PSYCH339 Lecture Notes - Lecture 10: Coefficient Of Determination, Psycinfo, Personnel Selection

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10 Aug 2016
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Lecture 10
Cross validation: degree to which derived from one sample are useful for predicting
performance in another sample
We can use a technique called multiple regressions to figure out the best way to
combine those two predictors
Essentially we want to add the tests together
Predicted performance = test 1+ test 2
Predicted grade school = GPA + GRE
Regression tells us how much to weight to give each test
Predicted performance = 17.54 + test 1 * 0.85 + test 2 * 1.04
Predicted performance is also called composite score
But here’s the problem
We need to cross validate
Apply those weights to another sample
Typically Rd > Rc
D = derivation (original sample)
C = cross validation sample
In my example Rc = 0.68
This decrease n validity is called shrinkage
Typical recommendation? Just add them together
Unit weighting
Predicted = test 1 + test 2
Easier to explain to clients anyway
Meta analysis
Also called validity generalization in context of selection
Average of many validity studies
oWeighted by sample size
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