Employee Selection and Staffing

1
Employee Selection and Staffing

• MGRS 467
• Dr. Yvonne Stedham

2
Pre-requisites

• MGRS 323 Organizational Behavior
• MGRS 467 Human Resource
• Management

3
Course Materials available on

• http//www.scsr.nevada.edu/ystedham/

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Employee Selection

• Outline -- Session 1
• Personal Introduction
• Course Introduction
• Content Overview
• Format Overview (Syllabus)
• External Environment
• The Labor Market (Demand and Supply)
• The Legal Environment

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Introductions

• Instructor
• Students Table Tents
• Name and Major
• Expected graduation
• Work Experience
• Interest in HR
• Aspirations

6
Relevance of this Course

• What is HRM? HRM Functions?
• Why a course in Selection?
• What is the relationship between HR and
  organizational performance?

7
HR and Organizational Performance

• What is an organization?
• What is organizational effectiveness?
• How does HR contribute?

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HR and Org effectiveness

• Individual effectiveness is the foundation for
  organizational effectiveness.
• Individual effectiveness depends on .

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HR and Org effectiveness

• Individual effectiveness
• f(Ability,Motivation)
• Performance
• Ability Motivation

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HR and Org. Effectiveness

• Match
• Individuals (Knowledge, Skills, Abilities)
• with
• Jobs (Requirements and Rewards)

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The HRM Framework
12
Session 2

• Handouts
• Readings Sign-up
• Readings
• WSJ and Fortune Sign-up
• Review
• Introductions Personal and Course
• Course Content

13
The HRM Framework
14
HR and Org effectiveness

• Individual effectiveness
• f(Ability,Motivation)
• Performance
• Ability Motivation

15
Course Format

• Syllabus

16
Session 3

• Readings Sign-up
• Extra Credit
• Today The HR Department and The External
  Environment (The LM)
• Review
• The HRM Framework Four Elements
• Course Format Syllabus
• What do we know about HR?

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What do you know about selection?

• Answer the following questions!

18
Session 4

• Quiz Reading 1 The Tie that Binds
• NNHRA Speaker
• Readings Sign-up
• Extra Credit
• Today The HR Department and The External
  Environment (The LM)
• Review
• What do we know about HR?

19
Competing in the Third Wave

• What is the third wave?
• Implications for HR?
• Implications for selection?

20
Session 5

• IGT Internship Compensation Megan LaKruse
  448-0350
• MHRA Next Meeting September 25 at 1215 p.m. in
  AB 210/ SHRM
• Current Issue HR represents the focus on equal
  and fair treatment .. Internationally
• President Bush 530p.m.
• What do we know?
• Results and Impact?
• Cause .. Reason? Purpose?
• Reaction? Purpose?

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Session 6

• Reading 2 today
• Questions for Reading 4
• Quiz on Reading 3
• Today External Environment
• The Economic Environment
• The Labor Market
• The Legal Environment

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The Staffing Function

• Recruitment and Selection
• Recruitment Generating a pool of qualified
  applicants
• Selection Assessing/Measuring Applicant KSAs
  Development of KSA Measures

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Selection

• The most important HR function
• Budget and time spent
• Definitions
• Staffing
• Mutual process by which
• the individual and the organization become
  matched to
• form the employment relationship.
• Mutual Process Series of interrelated activities
  - R, S, DM, job offers, hiring.

24
Selection

• Definitions (continued)
• Selection
• The process of obtaining
• and using information
• about job applicants
• to determine who should be hired.
• Focus here is on how to collect relevant info on
  applicants KSAs.

25
HR Department and Its Influence

• Department vs Functions/Activities (Japan,
  Europe)
• Influence of organizational units on
  organizational decisions two viewpoints
• Strategic contingencies
• Resource dependency

26
Role of HR

• The Human Equation
• Putting people first
• Strategy implementation

27
External Environment for HR and Selection

• Economic Conditions
• From land based to capital based to knowledge
  based
• From agriculture to factory to computer
• Information Age Implications for demand for
  labor - Types of jobs and KSA requirements
• Implications for selection KSA Assessment
• Management of Knowledge Workers
• Economic growth New Economy
• International competition

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Economic Conditions

• Old economy Mass production high volume and
  efficiency
• New economy current Info age trans
• private sector product - service and quality
  variety and choice customization convenience
  timeliness
• public sector taxpayer demand .. Education,
  healthcare, welfare, competition ? KSA
  requirements

29
External Environment

• Labor Market
• Demand Derived Demand
• Job growth quantity about 20, service industry
• Quality - Types of jobs and KSAs
• Supply Workforce number and composition
• ASAP survey

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External Enviro

• Supply- Workforce characteristics
• Values Psychological Contract - Reading
• Generations at work
• WWII Generation 60
• Baby Boom 40-60
• Generation X 20-40
• Millennial Generation birth-20 (Gen Y)

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Generations

• WWII
• Outlook practical
• Work Ethic dedicated
• View of Authority respectful
• Leadership by hierarchy
• Relationship personal sacrifice
• Perspective Civic

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Generations

• Baby Boom
• Outlook optimistic
• Work Ethic driven
• View of Authority love/hate
• Leadership by consensus
• Relationship personal gratification
• Perspective team

