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Re: st: linear probability model (LPM)
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.
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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