1. Applied Psychology in Human Resource Management seventh edition Cascio & Aguinis
PowerPoint Slides developed by
Ms. Elizabeth Freeman
University of South Carolina Upstate
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2. Chapter 14 Decision Making for Selection
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3. Why do we care about selection decisions?
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4. Selection decisions result in. employment. occupations. vocations
Selection decisions result in employment occupations vocations careers income
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5. Selection decisions are critical. 1. when applicants exceed jobs. 2
Selection decisions are critical when applicants exceed jobs when success depends on jobs
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6. Classical validity assessments of selection decisions focus on. 1
Classical validity assessments of selection decisions focus on accuracy of decisions or 2. prediction efficiencies
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7. Classical validity assessment statistics include 1. simple regressions or 2. multiple regressions
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8. Weaknesses of these statistics Simple regression may not consider all relevant variables Multiple regression may allow strong variables to offset weak variables
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9. Some placement decisions. cannot tolerate such. weaknesses in validity
Some placement decisions cannot tolerate such weaknesses in validity Pilots 2. Surgeons 3. Judges
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10. Decision theory addresses these weaknesses. 1. Recognizes predictions
Decision theory addresses these weaknesses Recognizes predictions 2. Accounts for the importance of outcomes & consequences a. individuals placed b. organizational survival c. payoffs – cost & benefit
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11. Selection decisions exist due to imbalances
between the numbers of people applying for jobs and
the numbers of jobs available to be filled
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12. Personnel selections involve decision makers
Decision makers must predict
Requiring measurements (behavioral data collection) &
Subsequent forecasts (predictions)
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13. Traditional selections focused on
technical psychometrics or classical validity studies
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14. Decision-making theory focuses on outcomes & consequences
to individuals selected
to organizations dependent on individual performance
to optimize the decision utility
(cost benefits)
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15. Selection decisions try to maximize individual differences
Goal = select those who possess the most predictive traits
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16. Classic Decision-Making Study
Conduct job analysis
Assess relationship
criterion predictor
Repeat the process until validation established
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17. Validation of Classical Selection Decisions
Assumes that linear models (linear algebra) are appropriate for job-success prediction
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18. Efficiency of Linear Models
1. Linear decision-making models are considered robust.
2. In this sense, robust means a strong and powerful way to make a selection decision.
3. Linear models work with
a. Least squares regression weights
b. Subjective weighting or variables
c. Unit weights for variables
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19. About Unit Weights – the power of 1 Appropriate when Scaling items
Populations change over time
Predictors are composites
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20. About Unit Weights – the power of 1 Optimal when
Applied to new samples
Sample < critical sample size
a. N = 40, 2 predictors
b. N = 105, 6 predictors
c. N = 194, 10 predictors
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21. About Unit Weights – the power of 1 Other strengths
Do not consume degrees of freedom
Estimated without error
Cannot reverse true relative weights of variables
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22. Suppressor variables Unit weighting is simple but
what about suppressor or
moderator variables?
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23. Suppressor variables not related to the criterion
may be related to the predictors
act like moderators
are understood to exist
true effects not easy to demonstrate
believed to have modest effects
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24. Remember Prediction outcome goals 1. identify predictor variables that
2. explain maximum variance
& have no relationship to other predictors
& have unique relationship to the criterion
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25. Can you eliminate the personal aspects of selection decisions?
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26. You can not remove the personal impact.
You can quantify the process to increase selection decision quality.
You can combine decision data strategically.
