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

2. COMPENSATORY PREDICTION MODELS
Regression Equations
Y = a + b1X1 + b2X2
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
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 Statistics

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 Statistics

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

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 Statistics

6. VG Three possible outcomes Refute or support Situational Specificity
Refute or support VG
Chap 7 Multivariate Statistics