Summary of the article 'The use of biodata for employee selection: Past research and future implications.' 2009,human resource management review - human resource,no. 3,pp. 219-231,vol. 19

1. The Use of Biodata for Employee Selection
Past research and future directions
Objectives:
1. Provide a selective but representative review of the research
that has been conducted on the use of biodata for employee
selection
2. To constructively critique this research to highlight
deficiencies that may limit the conclusions that should be
drawn
3. To stimulate important future research on biodata that avoids
the limitations of past research

2. Biodata
Article Overview
1. Biodata Research: A 2. Past Biodata Research: 4. Biodata: Future research
selective review of the Three potential concerns directions
research  2.1 The Heavy Reliance of Past  4.1 What is biodata?
 1.1. A Study by Goldsmith (1922) Studies on a Concurrent  4.2 Do results for concurrent
 1.2. Defining and operationalizing Validity Design validity studies generalize to a
biodata: Differences in definitions  2.2 The Type of Biodata Scale selection context?
and the types of items used Used  4.3 Increased research with an
 1.3. Methods of gathering biodata  2.3 The Lack of Information item-focus
 1.4. Strategies used for developing Provided on Biodata Items
biodata scales  4.4 Greater focus on the use of
 1.5. The Reliability of Biodata
technology
Scales 3. What is biodata? And why  4.5 Ways to increase the
1.6 The Validity of Biodata Scales accuracy of biodata information
 does it predict employee
 1.7 Adverse Impact behavior?  4.6 The value of a biodata
 1.8 Applicant Reactions to Biodata clearinghouse
 3.1 What is biodata?  Rethinking the use of a
 1.9 Incremental Validity
 1.10. The Accuracy of Biodata  3.2 Why Does Biodata factorial biodata development
 1.11. Computing a Biodata Scale Predict Employee Behavior? strategy
Score: Unit Weighting versus 5. Concluding Remarks
differential weighting
 1.12. The Generalizability of
Biodata Scales

3. 1.2. Defining and Operationalizing Biodata
Differences in definitions and the types of items used
 Factual information about life and work experiences, as well
as items involving opinions, values, beliefs, and attitudes that
reflect a historical perspective.
Narrowly defined
 Behaviors and events that occurred earlier in life
 How many jobs have you had in the past 5 years?
 How long have you been in your previous job?
Broadly Defined
 Temperament, assessment of working
conditions, values, preferences, skills, aptitudes, and abilities.
 I like doing things with other people.
 My teachers regarded me as a sociable boy/girl.

4. 1.2. Defining and Operationalizing Biodata
Differences in definitions and the types of items used
Mael’s (1991) definition:
 Does include items pertaining to historical events that may have
shaped the person's behavior and identity
 Does not include items that address such variables as behavioral
intentions, self-descriptions of personality traits, personal
interests, and ability
Advantages of Mael’s historical nature definition of biodata:
 Accuracy in reporting of discrete verifiable events
 More favorable view of questions that applicant views as job related
and reflect experience under applicants control

5. Biodata
Introduction
“One of the best selection devices for predicting
turnover”
 Organizations rarely use biodata (<17%)
 Less than one page devoted to biodata in Evers, Anderson, and
Voskuijl's Handbook of Personnel Selection (2005)
 A PsychINFO database search in 2008 for the term 'biodata'
turned up one article
*Survey of 255 HR professionals ranked biodata as lacking in terms of
validity, practicality, and legality

6. 1.1. A Study by Goldsmith (1922)
 Examined the ability of 9 “personal history” items to predict
the first-year sales of insurance agents
 Marital status
 Education
 Belonging to clubs
 Found that using a person‟s biodata score would improve hiring decisions made
 58 of the 259 individuals receiving a score of 4 or above were considered successful (22%)
 11 of the 243 individuals receiving a score less than 4 were considered successful (4%)
 Then and Now:
 Used few biodata items
 A number of items used would not be used today (age)
 Did not report data on the relationship of a given item and sales
 Provided an explanation for using each item

7. 1.3. Methods of Gathering Biodata
 Web-based
 Telephone
 Paper-and-pencil
 Study by Ployhart, Weekley, Holtz, and Kemp (2003) compared scores
from paper-and-pencil measure to those obtained from a web-based
version of the measure
o Web based group had lower mean score (may suggest less faking)
o Lower scores in terms of skew and kurtosis
Mumford (1999) suggested there may be benefits from using a
greater variety of data gathering methods.

