2008. * See ‘Explanatory variables in logistic regression.doc’ * Open & examine the univariate characteristics the data set u hsb2, clear d su hist read, norm [e.g.] gr box read [e.g.] tab ses [e.g.] * The dependent variable is hsci (science achievement score>=60). Graphically & numerically examine the pertinent univariate, bivariate & (insofar as possible) multivariate distributions, consider possible transformations or other manipulations, & perhaps save a new data set consisting of listwise (i.e. ‘complete’) observations (using the commands ‘mark’ and ‘markout’ [see Long/Freese]). * Select the explanatory variables * The procedure we'll use, as outlined in Hosmer & Lemeshow, preliminarily selects independent indvariables based on, first, substantive & theoretical relevance, and second, on pvalues<=.25 (which the procedure later modifies in view of more complete sets of variables and the modelling of nonlinearities). Begin by testing the potential explanatory variables with the dependent variable in 'mini' logit & logistic models. * Note: 'logit' yields the logit coefficient, while 'logistic' yields odds ratios. Specifying the option 'or' after logit makes logit display odds ratios. The Stata manual emphasizes that the only distinction between 'logit' & 'logistic' is logit coefficients versus odds ratios. The conclusions reached by the two approaches are identical. ci hsci, binomial [options: agresti, jeffreys, wilson] scatlog hsci math, ci [download ‘scatlog’] logit hsci math, or nolog estimates store full logit hsci, or lrtest full
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2 * Do the preceding graph & the tests--for each potential explanatory variable, & document the results for later reference. Regarding the tests, those variables that obtain pvalues<=.25 will be included in the 'preliminary main-effects model'. Let's suppose that we've done so. The following is our preliminary model, which we'll test for significance. If our sample is unweighted we can use the likelihood ratio test to assess nested models. If the sample is weighted we use Wald tests (i.e. Stata's 'test' command in logit/logistic regression). But first, run all of the preliminary variables in an OLS regression in order to run a multicollinearity test (see Menard on the justification for doing so).
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