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Psych 364 - Week 11 Notes

by: Allie S

Psych 364 - Week 11 Notes Psyc 3640

Marketplace > Clemson University > Psychlogy > Psyc 3640 > Psych 364 Week 11 Notes
Allie S
GPA 3.46

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About this Document

Psych week 11
Industrial Psychology 3640
Eric McKibben
Class Notes
Industrial Psych, psych, Clemson
25 ?




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This 6 page Class Notes was uploaded by Allie S on Monday November 9, 2015. The Class Notes belongs to Psyc 3640 at Clemson University taught by Eric McKibben in Summer 2015. Since its upload, it has received 39 views. For similar materials see Industrial Psychology 3640 in Psychlogy at Clemson University.


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Date Created: 11/09/15
Review: Utility Analysis • Assesses economic return on investment of HR interventions like staffing or training o Utility analysis can address the cost/benefit ratio of one staffing strategy versus another o Discovering worth of an employee based on Mean and STD • Includes consideration of the Base Rate, which is the percentage of the current workforce performing successfully o If performance is already high, then new staffing system will likely add little to productivity o How successful are they right now? The ROI to measure more is dramatically reduced – adding additional info will maybeeeee marginally benefit the organization if at all • Utility analysis calculations can be very complex, many other factors; uncertainty Reality: • We get messy data, and validity is somewhat hard to depict o We can correlate coefficients When we utilize this data, we can come up with: 1. False positive Applicant accepted, BUT performed poorly (Bad choice) 2. False negative Applicant rejected, BUT would have performed well (Bad choice) 3. True positive Applicant accepted AND performed well (Good choice) 4. True negative Applicant rejected AND would have performed poorly (good choice) *** Positive = ACCEPTED Negative = Rejected candidate True = AND False = BUT have a vertical line that indicates the cut off line The prediction line is sloped the horizontal line indicates actual performance ***Validity coefficient Feelings of unfairness regarding Staffing Strategies can lead to: • Initiation of lawsuits – if there are a lot of FALSE NEGATIVES, than the system seems unfair and that there is an underlying bias of some sort – PROCEDURALLY UNJUST; minority groups of some sort • Filing of formal grievances with company representatives – • Counterproductive behavior – can index based on the CPB Will index how good prediction system is Want a comprehensive idea of an individual – need the staffing model to cover everything we can think of • Problem is everyone has strengths and weaknesses; rarely do we get the ideal candidate o Most will lie in the middle and will need you to decipher which combo is going to be more effective Practical issues in staffing: • Staffing Model o Comprehensiveness § Enough high quality information about candidates to predict likelihood of their success o Compensatory § Candidates can compensate for relative weakness in one attribute through strength in another one, providing both are required by job job demands = outcomes desired * which outcomes are most important; which predictors do we look for Combining info 1. Clinical decision making • Human o Uses judgment to combine information & make decision about relative value of different candidates o Single hiring manager uses own judgment to hire § Combine all by self 2. Statistical decision making • Machine o Combines information according to a mathematical formula § Algorithm for combining info on person/ on job to find the best candidate *** Algorithm is the best – less bias 3. Hurdle system of combining scores • How we space the info out over time – all at once, or gradually? o Spacing out the predictor variable over time, creating hurdles; minimum qualifications need to be met at EACH step of the way o Ex: resume data gathered first and ONLY x candidates will make it to the next round; next hurdle is g-test; then interview hurdle • Non-compensatory strategy: individual has no opportunity to compensate at later stage for low score in earlier stage • Establishes series of cut scores – can narrow down candidates Compensatory approach • Multiple regression analysis o Results in equation for combining test scores into a composite based on correlations of each test score with performance score § Opposite of NON – may not pass hurdle, but you have another skill that would compensate for said deficiency o Cross-validation • Regression equation developed on first sample is tested on second sample to determine if it still fits well criterion – what we WANT to predict • need to establish How important each predictor is (weight) • need to look at overlap between the 2 predictors and between each and the criterion Score banding • Individuals with similar test scores can be grouped together in a category or score band • CREATE “bands” around predictions • Selection within band can be made based on other considerations • Score Banding is controversial • Score Banding uses the Standard error of measurement (SEM) for the test o SEM provides a measure of the amount of error in a test score distribution o Function of reliability of test & variability of test scores 2 types of Score Banding: 1. Fixed band system o Candidates in lower bands not considered until higher bands have been exhausted 2. Sliding band system o Permits band to be moved down a score point when highest score in a band is exhausted Primary reason for score banding is to help with diversity within an organization 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 a staffing strategy  


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