Wednesday, March 11, 2015

Biographical Data (Biodata) Tests

Biodata measures are based on the measurement principle of behavioral consistency, that is, past behavior is the best predictor of future behavior. Biodata measures include items about past events and behaviors reflecting personality attributes, attitudes, experiences, interests, skills and abilities validated as predictors of overall performance for a given occupation.

Often, biodata test items are developed through behavioral examples provided by subject matter experts (SMEs). These items specify situations likely to have occurred in a person’s life, and ask about the person’s typical behavior in the situation. In addition, biodata items reflect external actions that may have involved, or were observable by, others and are objective in the sense there is a factual basis for responding to each item. An item might ask “How many books have you read in the last 6 months?” or “How often have you put aside tasks to complete another, more difficult assignment?” Test takers choose one of several predetermined alternatives to best match their past behavior and experiences.

A response to a single biodata item is of little value. Rather, it is the pattern of responses across several different situations that give biographical data the power to predict future behavior on the job. For this reason, biodata measures often contain between 10 and 30 items and some wideranging instruments may contain a hundred or more items. Response options commonly use a 5- point scale (1 = Strongly Disagree to 5 = Strongly Agree). Once a group of biodata items is pretested on a sample of applicants, the responses are used to group the items into categories or scales. Biodata items grouped in this way are used to assess how effectively applicants performed in the past in competency areas closely matched to those required by the job.

A more recent development is targeted biodata instruments. In contrast to traditional biodata measures developed to predict overall job performance, targeted biodata measures are developed to predict individual differences in specific job-related behaviors of interest. Similar to the developmental process used for traditional biodata, the content of a targeted biodata measure is often driven by SME-generated behavioral examples relevant to the specific behavior(s) of interest.

An example of a targeted biodata measure is a job compatibility measure (sometimes referred to as a suitability measure) which focuses on the prediction of counterproductive or deviant behaviors. Counterproductive behavior is often defined as on-the-job behavior that is (a) harmful to the mission of the organization, (b) does not stem from a lack of intelligence, and (c) is willful or so seriously careless it takes on the character of being willful. Previous criminal misconduct (e.g., theft), employment misconduct (e.g., sexual harassment, offensiveness to customers, and disclosure of confidential material), fraud, substance abuse, or efforts to overthrow the Government are some major factors that may be relevant to suitability determinations. A job compatibility index is typically used to screen out applicants who are more likely to engage in counterproductive behavior if they are hired. Job compatibility measures are less costly to implement than other procedures typically used to detect counterproductive behaviors (e.g., interviews, polygraphs) and are beneficial for positions requiring employees to interact frequently with others or handle sensitive information or valuable materials.


• Validity – Biodata measures have been shown to be effective predictors of job success (i.e., they have a moderate degree of criterion-related validity) in numerous settings and for a wide range of criterion types (e.g., overall performance, customer service, team work); Biodata measures have also appeared to add additional validity (i.e., incremental validity) to selection systems employing traditional ability measures

• Face Validity/Applicant Reactions – Because some biodata items may not appear to be job related (i.e., low face validity) applicants may react to biodata tests as being unfair and invasive

• Administration Method – Administered individually but can be administered to large numbers of applicants via paper and pencil or electronically at one time

• Subgroup Differences – Typically have less adverse impact on minority groups than do many other types of selection measures; Items should be carefully written to avoid stereotyping and should be based on experiences under a person’s control (i.e., what a person did rather than what was done to the person)

 • Development Costs – The development of biodata items, scoring strategies, and validation procedures is a difficult and time-consuming task requiring considerable expertise; Large samples of applicants are needed to develop as well as validate the scoring strategy and additional samples may be needed to monitor the validity of the items for future applicants

• Administration Costs – Can be cost effective to administer and generally not time consuming to score if an automated scoring system is implemented

• Utility/ROI – High predictive ability can allow for the identification and selection of top performers; Benefits (e.g., savings in training, high productivity, decreased turnover) can outweigh developmental and administrative costs

 • Common Uses – Commonly used in addition to cognitive ability tests to increase validity and lower adverse impact

Elkins, T., & Phillips, J. (2000). Job context, selection decision outcome, and the perceived fairness of selection tests: Biodata as an illustrative case. Journal of Applied Psychology, 85(3), 479-484.

Hough, L. M., & Oswald, F. L. (2000). Personnel selection: Looking toward the future— Remembering the past. Annual Review of Psychology, 51, 631-664.

Mount, M. K., Witt, L. A., & Barrick, M. R. (2000). Incremental validity of empirically keyed biodata scales over GMA and the five factor personality constructs. Personnel Psychology, 53(2), 299-323.

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(2), 175-184.

Schmitt, N., Cortina, J. M., Ingerick, M. J., & Wiechmann, D. (2003). Personnel selection and employee performance. Handbook of Psychology: Industrial and Organizational Psychology, 12, 77-105. New York, NY: John Wiley & Sons, Inc.

@@@@@  Biographical Data

The content of biographical data instruments varies widely, and may include such areas as leadership, teamwork skills, specific job knowledge and specific skills (e.g., knowledge of certain software, specific mechanical tool use), interpersonal skills, extraversion, creativity, etc.   Biographical data typically uses questions about education, training, work experience, and interests to predict success on the job.  Some biographical data instruments also ask about an individuals attitudes, personal assessments of skills, and personality.    

  • Can be administered via paper and pencil or computerized methods easily to large numbers.
  • Can be cost effective to administer.
  • Have been demonstrated to produce valid inferences for a number of organizational outcomes (e.g., turnover, performance).
  • Are typically less likely to differ in results by gender and race than other types of tests.
  • Does not require skilled administrators.
  • Can reduce business costs by identifying individuals for hiring, promotion or training who possess the needed skills and abilities.

  • May lead to individuals responding in a way to create a positive decision outcome rather than how they really are (i.e., they may try to positively manage their impression or even fake their response).
  • Do not always provide sufficient information for developmental feedback (i.e., individuals cannot change their past).
  • Can be time-consuming to develop if not purchased off-the-shelf.

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