Chapter 7 Using Multivariate Statistics
P173
• Multiple Regression
• Multiple Correlation
– What’s the difference between regression and
correlation?
• Validity Generalization
Chap 7 Multivariate Statistics
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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
– Examples of suppressor 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?
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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?
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REPLICATION AND CROSS-VALIDATION
• What IS cross validation?
• Why is it necessary?
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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
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VG
• Three possible outcomes
– Refute or support Sit Specificity
– Refute or support VG
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