USING BIODATA

Elizabeth Allworth PhD. 

Allworth Juniper, Psychologists

13 August 1999

What is biodata?

Benefits and limitations

Alternative scaling methods

Research findings

BIODATA

What is it?

BIOgraphical DATA

A standardised method of assessing job-relevant biographical information. 

Information spans past experiences, behaviours or activities in any aspect of life (work, education, leisure, community involvement etc). 

Based on the premise that the best predictor of future performance is past performance - behavioural consistency. 

Origins of biodata:

1894 - Washington Life Insurance Company 

where applicant lives, how many dependents, marital status etc. 

items weighted according to empirical relationship with performance criteria and capacity to differentiate good and poor performers.

Atheoretical. 

Empirical keying approaches still most common but call for rational, theoretically driven item generation and scaling (Dunnette, 1962; Pace & Schoenfeldt, 1977).

SAMPLE BIODATA ITEMS

1.In the past 3 years, the number of different paying jobs I have held for more than 2 weeks is:

a.7 or more

b.5-6

c.3-4

d.1-2

e.None

2.In my leisure time, the activities I engage in most often are:

a.Reading

b.Social activities

c.Constructing things

d.Sports

e.None of the above


SAMPLE BIODATA ITEMS (cont)

3.Think about how you usually behave when changes are occurring in your life.

How often have you done each of the following?

1.Not at all 

2.Seldom

3.Occasionally

4.Often

5.Very often


 
a)Worked actively to ensure that change occurred
1
2
3
4
5
b)Delayed a change for as long as you could
1
2
3
4
5
c)Carefully planned what you would do to make a change
1
2
3
4
5
d)Left things for someone else to deal with
1
2
3
4
5



ATTRIBUTES OF BIODATA ITEMS

Biodata taxonomy (Mael, 1991) 
 
Historical
Have you worked in an open plan office?
vs
Hypothetical
How well would you work in an open plan office?
Behavioural
Do you always type your own reports?
vs
Attitudinal
What is your opinion on managers doing their own typing?
Verifiable
What was your grade point average at uni?
vs
Non-verifiable
How hard do you study?
Objective
How many sales did you make last year?
vs
Subjective
Would you describe yourself as a good salesperson?
Controllable
Have you ever worked in this industry?
vs
Non-controllable
Have any of your family worked in this industry?
First hand
Do you plan tasks before acting?
vs
Second hand
How would others describe your planning ability?



Benefits of Biodata

Relatively high predictive validity

Mean predictive validity of .30 - .40 for a range of criteria (training, job performance, tenure, promotions).

Reilly & Chao (1982). 

Schmitt, Gooding, Noe & Kirsch (1984).

Hunter & Hunter (1984).

Snell, Stokes, Sands & McBride (1994).

Compared with: 

cognitive ability .53

reference check.26

interview.14 - .38

academic achievement.11

personality -.13-.33

Less adverse impact than cognitive ability

Biodata among the most favourable of assessment techniques in terms of minimising adverse impact, although different keys may be needed for males and females (Reilly & Chao, 1982).

Particular types of items produce more adverse impact than others but these can be detected and removed (Whitney & Schmitt, 1997).

Validity of biodata not moderated by age, gender, race, age, education or experience (Rothstein et al., 1990).

Utility - Cost benefits of selection

Developmental costs modest compared with assessment centres and work samples (Hunter, 1986).

Easy to administer across large samples. 

Generalisable across situations (Rothstein et al., 1990).

Higher validity increases cost benefits, particularly when selection ratio is high, ie many candidates for few positions.

Incremental validity over cognitive ability

Prediction of typical rather than maximal performance.

Measures attitudinal aspects of performance not measured by cognitive ability.

Incremental validity over personality

McManus & Kelly (1999); Mael & Hirsch (1993).

More reliable than personality measures

Biodata less susceptible to faking and other forms of response distortion - responses verifiable.

Behavioural items more reliable than attitudinal items.

Alternative Biodata Scaling Methods

Empirical Scaling

Empirical keying at the item or item response-option level.Items weighted in accordance with correlation with criterion.

Benefits of empirical keying:

Maximises prediction within specific sample.

Less susceptible to faking.

Disadvantages:

Face validity.

Not conceptually linked with the criterion - post hoc analysis of predictor-criterion relationships.

Weak generalisability – capitalises on sample specific variance.

Construct-oriented biodata

Based on a job analysis.

Items that are conceptually related to the criterion are generated i.e., predictor-criterion relationships are hypothesised a priori. 

Homogeneous scales are developed (principal components and reliability analysis).

Benefits of construct-oriented approach:

Validated on smaller sample sizes.

Assist conceptual/theoretical understanding of predictor-criterion relationships.

Job-relatedness increases legal defensibility.

Generalisability across samples.

Validity higher for specific than global scales (Stokes & Searcy, 1999; Karas & West, 1998, 1999). 

Disadvantages:

Amenable to faking.

Possibly weaker predictive validity although evidence not consistent. 

Most biodata questionnaires rely on a combination of rational item generation and empirical keying techniques (Mael & Hirsch, 1993; Mitchell, 1986; Owens & Schoenfeldt, 1979).

CONCLUSIONS

Biodata amenable to measuring a range of cognitive and non-cognitive constructs.

Biodata shows incremental validity over cognitive performance.

Need to develop generalisable biodata scales.

