1. Chapter 10
Decision Making
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2. Chapter Learning Outcomes
After reading this chapter you should:
Appreciate the complexity of decision making in the employee selection context
Be familiar with the sources of common decision- making errors in employee selection
Understand the distinction between judgmental and statistical approaches to the collection and combination of applicant information
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3. Chapter Learning Outcomes (continued)
Understand the advantages and disadvantages of various decision-making models
Appreciate issues involved with group decision making
Know the basic principles in the application of cut-off scores, banding, and top-down selection
Be able to discuss the benefits of using best practices in recruitment and selection
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4. The Context of Selection Decisions
Satisficing: making an acceptable or adequate choice rather than the best or optimal choice
Organizational fit: an applicant’s overall suitability for the organization and its culture
Organizational fit tends to have promote-from-within policies, flexible job descriptions, or jobs that change rapidly, or they tend to practice job rotation or rapid promotion.
Another form of selection involves promotion or transfer.
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5. Selection Errors
Implicit theories: personal beliefs that are held about how people or things function, without objective evidence and often without conscious awareness
Implicit theories include maintaining direct eye contact while being interviewed; what characteristics or qualities are important or necessary for particular kinds of work; and employers making subjective decisions based on gut feelings about the applicant.
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6. Outcomes of the Selection Process
False positive error: occurs when an applicant who is assessed favourably turns out to be a poor choice
False negative error: occurs when an applicants who is rejected would have been a good choice
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7. Methods of Collecting and Combining Applicant Information Table 10.1
Pure judgment approach: an approach in which judgemental data are combined in a judgmental manner
Trait rating approach: an approach in which judgmental data are combined statistically
Table 10.1: Methods of Collecting and Combining Applicant Information (p )
Although all six of the basic decision-making approaches described have been used in employee selection, they are not equally effective. A considerable body of research indicates that the pure statistical and the statistical composite approaches are generally superior to the other methods in predicting performance. Both of these approaches involve combining information in a statistical manner.
Why do employers resist using statistical approaches? They prefer relying on gut feelings or instincts. Employers also tend to be overconfident in their decision-making abilities. They generally believe that they are quite successful in selecting good job candidates. Few employers don’t bother to keep track of their success rates by reviewing the job performance of those they hired. Some employers don’t want to spend the time or money on pursuing the statistical approaches.
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8. Methods of Collecting and Combining Applicant Information Table 10
Methods of Collecting and Combining Applicant Information Table 10.1(continued)
Profile interpretation: an approach in which statistical data are combined in a judgmental manner
Pure statistical approach: an approach in which data are combined statistically
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9. Methods of Collecting and Combining Applicant Information Table 10
Methods of Collecting and Combining Applicant Information Table 10.1(continued)
Judgmental composite: an approach in which judgmental and statistical data are combined in a judgmental manner
Statistical composite: an approach in which judgmental and statistical data are combined statistically

10. Group Decision Making
Researchers conclude that groups are generally better at problem solving and decision making than the average individual
Groups make better selection decisions than individuals
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11. Class Activity
Think about the types of decisions you make on a daily basis. What would be some examples of routine and non- routine decisions?
Is it better to discuss the decision making process as a group or on an individual basis? What has been your experience in this area?
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12. Incremental Validity and Cut-off Score
Incremental validity: the value in terms of increased validity of adding a particular predictor to an existing selection system
Cut-off score: a threshold; those scoring at or above the cut-off score pass, those scoring below fail
Selection ratio: the proportion of applicants for one or more positions who are hired
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13. Decision-Making Models
Unit and Rational Weighting
Multiple Regression Model
Multiple Cut-Off Model
Multiple Hurdle Model
Combination Model
Profile Matching Model
Unit and Rational Weighting: simply add together the scores applicants received on the various selection tools that were used and to give each score the same weighting (e.g., a value of 1.0). This approach is known as unit weighting.
Multiple Regression Model: uses the applicant’s scores on each predictor (e.g., tests, interviews, reference checks). The scores are also weighted and summed to yield a total score (e.g. predicted job performance).
Multiple Cut-Off Model: uses the scores on all predictors are obtained for all applicants, just as in the multiple regression model. Applicants are rejected if their scores on any of the predictors (tests, interviews, reference checking) fall below the cut-off scores.
Multiple Hurdle Model: applicants must pass the minimum cut-off for each predictor, in turn, before being assessed on the next predictor. As soon as an applicant has failed to meet the cut-off on a given predictor, the applicant ceases to be a candidate for the job and is not assessed on any of the remaining predictors.
Combination Model: all applicants are measured on all predictors and those falling below the cut-off on any of the predictors are rejected, just as in the multiple cut-off model.
Profile Matching Model: current employees who are considered successful on the job are assessed on several predictors. Their average scores on each predictor are used to form an ideal profile of scores required for successful job performance.
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14. Making Selection Decisions
Top-down selection: involves ranking applicants on the basis of their total score, selecting from the top down until the desired number of candidates has been selected
Based on the assumption that individuals scoring higher will be better performers on the job than those scoring low
Considered the best approach for maximizing organizational performance
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15. Banding
Banding: refers to a grouping process that takes into account the concept of standard error of measurement involves grouping applicants based on ranges of scores
Cut-off scores are actually a form of banding where there are two bands
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16. Making Selection Decisions: Conclusions
Selection systems are made more effective by the following recommendations:
Use valid selection instruments
Dissuade managers from making selection decisions based on gut feelings or intuition
Encourage managers to keep track of their own selection “hits” and “misses”
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17. Making Selection Decisions: Conclusions (continued)
Train managers to make systematic selection decisions
Periodically evaluate or audit selection decisions in order to identify areas needing improvement
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18. Recruitment and Selection Notebook 10.1
Making the Selection Decision
Identify all of the sources of information about the applicant available to you
Use reliable, valid selection instruments whenever possible
Determine which decision-making model you will use
Recruitment and Selection Notebook 10.1 (p. 492) provides guidelines that should help the HR professional in making a selection decision.
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19. Recruitment and Selection Notebook 10.1(continued)
If using the regression or combination models, collect and save data over a period of time for all predictors as well as job performance data for those applicants who are hired
If using multiple cut-off or multiple hurdle models, determine appropriate cut-off scores for each predictor

20. Recruitment and Selection Notebook 10.1(continued)
Combine data from different predictors statistically to yield an overall score
Offer the position(s) to the candidate(s) with the highest overall score(s)

21. Summary
Selection decisions are made by groups, rather than by individuals
Methods that involve combining applicant information in a statistical manner are better methods in reducing errors and predicting job performance
Various decision-making models are used
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22. Discussion Questions
What are the common decision-making errors made in employee selection? Can these be eliminated? If so, how? If they cannot be eliminated, can they be reduced? If so, how?
What is the difference between judgmental and statistical approaches to the collection and combination of applicant information?
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23. Discussion Questions (continued)
Why do organizations tend to use groups to make selection decisions? What are the advantages and disadvantages of group decision making?
Why is it better to use predictors that are uncorrelated or that have a low correlation with each other than predictors that are highly correlated with each other.
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