8. 1.4. Strategies Used for Developing Biodata
Scales
 Researchers use a combination of strategies:
1. Empirical
 “Dust-bowl empiricism”
2. Behavioral Consistency
 “Best predictor of future behavior is past behavior”
3. Rational/Deductive
 Job Analysis/Theories
4. Factorial
 Attempting to explain „why‟ there is a correlation
5. Subgrouping
 Different groups use different constructs when answering

9. Scale Development
1. The Empirical Approach
 “Dust Bowl Empiricism”
No theory is involved
behind the study. Solely
refers to instances arising
from entirely inductive
processes. We just want
to know which items are
significantly correlated to
form the scale.
 Large pool of items are
used, those that are
predictive are chosen for
use in the scale Example: Finding a high correlation between two
 Ideally a cross-validation variables, job turnover and amount of jobs held in
study would be past five years, and including „amount of jobs held
conducted in past five years‟ as part of your biodata scale.

10. Scale Development
2. Behavioral Consistency Approach
 Past behavior predicts future behavior
 Selects items that are consistent with the criterion of
interest
 Causal variables are usually not investigated
 Example: When interested in predicting turnover
ask, “how long have you been at your most recent job?”
 Stable work ethic?

11. Scale Development
3. Rational/Deductive Approach
 Conduct a job analysis to determine KSAs relevant for the
criterion of interest or
 Use recent research/theories in development of questions
 Criterion of interest = voluntary turnover
 Knowledge of the job (realistic expectations) reduces
voluntary turnover
 Example: “Do you know someone who works for the
organization?”

12. Scale Development
4. Factorial Approach
 Principal Axis Factor Analysis/ Principal
Components Analysis
 Explain why biodata scales predict the criterion of
interest
 Extracts underlying 'factors' that cause the statistical relationship to exist
 Empirical example: Job turnover and amount of jobs held in past five
years?
 Age
 Behavioral Consistency example: turnover and amount of time at most
recent job?
 Work ethic
 Rational/Deductive example: Knowing someone working for the
organization and voluntary turnover?
 Realistic expectations

13. Scale Development
5. Subgrouping
 Different groups may have different patterns of
constructs that underlie their responses to
biodata items
 Types of biodata items that best predicted military
suitability for high school graduates differed from
those that predicted suitability for non-graduates

14. 1.5. The Reliability of Biodata
Scales
 One construct
 e.g. past experience interacting with people
 Coefficient alpha appropriate
 Estimates range from .50-.80
 A variety of constructs
 e.g. marital status, age, schooling completed, and
number of jobs held in the past 5 years
 Coefficient alpha not appropriate
 Test-Retest reliability may be appropriate
 Estimates range from .60-.90

15. 1.6 The Validity of Biodata
Scales
 Criterion-related Validity
 Research shows that biodata is a good predictor of:
 Job performance
 Voluntary turnover
 1.9 Incremental Validity
 Mount, M. K., Witt, L. A., & Barrick, M. R. (2000)
 Biodata added unique variance in predicting supervisory ratings of
performance beyond that accounted for by tenure, general mental ability, and
the Big Five personality traits
 Allworth and Hesketh (2000)
 Biodata scale accounted for unique variance in performance ratings when
added after a cognitive ability test

16. 1.7 Adverse Impact
 Causes for concern occur when biodata items are
used regarding:
 Educational level
 Cognitive ability (GPA)
 Use careful item screening
 Compared to other selection devices, biodata has modest
adverse impact

17. 1.8 Applicant Reactions to Biodata
 Poor face validity
 Applicants are likely to react negatively to items that are perceived as
lacking job relatedness, fakable, and overly personal in nature
 Studies usually do not involve applicants
 Students or current employees

18. Biodata
Sample FBI Inventory
This inventory contains 40 questions about yourself.You are to read each question and
select the answer that best describes you from the choices provided. Answer the
questions honestly; doing otherwise will negatively affect your score.
1. How did you typically prepare for final 3. To what extent have you enjoyed being
exams in college? given a surprise party?
A. Studied a few hours every day across several weeks A. Not at all
B. Studied many hours over a few days B. To a slight extent
C. Studied the entire night before each exam C. To a moderate extent
D. Did not study D. To a great extent
E. I have never been given a surprise party
2. How often are your library books 4. In the past year, how many times have
overdue? you thrown something when you were
A. Always angry?
B. Often A. 0 times
C. Rarely B. 1 - 2 times
D. Never C. 3 - 4 times
E. I never take books out of the library D. 5 - 6 times
E. 7 or more

19. 1.10. The Accuracy of Biodata
 Students
 Fairly Accurate
 External verification from parents supports the self-reported student
data
 Applicants
 Accuracy was mixed when studies were conducted in a
selection context
 Faking 'good' answers

20. 1.11. Computing a Biodata Scale Score
Unit Weighting versus differential weighting
1. Correlational (Unit) Method
 Compute a simple correlation between an item and the
criterion, then use this value to weight the item
 More highly correlated items receive higher weights
2. Differential Regression Method
 Select all biodata items that are significantly correlated to
the criterion and unit weight them.
 Differential regression method is most beneficial when the
correlations among the items are low, there are relatively few
items, and there is a large sample
 Both methods tend to provide comparable results.

