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Re: st: linear probability model (LPM)


From   Steven Samuels <ssamuels@albany.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: linear probability model (LPM)
Date   Thu, 26 Apr 2007 11:52:48 -0400

A linear probability model is desirable because effects are risk differences, which are much easier to interpret than odds ratios. It's best for proportions that are not too close to 0 or 1; otherwise the model may predict probabilities outside those boundaries. (In this range linear, probit, and logit models give similar predictions-Cox, Analysis of Binary Data, 1972).

In Stata you can use the -reg- command with the "robust" option to produce proper standard errors. However you still need to check goodness of fit. With continuous covariates, use the -linktest- command. You should also group predictors, continuous or categorical, to visually see if the linear model is accurate. With continuous covariates, the -lowess- command, with the -noweight- and - adjust- options will give you a visual check of fit without grouping.

Good luck!

Steve

On Apr 26, 2007, at 10:12 AM, Vanessa Mahlberg wrote:


My dependent variable is dichotomous (zero for low work satisfaction and one for high work satisfaction). I would like to run a linear probability model (LPM) instead of a probit model- but I don?t really know the right stata command. I read in some articles that I should use the "regress" command?!? But in my opinion it must be something like STATISTICS- BINARY OUTCOMES-???
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