Structure of biodata

Mumford, Stokes and Owens’ (1990) ecology model postulated that biodata can be organized in terms of core knowledge, skill, ability, value and expectancy variables. These explain how people develop their characteristic patterns of adaptation at work and elsewhere. These constructs ‘facilitate the attainment of desired outcomes while conditioning future situational choice by increasing the likelihood of reward in certain kinds of situations’ (p. 81).

Dean and Russell (2005) replicated these constructs using 142 biodata items and over 6,000 newly hired air traffic controllers. Part of the success of this study can be attributed to the fact that the authors combined rationally designed items, based on Mumford and Owens’ (1987) approach, with traditional empirical data. Correlations between the various biodata scales, cognitive ability scores and a composite performance criterion can be found in this study. Overall, biodata correlated with job performance almost as well as cognitive ability. Furthermore, Dean and Russell corrected restriction of range in cognitive ability (the uncorrected correlation between cognitive ability and the criterion was 0.16, and the corrected correlation for biodata and the criterion was 0.43).

Although the wider literature has provided compelling evidence that cognitive ability tests, particularly general mental ability scores, are the best single predictor of work performance. Dean and Russell’s results provide robust evidence in support of the validity of coherently constructed and scored biodata scales, not least because they organized their items according to established constructs (interpersonal skills, personality and values). Among the different scales or aspects of biodata, intellectual resources predicted job performance best, followed by choice processes and social and personality resources; filter processes were only weakly related to job performance.

Studies have also shown that using purpose-built biodata that include a defined structure (different scales) can be used successfully to predict performance in college, even when entry exam scores (SATs) and personality factors are taken into account (Oswald, Schmitt, Kim, Ramsay & Gillespie, 2004). Oswald and colleagues looked at 115 items of biodata in a sample of 654 college students and identified 12 major dimensions. These included knowledge (‘Think about the last several times you have had to learn new facts or concepts about something. How much did you tend to learn?’), citizenship (‘How often have you signed a petition for something you believe in?’), leadership (‘How many times in the past year have you tried to get someone to join an activity in which you were involved or leading?’), and ethics (‘If you were leaving a concert and noticed that someone had left their purse behind with no identification, what would you do?’), which they used to predict final academic grades. Most a’s were higher than 0.6, with the exception of adaptability, career and interpersonal, which had lower internal consistencies. On the other hand, all factors except ethics correlated only modestly with impression management.

Oswald and colleagues (2004) also tested the extent to which their 12 biodata factors predicted GPA, absenteeism and peer ratings while controlling for SATs and personality scores. Their results showed that six facets were still significantly linked to these outcomes even when previous academic performance and psychometrically derived trait scores were included in the regression model. Leadership and health were linked to GPA, citizenship, interpersonal and learning predicted peer ratings, and absenteeism was predicted by health and ethics.

Manley, Benavidez and Dunn (2007) compared the predictive power of two self-reported measures of personality (locus of control and conscientiousness) with biodata measures of the same constructs. Results revealed that the biodata versions of these two constructs predicted ethical decision making better than the self-reported (personality-style) measures did.

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