EEOC Statements on PreEmployment Inquiries
“Although Title VII does not make pre-employment inquiries concerning
race, color, religion or national origin per se violations of the law, the
Commission’s responsibility to equal employment opportunity compels it to
regard such inquiries with extreme disfavor.”
“ … in the investigation of charges alleging the commission of unlawful
employment practices, the Commission will pay particular attention to
the use by the party against whom charges have been made of preemployment inquiries concerning race, religion, color, or national origin,
or other inquiries which tend directly or indirectly to disclose such
information. The fact that such questions are asked may, unless
otherwise explained, constitute evidence of discrimination, and will
weigh significantly in the Commission’s decision as to whether or not
Title VII has been violated”
Application Blanks
• Content of items (use of job analysis)
• Number of application blanks (one for each position or job category)
• Legal issues
• Image of organization (e.g., format, recruitment issue, perceived fairness)
• Accuracy of data
 Applicants overstated length of employment and past salary on application forms
(Goldstein, 1971)
 One serious lie on 25% of application forms and resumes (LoPresto, Mitcham, & Ripley,1986)
 40% to 60% candidates overstated their qualifications on resumes (George & Marett, 2005)
 Most frequent falsifications are job history, job duties, educational record, position title,
and previous salary (Broussard and Brannen, 1986)
Increasing Application Form Accuracy
• Inform applicants in verbally and in writing, that the information they
furnish will affect their employability
• Inform applicants that the data they provide will be thoroughly checked
• Require applicants to sign a statement certifying the accuracy of the
information they provided on the form.
• Include warnings of penalties (not being hired or termination upon
discovery) for deliberate falsification
• Include a statement that the application does not create a binding
obligation of employment for any specific period of time
Previous research studies examining employment applications
by date of study
Study
Results
Type of Application & Sample
Wallace &
Vodanovich (2002)
Fortune 500 sample = 2.99
inappropriate items
Customer Service sample =
5.35 inappropriate items
191 Fortune 500
Finance/Accounting applications
109 Customer service applications
(e.g., retail, food service)
Wallace, Tye, &
Vodanovich (2000)
Average of 4.2 inappropriate
items with most problematic:
salary, age, driver’s license
42 online state general employment
applications
Vodanovich &
Lowe
(1992)
Average of 7.4 inappropriate
items with most problematic:
age, convictions, & salary
Retail; 46 categories
Jolly & Frierson
(1989)
25% of 20 categories were
problematic (e.g., salary)
283 random applications from
American Society of Public
Administration members; 20
categories
Coady,
(1986)
Most problematic: improper use
of EEO worksheets
50 state libraries; 25 categories
Lowell & DeLoach,
(1982)
Most problematic: military
service & age
50 US firms; 17 categories
Burrington,
(1982)
Average of 7.7 inappropriate
items
50 general state applications; 30
categories
Miller,
(1980)
Average of 9.74 inappropriate
items
151 of Fortune 500; 72 categories
Note: Adapted from Wallace, Tye, and Vodanovich, 2000.
Frequency of Common Inappropriate Application Blank Questions
Item
Not
appropriate
Worded
Appropriate
Not asked
Past salary
98.9
0
1.1
Minimum
salary
72.7
0
27.2
Age
54.5
37.5
8.0
Information
about
relatives
50.0
10.2
39.8
Conviction
records
43.2
28.4
28.4
Health
40.9
2.3
56.8
Military
service
30.7
30.7
38.6
Marital
status
27.3
0
72.7
Emergency
contact
25.0
43.2
31.8
From: Vodanovich & Lowe (1992); Public Personnel Management
Years of experience and
previous salary are the
strongest predictors of
starting salary, and
starting salary is the
greatest predictor of
current salary.
