developing a hiring system l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Developing a Hiring System PowerPoint Presentation
Download Presentation
Developing a Hiring System

Loading in 2 Seconds...

play fullscreen
1 / 26

Developing a Hiring System - PowerPoint PPT Presentation


  • 172 Views
  • Uploaded on

Developing a Hiring System. OK, Enough Assessing: Who Do We Hire??!!. Who Do You Hire??. Information Overload!!. Leads to: Reverting to gut instincts Mental Gymnastics. Combining Information to Make Good Decisions. “Mechanical” methods are superior to “Judgment” approaches

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Developing a Hiring System' - studs


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
developing a hiring system

Developing a Hiring System

OK, Enough Assessing:

Who Do We Hire??!!

information overload
Information Overload!!
  • Leads to:
    • Reverting to gut instincts
    • Mental Gymnastics
combining information to make good decisions
Combining Information to Make Good Decisions
  • “Mechanical” methods are superior to “Judgment” approaches
    • Multiple Regression
    • Multiple Cutoff
    • Multiple Hurdle
    • Profile Matching
    • High-Impact Hiring approach
multiple regression approach
Multiple Regression Approach
  • Predicted Job perf = a + b1x1 + b2x2 + b3x3
    • x = predictors; b = optimal weight
  • Issues:
    • Compensatory: assumes high scores on one predictor compensate for low scores on another
    • Assumes linear relationship between predictor scores and job performance (i.e., “more is better”)
multiple cutoff approach
Multiple Cutoff Approach
  • Sets minimum scores on each predictor
  • Issues
    • Assumes non-linear relationship between predictors and job performance
    • Assumes predictors are non-compensatory
    • How do you set the cutoff scores?
how do you set cut scores
How Do You Set Cut Scores?
  • Expert Judgment
  • Average scores of current employees
    • Good employees for profile matching
    • Minimally satisfactory for cutoff models
  • Empirical: linear regression
multiple cutoff approach9
Multiple Cutoff Approach
  • Sets minimum scores on each predictor
  • Issues
    • Assumes non-linear relationship between predictors and job performance
    • Assumes predictors are non-compensatory
    • How do you set the cutoff scores?
    • If applicant fails first cutoff, why continue?
slide10

Multiple Hurdle Model

Finalist

Decision

Background

Interview

Test 1

Test 2

Pass

Pass

Pass

Pass

Fail

Fail

Fail

Fail

Reject

profile matching approach
Profile Matching Approach
  • Emphasizes “ideal” level of KSA
    • e.g., too little attention to detail may produce sloppy work; too much may represent compulsiveness
  • Issues
    • Non-compensatory
    • Small errors in profile can add up to big mistake in overall score
  • Little evidence that it works better
how do you compare finalists
How Do You Compare Finalists?
  • Multiple Regression approach
    • Y (predicted performance) score based on formula
  • Cutoff/Hurdle approach
    • Eliminate those with scores below cutoffs
    • Then use regression (or other formula) approach
  • Profile Matching
    • Smallest difference score is best
    • ∑ (Ideal-Applicant) across all attributes
  • In any case, each finalist has an overall score
making finalist decisions
Making Finalist Decisions
  • Top-Down Strategy
    • Maximizes efficiency, but also likely to create adverse impact if CA tests are used
  • Banding Strategy
    • Creates “bands” of scores that are statistically equivalent (based on reliability)
    • Then hire from within bands either randomly or based on other factors (inc. diversity)
slide16

Applicant Total Scores

94

93

89

88

87

87

86

81

81

80

79

79

78

72

70

69

67

limitations of traditional approach
Limitations of Traditional Approach
  • “Big Business” Model
    • Large samples that allow use of statistical analysis
    • Resources to use experts for cutoff scores, etc.
    • Assumption that you’re hiring lots of people from even larger applicant pools
a more practical approach
A More Practical Approach
  • Rate each attribute on each tool
    • Desirable
    • Acceptable
    • Unacceptable
  • Develop a composite rating for each attribute
    • Combining scores from multiple assessors
    • Combining scores across different tools
    • A “judgmental synthesis” of data
  • Use composite ratings to make final decisions
categorical decision approach
Categorical Decision Approach
  • Eliminate applicants with unacceptable qualifications
  • Then hire candidates with as many desirable ratings as possible
  • Finally, hire as needed from applicants with “acceptable” ratings
    • Optional: “weight” attributes by importance
numerical decision approach
Numerical Decision Approach
  • Eliminate applicants with unacceptable qualifications
  • Convert ratings to a common scale
    • Obtained score/maximum possible score
  • Weight by importance of attribute and measure to develop composite score
summary decision making
Summary: Decision-Making
  • Focus on critical requirements
  • Focus on performance attribute ratings
    • Not overall evaluations of applicant or tool
  • Eliminate candidates with unacceptable composite ratings on any critical attribute
  • Then choose those who are most qualified:
    • Make offers first to candidates with highest numbers of desirable ratings