DOC PREVIEW
MSU PSY 255 - Selection Continued

This preview shows page 1 out of 3 pages.

Save
View full document
View full document
Premium Document
Do you want full access? Go Premium and unlock all 3 pages.
Access to all documents
Download any document
Ad free experience
Premium Document
Do you want full access? Go Premium and unlock all 3 pages.
Access to all documents
Download any document
Ad free experience

Unformatted text preview:

PSY 255 1nd Edition Lecture 11 Outline of Last Lecture I. SelectionOutline of Current Lecture II. Selecting ApplicantsIII. Bottom Line ($$$)Current LectureSelecting Applicants- Approaches:o Multiple cutoffso Multiple hurdleso Multiple regression- (combinations of the approaches are also possible)- Multiple cutoffso Cutoffs (passing scores) are established for each predictor Set subjectively (SMEs) or statistically (contrast, borderline)o Non-compensatory Must meet/exceed each cutoff (overall performance on selection battery irrelevant) Requires minimal competence in all areas- Multiple hurdleso Variant of multiple cutoffs Also non-compensatoryThese notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.o Applicants are administered individual predictors in predetermined ordero Cost effective Selecting Applicants- Multiple regression (MR)o Statistical technique that forecasts criteria using one or more predictor scores Relationships between multiple predictors and outcome when considered simultaneously Creates equation that optimally combines predictor scores to predict the criterion- MR steps: o Validation study to create regression equation (remember to cross-validate)o Collect predictor scores from applicantso Plug applicant scores into regression equation and predict criterion scoreso Rank order applicants based on predicted criterion scores and start hiring- Combining all three methodso Set critical scores on each predictor o Administer predictors in serial fashion o Create equation to predict performance (criterion score) of ‘survivors’ Bottom-Line ($$$)- So what if the selection battery predicts performance? What’s in it for the organization?- Utility analysiso Determines how useful and cost efficient the selection battery iso Involves decision accuracy (validity), base rate, selection ratio, cost- Validityo Maximize hits & correct rejects, minimize false alarms and misses- Base rateo Percentage of current employees who are successful on the job Base rates tend to be higher for easier jobs vs. more difficult ones Reflects quality of old selection methodo As base rate drops, potential utility increases- Selection ratio (SR)o Number of job openings divided by number of applicants (Available positions)/(Applicants)o As selection ratio drops, potential utility increases 10 positions, 10 applicants = SR of 1.00 5 positions, 10 applicants = SR of 0.50 1 position, 10 applicants = SR of 0.10 1 position, 100 applicants = SR of 0.01- Costo Associated with development and implementation of selection battery- Calculating utilityo Taylor–Russell tables Using validity, base rate, and selection ratio data, these tables estimate percentage of workforce expected to be successful Utility = difference between new estimated success rate and the original base


View Full Document
Download Selection Continued
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...
Login

Join to view Selection Continued and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Selection Continued 2 2 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?