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© 2013 by Nelson Education 1
Decision Making
© 2013 by Nelson Education 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
© 2013 by Nelson Education 3
◦ 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
Chapter Learning Outcomes (continued)
© 2013 by Nelson Education 4
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
The Context of Selection Decisions
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Implicit theories: personal beliefs that are held about how people or things function, without objective evidence and often without conscious awareness
Selection Errors
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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
Outcomes of the Selection Process
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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
Methods of Collecting and Combining Applicant InformationTable 10.1
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Methods of Collecting and Combining Applicant InformationTable 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
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
Methods of Collecting and Combining Applicant InformationTable 10.1(continued)
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Researchers conclude that groups are generally better at problem solving and decision making than the average individual◦ Groups make better selection decisions than
individuals
Group Decision Making
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1. Think about the types of decisions you make on a daily basis. What would be some examples of routine and non-routine decisions?
2. 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?
Class Activity
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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
Incremental Validity and Cut-off Score
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Decision-Making Models
Unit and Rational Weighting Multiple Regression Model Multiple Cut-Off Model Multiple Hurdle Model Combination Model Profile Matching Model
© 2013 by Nelson Education 14
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
Making Selection Decisions
© 2013 by Nelson Education 15
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
Banding
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Selection systems are made more effective by the following recommendations:
1. Use valid selection instruments2. Dissuade managers from making
selection decisions based on gut feelings or intuition
3. Encourage managers to keep track of their own selection “hits” and “misses”
Making Selection Decisions: Conclusions
© 2013 by Nelson Education 17
4. Train managers to make systematic selection decisions
5. Periodically evaluate or audit selection decisions in order to identify areas needing improvement
Making Selection Decisions: Conclusions (continued)
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Recruitment and Selection Notebook 10.1
Making the Selection Decision1. Identify all of the sources of information
about the applicant available to you
1. Use reliable, valid selection instruments whenever possible
1. Determine which decision-making model you will use
Recruitment and Selection Notebook 10.1(continued)
4. 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
5. If using multiple cut-off or multiple hurdle models, determine appropriate cut-off scores for each predictor
Recruitment and Selection Notebook 10.1(continued)
6. Combine data from different predictors statistically to yield an overall score
7. Offer the position(s) to the candidate(s) with the highest overall score(s)
© 2013 by Nelson Education 21
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
Summary
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1. 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?
2. What is the difference between judgmental and statistical approaches to the collection and combination of applicant information?
Discussion Questions
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Discussion Questions (continued)3. Why do organizations tend to use groups
to make selection decisions? What are the advantages and disadvantages of group decision making?
4. 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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