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Generations

• Generation X
• Outlook skeptical
• Work Ethic balanced
• View of Authority unimpressed
• Leadership by competence
• Relationship reluctant to commit
• Perspective self

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Generations

• Millennials
• environmentally conscious
• connected
• more tolerant of differences
• generally optimistic
• achievement oriented
• team players
• sociable
• want to fit in not revolutionize

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External Enviro Summary

• Economic Conditions
• Information Age
• Management of Knowledge Workers
• Job growth
• The New Economy
• Implications for Selection

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Review - External Enviro

• Labor Market
• Demand
• - QUN and
• - QUL - Labor Shortage
• Supply
• - QUN and
• - QUL - Composition and KSA type and level and,
  needs and values of employees

37
Review

• HRM Framework
• HRM Framework is implemented through an HR
  department
• The HR Department and Its Influence
• Function/Activities
• Influence depends on what

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Review

• Labor Market
• Supply - Workforce
• Diversity of values
• Generational Differences
• WWII
• Baby Boomers
• Generation X
• Generation Y

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Review

• Psychological Contract -- Today
• Legal Conditions relevant to selection
• Employment Contract
• Employment-at-will
• Assignment back
• Common Law refers to laws applied in the
  English-speaking world when there were few
  statutes. Courts wrote opinions explaining the
  bases for their decisions -- these opinions
  became precedents for later decisions in similar
  cases

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Session 7

• Review External Environment
• The Economic Environment
• The Labor Market
• The Legal Environment
• Hand back Readings 1 and 2
• Quiz on Reading 3
• Questions for Reading 4 next time
• Case Review next time
• Today Legal Environment

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Review

• Labor Market
• Supply - Workforce
• Diversity of values
• Generational Differences
• WWII
• Baby Boomers
• Generation X
• Generation Y

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Legal Environment

• The Employment Relationship
• Psychological Contract
• Employment Contract
• Formal agreement, voluntary Defines and governs
  the terms and conditions of the employment
  relationship promises and expectations change
  with time
• Written or oral --- both are legally enforceable

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Employment at Will

• Reading 2
• Right of both parties to terminate the employment
  relationship
• If set-term contract Termination for
• Just cause
• Failure to perform
• If indeterminate-term contract --- employment
  at will (common law) most are at-will.

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Session 8

• Quick Decision Reading
• Hand in Case Review
• Hand in Answers to Reading 4
• SH Exercise for next time
• Kristin - Article
• Review External Environment
• The Legal Environment
• Today Legal Environment
• EEO Laws
• Readings 3 and 4
• BFOQ Exercise

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Workplace Torts

• Breaches of legal duty by ER when establishing
  or modifying the initial relationship (common
  law)
• Tort civil wrong violation of a duty by the ER
  that leads to harm or damages suffered by others
• Examples
• 1. Fraud or misrepresentation lie/mislead
  applicant when communicating conditions and terms
  -gt ER violates a duty to be truthful in the
  presentation of information
• 2. Negligent hiring ER violates duty to protect
  Ees and customers against unreasonable and
  foreseeable risk of harm

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Need for Laws and Regulations

• Balance of Power ? Laws limit discretion of ER in
  establishment of terms and conditions
• Protection of EEs
• Employment Standards Minimum acceptable terms
  and conditions of employment min. wage,
  overtime, safety and health (FLSA 1938, OSHA)

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Need for laws

• Individual Rights Labor Relations, Civil Rights
  Protection, Restrictions on employment-at-will
  implied contract

48
Legal Environment

• Consistency of Treatment Procedural justice ?
  Standardized Systems
• Protection of ERs
• Permissble and impermissible practices CRA
  specifies what is OK e.g., to use ability tests
• Administrative predictability and stability

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Sources of Laws and Regulations

• Common Law England Court-made Law Case-by-case
  decisions ? Precedence (Germany and other country
  code-based law) States develop and administer
  own common law.
• Constitutional Law Supersedes Prohibits
  deprivation of employment right without due
  process.

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Legal Environment

• Statutory Law Derived from written statutes that
  are passed by legislative bodies (Federal
  Congress State Legislature/Assemblies Local
  Municipal/Councils)
• Agencies Interpret, administer, enforce law. DOL
  (OFCCP) EEOC FEP publish rules and regulatory
  guidelines that are given great deference by
  courts. Federal Register Code of Federal
  Regulations.

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EEO Framework - Specific Laws

• I. U.S. Constitution
• 5th Amendment
• Due Process of law --- Prohibition upon federal
  government no person shall be deprived of life,
  liberty, or property does not speak directly to
  specific subjects such as employment ? Courts
  prefer to defer to existing statutory laws
  because it is more specific!!
• 14th Amendment
• Prohibition for States to enacts any law that
  does not guarantee equal protection for all.

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II.Statutory Laws

• Civil Rights Act 1866
• Right to make and enforce contracts for
  employment for all citizens as enjoyed by white
  citizens.
• Civil Rights Act of 1871
• Right to sue if deprived of any rights or
  privileges guaranteed by the Constitution and
  laws for ALL citizens. Must show intention.
• Equal Pay Act 1963
• Equal pay for equal work regardless of SEX
  (female employees only) amendment to FLSA .