--- Mickey Silberman,
Jackson Lewis, LLP
Industry Liason
Conference (2011)
Percentage of most commonly identified inadvisable application blank items by sample
[from Wallace & Vodanovich, 2004) Public Personnel Management]
Customer Service
Fortune 500
Inadvisable
Legitimate
Inadvisable
Legitimate
Desired Salary
66.1
15.0
20.8
5.2
Personal E-Mail Address *
49.5
0.0
88.0
2.5
Lowest Acceptable Salary
46.8
19.2
25.0
1.2
Graduation Date
33.9
3.1
54.2
3.5
Work Schedule
33.0
36.2
1.6
0.6
Conviction (w/o disclaimer)
26.6
56.8
3.6
38.2
References
26.6
42.4
3.1
23.6
Gender (w/o EEO disclaimer)
25.7
38.0
20.3
19.5
Race (w/o EEO Disclaimer)
24.8
25.1
15.1
18.9
Driver’s License
22.9
16.2
1.6
0.8
Relatives
21.1
5.1
2.6
0.0
EEO Worksheet
16.5
8.1
14.1
25.6
Handicap (w/o EEO disclaimer)
14.7
3.2
3.6
15.6
Age (w/o EEO disclaimer)
14.7
3.2
3.1
15.8
Language Fluency
11.9
1.5
3.1
0.7
Emergency Contact
7.3
0.0
.5
0.0
Marital Status
3.7
0.8
5.7
0.3
Personal Web Page Address
3.7
0.0
16.7
0.0
National Origin (w/o EEO disclaimer)
2.8
1.8
5.7
23.1
Category
Effect of Name on Resumes and
Interview Rates
Name type
Resume Quality
Low
High
“White”
sounding
name
“Black”
sounding
name
50% less chance of
being invited for
an interview
versus “Whites”
with high
qualifications
Research on court cases: Most common application blank challenges were based on questions
about sex (28%), age (25%), and race (12%). Source: Kethley & Terpstra, 2005
2012 EEOC Guidance on Arrest and Conviction Records
In 2012, the EEOC issued guidelines on the use of arrest and conviction records for
making selection decisions, its first update since 1990.
Refusing to hire those with arrest records is not considered to be justifiable. However,
if an individual's behavior underlying an arrest makes the person unfit for a given job,
then a decision to not hire may be legitimate.
Conviction records are generally easier to defend from a legal perspective. But, the EEOC
stresses the consideration of the following three factors:
1) the nature and severity of the offense,
2) the amount of time that has passed since the conviction (or completion of one's
sentence), and
3) the nature and type of job sought.
The EEOC suggests not asking about conviction records on application forms. If
included, such questions ought to be restricted to convictions “for which exclusion would
be job related for the position in question and consistent with business necessity.” See:
http://www.eeoc.gov/laws/guidance/arrest_conviction.cfm
“Ban The Box” Movement
Best Practices: EEOC Use of Criminal Records
(http://www.eeoc.gov/laws/guidance/arrest_conviction.cfm#VIII)
The following are examples of best practices for employers who are considering
criminal record information when making employment decisions.
General
·
·
Eliminate policies or practices that exclude people from employment based on
any criminal record.
Train managers, hiring officials, and decisionmakers about Title VII and its
prohibition on employment discrimination
Developing a Policy
·
Develop a narrowly tailored written policy and procedure for screening
applicants and employees for criminal conduct.
o Identify essential job requirements and the actual circumstances under
which the jobs are performed.
o Determine the specific offenses th at may demonstrate unfitness for
performing such jobs.
·
Identify the criminal offenses based on all available evidence.
o Determine the duration of exclusions for criminal conduct based on all
available evidence.
·
Include an individualized assessment.
o Record the justification for the policy and procedures
o Note and keep a record of consultations and research considered in
crafting the policy and procedures.
· Train managers, hiring officials, and decisionmakers on how to implement the
policy and procedures consistent with Title VII.
Questions about Criminal Records
·
When asking questions about criminal records, limit inquiries to records for
which exclusion would be job related for the position in question and consistent
with business necessity.
Confidentiality
Keep information about applicants’ and employees’ criminal records.
confidential. Only use it for the purpose for which it was intended.
SHRM Credit Background Check Survey Results
SHRM Survey Credit Checks
2004
In general, how frequently does your
organization, or an agency hired by
your organization check any of the
following references for its job
candidates?
2010
Does your organization, or an agency
hired by your organization, conduct
credit background checks for any job
candidates by reviewing the candidates’
consumer reports?
