1. Chapter 7 Using Multivariate Statistics P173 Multiple Regression Multiple Correlation – What’s the difference between regression and correlation? Validity Generalization Chap 7 Multivariate Statistics1

2. COMPENSATORY PREDICTION MODELS Regression Equations – Y = a + b 1 X 1 + b 2 X 2 – what’s the difference between b and β weights? – Why use one or the other? Multiple Correlation – How are the correlations among the predictors related to the multiple R? – Would you want high correlations among predictors? Suppressors and Moderator Variables – Examples of suppressor variables Examples of suppressor variables – Suppressor variables explained Suppressor variables explained – Suppressors How could reading ability act as a suppressor for security guard performance? – Moderators How could social skills moderate the conscientiousness-performance relationship? Other Additive Composites – Unit weighting is usually sufficient – Could you add veterans’ preference or religious preference? Chap 7 Multivariate Statistics2

3. NONCOMPENSATORY PREDITION MODELS Multiple Cutoff Models – Two situations warrant it: 1. vital trait 2. if variance is too low (small) to yield sig r. – What can happen if cutoffs are all very low? – What can happen if cutoffs are all very high? Sequential Hurdles – When could this be useful? Chap 7 Multivariate Statistics3

4. REPLICATION AND CROSS-VALIDATION What IS cross validation? Why is it necessary? Chap 7 Multivariate Statistics4

5. VG Situational Specificity v. Validity Generalization – Special form of meta-analysis – All validity coefficients (across studies) Would be the same if not for Artifacts – Hunter & Schmidt (‘90) If var in coefficients is explained by artifacts, reject SS Reject Sit Spec if artifacts 75 % of the variance in coefficients is explained by known artifacts Chap 7 Multivariate Statistics5

6. VG Three possible outcomes – Refute or support Sit Specificity – Refute or support VG Chap 7 Multivariate Statistics6