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Developing a Hiring System

Developing a Hiring System. Measuring Applicant Qualifications. or Statistics Can Be Your Friend!. Individual Differences & Hiring. Purpose of selection is to make distinctions based on individual differences Differences in job performance: Criteria (Y)

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Developing a Hiring System

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

  4. 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.

  5. 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?

  6. 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 p < .05 ?? • Less than 5% probability that results are due to chance

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

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

  9. 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”?

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

  11. 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?”

  12. 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 +b1X1+b2X2+b3X3

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

  14. 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

  15. How Big is Big Enough?

  16. 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

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