21. 1.12. The Generalizability of Biodata Scales
Will a biodata scale developed in one organization be
valid if applied in another organization?
 In the U.S., research shows that biodata scales have
predicted:
 Brown (1981)
 Sales volume for insurance agents across 12 companies
 Rothstein, Schmidt, Erwin, Owens, and Sparks (1990)
 Performance of supervisors across organizations
 Carlson, Scullen, Schmidt, Rothstein, and Erwin (1999)
 Rate of promotions across 24 organizations

22. 1.12. The Generalizability of Biodata Scales
(International)
 Laurent (1970)
 Valid scale for managers in the US was also valid in
predicting management success in Denmark, Norway, and
the Netherlands
 Dalessio, Crosby, and McManus (1996)
 Scale used to select insurance agents in the US used with
equal effectiveness in the United Kingdom and Ireland

23. 1.12. The Generalizability of Biodata Scales
Overtime
 Brown (1978)
 Scale developed in 1933 for selecting insurance agents predicted
survival and performance of agents in 1969-1971
 Rothstein, H. R., Schmidt, F. L., Erwin, F. W., Owens, W.
A., & Sparks, C. P. (1990)
 Validity coefficients of studies done in 1974 and 1985 were similar
 Carlson, K. D., Scullen, S. E., Schmidt, F. L., Rothsteing, H., &
Erwin, F. (1999)
 Scoring key for the Manager Profile Record yielded valid scores up
to 11 years after the key was developed
*Stability likely due to researchers using items that were
generic/attributes of the jobs tapped by the biodata items have not
changed greatly

24. 2.Past Biodata Research
Three potential concerns
 2.1 Heavy reliance on concurrent validity designs
 2.2 Type of biodata scale used
 2.3 Lack of information provided on biodata items

25. 2.1. The Heavy Reliance of Past Studies on a
Concurrent Validity Design
Are results taken from current employees comparable to job applicants?
 Stokes, Hogan, and Snell (1993)  Harold, McFarland, & Weekley (2006)
 Studied sample of incumbents working in a  425 call center employees and 410
sales position and applicants who had applied applicants respond to 20 biodata item
for the position  Validity coefficients higher for job incumbents
o Developed two scales to predict (.27) than job applicants (.18)
turnover (i.e. job applicant scale and job
incumbent scale)
o Validities of scale were similar
• Job Incumbent .22
• Job Applicant .23
 Switched the scales, i.e. gave job applicant the
job incumbent scale
● Validity Coefficient = .08
● Biodata scales developed for each group had
no items in common

26. 2.2 The Type of Biodata Scale Used
 Generic vs. Situation-specific Scales
 Developing situation-specific biodata scales may
result in higher validity than a more generic scale
o Situation-specific validity coefficient = .33
o General validity coefficient = .22
 Expensive to develop
• Writing items
• Pilot testing
*Generic scale is better than no scale

27. 2.3 The Lack of Information Provided on
Biodata Items
 Researchers often do NOT report the actual items they
used due to:
 Lengthy biodata measures, < 100 items, journal space issue
 Used biodata items sold by vendors who do not allow publication of their
items
 Therefore, most studies have not reported:
 1. Correlation between each biodata item and the criterion used in the study
 2. How each item was weighted in creating the scale
 3. Whether an item provided unique variance in predicting a criterion variable
 4. Whether an item had adverse impact
 5. Correlations among biodata items

28. 2.3 The Lack of Information Provided on
Biodata Items
 Imagine you are developing a new biodata scale.
How would this omitted information be
beneficial?
 Valid predictors in past studies
 Adverse impact
 Non-significant findings
 Allow selection of biodata items that are of
maximum value while limiting the number of
items that are used

29. 3. What is Biodata? And Why Does It
Predict Employee Behavior?
 3.1 What is Biodata?
 3.2 Why does it predict employee behavior?

