1. Chapter 6 Predicting Future Performance
Criterion-Related Validation
Kind of relation between X and Y (regression)
Degree of relation (validity coefficient)
Strength?
Significant?
How accurate are predictions?
Regression & Correlation
What’s the difference between the two?
Significance Testing
Chapter 6 Predicting Future Performance
Criterion related: evidence for job –relatedness; a technique for validating relational inferences

2. Predicting Future Performance
Criterion-related validation
What it the Kind of relationship?
What is the degree of relationship?
What tells us this?
VALIDATION AS HYPOTHESIS TESTING
Use a valid criterion, not just because it is measurable
Example?
BIVARIATE REGRESSION
Linear Functions Y = f(X) usually positive and monotonic
General Linear regression equation Y = a + b(X)
Be sure to examine the scatter plot. Why?
MEASURES OF CORRELATION
Basic Concepts in Correlation
Residual and Error of Estimate
Generalized Definition of Correlation
Coefficient of Determination
Third Variables
Null Hypothesis and its Rejection
Chapter 6 Predicting Future Performance

3. Measures of Correlation
The Product-Moment Coefficients of Correlation
What is it?
Non-linearity
Homoscedasticity and Equality of Prediction Error
Correlated Error
Unreliability (Chap 5 correct for attenuation)
Reduced Variance (can be corrected for)
Group Heterogeneity (check subgroup diff)
Questionable Data Points (check for them)
A summary Caveat
Don’t over-estimate what you have
Sometimes you can’t control everything
You may need to get more data
Work with what you have
Chapter 6 Predicting Future Performance

4. Statistical Significance
The Logic of Significance Testing
Under what conditions could a low validity coefficient of .20 be useful?
Type I and Type II Errors and Statistical Power
Which is more important I or II?
How can you control power?
Three things affect power
Sample size (N)
Effect size in population
Alpha level
Explain why for each
Chapter 6 Predicting Future Performance

5. COMMENT ON STATISTICAL PREDICTION
What is the standard error of estimate?
Why is it an important consideration for prediction?
What is a problem with restriction range restriction in
The predictor?
The criterion?
What could be done about it?
Give an example of a curvilinear relationship between a predictor and criterion
Chapter 6 Predicting Future Performance