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The statistical equations that Mr. Heckman developed to meld the workers and nonworkers -- thus correcting for "selection bias" and more accurately calculating the probability of going to work -- won him the Nobel.
Webber ran the numbers both ways: with and without correcting for selection.
I find that even after correcting for selection bias, return venture capitalists are less productive than comparable non-migrants in targeting promising projects and/or providing value-added services.
Without correcting for selection biases, effect sizes and between-study variances therefore tend to be overestimated.
Correcting for selection bias in genome scans is problematical because no unbiased estimator exists for the scan data alone.
Correcting for selection on synonymous sites, we find that the correlation between ωa and Ne is positive but not significant, whereas the correlation between ωa and log(Ne) is positive and significant with ANCOVA (slope P = 0.028, intercept P = 0.032).
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This two-stage method (first developed by Heckman, 1976) corrects for selection bias.
Thus, estimates from the selection equation are used to computeλ1 and λ0, which are then added to the outcome equations to correct for selection bias.
We use our measure of high school achievement to correct for selection into higher education, which, as discussed above, is unobservable in most studies.
To correct for selection bias, a two-stage non-linear procedure is adopted which departs in few features from the standard Heckman correction.
Based on Heckman (1976), when a farmer's decision process about the adoption of a new technology requires more than one step, models with two-step regressions, such as Heckman's sample selection, are appropriate to correct for selection bias generated during the decision-making processes.
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