Applied Psychology in Human Resource Management seventh edition Cascio & Aguinis PowerPoint Slides developed by Ms. Elizabeth Freeman University of South Carolina Upstate Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Chapter 14 Decision Making for Selection Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Why do we care about selection decisions? Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Selection decisions result in. employment. occupations. vocations Selection decisions result in employment occupations vocations careers income Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Selection decisions are critical. 1. when applicants exceed jobs. 2 Selection decisions are critical 1. when applicants exceed jobs 2. when success depends on jobs Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Classical validity assessments of selection decisions focus on. 1 Classical validity assessments of selection decisions focus on 1. accuracy of decisions or 2. prediction efficiencies Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Classical validity assessment statistics include 1. simple regressions or 2. multiple regressions Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Weaknesses of these statistics Simple regression may not consider all relevant variables Multiple regression may allow strong variables to offset weak variables Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Some placement decisions. cannot tolerate such. weaknesses in validity Some placement decisions cannot tolerate such weaknesses in validity 1. Pilots 2. Surgeons 3. Judges Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision theory addresses these weaknesses. 1. Recognizes predictions Decision theory addresses these weaknesses 1. Recognizes predictions 2. Accounts for the importance of outcomes & consequences a. individuals placed b. organizational survival c. payoffs – cost & benefit Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Selection decisions exist due to imbalances between the numbers of people applying for jobs and the numbers of jobs available to be filled Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Personnel selections involve decision makers Decision makers must predict Requiring measurements (behavioral data collection) & Subsequent forecasts (predictions) Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Traditional selections focused on technical psychometrics or classical validity studies Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-making theory focuses on outcomes & consequences to individuals selected to organizations dependent on individual performance to optimize the decision utility (cost benefits) Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Selection decisions try to maximize individual differences Goal = select those who possess the most predictive traits Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Classic Decision-Making Study Conduct job analysis Assess relationship criterion predictor Repeat the process until validation established Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Validation of Classical Selection Decisions Assumes that linear models (linear algebra) are appropriate for job-success prediction Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Efficiency of Linear Models 1. Linear decision-making models are considered robust. 2. In this sense, robust means a strong and powerful way to make a selection decision. 3. Linear models work with a. Least squares regression weights b. Subjective weighting or variables c. Unit weights for variables Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

About Unit Weights – the power of 1 Appropriate when Scaling items Populations change over time Predictors are composites Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

About Unit Weights – the power of 1 Optimal when Applied to new samples Sample < critical sample size a. N = 40, 2 predictors b. N = 105, 6 predictors c. N = 194, 10 predictors Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

About Unit Weights – the power of 1 Other strengths Do not consume degrees of freedom Estimated without error Cannot reverse true relative weights of variables Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Suppressor variables Unit weighting is simple but what about suppressor or moderator variables? Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Suppressor variables not related to the criterion may be related to the predictors act like moderators are understood to exist true effects not easy to demonstrate believed to have modest effects Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Remember Prediction outcome goals 1. identify predictor variables that 2. explain maximum variance & have no relationship to other predictors & have unique relationship to the criterion Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Can you eliminate the personal aspects of selection decisions? Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

You can not remove the personal impact. You can quantify the process to increase selection decision quality. You can combine decision data strategically. Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Data-combination Strategies A. Data measurement 1. Mechanical 2. Judgmental B. Data Collection Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Mechanical – statistical a. individual measurements ability tests, Data Measurement Mechanical – statistical a. individual measurements ability tests, aptitude assessments, knowledge inventories b. combine scores, predict outcomes 2. Judgmental – clinical a. include subjective assessments based on impressions Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Pure Mechanical (Statistical) – data is collected and Data Collection Pure Mechanical (Statistical) – data is collected and combined by equations Scorable application blanks Biographical information blanks c. Test batteries Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

2. Pure Judgmental (Clinical) – data is collected and Data Collection 2. Pure Judgmental (Clinical) – data is collected and combined judgmentally open-ended interview ratings subjective impression ratings Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

3. Modified Mechanical Strategy – Data Collection 3. Modified Mechanical Strategy – Behavior Trait Rating – interview information summarized on standard form Profile Interpretation – based on objective inventory information, interpret results without knowing or meeting a candidate i. California Personality Inventory ii. Myers Briggs Inventory Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

4. Clinical Composite Strategy – most common Data Collection 4. Clinical Composite Strategy – most common collected objectively & subjectively combined subjectively Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

5. Mechanical Composite Strategy – Data Collection 5. Mechanical Composite Strategy – collected objectively & subjectively combined mechanically, for example, multiple regression Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

With all of the data collection and options, what is best? Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