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Session 9

• Quick Decision Reading
• Case Review Next Time and Statistics
  Assignments
• Reading 3 today and 4 next time
• Articles?
• Review External Environment
• The Legal Environment Workplace Torts FLSA,
  NLRA Constitution Amendments Early Civil
  Rights Acts EEO Framework EPA CRA 1964 TVII
• Today Legal Environment
• Title VII
• BFOQ Exercise SH Exercise
• SH Reading Reading 4 (Cassandra and Sarah)
• ADEA
• ADA Reading 3

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EPA

• Equal pay for equal work regardless of SEX
  (female employees only) amendment to FLSA .
• Equal Work Substantially similar
  Requirements concerning skill, effort,
  responsibilities, working conditions.
• Exceptions Seniority Merit Quantity of
  production
• Note If in violation of EPA, ER may not LOWER
  wages.
• Consider --- Internal equity and job evaluation
  Comparable worth.

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Title VII of CRA 1964

• Coverage ERs with 15 or more employees Federal,
  State, Local governments Educational
  Institutions Employment Agencies Labor Unions
• Not covered Until recently Congress Private
  Clubs Religious Organizations.
• CRA 1964 Several Titles each focusing on
  discrimination in a different sectors of
  society (education, right to vote, ) Title VII
  focuses on discrimination in employment.

56
Title VII

• Enforcement EEOC
• Contents of TVII
• 703 (a) Employer may not discriminate on the
  basis of race, color, national origin, sex, and
  religion in any employment decision.

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Title VII

• Color White, black, yellow, brown, red.
• Race Local geographic or global human population
  distinguished by genetically transmitted physical
  characteristics Caucasian Negro Hispanic
  Oriental Indian.
• National Origin Citizenship Heritage Any
  country, nation.
• Religion All kinds not associated with any of
  the other characteristics Christian, Hindu,
  Muslim, Buddhist.

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Title VII

• 703 (b) . Nondiscriminatory apprenticeship
  program
• 704 (a) . Unlawful to discriminate if opposed
  unlawful employment practice assisted in TVII
  investigation.
• 704 (b) . Prohibits ads concerning employment
  indicating preference for any of the prohibited
  factors.
• 1978 Amendment
• Pregnancy Discrimination Act --- prohibits
  discrimination on the basis of pregnancy,
  childbirth, or related condition. Reinstatement
  right for similar position no loss of seniority
  coverage of disability insurance.

59
Title VII

• Exemptions that are written into the law
• Discrimination on the basis of the protected
  factors is permissible when such qualification
  is a bona-fide occupationl qualification (BFOQ)
  reasonably necessary to the operation of that
  particular business or enterprise burden of
  proof is with ER very narrowly interpreted ---
  preferences of ER, coworkers, clients are
  irrelevant.
• Seniority Systems Bona fide seniority or merit
  systems are lawful if no intention to
  discriminate job or departmental systems are not
  seen as bona fide, plant or company-wide
  systems are seen as bona fide.

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Exemptions to TVII

• Testing Employer may give and act upon
  professionally developed ability tests if they
  are not used to discriminate on the basis of the
  protected factors.
• Preferential Treatment It is unlawful to
  interpret TVII as requiring preferential
  treatment of individuals of protected groups -
  reverse discrimination
• National Security Discrimination is permitted

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Further TVII Issues

• Fetal Protection -- Johnson Controls 1991 An
  employers exclusion of fertile women, but not
  fertile men, could not be justified on grounds
  that the rule protected the womans reproductive
  capacity and the physical welfare of the fetus.
  The safety qualification is limited to those
  instances where sex or pregnancy presents danger
  to customers or third parties. A fetus is not a
  third party whose safety is essential to the
  operation of the employers business, and thus
  cannot be the basis of a BFOQ.

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Sexual Harassment

• Unwelcome sexual advances in exchange for a
  favorable employment condition. Quid pro quo
  hostile work environment sexual harassment.
  Employer is liable. Pattern of behavior. Policy
  and process. Onclae v. Sundowner --- same sex.
  Faragher v. Boca Raton --- ER liable even if the
  employer had no knowledge of the harassment.
  Burlington v. Ellerth allows employers to be sued
  for quid pro quo even if the employee suffered no
  tangible loss of job benefits for declining the
  supervisors sexual advances

63
Session 10
64
Executive Order 11246

• Contractors doing business with federal
  government ( amount of contract specified). Same
  provisions as TVII AND requires contractors to
  develop affirmative action plans Formal,
  specific personnel programs that are designed to
  increase the participation of protected groups.
• 1967 sex-based discrimination added as
  prohibited

65
Age Discrimination in Employment Act 1967

• Amended 1986. Protects EEs and applicants who are
  40 years old and above (no upper limit).
• No mandatory retirement age (except law
  enforcement officers, firefighters, tenured
  professors, executive under certain conditions,
  top policy makers.)
• No reverse discrimination.

