Staffing Decisions in Industrial Organizational Psychology

1. 1
Work in the 21st Century
Chapter 6
Staffing Decisions

2. 2
Module 1:
Conceptual Issues in Staffing
• Staffing decisions
– Associated with recruiting, selecting,
promoting, & separating employees
Keith
Brofsky/Getty
Images

3. 3
Sequential View of
the Staffing Process
Figure 6.1
SOURCE: Guion (1998).

4. 4
Impact of Staffing Practices on
Firm Performance
• High performance work practices
– Include use of formal job analyses, selection
from within for key positions, & use of formal
assessment devices for selection
• Staffing practices have positive associations
with firm performance

5. Table 6.1:
Stakeholder Goals in the Staffing Process
5

6. 6
Staffing from
International Perspective
• Job descriptions used universally
• Educational qualifications & application
forms widely used for initial screening
• Interviews & references are common post-
screening techniques
• Cognitive ability tests used less frequently;
personality tests used more frequently

7. 7
Module 2: Evaluation of
Staffing Outcomes
• Validity: Accurateness of inferences made
based on test or performance data
• Validity designs
• Criterion-related: the one you can apply the most at
the organizational level (this provides a coefficient)
• Content-related: whether we are capturing the entire
domain of performance within one test
• Construct-related:

8. 8
Figure 6.2: Scatterplots Depicting Various Levels
of Relationship between a Test and a Criterion

9. 9
Selection Ratio (SR)
• Selection Ratio (SR)
– Index ranging from 0 to 1 that reflects the ratio
of available jobs to applicants
n = number of available jobs
N = number of applicants assessed
SR = n/N

10. 10
Selection Decisions
False positive
• Applicant accepted but performed poorly
False negative
• Applicant rejected but would have performed well
True positive
• Applicant accepted & performed well
True negative
• Applicant rejected & would have performed poorly

11. 11
Cut score or cutoff score
• Specified point in distribution of scores
below which candidates are rejected
• Raising cut score will result in fewer false
positives but more false negatives
• Strategy for determining cut score depends
on situation

12. 12
Figure 6.3: Effect on Selection
Errors of Moving the Cutoff Score

13. 13
Establishing Cut Scores
• Criterion-referenced cut score
• Consider desired level of performance & find test
score corresponding to that level
• Looking at actual performance and have SME (ex:
in order for a person to excel on a job they have to
answer this set of questions correctly)
• Norm-referenced cut score
• Based on some index of test-takers’ scores rather
than any notion of job performance
• Pick a # that is based on how the tester’s are
performing on the exam (on average our applicants
answer 80% correctly)

14. 14
Utility Analysis
• Assesses economic return on
investment of HR interventions like
staffing or training
• Utility analysis can address the cost/benefit
ratio of one staffing strategy versus another

15. 15
Utility Analysis
• Includes consideration of the Base Rate, which is
the percentage of the current workforce
performing successfully
– If performance is already high, then new staffing
system will likely add little to productivity
• Utility analysis calculations can be very
complex- as this increase it decreases the
likelihood that people will actually use
these

16. 16
Feelings of unfairness regarding
Staffing Strategies can lead to:
• Initiation of lawsuits
• Filing of formal grievances with
company representatives
• Counterproductive behavior

17. 17
Module 3: Practical
Issues in Staffing
• Staffing Model
– Comprehensiveness
• Enough high quality information about candidates to
predict likelihood of their success
– Compensatory
• Candidates can compensate for relative weakness in
one attribute through strength in another one,
providing both are required by job

18. Table 6.2: The Challenge of Matching
Applicant Attributes and Job Demands
18

19. 19
Combining Information
• Clinical decision making
– Uses judgment to combine information &
make decision about relative value of
different candidates
• Statistical decision making
– Combines information according to a
mathematical formula

20. Table 6.3: Using Multiple
Predictors to Choose a Candidate
20

21. 21
Combining Information (cont'd)
• Hurdle system of combining
scores
– Non-compensatory strategy:
individual has no opportunity to
compensate at later stage for low
score in earlier stage
– Establishes series of cut scores
Anthony Saint James/Getty Images

