PSY 3711 1st Edition Lecture 17 Outline of Last Lecture I Disparate treatment II Disparate impact III Us legal backdrop IV Civil rights act of 1991 V Americans with disabilities act of 1990 VI Characteristics of medical screen VII Equal employment opportunity commission EEOC VIII Testing and the civil rights act IX Clinical synthesis Outline of Current Lecture I Humans behind the machine II Meta analytics weights III Expert Weights IV Bootstrapped weights V Which is best Current Lecture I II III Why does mechanical data combination perform so well a We can mechanically combine predictors in a number of different ways b Equal unit weights c Differential weights i Expert judgment based weights ii Bootstrapped weights iii Criterion weighting Meta analytic weights a Advantage of using more stable estimates b Can be used in two ways i Use the predictive validity ii Use both the predictive validity and the info about its interrelations with other predictors Expert weights a For expert judgment based weights we do a study of experts and ask them how they weight the info b We simply ask them how they do it These notes represent a detailed interpretation of the professor s lecture GradeBuddy is best used as a supplement to your own notes not as a substitute IV V c Only as good as the experts asked Bootstrapped weights a Try to model what experts do and then use hteir weighting scheme consistently b One way to do this is to gather the judgments of a large nubmero f experts and then predict their judgments based on the data they had available c Much like optimal weights a multiple regression is used What predictors are best a When predictors are of similar strength and there is not a lot of overlap unit weights work just fine b Difference between equal unit weights and differential weights is typically small c If there are large difference in predictive strength of your measure then differential weights can yield some improvement d But even if you only use unit weights you are already way ahead
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