66
EEO Legislation - How effective?

• EEO Laws clearly address societal problems ---
  safeguarding fair treatment in employment of
  traditionally disadvantaged groups.
• Hire the most qualified applicant -- the role and
  effect of stereotypes

67
Review

• Statutory Laws
• Early Civil Rights Acts
• Equal Pay Act
• Title VII of CRA 1964
• Coverage
• Who is protected?
• How?
• Pregnancy Dicrimination Act 1978

68
Review

• Exemptions BFOQ, business necessity, seniority
  system, testing
• Preferential Treatment and Reverse Discrimination
• Fetal Protection
• Sexual Harassment (Training Handout)

69
Review

• Readings
• Employment at Will and recent Legal Developments
• Sexual Harassment - Assignment back

70
Executive Order 11246

• Contractors doing business with federal
  government ( amount of contract specified). Same
  provisions as TVII AND requires contractors to
  develop affirmative action plans Formal,
  specific personnel programs that are designed to
  increase the participation of protected groups.
• 1967 sex-based discrimination added as
  prohibited - Executive Order 11375
• AAP and reverse discrimination

71
Age Discrimination in Employment Act 1967

• Amended 1986. Protects EEs and applicants who are
  40 years old and above
• (no upper limit). No mandatory retirement age
  (except law enforcement officers, firefighters,
  tenured professors, executive under certain
  conditions, top policy makers.) no reverse
  discrimination.

72
Age Discrimination in Employment Act 1967

• Amended 1878, 1986.
• Protects EEs and applicants who are 40 years old
  and above (no upper limit).
• No mandatory retirement age (except law
  enforcement officers, firefighters, tenured
  professors, executives under certain conditions,
  top policy makers.)
• No reverse discrimination.

73
Americans with Disabilities Act 1990

• Since 1994 covers Ers with 15 or more Ees.
• 43 mill. Disabled Americans.
• Protects
• Physical or mental impairment that substantially
  limits one or more life activities (walk, see,
  ..)
• Record of impairment
• Regarded as having impairment
• about 1,000 disabilities (affective disorders,
  biochemically based disorders - AIDS, Cancer,
  Anxiety Disorders, Eating Disorders, Infertility,
  Epilepsy)
• Disability evaluated with adjustive equipment
  (glasses)

74
ADA

• How it protects
• .Punitive damages
• .Essential job functions
• .Reasonable accommodations
• .Restructuring of physical facilities
• .Perceptual restructuring
• 1994 5,500 complaints (25 more than were
  expected)
• ADA Reading - Quiz Back

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ADA

• cultural change education vs compliance
• Be reasonable, thoughtful, caring, and you can
  comply
• Janet Reno

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Other Laws

• Rehabilitation Act 1972
• Vietnam Era Readjustment Act 1974

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Family and Medical Leave Act 1993

• Employers with more than 50 employees have to
  provide 12 weeks of unpaid leave for family or
  medical emergencies.
• Employer must guarantee the employee the same or
  a comparable job. The employer must also pay the
  health-care coverage for the EE --- which the EE
  has to be back if he/she fails to return to work.
  ERs are allowed to exempt key employees
  defined as the highest paid 10 of their work
  force whose leave would cause substantial
  economic harm to the employer. Also exempt are
  EEs who have not worked at least 1,250 hours (25
  hrs a week) in the previous 12 months.

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Session 11

• Stuff back
• Adjustements to syllabus
• No SH Video
• Stat Assignment 1 due October 9th
• Stat Assignment 2 due October 16th
• Review
• Statutory Laws Who covered Who protected, How
  protected
• Readings ADA SH
• Exercises BFOQ SH
• Cases
• Finish Legal Enviro

79
Enforcement of Laws and Court Process

• Filing a Discrimination Complaint
• Local EEO Agency
• NERC (Nevada Civil Rights Commission)
• EEOC
• Investigation
• Right to sue letter

80
Evidence of Discrimination

• Intentional Discrimination
• Disparate Treatment different standards applied
  to various groups
• Adverse Impact same standards are applied but
  disproportionately less minority applicants are
  selected

81
Session 12

• MHRA Tuesday, October 9, 400pm AB 209
• Speaker
• October 18 Midterm October 9 Study Guide
• Graduate Project
• Stat Assignment 1 due October 9th
• Review
• Cases
• Finish Legal Enviro

82
Federal Court Process

• PRESENTATION OF EVIDENCE IN
• TITLE VII CASES
• Burden of Proof
• Plaintiff ? Defendant ? Plaintiff
• Prima Facie Evidence
• 1. Disparate Treatment 1. Job-based/legitimate 1.
  Defendant explanation pretext true
  McDonnell Rule 4 conditions reason was
  rejection for prejudice
• 2. Adverse Impact 2. Business Necessity, 2.
  Other method
• 80 or 4/5 Rule BFOQ, Validation

83
Disparate Treatment 4 Conditions- McDonnel Rule

• Plaintiff must show
• belongs to protected class
• applied and was qualified for the job
• despite the qualifications - was rejected
• position remained open and the employer continued
  to seek applications from persons with the
  complainants qualifications
• Applied also for ADEA cases

