Chapter 6 Predicting Future Performance 1

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

Chapter 6 Predicting Future Performance 2

• VALIDATION AS HYPOTHESIS TESTING• BIVARIATE REGRESSION– Linear Functions

• 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

– The Product-Moment Coefficient of Correlation– What are these? Explain each

• Non-linearity• Homoscedasticity and Equality of Prediction Error• Correlated Error• Unreliability • Reduced Variance• Group Heterogeneity• Questionable Data Points

• 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?– What are the three things power is affected by?

» 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 creiterion