Credit Checks
Credit Checks
Always: 19% Sometimes: 24%/
42% Rarely: 18%
Never: 39%
All job candidates: 13% Select job
candidates: 47%
No: 40%
Survey margin of error: +/- 5%
Note: n = 296. Excludes respondents who
responded “Don’t know.” Source: SHRM
Reference and Background Checking Survey
(2004)
Survey margin of error: +/- 5%
Note: n = 343. Excludes respondents who
responded “Not sure.” Source: SHRM
Background Checking Survey (2010)
SHRM Survey on Use of Credit Background Checks
(2010)
 On which categories of job candidates does your organization conduct
credit background checks?
SHRM Survey (cont.)
 When does your organization, or any agency hired by your organization,
initiate credit background checks on job candidates?
SHRM Survey (cont.)
Does your organization allow job candidates, in certain circumstances, the
opportunity to explain the results (e.g., high debt, bankruptcy, etc.) of their
consumer report that might have an adverse effect on an employment decision?
SHRM Credit Check Survey Research Summary
• The use of credit background checks in employment decisions has not
changed in any discernable way over the past 6 years.
• Most organizations do not conduct credit background checks on all job
candidates.
• Organizations conduct credit background checks for those positions where
this information is most job-relevant.
• Employers place lower relative importance on credit background checks
than other job-related factors in making hiring decisions.
• Employers do not use credit background checks to screen out mass
numbers of candidates in the early phases of the application process.
• Credit background check results are seldom used as a definitive hiring
criterion.
 Two large studies by the Federal Reserve System in 2003 and Freddie Mac in 2000
concluded that Asians and Whites have higher credit scores than do Hispanics and
African Americans
Meta-Analysis
Criterion
Work problems
K
10
N
7,464
r
.149
Discipline
5
5,946
.131
Absenteeism
6
1,678
.211
Performance ratings
3
561
.069
K = number of studies, N = total sample size, r = sample-size weighted uncorrected average
correlation
Credit score: A number which provides a “snapshot” over a certain period of time (not shown to
employers)
Credit report: Generates information about an individual’s debt over a longer time frame than a
credit score
From: Statement of Michael Aamodt, Ph.D., Principal Consultant, DCI Consulting Group, Inc. EEOC Meeting of
October 20, 2010 - Employer Use of Credit History as a Screening Tool
Credit Scores
No relationship between credit ratings and performance scores or termination
decisions (Bryan & Palmer, 2012) – over 170 employees in a financial organization
A recent study (Bernerth, Taylor, Walker, & Whitman, 2011) found credit scores to be
predictive of certain work-related outcomes and Big 5 personality scores. The
authors found that credit scores were significant and negatively related to
supervisor ratings of:
• Task performance and employee engagement in OCBs
Credit score were also predictive of Big 5 personality scores of
• Greater conscientiousness
• Low agreeableness
• But, credit scores were NOT found to predict supervisor ratings of:
workplace deviance (e.g., theft, aggressiveness)
However, the authors caution the use of credit scores absent data demonstrating
their job relatedness for certain jobs and the potential for adverse impact.
Recent Rulings on Use of Credit and
Criminal Background Checks
EEOC v. Kaplan Higher Learning (2013)
EEOC position: Kaplan’s use of credit reports adversely impacted Black applicants. Had
to use “race raters” to decide race from driver’s license photos of applicants
But, circuit court judge (N.D. Ohio) ruled this approach to be unreliable and not
scientifically rigorous enough (did NOT meet the Daubert standard):
1)
2)
3)
4)
5)
technique or theory can be or has been tested
whether it has been subject to peer review and publication
the known or potential rate of error of the technique or theory
the existence and maintenance of standards and controls
whether the technique or theory has been generally accepted in the
scientific community
>>> Judge issued a SJD
EEOC v. Freeman (2013)
EEOC evidence:
• 51 Black applicants passed over between March 23, 2007, and Aug. 11, 2011
because of credit histories
• 83 Black and male workers passed over between Nov. 30, 2007 and July 12,
2012 based on criminal records.