84
Adverse Impact 80 or 4/5 Rule

• Selection Ratios
• Number of nonminority applicants selected
• DIVIDED BY
• Number of nonminority applicants applied
• THIS IS
• Nonminority selection ratio
• Number of minority applicants selected
• DIVIDED BY
• Number of minority applicants applied
• THIS IS
• Minority selection ratio

85
Adverse Impact

• Compare the two selection ratios
• If the ratio for nonminorities is smaller there
  may be evidence of discrimination
• If the ratio is less than 80 or 4/5 of the
  nonminority ratio, then there is evidence of
  adverse impact (because the difference in the
  ratios is statistically significant)

86
Adverse Impact - Example

• 100 White applicants
• 100 African American applicants
• 20 of the White applicants are selected
• 5 of the African Americans are selected
• 20100 .2 Nonminority Selection Ratio
• 5100 .05 Minority Selection Ratio
• .05 .2 .25 This does not meet the 80
  rule!

87
Adverse Impact - Example

• 100 White applicants
• 100 African American applicants
• 20 of the White applicants are selected
• 16 of the African Americans are selected
• 20100 .2
• 16100 .16 AND .16 .2 .80 meets the 80
  rule BUT 16 80

88
General Statistical Evidence for Discrimination

• Stock Statistics
• of women managers in org.
• DIVIDED BY
• of skilled women managers in the work force
• Total of managers in the org.
• DIVIDED BY
• Total of skilled managers in the work force
• What is the relevant labor market?
• EEO 1 form

89

• Flow Statistics
• of nonminority applicants selected
• DIVIDED BY
• of nonminority applicants
• of minority applicants selected
• DIVIDED BY
• of minority applicants
• When is the difference between these two ratios
  significant?
• .80 or 4/5 rule
• Standard Deviation Rule

90
Standard Deviation Rule

• Provides a rule of thumb to judge whether or not
  the of minorities selected is representative of
  their proportion in the applicant pool.
• S.D. Square Root of
• Total of minority applicants/Total applicants
• MULTIPLIED BY
• Total of nonminority applicants /Total
  applicants
• MULTIPLIED BY
• Total of persons selected
• The number of minorities selected should be in
  the following range
• - 2S.D. lt Mean lt 2S.D.

91
Landmark Selection Cases

• Page 66
• Resulted in establishing burden of proof
  requirements

92
Session 13

• Graduate Project
• Review
• Definition of Discrimination
• Stock Statistics and Flow Statistics
• Landmark Selection Cases
• Measurement in Selection
• Principles
• Reliability
• Validity

93
Review - Measurement

• Why measurement in selection?
• How can we mess up?
• How to capture applicant KSAs? Why is this
  difficult?
• We need criterion and predictor measures.
  Explain.
• What are measurement scales? What type of
  measurement scales are distinguished?
• What is a frequency distribution? Measures of
  central tendency and variation?
• Describe the characteristics of the Normal
  Distribution.
• What is the purpose of Hypothesis Testing?

94
Measurement in Selection

• I Overview
• Selection decisions are based on what
  information?
• Purpose is to ..

95
Measurement in Selection

• I Overview
• Selection decisions are based on what
  information?
• Purpose is to match the ind and the job
• Need information about both
• JD -gt KSAs required for the job
• ?? -gt KSAs of the individual
• How can we mess up?
• Measure irrelevant KSAs
• Measure KSAs inaccurately

96
How can we accurately capture applicants KSAs?

• Ask Observe Test
• We must determine the type and level of KSAs
  that applicants have.
• The assumption is that the higher the level of
  KSAs the higher the level of predicted
  performance.
• Level? Measurement Quantification

97
Definition of Measurement

• Application of rules for assigning numbers to
  objects to represent quantities of attributes.
• Differences between applicant scores are due to
  actual differences in KSAs.
• Rules
• Specified algorithms to assign numbers (She is a
  10) same results by different users

98
Measurement

• Attributes of an object Physical and
  psychological which is are intangible and must
  be inferred from indicants of these objects.
• Criterion measure or definition of what is meant
  by employee success on the job it is the
  dependent variable to be predicted by KSAs
  e.g., employee behaviors, attitudes, supervisor
  ratings
• Predictorsindicants of relevant attributes
  predict criteria
• Must be important to the job (job related)
• Must be measures of attributes that are
  identified as critical to job success

99
II Measurement and Individual Differences

• Scales of Measurement
• Measurement is prerequisite for any statistical
  analysis how precisely can we measure can we
  detect small differences (classification of
  success as yes or no OR degree of success??)
• Scale
• Means by which individuals can be distinguished
  on a specific variable
• Nominal scale Scale composed of mutually
  exclusive categories (sex, race, job class) the
  numbers are labels only frequencies.

100
Scales

• Ordinal scale Ranks objects (hi, lo)
  differences between numbers yield additional
  information but not on the magnitude of the
  differences among ranks e.g., percentile
  (represents the proportion of persons taking a
  test who made below a given score 70th
  percentile means that 70 scored lower and 30
  scored higher does not tell how much higher and
  lower)
• Interval scale Arbitrary no absolute zero
  interpretation of differences 40 vs 80 points
  does not mean 2 times the level of skill e.g.,
  zero on a test for math skills does not mean that
  the individual has zero math skills
• Ratio scale physical measures (height) and
  counting has absolute zero.