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27. Data-combination Strategies A. Data measurement 1. Mechanical
2. Judgmental
B. Data Collection
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28. Mechanical – statistical a. individual measurements ability tests,
Data Measurement
Mechanical – statistical
a. individual measurements ability tests,
aptitude assessments, knowledge inventories
b. combine scores, predict outcomes
2. Judgmental – clinical
a. include subjective assessments based on impressions
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29. Pure Mechanical (Statistical) – data is collected and
Data Collection
Pure Mechanical (Statistical) –
data is collected and
combined by equations
Scorable application blanks
Biographical information blanks
c. Test batteries
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30. 2. Pure Judgmental (Clinical) – data is collected and
Data Collection
2. Pure Judgmental (Clinical) –
data is collected and
combined judgmentally
open-ended interview ratings
subjective impression ratings
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31. 3. Modified Mechanical Strategy –
Data Collection
3. Modified Mechanical Strategy –
Behavior Trait Rating – interview information summarized on standard form
Profile Interpretation – based on objective inventory information, interpret results without knowing or meeting a candidate
i. California Personality Inventory
ii. Myers Briggs Inventory
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32. 4. Clinical Composite Strategy – most common
Data Collection
4. Clinical Composite Strategy – most common
collected objectively & subjectively
combined subjectively
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33. 5. Mechanical Composite Strategy –
Data Collection
5. Mechanical Composite Strategy –
collected objectively & subjectively
combined mechanically, for example, multiple regression
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34. With all of the data collection and options, what is best?
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35. 1. Accuracy of the decisions may depend on accurate weightings
Consider:
1. Accuracy of the decisions may depend on accurate weightings
2. Accurate weightings are not possible subjectively (judgmentally)
3. Mechanical methods can continue to adjust findings while humans seem to be limited in ability to absorb more & more information
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36. Result? a. Collect data objectively b. Collect data subjectively
c. Make final decisions on mechanically combined data
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37. Are there alternative selection decision
prediction models?
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38. Yes, at least 3. Multiple regressions Multiple cutoffs
Multiple hurdles
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39. Multiple Regressions Model Assumptions a. Linearity b. Trait
c. Compensatory interaction
2. Larger samples better
3. Strengths – deals with
a. nominal data
b. nonlinear relationships
c. linear & nonlinear interactions
d. multiple jobs with same or different predictors
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40. Multiple-Cutoff Approach Assumptions
a. Minimal proficiency required on one or more variables
b. Curvilinear relationships
2. Setting a Cutoff
One cutoff, Angoff Method or Expectancy Chart
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41. Multiple-Cutoff Approach Cutoff considerations
1. number of openings per position
2. number of applicants
3. ratio of successful applicants given to historical performance of incumbents
4. typical hiring rate
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42. Multiple-Cutoff Approach Guidelines
1. Decide - cutoff scores sensibility
2. Recognize - may not be a single best
3. Begin with a job analysis
4. Consider validity & job-relatedness of all assessments
5. Relate minimum cutoffs & performance
6. Use adequate number of judges
7. Consider statistical & legal issues
8. Set cutoffs high enough
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43. Multiple-Cutoffs by Angoff Method
1. Multiple judges rate item minimums with barely competent person in mind
2. Judges’ ratings per item averaged
3. Items’ averaged ratings are summed for minimally acceptable score
Advantages
Easy to administer
Reliable as other judgmental methods
Intuitive appeal as expert judges form the decisions
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44. Multiple-Cutoffs by Expectancy Charts
Compare past performance data as comparison with a given predictor score
Analyze the number of successful employees expected given selection ratios
Use raw hiring data to determine
Number of applicants
Number hired
Tenure of hires
Performance of hires
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45. Multiple-Hurdle Approach
Includes multiple-regression and multiple-cutoff decisions
Adds perspective of time
Temporarily or permanently hired
3. Appropriate when training is long, complex, expensive
Air traffic controllers
Surgeons
Financial specialists
Tenure track professionals
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46. From Classical Validity to Decision-Theory
Classical validity – explains the highest proportion of variance in selection decisions
Decision-Theory –emphasizes selection ratios, base rates, and the organizational impacts of HR judgments
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47. Selection Ratios – applicants : hires