Data in report issued by EEOC deemed to be problematic: (e.g., did not include
data on all available applicants for the two classes for the entire class period)
Words used by the judge in this case: “flawed,” “skewed,” “rife with analytical
errors,” “laughable,” and “an egregious example of scientific dishonesty”
Judge also ruled that the EEOC did not identify a specific employment
practice that caused the alleged adverse impact
Also in 2013: 9 State Attorney Generals Letter Opposing EEOC Guidance on
Criminal Background Data
Characteristics of Training & Experience Evaluations (e.g.,
information from application blanks, resumes)
 A listing or description of tasks, KSAs, or other job-
relevant content areas
 A means by which applicants can describe, indicate, or
rate the extent of their training or experience with these
job content areas
 A basis for evaluating or scoring applicants’ self-
reported training, experience, or education
Some Uses of Training and Experience Evaluations (e.g.,
gleamed from application blank information, resumes)
 As the sole basis for deciding if an individual is or is not
minimally qualified
 As a means for rank-ordering individuals from high to low
based on a T&E score
 As a basis for prescreening applicants prior to
administering more expensive, time-consuming predictors
(for example, an interview)
 In combination with other predictors used for making an
employment decision
Applications via the Internet
Increasing frequency of requiring ABs and resume information
via the Internet (e.g., preset fields, check boxes)
Greater convenience and standardization -- but can lead to less
applicants
Effect on those without access to Web (adverse impact)
Use of 3rd party companies for resume submission
•
Privacy concerns (tell users how data will be handled)
Social Media and Selection
Frequency of Use --•18% indicated that they have used social networking websites to screen applicants,
while 11% planned on using such sites in the future (survey of over 400
organizations by the Society for Human Resource Management in 2011)
• 45% of employers used social networking sites to investigate job applicants
(survey of over 2,600 hiring managers by Harris Interactive for CareerBuilder.com)
Consequences?
• 35% of organizations in Harris survey said they did not hire candidates due to
content available on social networking sites. Most common examples of negative
information included
Provocative attire
Images of drug or alcohol use,
Complaints about previous employers
http://www.siop.org/tip/oct12/05davison.aspx (TIP article of social media &
selection)
Password Protection Act
Introduced March of 2012
• Prohibits an employer from forcing prospective or current employees to provide
access to their own private account as a condition of employment
• Prohibits employers from discriminating or retaliating against a prospective or
current employee because that employee refuses to provide access to a passwordprotected account.
Example:
Scoring resume
data for sales
and accounting
jobs
Brief Training and Experience Evaluation Used for
Appraising Applications Submitted for the Job of Clerk
An Example Training and Experience Evaluation Form for the
Job of Personnel Research Analyst
An Example Rating Form for Use in Evaluating Training and Experience of
Applicants for the Job of Personnel Research Analyst
Decision-Making Methods for T&E Data
• Holistic Judgment
 An informal, unstructured approach that an individual
takes when reviewing an application or T&E form
 An individual makes a cursory review of the information
and arrives at a broad, general judgment of the
applicant’s suitability
 Because of its unstandardized nature and unknown
reliability and validity, it should be avoided as an
approach to T&E evaluations.
Decision-Making Methods for T&E Data (cont.)
• Point Method
 A pre-established rating system for crediting applicants’
prior training, education, and experience considered relevant
to the job
 Points are assigned based on the recentness, type, and
amount of training, job experience, and education received
 Analysts using the point method make their ratings and then
sum the credited points assigned
Decision-Making Methods for T&E Data (cont.)
• Grouping Method
 Divides applicants into groups that best represent each
applicant’s level of qualifications
 The number of groups used will depend on the particular
situation




High Group: suitable applicants well qualified for the job
Middle Group: applicants not fitting in either the high or low group
Low Group: applicants with minimum qualifications but poorly suited
because of limited experience or training
Unqualified Group: applicants lacking minimum qualifications
Grouping Method Example
Decision-Making Methods for T&E Data (cont.)
• Behavioral Consistency Method
 Applicant descriptions of achievements related to key job
requirements or competencies are formally scored using scales
derived from subject matter experts
• Principles of the Method
 Behaviors evaluated have been identified by SMEs as showing
differences between superior and minimally acceptable
workers.
 Applicants’ past accomplishments can be reliably rated by
SMEs.
 Past accomplishments are considered predictive of future
behaviors
Sample of Behavior Consistency Model
Concerns the conduct of research activities including designing a research study, collecting and analyzing data to
test specific research hypotheses or answer research questions, and writing up research results in the form of a
formal report.