101
Standardization of Selection Measures

• Definition Systematic instrument, technique, or
  procedure for assigning scores to a
  characteristic or attribute of an individual
• Detect a true difference
• Standardized if Content measures by same
  information Administration - information
  collected the same way Scoring rules for
  scoring are pre-specified
• Individual Differences
• Applying the scale, each applicant receives a
  score how do we interpret the scores? What do
  they mean?

102
Interpreting Individual Scores

• Frequency Distribution
• How many time did we get each score? First
  understanding of what our sample (applicant pool
  looks like) did applicants tend to score higher
  or lower or are scores evenly distributed?
• Frequency distribution is a frame of reference to
  give meaning to scores.
• Most characteristics are normally distributed
  bell curve! That means that most applicants score
  around average (have an average level of the
  characteristic, a few have more and a few have
  less.

103
Distributions differ with respect to

• Central tendency
• Mean
• Mode most often observed score
• Median 50 of the scores are above and 50 are
  below this score
• Variation Mean of squares of the deviation
  scores (variance) depends on the extent to which
  scores cluster together the square root of the
  variance is the standard deviation a large
  standard deviation means that the scores a widely
  spread around the mean, a small standard
  deviation means that the scores are clustered
  around the mean

104
Skewness and Kurtosis

• SkwenessScores are symmetrically or
  asymmetrically distributed around the mean if
  the scores are symmetrically distributed then the
  mean median the distribution is positively
  skewed if the bulk of the scores is above the
  mean and the mean is larger than the median
  negatively skewed if the bulk of the scores is
  below the mean and the mean is smaller than the
  median.
• Kurtosis Peaked or flat

105
Probability Distributions

• In order to draw conclusions about the scores
  that applicants receive we have to evaluate
  whether our results are statistically
  significant we want to make inferences from our
  sample about the population statistical
  significant results are not random but are truly
  describing the population.
• Probability distribution or probability density
  function the normal distribution is a
  theoretical density function
• Decreasing probability values when the variable
  values grow extreme from the mode
• It is symmetric if meanmodemedian
• Entire area under the curve 1.00

106
Session 14

• Important Dates
• October 16 Statistics Assignment 2
• October 18 Midterm Exam (Study Guide )
• October 30 Quiz on Reading 5 JA
• Graduate Projects
• Cassandra Selection Methods for Executives in
  Japan, Australia, the UK, and the U.S.
• Mike - Interviewing and Organizational Fit
• Ben Suspect Selection Methods
• Review
• Measurement in Selection
• Principles Hypothesis Testing
• Reliability

107
Review

• How do we evaluate whether two variables are
  related to each other?
• Why and how do we determine whether our result is
  statistically significant?
• Why do we need to evaluate the quality of our
  measures?
• What does it mean when a measure is reliable?
• How do we assess whether a measure is reliable?
• What does a p-value of .02 imply?

108
Tests and Reference Sources

• Buross Mental Measurement Yearbooks
• The Mental Measurement Yearbooks Database
• Journals

109
Normal Distribution

• Standardized normal distribution
• mean 0 and SD 1
• 68 of the scores are within and 1 SD around
  the mean
• In selection we assume that most characteristics
  that we measure are normally distributed in the
  population that means if had an endless number
  of observations our frequency distribution would
  look like a normal curve
• This is important because evaluating the
  statistical significance of what we are
  interested (hypotheses testing) is based on the
  assumption of a normal distribution.

110
Normal Distribution

• If we assume a normal curve we can calculate
  z-scores that means we can transform our raw
  scores (the score that the applicants received)
  into a score on the normal curve by deducting
  the mean from each raw score and dividing that
  number by the SD (this is called the Z score)
• so for each raw score we get a z score that is
  important because we can now more simply
  calculate other statistics such as correlations

111
Session 15

• Important Dates
• October 18 Midterm Exam
• October 30 Quiz on Reading 5 JA
• Review and Assignment 1
• Measurement in Selection
• Principles Hypothesis Testing
• Reliability

112
Hypothesis Testing

• In order to draw valid conclusions from our
  sample we must show that our results are
  statistically significant (representative of the
  population) and not random.
• We would like to reject the null hypothesis which
  says that our results are not truly reflecting
  the population
• For example We want to conclude that the
  correlation between test scores and performance
  that we got for our sample is true
• Null Hypothesis rxy 0 which means that there
  is no relationship between x (test scores) and y
  (performance score).

113
Hypothesis Testing

• If H0 is true and we reject it we make a Type I
  error which would be bad and we want to avoid it
• Therefore, we allow only a minimum level of error
  in rejecting the H0 (traditionally .05 or .01
  this is your alpha level).
• Based on the observed correlation and the number
  of observations in our sample we calculate a
  t-statistic. We then find the corresponding
  values, based on the sample size and the alpha
  level in the table for the t-distribution.
• If our obtained t-value is larger than the value
  in the table then our result is significant and
  we can reject the notion that there is not really
  a relationship between the two variables.

