developing a hiring system
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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) - PowerPoint PPT PresentationTRANSCRIPT
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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