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【精品】PPT课件 Chapter 11 Decision MakingChapter 12 Final Match.ppt

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【精品】PPT课件 Chapter 11 Decision MakingChapter 12 Final Match.ppt

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【精品】PPT课件 Chapter 11 Decision MakingChapter 12 Final Match.ppt

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文档介绍:Chapter 11: Decision Making
Chapter 12: Final Match
Part 5 Staffing Activities: Employment
McGraw-Hill/Irwin
© 2006 The McGraw-panies, Inc., All Rights Reserved.
CHAPTER ELEVEN
Decision Making
Screen graphics created by:
Jana F. Kuzmicki, PhD
Troy State University-Florida and Western Region
Organization Strategy
HR and Staffing Strategy
Staffing Policies and Programs
Staffing System and Retention Management
Support Activities
pliance
Planning
Job analysis
Core Staffing Activities
Recruitment: External, internal
Selection: Measurement, external, internal
Employment: Decision making, final match
Organization
Vision and Mission
Goals and Objectives
anizations Model
Chapter Outline
Choice of Assessment Method
Validity Coefficient
Correlation with Other Predictors
Adverse Impact
Utility
Determining Assessment Scores
Single Predictor
Multiple Predictors
Hiring Standards and Cut Scores
Description of Process
Consequences of Cut Scores
Methods to Determine Cut Scores
Professional Guidelines
Methods of Final Choice
Random Selection
Ranking
Grouping
Decision Makers
HR Professionals
Managers
Employees
Legal Issues
Choice of Assessment Method
Validity coefficient
Correlation with other predictors
Adverse impact
Utility
Validity Coefficient
Practical significance
Extent to which predictor adds value to prediction of job ess
Assessed by examining
Sign
Magnitude
Validities above .15 are of moderate usefulness
Validities above .30 are of high usefulness
Statistical significance
Assessed by probability or p values
Reasonable level of significance is p < .05
Face validity
Correlation With Other Predictors
To add value, a predictor must add to prediction of ess above and beyond forecasting powers of current predictors
A predictor is more useful the
Smaller its correlation with other predictors and
Higher its correlation with the criterion
Predictors are likely to be highly correlated with one another when their content domain is similar
Adverse Impact
R