For the behavior Conducting Empirical Research that is defined above, think about your past activities and
accomplishments. Then write a narrative description of your activities and accomplishments in the space below.
In your description, be sure to answer the following questions:
1. What specifically did you do? When did you do it?
2. Give examples of what you did that illustrate how you accomplished the above behavior.
3. What percentage of credit do you claim for your work in this area?
Description: During my senior year (2005–2006), I wrote a senior research thesis as a partial requirement
for graduation with honors in psychology. I designed a research study to investigate the effects of
interviewer race on interviewee performance in a structured interview. I personally designed the
research study and conducted it in a metropolitan police department. White and African-American
applicants for the job of patrol police officer were randomly assigned to White and African-American
interviewers. After conducting an analysis of the patrol police job, a structured interview schedule was
developed. The various interviewee-interviewer racial combinations were then compared in terms of
their performance in the structured interview. I consider the vast majority of the work (80 percent) to be
my own. My major professor accounted for about 20 percent of the work. Her work consisted of helping
to obtain site approval for the research, helping to design the study, and reviewing my work products.
Name and Address of an Individual Who Can Verify the Work You Described Above:
Name:
Dr. Amy Prewett
Address:
Department of Psychology
Pascal Univ. State College, ID
Phone:
607-555-0821
An Example Rating Scale for Scoring the Behavioral Consistency
Method of T&E Evaluation
Decision-Making Methods for T&E Data (cont.)
• Task-Based Method
 Critical job tasks identified from comprehensive job
analysis serve as the basis for the task-based method.


Applicants indicate on a list of tasks if they have performed the tasks
and, if so, how often
Applicants furnish specific information such that their self-ratings can be
verified
• KSA-Based Method
 Similar to the task-based method with the substitution of
KSAs on the questionnaire for applicant self-ratings
Psychometrics of T&E Evaluations
• Reliability
 T&E evaluations reflect high inter-rater reliability estimates
(.80s)
 Task-based method has the highest reliability Grouping
method producing the lowest
• Validity
 Validity of T&E ratings varies with the type of procedure
used




The behavioral consistency method demonstrated the highest validity
The point- and task-based methods show useful validities for applicant
groups having low levels of job experience
Past work experience predicts job performance (.27) and task performance is
rather than job or organizational experience
GPA r with performance = .32 but years of education = .10; Job tenure
(organizational tenure or hours worked (r = .18, .20 respectively)
Research Findings on T&E Evaluations
• T&E Evaluations
 Consistently predict important work outcomes
 Vary significantly in the strength of their predictive
validity


Some methods of evaluating experience and training exhibit substantial
correlations, .45, with success (e.g., the “behavioral consistency” method)
Other methods reflect low validities (e.g., the point method: .15, task
method: .11)
 Are particularly valuable for the first three to five years
on the job
T&E Recommendations
• Use T&E evaluations to set specific minimum job
qualifications (KSAs), rather than using a selection standard
• Replace holistic methods with competency-based
approaches—behavioral consistency and grouping methods
• T&E evaluations are subject to the Uniforms Guidelines
• Use T&E evaluations only as rough screening procedures
for positions where previous experience and training are
necessary
• Forms and procedures for collecting and scoring T&E
evaluations should be standardized/structured as much as
possible
• Verify self-report data, particularly of data given by
applicants who are going to be offered a job
• Base final hiring decisions on other selection measures
when distortion of self-evaluation information is likely to be a
problem
Reference Checks
(Exceptionally common technique; e.g., 95% usage by organizations)
Basic Purposes:
• Verify information provided by the applicant (check for
inconsistencies)
• Uncover unreported or additional information
• Predict job performance (pass or fail decisions)
Typical Types of Information Collected
• Employment dates
• Rehire?
