1 Chapter 7 Staffing Decisions Copyright © The McGraw-Hill Companies, Inc

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Chapter 7

Staffing Decisions

Copyright © The McGraw-Hill Companies, Inc.

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Module 1:Conceptual Issues in Staffing

• Staffing decisions– Associated with recruiting, selecting,

promoting, & separating employees


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Staffing From Strategic Perspective

• Model 1: Staffing as person-job match

– Assumes well-defined jobs in a stable industry

• Model 2: Staffing as strategy implementation

– Staffing as support mechanism to implement a predetermined strategy

• Model 3: Staffing as strategy formation

– Identify individuals with core attributes who will help develop organizational strategy


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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


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Stakeholders in the Staffing Process

• Line managers– Seek accurate, easy-to-administer, & easy-to-defend

staffing process

• Co-workers– Among other considerations, layoff decisions have

practical & emotional consequences

• Applicants– Manner in which staffing decision carried out will be

influential in their perception of organization


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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 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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Levels of Relationship Between a Test & a Criterion


Figure 7.3Scatterplots DepictingVarious Levels ofRelationship between aTest and a Criterion

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Validity

• Selection ratio (SR)

n = number of available jobs

N = number of people 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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Effect on Selection Errors of Moving the Cutoff Score


Figure 7.4The Effect on SelectionErrors of Moving theCutoff Score

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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

• Addresses cost/benefit ratio of one staffing strategy versus another

• Base rate– % of current workforce performing successfully– If performance is high, then new system will

likely add very little to productivity


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Utility Analysis

• Assesses economic return on investment of HR interventions like staffing or training

• Calculations can be very complex


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Feelings of unfairness lead to:

• Initiation of lawsuits

• Filing of formal grievances with company representatives

• Counterproductive behavior


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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


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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


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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)

• Combination scores by regression (compensation approach)– Multiple regression analysis

• Results in equation for combining test scores into a composite based on correlations of each test score with performance score


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Relationship Between Predictor Overlap & Criterion Prediction


Figure 7.5The Relationship betweenPredictor Overlap andCriterion Prediction

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Combination Scores by Regression

• Cross-validation– Regression equation developed on first

sample is tested on second sample to determine if it still fits well


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Score banding

• Individuals with similar test scores grouped together in category (score band)

• Selection within band made based on other considerations

• Controversial


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Score Banding

• Standard error of measurement (SEM)– Provides measure of 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 staffing strategy

– However, there is no explicit prohibition of age norming


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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 not feasible

• Requires an actuarial strategy• Utility can be an issue (Cost of testing can be

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 4: Legal Issues inStaffing 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


C. Sherburne/PhotoLink/Getty Images

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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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AI Determination

• 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 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)

• Crude & can be substantially affected by sample sizes

• Burden of proof shifts to employer once AI is demonstrated


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Permissions

Slide 2: McGraw-Hill Education Digital Image Library, Keith Brofsky/Getty Images, Source Image ID: MD002231, Filename: 59212.JPG

Slide 4: Figure 7.1 from Guion, R. M. (1998). Assessment, measurement, and prediction, p. 6. Mahwah, NJ: Lawrence Erlbaum Associates. Reprinted by permission of Lawrence Erlbaum Associates, Inc.

Slide 5: Figure 7.2 from Howard, A. (1991). Current influences on the staffing process. In D. W. Bray (Ed.), Working with organizations and their people: A guide to human resources practice. New York: Guilford Press. Used by permission.

Slide 21: McGraw-Hill Education Digital Image Library, Anthony Saint James/Getty Images, Source Image ID: SP004984, Filename: SS31010.JPG

Slide 24: McGraw-Hill Education Digital Image Library, C. Sherburne/PhotoLink/Getty Images, Source Image ID: SO000477, Filename: 25057.JPG

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1 Chapter 7 Staffing Decisions Copyright © The McGraw-Hill Companies, Inc