114
p -Value

• P value for a sample outcome is the probability
  that the sample outcome could have been more
  extreme than the observed one.
• Large p-values support H0 while small p-values
  support the alternative hypothesis. Compare the
  p-value to the specified alpha risk. If p lt alpha
  then conclude Ha (significance)

115
Stat Assignment 1

• Descriptive Statistics
• Two employment tests with scores for 15 employees
• Which test should be used? Which applicant should
  be hired?
• Determine the correlation between test scores and
  performance. The magnitude and significance of
  the correlation are used to determine which test
  should be used.

116
Assignment 1

• Purpose Review of Statistical Concepts and
  Overview of Measurement Concepts
• Test A and Test B - measure the same KSA
• Type of Data -- Interval, no absolute zero
• Descriptive Statistics - Range A 18-47 (29) and
  Range B 10 - 27 (17) gt SD for A gt than for B
• Interval size for Test B 4 or 3 points per group
  (175) Test A 6 points
• Normally distributed test scores VS our test
  results
• Negatively skewed mean (31.5) lt median (32.5)
• Significance t-test and p-value

117
Quality of Measures Reliability

• How good a measure is my test? To what extent
  does the measure accurately capture the KSA we
  are interested?
• The scores obtained on a measure are
• X obtained X true X error
• If there was no error in the measure, every time
  we apply the measure to the same person we should
  get the same score.
• A reliable measure is a consistent measure.
• The reliability of a measure reflects the
  measures consistency.

118
Reliability

• Three methods to evaluate the reliability of a
  measure
• Each method focuses on a different source of
  measurement error. Measurement error are those
  factors that impact the obtained score but are
  not at all related to the attribute that is being
  measured.
• The methods
• Test-Retest Reliability
• Parallel or Equivalent Forms Reliability
• Internal Consistency Reliability
• Split-Half and Odd-Even Cronbach Alpha
• Spearman-Brown Adjustment

119
Spearman-Brown Formula to Correct a Split-Half
Reliability Coefficient

120
Reliability

• The conclusion that a measure is reliable can
  only be drawn if, and only if, the reliability
  coefficient (a correlation coefficient) is
  statistically significant (as determined by a
  t-test.

121
Meaning of Reliability Coefficient

• The extent (in percentage terms) to which
  individual differences in scores of a measure are
  due to true differences in the attribute
  measured and the extent to which they are due to
  chance error

122
Reliability

• Interpretation of the reliability coefficient
• The reliability coefficient is equal to the
  correlation coefficient between the obtained and
  the true score squared page 141
• Acceptable magnitude of reliability
• The standard error of measurement - is the amount
  of error to be expected in an individuals score.
  
• We calculate the standard error of measurement as
  the SD of the sample multiplied by the square
  root of 1 minus the reliability coefficient

123
Standard Error of Measurement

124
Reliability

• Important
• The difference in the score between two
  applicants is only significant if it is at least
  two times the standard error of measurement.
• Example
• The standard error of measurement for a test is
  1.5. Candidate A scores 18, candidate B scores 24
  - does candidate B really have more of the
  attribute that is being measured?

125
Assignment 2

• Reliability of written test
• Test - Re-test
• Internal Consitency - Split Half
• Reliability of the final interview
• Interrater
• Screening with supervisor
• Screening with technician
• SEM - which candidate to choose

126
Quality of MeasuresValidity

• Validity in Selection concerns the following
  question How appropriate is it to make
  inferences from the scores on a measure to
  performance?
• Is the score a good predictor of performance? Is
  the score actually related to future performance?
• Relationship between reliability and validity

127
Quantitative Relationship between Validity and
Reliability

128
Validity

• Three methods to evaluate the validity of a
  measure.
• Criterion-Related (Empirical) Validity
• Predictive Validity
• Concurrent Validity
• Content Validity
• Construct Validity

129
Major Steps in Conducting Concurrent Validation
Studies

• Conduct analyses of the job
• Determine relevant KSAs and other characteristics
  required to perform the job successfully
• Choose or develop the experimental predictors of
  these KSAs.

130
Major Steps in Conducting Concurrent Validation
Studies

• Select criteria of job success
• Administer predictors to current employees and
  collect criterion data
• Analyze predictor and criterion data

131
Major Steps in Conducting Predictive Validation
Studies

• Conduct Analyses of the job
• Determine relevant KSAs and other characteristics
  required to perform the job successfully
• Choose or develop the experimental predictors of
  these KSAs.

132
Major Steps in Conducting Predictive Validation
Studies

• Select criteria of job success
• Administer predictors to job applicants and file
  results
• After passage of a suitable period of time,
  collect criterion data
• Analyze predictor and criterion data

133
Requirements for a Criterion-Related Validation
Study

• The job should be reasonably stable and not in a
  period of change or transition
• A relevant, reliable criterion that is free from
  contamination must be available or feasible to
  develop

134
Requirements for a Criterion-Related Validation
Study

• It must be possible to base the validation study
  on a sample of people and jobs that is
  representative of people and jobs to which the
  results will be generalized
• A large enough sample of people on whom both
  predictor and criterion data have been collected
  must be available

135
Content versus Face Validity

• Content Validity deals with the representative
  sampling of the content domain of a job by a
  selection measure
• Face Validity concerns the appearance of whether
  a measure is measuring what is intended

