Chapter 6 Predicting Future Performance

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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

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

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

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

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

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