• Job title
• Job tasks
• Salary (e.g. beginning and ending): Technically not illegal to ask but
can potentially be problematic – leading to pay offers that are different
(lower) for minorities and females
(From SHRM survey, 2005 -- Reference and Background Checks)
Sources of Reference Data
• Supervisor (most common and most useful)
• Personal reference
• Agencies (e.g., credit ratings)
• Public Records (criminal background, driving records,
court records, workers compensation)
• Educational background (verification)
Reference Check Methods
In-Person (e.g., interview)
• Costly, time consuming
• Used in jobs that involve the concern for risks (e.g., security, $)
• Can elicit different types of information (differences
between in-person and written reference information)
Mail (or e-mail) -- See Table 9.4 on page 409
• Low return rate with use of “snail” mail (e.g., 56 – 64%)
(many chose to set up phone reference checks via email)
• Standardized questions, format
• Written record of responses
• Ensure confidentiality of responses (signed statement by applicant)
Telephone Checks (see Table 9.9 on page 407)
• Allows follow-up or clarification of answers given
• Less resistance to giving certain types of information can be collected
• Structured phone interview validity (.25)
• Relatively quick process
• Important data can be gleaned from various verbal cues (e.g., pauses,
hesitations, voice inflections, voice level, intonations)
• Relatively high return rate (especially if time was set earlier)
• Better responsiveness, more interactive nature of the method, and more
confidence in the identity of responder
Reference Check Recommendations
• Use of job-related questions (e.g., KSAs from a job analysis)
• Use of multiple reference check forms (job specificity)
• Follow provisions contained in the Uniform Guidelines
(e.g., regarding fairness, validity)
• Behaviorally-focused and objective set of questions
• Get written permission for applicants
• Training of interviewers (phone, interview) and recordkeeping
• Ask for additional references if one’s submitted not available
• Verify information that is collected!
Usefulness of Reference Information
• Relatively low validity; relationship to performance measures (e.g.,
.18, .25)
• Relatively low interrater reliability (e.g., .40, but sometimes from
different sources)
Most useful if:
• Data collected from immediate supervisor
• Referee knows applicant well (chance to observe job behavior) and have
similar demographic characteristics
• Similarity between the prior job and the one being applied for
Reference Checks --- Legal Issues
Defamation
• Content can be written or oral
• Statement must be false
• Injury must occur (e.g., not hired)
• Company does not have privilege:
Absolute privilege: Immunity from legal challenge
Qualified privilege: Statement is knowingly false or malicious
Reference Checks --- Legal Issues
Negligent Hiring
 An injury to a third party is caused by an employee
 The employee is shown to be unfit for the job that he or she
holds
 The employer knew or should have known that the
employee was unfit if a background check or criminal check
had been conducted
 The injury to the third party was a foreseeable outcome
resulting from hiring the unfit employee
 The injury is a reasonable and probable outcome of what the
employer did or did not do in hiring the individual
More lawsuits regarding reference and background checks than ones involving medical
exams, drug tests, or polygraphs – but, still small percent. Applicants sign waiver?
Letters of Recommendation
(Mainly used in highly skilled or professional jobs)
Some generic indicators:
• Meaning of certain adjectives (e.g., mental ability – performance;
cooperativenss/personality – not related to performance)
• Number of words used or length of letter (longer letter is better)
Concerns:
• Pre-selection of referees (often only positive information included)
• Verbal and organizational skill of writer
• Unstructured content
• Omissions
• Time availability
• Subjective scoring (e.g., focus on irrelevant information, status of
writer)
Biographical Data (Bio-Data) Process
Developing Biodata Items --Task/job-based approach --- To assess safety orientation; “How often
have you been a participant in safety meetings?”
Assumes prior experience in the job domain
KSAO approach --[Example of assessing a job-related construct such as initiative --“On past jobs, how often did you volunteer to work on specific projects?”
More generic but not explicitly related to job duties
•
Item Screening
• Relevance of items to the intended construct or job/task relevance of the item
• Potential overlap with other constructs
• Social desirability and/or faking potential
• Possible for bias against protected groups
• Privacy concerns
Eliminate an item from the Bio-data inventory if items:
• Have little variability
• Has a skewed response distribution
• Is correlated with protected-group characteristics such as ethnicity
• Has no correlation with other items thought to be measuring the same
life history construct
• Has no correlation with the criterion (no item validity)
Classification of Biographical Items
Historical
Hypothetical
How old were you when you got your first job?
What job do you think you’ll have in 10 years?
External
Internal
Did you ever get fired from a job?
What is your attitude towards friends who smoke
marijuana?
Objective
Subjective
How many hours did you study for your math
tests?