136
Session 17

• Midterm Exam Next Week
• Review and Assignment 2
• Reliability
• Content Validity

137
Assignment 2

• Reliability of written test
• Test - Re-test
• Internal Consitency - Split Half
• Reliability of the final interview
• Interrater
• Screening with supervisor
• Screening with technician
• SEM - which candidate to choose

138
Session 18

• Content Validity
• Utility Analysis
• Regression
• Selection Methods - KSA Measures

139
Key Elements of Implementing a Content Validity
Strategy

• Conduct a comprehensive job analysis
• Selection of experts participating in a content
  validity study SMEs
• Specification of selection measure content
• Assessment of selection measure and job content
  relevance

140
Major Steps for Implementing a Construct
Validation Study

• The construct must be carefully defined and
  hypotheses formed concerning the relationships
  between the constructs and other variables
• A measure hypothesized to assess the construct is
  developed

141
Major Steps for Implementing a Construct
Validation Study

• Studies testing the hypothesized relationships
  between the construct measured and other,
  relevant variables are conducted.

142
Major Factors Affecting the Size of Validity
Coefficients

• Reliability of Criterion and Predictor
• Restriction of Range
• Criterion Contamination

143
Validity

• Interpretation of Validity Coefficients
• Magnitude and Significance
• Standard error of estimate shows how much error
  there may be in the predicted score.
• It is determined as the SD in the performance
  scores times the square root of 1 minus the
  squared validity coefficient
• Cross-validation
• Correction for attenuation
• Correction for Restriction of Range
• Criterion Contamination

144
Utility Analysis

• Using dollar-and-cents terms as well as other
  measures
• such as percentage increase in output,
• it shows the degree to which the use of a
  selection measure improves the quality of
  individuals selected
• over what would have happened if the measure had
  not been used.

145
An Equation for Calculating the Utility of a
Selection Program

• Expected Dollar Gain from Selection
• NsrxySDyZx-NT(C)
• Expected Dollar Gain from Selectionreturn in
  dollars to the organization for having a valid
  selection program

146
An Equation for Calculating the Utility of a
Selection Program

• Nsnumber of job applicants selected
• rxyvalidity coefficient of the selection
  procedure
• SDystandard deviation of job performance in
  dollars

147
An Equation for Calculating the Utility of a
Selection Program

• Zxaverage score on the selection procedure of
  those hired expressed in z or standardized score
  form as compared to the applicant pool
• NTnumber of applicants assessed with the
  selection procedure
• Ccost of assessing each job applicant with the
  selection procedure

148
Strategies for Selection Decision-Making

• How to transform DATA into relevant information
• .Data Collection
• .Data Combination
• Judgmental and Mechanical Methods
• Selection Decision-Making Strategies
• .Multiple Regression - Compensatory Model
• .Multiple Hurdles
• .Combination
• .Profile Matching

149
Regression Analysis

• Y f(X) - linear relationship
• Collect data on X and Y
• Scatterplot
• Estimate the equation that describes the linear
  relationship between X and Y
• Estimate in such a way so that the predictions
  that are made for based on X using the equation
  contain a minimal amount of error
• Least Squares Estimates - beta regression
  coefficient
• The equation for estimation is Y beta0 beta1X1

150
Example

• Assignment 3 - Multiple Regression
• Empirical Weights for Selection Devices
• X1 Initial Screening Interview
• X2 Ability Test
• X3 Final Interview
• Y beta beta1 X1 beta2 X2beta3 X3
• The weights reflect the extent to which each
  selection device contributes to explaining
  performance
• Question Is a compensatory model what we want?

151
Session 19

• Quiz on Job Analysis Reading
• Measurement Quiz
• Assignment 3
• Discussion on JA Reading
• Selection Methods - KSA Measures
• Application Blanks
• Midterm Exam back

152
Measurement Review Quiz

• Handout

153
Stat Assignment 3

• Validity

154
Assignment 3

• Validation of several selection devices and
  interpretation and use of validity information
• Initial interview
• Supervisor interview
• Technician interview
• Mechanical ability test
• Reliability Best was mechanical ability test,
  then technician interview

155
Questions 1 and 2

• Steps in concurrent validation page 164
• Weaknesses of concurrent validation page 162
• Compare concurrent and predictive validation
  results in general page 166

156
Validity Questions

• Validity of the initial interview
• Validity of the supervisor interview
• Validity of the technician interview
• Validity of the mechanical ability test

157
Midterm Exam

• Frequencies
• 95-90 2
• 89-85 1
• 84-80 7
• 79-75 4
• 74-70 1
• 69-65 2
• 64-60 3
• Mean 76
• Letter grades
• 90 A, 88 A-, 85 B, 80 B, 78 B-, 75 C, 70 C

158
Session 20

• Syllabus Update
• November 8, 11, or 13 - Guestspeaker
• November 6
• Bio Item Reading Quiz
• Selection Project Instructions
• November 13
• Selecting Top Corporate Leaders - Questions
• November 20
• Interview Quick Decision - Questions
• November 27
• Physical Attractiveness - Quiz
• November 29
• Intelligence and Conscientiousness - Questions