Human Resource Management Week Four Signature Assignment
Staffing Organizations
Chapter 11:
Decision Making
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Decision Making 1
Choice of Assessment Method
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Validity Coefficient
Relationship between predictor and criterion scores
Measured through correlations
Practical significance
Sign is positive versus negative
Magnitude ranges from -1 to +1; zero means no relationship
Statistical significance
Likelihood that other samples will get the same result
Validity for multiple criteria
Core job tasks
Organization and goal direction
Cooperation and group facilitation
Creativity
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Workforce Diversity
Concerns about factors not related to performance on the job:
al credentials may screen low-income applicants
Leadership behaviors that are more typical of men than of women fail to recognize the value of alternative modes of leadership
Difficult issue: one predictor has high validity and high disparate impact while another predictor has low validity and low disparate impact
Using a variety of different selection tools together
Putting greater weight on lower disparate impact sections of the test
Adding measures with lower disparate impact
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Correlation with other predictors
Small correlation with other existing predictors is better
Hypothetical example in setting where experience and GPA are already used
Cognitive ability and structured interviews have moderate validity and incremental prediction
Situational judgment has high validity and but low incremental prediction
Means situational judgment (in this case) is very similar to what’s already being used, whereas cognitive ability and structured interviews add more
Table divided into three columns summarizes incremental validity assessment. Column 1 notes points for assessment. The column header Task Performance. Columns 2 and 3, task performance is further divided into two sub-columns as: Validity and increment.
| Task performance Validity | Task performance Increment | |
| Cognitive ability | 0.35 | 0.27 |
| Conscientiousness | 0.18 | 0.12 |
| Situational judgment | 0.43 | 0.13 |
| Structured interview | 0.22 | 0.18 |
Table divided into three columns summarizes incremental validity assessment. Column 1 notes points for assessment. The column header Innovation. Columns 2 and 3, task performance is further divided into two sub-columns as: Validity and increment.
| Innovation Validity | Innovation Increment | |
| Cognitive ability | 0.28 | 0.20 |
| Conscientiousness | 0.08 | 0.02 |
| Situational judgment | 0.35 | 0.14 |
| Structured interview | 0.39 | 0.33 |
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Hiring Success Gain
Selection ratio
Number hired divided by number of applicants
High selection ratio means nearly every applicant must be hired
Low selection ratio means the organization can be more selective
Base rate
Number of successful employees divided by number of employees
Indicates how difficult the job is—higher base rate means easier job
Taylor-Russel Tables
Combine information on selection ratios, base rates, and validity
Conclusion is that selection tools are most valuable when the selection ratio is low, the base rate is low, and validity coefficient is high
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Taylor-Russell Tables
Access the text alternative for slide images.
Source: H. C. Taylor and J. T. Russell, "The Relationship of Validity Coefficients to the Practical Effectiveness of Tests in Selection," Journal of Applied Psychology, 1939, 23, pp. 565 -578.
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Economic Gain
Table divided into four columns summarizes assessment of economic impact. The column headers are marked from left to right as: Technique, sources of information, what is evaluated, and links to other areas.
| Technique | Sources of information | What is evaluated? | Links to other areas |
| Utility analysis | Data on predictor validity, applicant test scores, and estimated dollar value of performance variability | Expected value of improved job performance if a new selection tool is implemented | Line manager judgments of employee value; expected financial returns for investing in selection |
| Predictive analytics | Historical information on performance outcomes for business units | Contribution of different characteristics of the workforce to performance outcomes | Existing “hard” data from organizational records for valued outcomes |
| Kano analysis | Line manager and director descriptions of strategic impact of performance across domains | Changes in economic performance from enhanced levels of different types of employee skills | Tool from marketing, manager judgments regarding critical competencies are incorporated |
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Decision Making 2
Determining Assessment Scores
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Determining Assessment Scores
Single predictor
Simple and fast
Does not capture many candidate qualifications
Multiple predictors
More complicated and time consuming
More complete picture of the candidates
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Multiple Predictor Methods 1
Compensatory model
Adds all scores together into a single number
Can be done through informal “clinical” weighting, unit weighting, rational weighting, or regression weighting
Multiple hurdles model
Uses selection tools in order from cheapest to most expensive
Cuts candidates at each stage
Results similar to compensatory model, but costs are much lower
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Multiple Predictor Methods 2
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Combining Multiple Hurdle and Compensatory Models
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Selecting the Best Weighting Scheme
Do decision makers have considerable experience and insight into selection decisions?
Is managerial acceptance of the selection process important?
Is there reason to believe each predictor contributes relatively equally to job success?
Are there adequate resources to use involved weighting schemes?
Are conditions under which multiple regression is superior satisfied?
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Decision Making 3
Hiring Standards and Cut Scores
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Consequences of Cut Scores 1
Issue -- What is a passing score?
Score may be a
Single score from a single predictor or
Total score from multiple predictors
Description of process
Cut score - Separates applicants who advance from those who are rejected
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Consequences of Cut Scores 2
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Methods to Determine Cut Scores
Minimum competency
set on the basis of the minimum qualifications deemed necessary to perform the job
Compensatory: single aggregate score across predictors
Conjunctive: must pass standards for each predictor
Maximum competency
Screen for “overqualified” candidates
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Use of Cut Scores
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Decision Making 4
Methods of Final Choice
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Methods of Final Choice 1
Random selection
Each finalist has equal chance of being selected
Ranking
Finalists are ordered from most to least desirable based on results of discretionary assessments
Grouping and banding
Finalists are banded together into rank-ordered categories
Differential weighting
Incorporating weights on scores for determining final candidate eligibility
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Methods of Final Choice 2
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Decision Making 5
Decision Makers
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Decision Makers
Organizational leaders
Uniquely valuable, holistic understanding of the purpose of a selection system.
Buy-in enhances the success of any policy initiative
Human Resource Professionals
Technical expertise needed to develop sound selection decisions
Access to quantitative information from HR information systems that can be used to quantify predictor-outcome relationships
Line managers
Accountable for the success of the people hired
Identify critical needs in the selection system that might not be addressed
Coworkers
Select members compatible with the goals of the work team
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Decision Makers in Selection
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Decision Making 6
Legal Issues
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Legal Issues
Legal issue of importance in decision making
Cut scores or hiring standards
Uniform Guidelines on Employee Selection Procedures (UGESP)
If no adverse impact, guidelines are silent on cut scores
If adverse impact occurs, guidelines become applicable
Diversity and hiring decisions
Exclude issues of demography in hiring decisions
Evaluation based on KSAOs relevant to job-related diversity competence
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Ethical Issues in Staffing
Issue 1
Do you think companies should use banding and related methods to enhanced diversity in selection decisions? Defend your position.
Issue 2
Is clinical prediction the fairest way to combine assessment information about job applicants, or are statistically based weighting methods more fair? Why?
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