Developing a Hiring System

Slides:



Advertisements
Similar presentations
2  How to compare the difference on >2 groups on one or more variables  If it is only one variable, we could compare three groups with multiple ttests:
Advertisements

Combining Test Data MANA 4328 Dr. Jeanne Michalski
Developing a Hiring System OK, Enough Assessing: Who Do We Hire??!!
Chapter 10 Decision Making © 2013 by Nelson Education.
Simple Regression Equation Multiple Regression y = a + bx Test Score Slope y-intercept Predicted Score  y = a + b x + b x + b x ….. Predicted Score 
Marketing 334 Consumer Behavior
Chapter 7 – Classification and Regression Trees
T-tests Computing a t-test  the t statistic  the t distribution Measures of Effect Size  Confidence Intervals  Cohen’s d.
Chapter Five Selection © 2007 Pearson Education Canada 5-1 Dessler, Cole, Goodman, and Sutherland In-Class Edition Management of Human Resources Second.
Aaker, Kumar, Day Ninth Edition Instructor’s Presentation Slides
A semantic learning for content- based image retrieval using analytical hierarchy process Speaker : Kun Hsiang.
INTRO TO RATING SCALES 2014 v1.0. Define Decision: Build Rating Scales 2 Identify Alternatives Identify Criteria Identify Participants Build Ratings Scales.
DEFINING JOB PERFORMANCE AND ITS RELATIONSHIP TO ASSESSMENTS.
Chapter 10 The t Test for Two Independent Samples PSY295 Spring 2003 Summerfelt.
Hiring Matrix Tutorial
© Laura Portolese Dias 2011, published by Flat World Knowledge Human Resource Management By Laura Portolese Dias 5-1.
Repeated Measures ANOVA
Office of Institutional Research, Planning and Assessment January 24, 2011 UNDERSTANDING THE DIAGNOSTIC GUIDE.
TECHNICAL APPENDIX METHODOLOGY SUMMARY FOR CPM RESEARCH Missions International PO Box – Franklin, TN –
1 Tests with two+ groups We have examined tests of means for a single group, and for a difference if we have a matched sample (as in husbands and wives)
Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.
Analysis and Visualization Approaches to Assess UDU Capability Presented at MBSW May 2015 Jeff Hofer, Adam Rauk 1.
 How to choose the best concept?  How to decide as a team?  How to document the process?
© 2005 SHRM SHRM Weekly Online Survey: March 15, 2005 Hiring Decisions Sample comprised of 282 randomly selected HR professionals. Analyzing 282 response.
Statistics for the Behavioral Sciences Second Edition Chapter 11: The Independent-Samples t Test iClicker Questions Copyright © 2012 by Worth Publishers.
One-sample In the previous cases we had one sample and were comparing its mean to a hypothesized population mean However in many situations we will use.
Selection Decisions MANA 5341 Dr. George Benson
Chapter 9 – Classification and Regression Trees
Selection 1- Measurement 2- External. Organization Strategy HR and Staffing Strategy Staffing Policies and Programs Staffing System and Retention Management.
Chapter Twelve Copyright © 2006 John Wiley & Sons, Inc. Data Processing, Fundamental Data Analysis, and Statistical Testing of Differences.
Combining Test Data MANA 4328 Dr. Jeanne Michalski
Analysis of Variance (One Factor). ANOVA Analysis of Variance Tests whether differences exist among population means categorized by only one factor or.
KNR 445 Statistics t-tests Slide 1 Introduction to Hypothesis Testing The z-test.
Assessing Recruiting Effectiveness  Cost per hire  Time to hire  Tenure of employees recruited  Job performance of employees recruited  Yield ratios.
1 Testing of Hypothesis Two Sample test Dr. T. T. Kachwala.
Statistics and Nutrient Levels Julie Stahli Metro Wastewater Reclamation District March 2010.
©2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Chapter Twelve Copyright © 2006 McGraw-Hill/Irwin Attitude Scale Measurements Used In Survey Research.
Rubrics.
Micro array Data Analysis. Differential Gene Expression Analysis The Experiment Micro-array experiment measures gene expression in Rats (>5000 genes).
Introduction to Power and Effect Size  More to life than statistical significance  Reporting effect size  Assessing power.
© 2013 by Nelson Education1 Decision Making. Chapter Learning Outcomes  After reading this chapter you should:  Appreciate the complexity of decision.
© 2013 by Nelson Education1 Selection I: Applicant Screening.
Hiring Matrix Tutorial
Marketing Research Aaker, Kumar, Leone and Day Eleventh Edition
Alternative Evaluation and Selection
Welcome Reference Checking Reducing the Risk Reference Checking
RECRUITMENT & SELECTION
Scoring the Technical Evaluation Maximum possible score
INF397C Introduction to Research in Information Studies Spring, Day 12
Human Resource Selection, 8e
Analytic Hierarchy Process (AHP)
Informed Decision Making
Dr. Salim Abdullah Alshukaili
MANA 4328 Dr. Jeanne Michalski
1. Hiring managers have the responsibility to identify and hire the most qualified candidate that “best meets the needs of that position.”
Rubrics.
Decision Focus® 6.0 DECISION ANALYSIS Decision Statement
Evaluating Recruiting Methods
MANA 4328 Dr. George Benson Combining Test Data MANA 4328 Dr. George Benson 1.
Chapter 13 Individual and Group Assessment
Last class Tutorial 1 Census Overview
Objective Decision-making
Evaluating Recruiting Methods
Chapter 13 Individual and Group Assessment
Non-Parametric Statistics Part I: Chi-Square
Doing t-tests by hand.
Chapter 14: Decision Making Considering Multiattributes
Informed Decision Making
Survey Design.
Presentation transcript:

Developing a Hiring System OK, Enough Assessing: Who Do We Hire??!!

Performance-Based Hiring: Summary

Common Decision-Making Errors Switching to non-performance factors Reverting to “intuition” or “gut feel” Aggregating across all attributes Succumbing to the “Tyranny of the Best” Rating by method

Who Do You Hire??

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 Focus on tools not attributes

A Better Approach: Focus on Attributes For each candidate, rate each attribute on each tool Desirable Acceptable Unacceptable For example, Consider Lee:

A Better Approach: Focus on Attributes For each candidate, Rate each attribute on each tool Develop composite attribute rating Combining scores from multiple assessors Combining scores across different tools A “judgmental synthesis” of data For example, Consider Lee:

A Better Approach: Focus on Attributes For each candidate, Rate each attribute on each tool Develop composite attribute rating Create a Decision Table combining the composite ratings for all applicants

Sample Decision Table

A Better Approach: Focus on Attributes For each candidate, Rate each attribute on each tool Develop composite attribute rating Create a Decision Table combining the composite ratings for all applicants Use Decision Table to make final decisions Categorical Numerical

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

Using the Decision Table 1: More Positions than Applicants

Using the Decision Table 2: : More Applicants than Positions

Numerical Decision Approach Eliminate applicants with unacceptable qualifications Convert ratings to a common scale Obtained score/maximum possible score Example: Attribute—Dependability Interview: 3.5 on a five point scale Personality Test: 8/10 Converted scores? Weight by importance of attribute and measure to develop composite score Weighted converted score = converted score X importance of attribute X importance of measure

Numerical Decision Approach

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