1. Developing a Hiring System Measuring Applicant Qualifications or Statistics Can Be Your Friend!

2. Individual Differences & Hiring Purpose of selection is to make distinctions based on individual differences –Differences in job performance: Criteria (Y) –Differences in worker attributes: Predictors (X) Measurement: Assigning numbers to objects to represent the quantities of an attribute of the object

3. Key Measurement Issues Reliability--how accurately do our measurements reflect the underlying attributes? Validity --how accurate are the inferences we draw from our measurements? –refers to the uses we make of the measurements But first need to review basic statistics

4. What is Reliability? The extent to which a measure is free of measurement error Obtained score = –True Score + –Random Error + –Constant Error

5. What is Reliability? Reliability coefficient = % of obtained score due to true score –e.g., Performance measure with r yy =.60 is 60% “accurate” in measuring differences in true performance Different “types” of reliability reflect different sources of measurement error

6. What is Validity? The accuracy of inferences drawn from scores on a measure Example: An employer uses an honesty test to hire employees. –The inference is that high scorers will be less likely to steal. –Validation confirms this inference.

7. Descriptive & Inferential Statistics Descriptive: Useful for summarizing groups –Central tendency (mean, median, mode) –Variability (range, standard deviation) Inferential: Can results from a particular sample be generalized, or are they due to chance? How do we know?

8. What is Statistical Significance? The probability that the results of a statistical test are due to chance alone, or The probability of being wrong if you accept the results of a statistical test Less than 5% probability that results are due to chance p <.05 ??

9. Examples of Inferential Statistics: Hiring Security for a Concert “Are men stronger than women?”

10. Females M = 40 SD = 13 Males M = 62 SD = 15 Weight Lifted 0 10 20 30 40 50 60 70 80 90

11. Examples of Inferential Statistics: Hiring Security for a Concert “Do differences in strength affect job performance?” Put differently, “Do differences in strength correspond to differences in job performance”?

12. Correlation Coefficients Summarizes the linear relationship between two variables (example)example Symbolized as “r” (e.g., r =.30) Number indicates magnitude (strength) (.00 through 1.00) Sign (+ or -) indicates direction of relation

13. Examples of Inferential Statistics: Hiring Security for a Concert “Are men stronger than women?” –tests of group differences (t-tests, ANOVA) “Do differences in strength affect job performance?” –tests of association (scatterplots, correlations) “What’s the relative importance of strength and communication skills?”

14. The Payoff Statistically significant results can be used to predict results for future groups e.g., linear regression can be used to predict job performance based on test scores –simple: Y = a + bX –multiple: Y = a +b 1 X 1 +b 2 X 2 +b 3 X 3

15. Y=2.61 + (.7*5) = 6.1

16. Factors Affecting Statistical Significance Magnitude of finding (group difference or correlation) –Bigger is better! r =.5 is more likely to be significant than r =.3 Size of sample it was based on –Small samples are less likely to be similar to the population

17. How Big is Big Enough?

18. Example of Small Sample Problem Two firms use same test for same job –Firm A employs 30 people –Firm B employs 35 people Both find r =. 35 between test scores and job performance r is significant (“real”) for Firm B, but not A