Chapter 6 (p153)
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
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• VALIDATION AS HYPOTHESIS TESTING
• BIVARIATE REGRESSION
– Linear Functions
• MEASURES OF CORRELATION
– Basic Concepts in Correlation
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Residual and Error of Estimate
Generalized Definition of Correlation
Coefficient of Determination
Third Variables
Null Hypothesis and its Rejection
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– The Product-Moment Coefficient of Correlation
– What are these? Explain each
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Non-linearity
Homoscedasticity and Equality of Prediction Error
Correlated Error
Unreliability
Reduced Variance
Group Heterogeneity
Questionable Data Points
• A summary Caveat
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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
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– 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?
– What are the three things power is affected by?
» Explain why for each
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• 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 creiterion
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