1. Accuracy of the decisions may depend on accurate weightings Consider: 1. Accuracy of the decisions may depend on accurate weightings 2. Accurate weightings are not possible subjectively (judgmentally) 3. Mechanical methods can continue to adjust findings while humans seem to be limited in ability to absorb more & more information Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Result? a. Collect data objectively b. Collect data subjectively c. Make final decisions on mechanically combined data Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Are there alternative selection decision prediction models? Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Yes, at least 3. Multiple regressions Multiple cutoffs Multiple hurdles Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Multiple Regressions Model Assumptions a. Linearity b. Trait c. Compensatory interaction 2. Larger samples better 3. Strengths – deals with a. nominal data b. nonlinear relationships c. linear & nonlinear interactions d. multiple jobs with same or different predictors Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Multiple-Cutoff Approach Assumptions a. Minimal proficiency required on one or more variables b. Curvilinear relationships 2. Setting a Cutoff One cutoff, Angoff Method or Expectancy Chart Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Multiple-Cutoff Approach Cutoff considerations 1. number of openings per position 2. number of applicants 3. ratio of successful applicants given to historical performance of incumbents 4. typical hiring rate Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Multiple-Cutoff Approach Guidelines 1. Decide - cutoff scores sensibility 2. Recognize - may not be a single best 3. Begin with a job analysis 4. Consider validity & job-relatedness of all assessments 5. Relate minimum cutoffs & performance 6. Use adequate number of judges 7. Consider statistical & legal issues 8. Set cutoffs high enough Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Multiple-Cutoffs by Angoff Method 1. Multiple judges rate item minimums with barely competent person in mind 2. Judges’ ratings per item averaged 3. Items’ averaged ratings are summed for minimally acceptable score Advantages Easy to administer Reliable as other judgmental methods Intuitive appeal as expert judges form the decisions Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Multiple-Cutoffs by Expectancy Charts Compare past performance data as comparison with a given predictor score Analyze the number of successful employees expected given selection ratios Use raw hiring data to determine Number of applicants Number hired Tenure of hires Performance of hires Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Multiple-Hurdle Approach Includes multiple-regression and multiple-cutoff decisions Adds perspective of time Temporarily or permanently hired 3. Appropriate when training is long, complex, expensive Air traffic controllers Surgeons Financial specialists Tenure track professionals Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

From Classical Validity to Decision-Theory Classical validity – explains the highest proportion of variance in selection decisions Decision-Theory –emphasizes selection ratios, base rates, and the organizational impacts of HR judgments Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Selection Ratios – applicants : hires Decision-Theory Models Selection Ratios – applicants : hires Value influenced by quotas 1. When ratio is 1:1, hire essentially every applicant 2. When the ratio is < 1, hire very few 3. Surface value may support selectivity but increases recruiting expenses 4. Flexible SRs allow satisfactory performance 5. Challenge is the statistical logic Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-Theory Models Selection Ratios – applicants to hires For example, flexible SRs should decrease erroneous acceptances (hiring the wrong candidate) & decrease erroneous rejections (not hiring a good candidate) Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-Theory Models Selection Ratios – applicants to hires With flexible SRs, most organizations concentrate on the erroneous acceptances & ignore the consequences of erroneous rejections Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-Theory Models To understand impact of erroneous acceptances and erroneous rejections, must understand SRs and base rates Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-Theory Models Base Rates (BRs) – successful hires 1. If BRs high (jobs are easy to perform), measures may not matter 2. If BRs low (jobs difficult to perform), measures may be critical 3. Predictors should be added based on their ability to increase predictions Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-Theory Models Utility Considerations Erroneous acceptances are costly a. Risk of life b. Threat of lawsuits c. Injury to existing employees Erroneous rejections hard to quantify beyond out-of-pocket expenses a. Administrative expenses b. Legal defense Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-Theory Models Utility Considerations Potential Decision-Theory Weaknesses Some people may be treated unjustly (falling just below cutoffs) To minimize, use relative decisions predict attributes do not predict measurements Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

How do you determine utility or costs to an organization? Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-Theory Utility Models (the language of business) Taylor Russell Model (TRM) includes 1. Classical validity and 2. Selection ratio (openings to applicants) and 3. Base rate (successful performers) Documents success ratios a. sensitive to contextual issues b. sensitive to organizational issues Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-Theory Utility Models (the language of business) Naylor Shine - more applicable than TRM Assumes linear relationship between validity and utility of selections Defines utility as increase in average criterion scores 3. Assumes that new predictors added to existing selection battery 4. Does not require employee classifications 5. May use when performance cannot be put into dollars (turnover prediction) Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-Theory Utility Models (the language of business) Naylor-Shine Model answers questions a. What is average performance? b. What will the mean criterion score be? c. What SR should be used? Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-Theory Utility Models (the language of business) Brogden-Cronbach-Gleser Model (BCG) Assumes linearity only for test scores & actual performance Assumes normal distribution of test scores & performance 3. Supports decisions of low validity selection when standard deviations are high Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-Theory Utility Models (the language of business) BCG Model ALSO Separates recruitment & selection costs Allows alternative SD methods 3. Integrates selection with budgets Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-Theory Utility Models (the language of business) BCG Model Weaknesses Does not look at time value of money Ignores concept of risk Ignores taxation payoffs Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-Theory Utility Models (the language of business) BCG Model consideration selection decision costs are long-term investments, not short-term a. Developmental in scope b. Productivity increases c. Labor cost reductions d. Total employees required reduced e. Financial returns Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Decision-Theory Utility Models (the language of business) BCG Model Challenges Top scoring candidates refuse offers Expected & actual performances 3. General economic conditions 4. Managerial resistance a. may not trust the motivation b. may not believe in the source c. may prefer individual responsibility d. may not understand the context Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Strategic Context Selection Profit maximization Cost minimization Assume selection systems have fixed validity & standard deviations 1. assumption may be false 2. assumption may be true 3. risks associated with either need to be considered Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

Summary Classical validity 1. measurement & prediction 2. multiple regressions 3. compensatory variables Noncompensatory models 1. Taylor-Russell 2. Naylor-Shine 3. Brogden-Cronbach-Gleser 4. useful for planning Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall

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