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How Well Does Biodata Predicts Performance in the Workplace?

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How well does biodata predict performance in the workplace?
Biodata are biographical accounts of past events involving an individual’s background, including education, childhood, health and job history. To access the biodata of interviewee, interviewers usually ask factual kinds of questions about life and work experiences, as well as items involving opinions, values, beliefs, and attitudes that reflect a historical perspective (Schaffer & Lautenschlager, 1987). The individual can then be given a score based on their biodata. For example, the Weighted Application Blank (Goldsmith, 1922) assigns +1 for 8 years in education, +2 for 10, +3 for 12 and +2 for 16. This method of scoring allows a quick and easy comparison between applicants biodata. There are many advantages to using biodata, including allowing comparisons between candidates on their non-cognitive aspects. Applicants are less likely to lie due to the lack of demand characteristics (no “right” or “wrong” answers), which increases the internal validity of the data. In addition, the biodata must be in a sensitive and acceptable form for the candidates which can be difficult due to the nature of the information. Finally, the predictor variable must be clear, robust and measured by a reliable scale.
Research suggests that biodata may be able to predict job performance (Vinchur, Schippmann, Switzer & Roth, 1998), staff turnover (Mael & Ashforth, 1995) and absenteeism (Stokes & Cooper, 1994). In a study by Carraher & Buckley (2008), 386 nurses completed questionnaires assessing their intentions to quit, look for a new job, be absent and their ratings of performance. The results suggested that behavioural intentions to be absent, to quit, and to look for a new job were all related to performance and turnover but not absenteeism. These findings suggest that attitudes towards something may not always translate into the associated behaviour. Perhaps there are other, more important factors than an individual’s intentions that were not considered in this study. This idea is supported by several studies, reporting that the best predictor for absenteeism is a component of personality - lower emotional stability (Barrick & Mount, 1996; George, 1990).
Similar to the study conducted by Carraher & Buckler (2008), a study by Becton et al., (2009) found that biodata predicted turnover, organizational commitment, and job performance for those working in healthcare organizations. Research into the area of biodata is often conducted using healthcare professionals due to large numbers, low drop outs over time and ease of access. This in turn raises questions about the reliability of the results when generalised to the rest of the population. However, findings like these seem not to be limited to those working in the healthcare profession. A meta-analysis conducted by Vinchur et al., (1998) found biodata could predict supervisor ratings of performance and objective number of sales made. Vinchur et al., (1998)’s study was strong in terms of examining large numbers of studies and having rigorous inclusion criteria. Interestingly, Vinchur et al., (1998) found that general cognitive ability was only correlated (r = .04) with objective sales. This suggests that biodata may be far better at predicting job performance when compared with objective measures of cognitive ability. However, using supervisor ratings of performance is a subjective measure which may be biased by other variables such as how much the supervisor liked the individual. Objective and psychometrically sound scales should be used to measure performance, to allow reliable, unbiased comparisons across individuals. In addition, the results suggested that only the biodata relating to the Big 5 personality traits (Goldberg, 1990) were accurate in predicting job performance. Further research is required to determine which aspects of biodata are the strongest predictors for job performance. The correlations from this study were weak to just moderate (r = .26 - .41) suggesting there may be other important variables which have not been controlled for.
Absenteeism is an important negative work behaviour due to the loss of money associated with the loss of an employee. The findings for biodata accurately predicting absenteeism in the workplace are mixed, probably due to the unpredictability of individuals requiring time off. A study by Argyle, Cioffi and Gardner (1958) found that the type of supervisor was more influential in predicting absenteeism than biodata items. Interestingly, employees who had ‘democratic’ supervisors were less likely to be absent but showed no differences in staff turnover. Many studies looking at students have found similar relationships between biodata items and class absenteeism and intent to quit university (Schmitt, Oswald, Kim, Imus, Merritt, Friede & Shivpuri (2007). However, generalising the findings from students to adults in a work place is likely to be unreliable due to the age-related changes of absenteeism. Several studies have found that absenteeism is inversely related to age (Hackett, 1990; Martocchio, 1989) which in turn suggests that age is an important biodata predictor for job performance.
Although there is a large amount of evidence that suggests biodata predicts performance well, there are other studies which have highlighted certain circumstances can affect its validity. For example, biodata has been shown to be fakeable (Goldstein, 1971). In this experiment, Goldstein (1971) checked the answers applicants had given with their previous employers. Surprisingly, half of the sample overestimated the length of time they worked in their previous job. More seriously, a quarter of applicants gave reasons for leaving the job which their previous employer did not agree with. Shockingly, 17% gave as their previous employer someone who had never heard of them. This experiment demonstrates that biodata may not accurately predict job performance if the individual has given incorrect information. The incorrect information is likely to make the applicant seem better than they are, meaning there will then be a gap between their actual potential performance and the performance their fake biodata predicts. Checking that the applicant has been honest in their biodata would take a lot of time, money and effort which many employers do not have. Another problem with biodata is that in order for it to work well, the individuals in the work place need to be relatively homogenous. Several studies have shown that if only subgroup (within a company) information is used in predictors, all predictive efficiency is lost (Schmidt & Rothstein, 1994). The researchers went onto explain that it is important to take into account subgroup information as well as individual information (to account for lack of homogeneity) to be able to able to accurately predict work behaviours.
In conclusion biodata may be able to predict performance, provided that the applicant is honest and are similar to their colleagues, which research has suggested is not always the case. Age in particular appears to be an important piece of biodata in predicting job performance due to its strong association with absenteeism. The area of biodata is controversial, due to the negative associations with certain traits which can cause many other positive traits to be overlooked. Further research should be conducted in a sensitive way and is required to establish stronger correlations between biodata and work performance.

