Developing a Hiring System

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Developing a Hiring System

Measuring Applicant Qualifications or

Statistics Can Be Your Friend!

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0011 0010 1010 1101 0001 0100 1011Individual 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

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0011 0010 1010 1101 0001 0100 1011What 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

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0011 0010 1010 1101 0001 0100 1011What 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.

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0011 0010 1010 1101 0001 0100 1011Descriptive & 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?

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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 ??

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Examples of Inferential Statistics:Hiring Security for a Concert

• “Are men stronger than women?”

FemalesM = 40SD = 13

MalesM = 62SD = 15

Weight Lifted

0 10 20 30 40 50 60 70 80 90

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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”?

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0011 0010 1010 1101 0001 0100 1011Correlation 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

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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?”

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0011 0010 1010 1101 0001 0100 1011The 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

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

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0011 0010 1010 1101 0001 0100 1011Factors 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

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How Big is Big Enough?Sample Size Minimum r

5 .88 10 .63 15 .51 20 .44 25 .40 30 .36 35 .33 40 .31 50 .27 70 .23

100 .19

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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