Decision-Theory Models
Selection Ratios – applicants : hires
Value influenced by quotas
1. When ratio is 1:1,
hire essentially every applicant
2. When the ratio is < 1,
hire very few
3. Surface value may support selectivity but increases recruiting expenses
4. Flexible SRs allow satisfactory performance
5. Challenge is the statistical logic
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48. Decision-Theory Models
Selection Ratios – applicants to hires
For example, flexible SRs should
decrease erroneous acceptances
(hiring the wrong candidate)
&
decrease erroneous rejections
(not hiring a good candidate)
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49. Decision-Theory Models
Selection Ratios – applicants to hires
With flexible SRs, most organizations concentrate on the erroneous acceptances
& ignore the consequences of
erroneous rejections
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50. Decision-Theory Models
To understand impact of
erroneous acceptances and
erroneous rejections,
must understand SRs and base rates
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51. Decision-Theory Models
Base Rates (BRs) – successful hires
1. If BRs high (jobs are easy to perform), measures may not matter
2. If BRs low (jobs difficult to perform), measures may be critical
3. Predictors should be added based on their ability to increase predictions
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52. Decision-Theory Models
Utility Considerations
Erroneous acceptances are costly
a. Risk of life
b. Threat of lawsuits
c. Injury to existing employees
Erroneous rejections hard to quantify beyond out-of-pocket expenses
a. Administrative expenses
b. Legal defense
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53. Decision-Theory Models
Utility Considerations
Potential Decision-Theory Weaknesses
Some people may be treated unjustly (falling just below cutoffs)
To minimize, use relative decisions predict attributes
do not predict measurements
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54. How do you determine utility or costs to an organization?
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55. Decision-Theory Utility Models (the language of business)
Taylor Russell Model (TRM) includes
1. Classical validity and
2. Selection ratio (openings to applicants) and
3. Base rate (successful performers)
Documents success ratios
a. sensitive to contextual issues
b. sensitive to organizational issues
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56. Decision-Theory Utility Models (the language of business)
Naylor Shine - more applicable than TRM
Assumes linear relationship between validity and utility of selections
Defines utility as increase in average criterion scores
3. Assumes that new predictors added to
existing selection battery
4. Does not require employee
classifications
5. May use when performance cannot be
put into dollars (turnover prediction)
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57. Decision-Theory Utility Models (the language of business)
Naylor-Shine Model answers questions
a. What is average performance?
b. What will the mean criterion score be?
c. What SR should be used?
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58. Decision-Theory Utility Models (the language of business)
Brogden-Cronbach-Gleser Model (BCG)
Assumes linearity only for test scores & actual performance
Assumes normal distribution of test scores & performance
3. Supports decisions of low validity
selection when standard deviations
are high
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59. Decision-Theory Utility Models (the language of business)
BCG Model ALSO
Separates recruitment & selection costs
Allows alternative SD methods
3. Integrates selection with budgets
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60. Decision-Theory Utility Models (the language of business)
BCG Model Weaknesses
Does not look at time value of money
Ignores concept of risk
Ignores taxation payoffs
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61. Decision-Theory Utility Models (the language of business)
BCG Model consideration
selection decision costs are
long-term investments, not short-term
a. Developmental in scope
b. Productivity increases
c. Labor cost reductions
d. Total employees required reduced
e. Financial returns
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62. Decision-Theory Utility Models (the language of business)
BCG Model Challenges
Top scoring candidates refuse offers
Expected & actual performances
3. General economic conditions
4. Managerial resistance
a. may not trust the motivation
b. may not believe in the source
c. may prefer individual responsibility
d. may not understand the context
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63. Strategic Context Selection
Profit maximization
Cost minimization
Assume selection systems have
fixed validity & standard deviations
1. assumption may be false
2. assumption may be true
3. risks associated with either
need to be considered
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64. Summary Classical validity 1. measurement & prediction
2. multiple regressions
3. compensatory variables
Noncompensatory models
1. Taylor-Russell
2. Naylor-Shine
3. Brogden-Cronbach-Gleser
4. useful for planning
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65. Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall
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