ntroduction to Industrial and Organizational Psychology
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Work in the 21st Century
Chapter 6
Staffing Decisions
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Module 6.1:
Conceptual Issues in Staffing
Staffing decisions
Associated with recruiting, selecting, promoting, & separating employees
Keith Brofsky/Getty Images
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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
Table 6.1:
Stakeholder Goals in the Staffing Process
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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
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Module 6.2: Evaluation of
Staffing Outcomes
Validity: Accurateness of inferences made based on test or performance data
Validity designs
Criterion-related
Content-related
Construct-related
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Figure 6.1: Scatterplots Depicting Various Levels of Relationship between a Test and a Criterion
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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
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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
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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
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Establishing Cut Scores
Criterion-referenced cut score
Consider desired level of performance & find test score corresponding to that level
Norm-referenced cut score
Based on some index of test-takers’ scores rather than any notion of job performance
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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
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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
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Feelings of unfairness regarding Staffing Strategies can lead to:
Initiation of lawsuits
Filing of formal grievances with company representatives
Counterproductive behavior
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Module 6.3: 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
Table 6.2: The Challenge of Matching Applicant Attributes and Job Demands
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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
Table 6.3
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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
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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
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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
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Figure 6.3: Relationship Between Predictor Overlap & Criterion Prediction
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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
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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
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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
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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
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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
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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
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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
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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
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Module 6.4: 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
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Intentional Discrimination or Adverse Treatment
Plaintiff attempts to show that employer treated plaintiff differently than majority applicants or employees
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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
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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
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“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
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“80%” or “4/5ths” Rule (cont'd)
Can be substantially affected by sample sizes
Burden of proof shifts to employer once AI is demonstrated
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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.