30. 3.1 What is Biodata?
 Article‟s Position: Biodata consists of applicant‟s past
behavior and experiences
 Past behaviors and experiences can reflect events that occurred in a work context (quit a
job without giving notice), an educational setting (graduated from college), a family
environment (traveled considerably growing up), community activities (led a cub scout
troop), or other domains (active in local politics)
 Does not mean that past experiences are unrelated to such variables as
interests, personality, values, knowledge, and skills
 Schmidt et al. (1999)
 It is likely that an individual who possesses certain interests, personality
traits, values, and/or KSAs will be more likely to seek out certain situations that are
captured by historical biodata
 In summary: Many of the variables (personality traits) that have commonly been
confounded with biodata are actually antecedents of consequences of the personal
experiences that biodata taps

31. 3.2 Why Does Biodata Predict Employee
Behavior?
 Most studies focus on criterion-related validity and few models offer
an explanation to „why‟
 Mumford, Owens, and Stokes (1987, 1990) developed the
(Interactive) Ecological Model to help determine the “why”

32. 3.2 Why Does Biodata Predict Employee
Behavior?
Person's life begins with certain environmental and
hereditary resources...
• A nurturing mother
• excellent eyesight
...and certain limitations...
• Substandard nutrition
• Poor coordination
...which determine individual differences early in life.
• High cognitive ability
• Poor health
• Self-confidence
 Given these individual differences, an individual attempts
to maximize adaptation to the environment.

33. 3.2 Why Does Biodata Predict Employee
Behavior?
• The ecological model presumes that an individual makes
decisions about what situations to enter..
• What college to attend
• Whether to accept a job offer
…based upon the perceived value of the outcomes
• Social status
• Financial rewards
• Intrinsic satisfaction
…that are likely to be derived from the situations.
• Interactive: An individuals choice at a given point in time about what situation to enter
affects his subsequent development, which influences his future choices of situations, which
affect future development, etc. Thus, over time, an individual may develop new skills, satisfy
existing needs, increase academic goals, or decrease his work ethic.

34. Environmental Experience at Time 2
Non-choice/Uncontrollable events:
e.g. unemployment, health problems
Environmental Experience at Time 2
Time 1 Time 2 Time 3
An individual possesses several The experience of the new The individual is now
attributes, based on these attributes environment will lead to changes different on one or
the model suggests the individual will in the person’s attributes. more attributes than at
. actively choose to enter a new Time 1.
situation/environment that is
perceived to aid in development.

35. 3.2 Why Does Biodata Predict Employee
Behavior?
 By limiting the definition of biodata to past behaviors and
experiences, the data collection is on events reflected in the boxes
labeled 'Environmental Experience at Time 2'.
 Did you graduate from college?
 How long were you at your most recent job?
 What percentage of you college expenses have you paid?
 This model explains how defining biodata by past behavior and experiences does NOT
mean a biodata score is unrelated to such variables as
conscientiousness, ability, interests, knowledge, etc. Rather, it shows how such variables
are likely antecedents and/or consequences of an individual's behaviors and experiences.
 Better way to get information that could be faked
 Asking how long someone was in a prior position in sales helps gauge the persons
dependability, knowledge of a sales position (realistic job
expectations), communication skills, etc. without asking 'Are you a dependable
person'

36. 4. Biodata: Future Research Directions
4.1 What is biodata?
 Doubtful that rigorously designed empirical biodata studies will result in a consensus, more likely
that cogent arguments by experts will need to persuade the research community
4.2 Do results for concurrent validity studies generalize to
a selection context?
 Increase predictive validity research designs
 Concurrent validity coefficients may overestimate coefficients for applicants
4.3 Increased research with an item-focus
 Increase attention to item-level issues
 Work vs. education, amount vs. time
4.4 Greater focus on the use of technology
 Individual differences  different questions  different scoring keys?

37. 4. Biodata: Future Research Directions
4.5 Ways to increase the accuracy of biodata information
 Elaboration lowers scores (decrease faking), but does this increase validity?
4.6 The value of a biodata clearinghouse
 Easier scale development but risk of compromising „answers‟
4.7 Rethinking the use of a factorial biodata development
strategy
 Varimax rotation-constructs found may be correlated (explain little
variance)
 PCA-large number of biodata items used warrants large sample size
 May result if valid biodata items being dropped from final scale
 Confirmatory Factor Analysis may be more appropriate

38. 5. Concluding Remarks
Concerns are unfounded or only true for certain biodata scales
 Validity
 Research has shown biodata to be an excellent predictor
 Legality
 Adverse impact and a lack of face validity may be minimized by careful selection
of items
 Practicality
 Biodata scales do not need to involve a large number of items
 Barrick and Zimmerman (2005) and O‟Connell et al. (2002) used < 10 items
To further use of biodata and research 3 issues need to be addressed
1. Agreement on what biodata is
2. Greater reliance on predictive validity designs
3. Greater attention given to the specific biodata items used in studies