22. 22
Hurdle System of Combining Scores
• Constructed from multiple hurdles so
candidates who don’t exceed each of the
minimum dimension scores are excluded
from further consideration
• Often set up sequentially
• More expensive hurdles placed later
• Used to narrow a large applicant pool

23. 23
Combining Information (cont'd)
• Compensatory approach
– Multiple regression analysis
• Results in equation for combining test scores into a
composite based on correlations of each test score
with performance score
• Cross-validation
– Regression equation developed on first sample
is tested on second sample to determine if it
still fits well

24. 24
Figure 6.4: Relationship Between
Predictor Overlap & Criterion Prediction

25. 25
Score banding
• Individuals with similar test scores can
be grouped together in a category or
score band
• Selection within band can be made
based on other considerations
• Score Banding is controversial

26. 26
Score Banding
• Score Banding uses the Standard error
of measurement (SEM) for the test
– SEM provides a measure of the amount
of error in a test score distribution
– Function of reliability of test &
variability of test scores

27. 27
Score Banding
• Fixed band system
– Candidates in lower bands not considered
until higher bands have been exhausted
• Sliding band system
– Permits band to be moved down a score
point when highest score in a band is
exhausted

28. 28
Subgroup Norming
– Develop separate lists for individuals in
different demographic groups who are
then ranked within their respective group
– In general, subgroup norming is not
allowed as a staffing strategy

29. 29
Selection vs. Placement
• Sometimes, the challenge is to place an individual
rather than simply select an individual
• Placement
– Process of matching multiple applicants & multiple job
openings
– Strategies
• Vocational guidance
• Pure selection
• Cut & fit

30. Challenge of Placing Multiple
Candidates
30

31. 31
Deselection
• 2 typical situations
– Termination for cause
• Individual is fired for a particular reason
• Generally not unexpected
– Layoff
• Job loss due to employer downsizing or
reductions in force
• Often occurs with little or no warning

32. 32
Large Staffing Projects
• Concessions must be made: Labor intensive
assessment procedures are often not feasible
• Cost of testing can be quite expensive
• Fairness is a critical issue
• Standard, well-established, & feasible selection
strategies are important

33. 33
Small Staffing Projects
• Luxury of using wider range of assessment
tools
• Adverse impact is less of an issue
• Fairness is still a key issue
• Rational, job-related, & feasible selection
strategies are important

34. 34
Module 4: Legal Issues in
Staffing Decisions
• Charges of employment discrimination
– Involve violations of Title VII of 1964 CRA,
ADA, or ADEA
– I-O psychologists often serve as expert
witnesses in these lawsuits
– Consequences can be substantial
– Most often brought by individual claiming
unfair termination

35. 35
Intentional Discrimination or
Adverse Treatment
• Plaintiff attempts to show that
employer treated plaintiff differently
than majority applicants or employees

36. 36
Unintentional Discrimination or
Adverse Impact (AI)
• Acknowledges employer may not have
intended to discriminate against
plaintiff but employer practice had AI
on group to which plaintiff belongs

37. 37
Determination of Adverse Impact
• Burden of proof on plaintiff to show:
a) he/she belongs to a protected group, &
b) members of protected group were
statistically disadvantaged compared to
majority employees

38. 38
“80%” or “4/5ths” rule
– Guideline for assessing whether there is
evidence of Adverse Impact (AI)
– Plaintiffs must show that protected group
received only 80% of desirable outcomes
received by majority group in order to meet
burden of demonstrating AI
– Results in AI ratio

39. 39
“80%” or “4/5ths” Rule (cont'd)
• Can be substantially affected by sample
sizes
• Burden of proof shifts to employer once AI
is demonstrated

40. 40
Social Networking Sites
and the Workplace
• Employees (or applicants) posting information on a social
networking site (e.g., Facebook, Twitter) that is accessed by an
employer have been increasingly getting in trouble.
• Job candidates who have been found to post on SNS that they
like to “shoot people” or “blow things up” have been removed
from hiring consideration.
• Employment lawyers are still debating the legality of
employment decisions based on information on social
networking sites.