Job requirements biodata offers a potentially productive research pursuit.

Preferred scaling method may depend on goal of biodata development – situation-specific versus generalisable.


Biodata References

Allworth, E., & Hesketh, B. (1998).Generalisability of Construct-oriented Biodata Scales in Predicting Adaptive Performance. Paper presented at the annual meeting of The Society for Industrial and Organisational Psychology, Dallas, TX, USA, April.

Allworth, E., & Hesketh, B. (1999).Construct-oriented biodata:Capturing change-related and contextually relevant future performance. International Journal of Selection & Assessment, 7, 97-111.

Borman, W. C., & Motowidlo, S. J. (1993).Expanding the criterion domain to include elements of contextual performance.In N. Schmitt, W.C. Borman, & Associates (Eds.), Personnel Selection in Organisations.San Francisco: Jossey-Bass Publishers

Dunnette, M. D. (1962).Personnel Management.Annual Review of Psychology, 13, 285-314.

Hesketh, B., & Allworth, E. (1999).Job requirements biodata as a predictor of performance in customer service roles.Paper presented at The Third Annual Australian Industrial and Organisational Psychology Conference, Brisbane, Australia, June

Hunter, J. E. & Hunter, R. F. (1984) Validity and utility of alternative predictors of job performance.Psychological Bulletin, 96 (1) 72-98.

Hunter, J.E. (1986) Cognitive ability, cognitive aptitudes, job knowledge, and job performance.Journal of Vocational Behaviour, 29, 340-362.

Karas, M., & West, J. (1998).An explicit construct-oriented approach to the development of rational-empirical biodata.Paper presented at the annual meeting of The Society for Industrial and Organisational Psychology, Dallas, TX, USA, April.

Karas, M., & West, J., (1999).Construct-oriented biodata development for selection to a differentiated performance domain. International Journal of Selection & Assessment, 7, 86-96.

Kilcullen, R. N., White, L. A., Mumford, M. D. & Mack, H. (1995). Assessing the construct validity of rational biodata scales.Military Psychology, 7, (1), 17-28.

Kluger, A. N. & Colella, A. (1993). Beyond the mean bias: The effect of warning against faking on biodata item variances.Personnel Psychology, 46, 763-780.

Mael, F. A. & Hirsch, A. C. (1993). Rainforest empiricism and quasi-rationality: Two approaches to objective biodata. Personnel Psychology, 46, 719-738.

Mael, F. A. (1991) A conceptual rationale for the domain and attributes of biodata items.Personnel Psychology, 44, 763-792.

McManus, M. A., & Kelly, M. L. (1999) Personality measures and biodata:Evidence regarding their incremental predictive value in the life insurance industry.Personnel Psychology, 52, 137-148.

Mitchell, T. W. (1994). The utility of biodata. In Stokes, G.S., Mumford, M.D. and Owens, W.A. (Eds.), Biodata Handbook: Theory, Research and Use of Biographical Information in Selection and Performance Prediction. CPP books, Pan Alto.

Mumford, M. D. & Owens, W. A. (1987). Methodology review:Principles, procedures, and findings in the application of background data measures.Applied Psychological Measurement, 11, 1-31.

Owens, W. A. & Schoenfeldt, L. F. (1979). Toward a classification of persons. Journal of Applied Psychology Monograph, 65, 569-607.

Pace, L. A., & Shoenfeldt, L. F. (1977).Legal concerns in the use of weighted applications.Personnel Psychology, 35, 1-63.

Reilly, R. R. & Chao, G. T. (1982) Validity and fairness of some alternative employee selection procedures.Personnel Psychology, 35, 1-62.

Rothstein, H. R., Schmidt, F. L., Erwin, F. W., Owens, W. A., & Sparks, C. P. (1990).Biographical data in employment selection:Can validities be made generalizable?Journal of Applied Psychology, 75, 175-184.

Schmitt, N., Gooding, R. Z., Noe, R. A. & Kirsch, M. (1984). Meta-analyses of validity studies published between 1964 and 1982 and the investigation of study characteristics. Personnel Psychology, 37, 407-422. 

Snell, A. F., Stokes, G. S., Sands, M. M. & McBride, J. R. (1994). Adolescent life experiences as predictors of occupational attainment. Journal of Applied Psychology, 79, (1), 131-141.

Stokes, G. S., & Searcy, C. A.. (1999).Specification of scales in biodata form development:Rational vs. empirical and global vs specific.International Journal of Selection & Assessment, 7, 72-85.

Stokes, G. S., Mumford, M. D., & Owens, W. A. (1994).Biodata Handbook: Theory, Research and Use of Biographical Information in Selection and Performance Prediction. CPP Books, Pan Alto.

Stokes, G. S., & Searcy, C. A.. (1999).Specification of scales in biodata form development:Rational vs. empirical and global vs specific.International Journal of Selection & Assessment, 7, 72-85.

Whitney, D. J. & Schmitt, N. (1996) The influence of culture on responses to biodata employment items.Paper presented at 11th Annual Conference of the Society for Industrial and Organizational Psychology, San Diego, CA.

Dr Elizabeth Allworth
Allworth Juniper, Psychologists
Suite 3, Level 6, 99 Elizabeth St
SYDNEY NSW 2000
AUSTRALIA
Ph:  61-2-9223 2774
Fax: 61-2-9223 2894
Email:  ajpsych@zip.com.au