References
Argyle, M., Gardner, G., & Cioffi, F. (1958). Supervisory methods related to productivity, absenteeism, and labour turnover. Human relations.
Barrick, M. R., & Mount, M. K. (1996). Effects of impression management and self-deception on the predictive validity of personality constructs.Journal of applied psychology, 81(3), 261.
Becton, J., Matthews, M. C., Hartley, D. L., & Whitaker, D. H. (2009). Using biodata to predict turnover, organizational commitment, and job performance in healthcare. International Journal of Selection and Assessment, 17(2), 189-202.
Carraher, S. M., & Buckley, M. R. (2008). Attitudes towards benefits and behavioral intentions and their relationship to Absenteeism, Performance, and Turnover among nurses. Academy of Health Care Management Journal,4(2), 89.
Goldberg, L. R. (1990). An alternative" description of personality": the big-five factor structure. Journal of personality and social psychology, 59(6), 1216.
George, J. M., & Bettenhausen, K. (1990). Understanding prosocial behavior, sales performance, and turnover: A group-level analysis in a service context.Journal of applied psychology, 75(6), 698.
Goldsmith, D. B. (1922). The use of the personal history blank as a salesmanship test. Journal of Applied Psychology, 6(2), 149-155.
Hackett, R. D. (1990). Age, tenure, and employee absenteeism. Human Relations, 43(7), 601-619.
Lautenschlager, G. J., & Shaffer, G. S. (1987). "Reexamining the component stability of Owen's biographical questionnaire". Journal of Applied Psychology, 72(1), 149-152.
Mael, F. A., & Ashforth, B. E. (1995). Loyal from day one: Biodata, organizational identification, and turnover among newcomers. Personnel Psychology, 48(2), 309-333.
Martocchio, J. J. (1989). Age-related differences in employee absenteeism: A meta-analysis. Psychology and Aging, 4(4), 409.
Neiner, A. G., & Owens, W. A. (1985). Using biodata to predict job choice among college graduates. Journal of Applied Psychology, 70(1), 127.
Schmitt, N., Oswald, F. L., Kim, B. H., Imus, A., Merritt, S., Friede, A., & Shivpuri, S. (2007). The use of background and ability profiles to predict college student outcomes. Journal of Applied Psychology, 92(1), 165.
Schmidt, F. L., & Rothstein, H. R. (1994). Application of validity generalization to biodata scales in employment selection.
Stokes, G. S., & Cooper, L. A. (1994). Selection using biodata: Old notions revisited.
Vinchur, A. J., Schippmann, J. S., Switzer III, F. S., & Roth, P. L. (1998). A meta-analytic review of predictors of job performance for salespeople.Journal of applied psychology, 83(4), 586.…...

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