Would you describe yourself as shy?
First-hand
Second-hand
How punctual are you about coming to work?
How would your teachers describe your
punctuality?
Discrete
Summative
At what age did you receive your driver’s license?
How many hours do you study during an average
week?
Verifiable
Non-Verifiable
What was your GPA in college?
How many fresh vegetables do you eat daily?
Controllable
Non-controllable
How many times did you drop classes in college?
How many siblings do you have?
Best results if items are: Historical, objective, discrete, job relevant, and external
Classification of Biographical Items (cont.)
1. Verifiable:
Did you graduate from college?
2. Historical:
How many jobs have you held in the past five
years?
Unverifiable:
How much did you enjoy high school?
Futuristic:
What job would you like to hold five years from
now?
3. Actual Behavior:
Have you ever repaired a broken radio?
4. Memory:
How would you describe your life at home
while growing up?
5. Factual:
How many hours do you spend at work in a
typical week?
6. Specific:
While growing up, did you collect coins?
7. Response:
Which of the following hobbies do you
enjoy?
8. External Event:
When you were a teenager, how much time
did your father spend with you?
Hypothetical Behavior:
If you had your choice, what job would you like to
hold now?
Conjecture:
If you were to go through college again, what
would you choose as a major?
Interpretive:
If you could choose your supervisor, what
characteristic would you want him or her to have?
General:
While growing up, what activities did you enjoy
most?
Response Tendency:
When you have a problem at work, to whom do you
turn for assistance?
Internal Event:
Which best describes the feelings you had when
you last worked with a computer?
1. Yes-No Response:
Are you satisfied with your life?
a. Yes
b. No
2. Continuum, Single-Choice Response:
About how many fiction books have you read in the past year?
a. None
b. 1 or 2
c. 3 or 4
d. 5 or 6
e. More than 6
3. Noncontinuum, Single-Choice Response:
Which one of the following would you most prefer to do in your leisure time?
a. Read a book
b. Work crossword puzzles
c. Attend a party
d. Play golf, tennis, or softball
e. Repair a broken appliance or make minor home repairs
4. Noncontinuum, Multiple-Choice Response:
Check each of the following activities you had participated in by the time you were 18.
a. Shot a rifle
b. Driven a car
c. Worked a full-time job
d. Traveled alone more than 500 miles from home
e. Repaired an electrical appliance
5. Continuum, Plus Escape Option:
When you were a teenager, how often did your father help you with your schoolwork?
a. Very often
b. Often
c. Sometimes
d. Seldom
e. Never
f. Father was not at home
6. Noncontinuum, Plus Escape Option:
In what branch of the military did you serve?
a. Army
b. Air Force
c. Navy
d. Marines
e. Never served in the military
7. Common Stem, Multiple Continuum: In the last 5 years, how much have you enjoyed each of the
following? (Use the rating scale of 1 to 4: (1) Very Much, (2) Some, (3) Very little, (4) Not at all
a. Reading books
b. Watching TV
c. Working at your job
d. Traveling
e. Outdoor recreation
Biodata Scoring
Empirical Keying Approach (correlation between items and a criterion)
High
Items
Performance
Groups (e.g.,
median split, upper
vs. lower thirds)
Low
Issues:
• Validity of the criterion measure
• Contamination/bias in criterion measure
• Items (and their weights) are specific to the criterion
Summary of Bio-Data Validity Studies
Bio-Data (cont)
• Reliability: .60 to .80 across several studies [higher for more
verifiable items]
• Validity: Many validity coefficients above .30
• Accuracy: Some distortions exist. Mainly on unverifiable items (e.g.,
interests, preferences) and more if desirability of answers is apparent
(e.g., faking can occur)
Bio-Data [Why does it work?]
• Use of life history items e.g., personal background, life experiences,
interests (past behavior is best predictor of future behavior)
• Only relevant (empirically significant) items/constructs are selected
• Correlation between BIB content and criterion
• Wide range of information (lots of different questions and types)
Some Bio-Data Issues
• Situational specificity
• Need large sample to construct properly
• Assumption of a “correct” life history
• Pure empirical approach (e.g., versus content approach)
• Legal issues (e.g., adverse impact, validity, reliability)