Notes from 3rd year Organisational Psychology module
includes a write-up of lecture slides, key studies within the syllabus, relevant studies beyond the scope of teaching (required to get a first class honours) and future research suggestions
Aim: To explore the history, use and abuse of biodata as a selection device in
industry.
Objective: To let students see how to, devise, and validate a method of selection
slowly growing in popularity in certain areas.
Theoretical background
Biodata – biographical accounts of past events – are information concerning an
individual’s personal life history and experience. Biodata are theoretically based
on the assumption that past behaviour is the best predictor of future behaviour;
that is, the sort of experiences that a person has had and the ways they have
coped with them best predict their future behaviour. Applicants’ behaviour up to
the point at which they are applying for a job should be a good predictor of their
performance on the job after they are being hired.
Breaugh (2009) further suggests that past behaviours and experiences serve as
a proxy (alternative) measure for person attributes. For example, a researcher
might assume that, having spent considerable time in a prior position that
involved external sales, the person is dependable, knows what selling involves,
and has good communication skills. Such an indirect measure may be
appropriate when the person variables of interest are not easily measured (e.g.
applicants may be motivated to distort their responses). Given that biodata has
been shown to be one of the best predictors of new employee performance and
retention, such an indirect approach to person measurement seems to be well-
grounded.
The social identity theory (SIT) has been used to account for the predictive value
of specific types of events and behaviours. SIT states that every person has a
self-concept, which comprises his personal identity (attributes of the person that
are personal in nature) and social identity (defining aspects of a person
expressed in terms of psychologically belonging to a perceived social category).
From an SIT perspective, every experience has the potential to shape
subsequent behavioural patterns, though mitigated by the effects of the personal
identity and social identifications. For instance, when a person associates with a
team, club, school, or any other psychological group, the person takes on to
varying degrees the syndrome of aspirations, preferences, values, and self-
perceptions that are endemic to group members. Peripheral compulsions or
inhibitions may also become part of the person’s behavioural repertoire. These
influences on behaviour may outlive the person’s active or continued
involvement in the group. Thus, biodata items encompass the effects of all
characteristics internalized through identification with the psychosocial entities
with whom one interacts.
Owens (1971) developed a theoretical framework for biodata, the
Developmental/Integrative model. Underlying the DI model is the notion that
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, different kinds of individuals undergo differing patterns of experiences as they
develop, and that identifying these experiential patterns also identifies the kinds
of persons. Past experiences need to be described not only in terms of their
outcomes, but also in terms of the values, traits and behaviours of the individual.
Hence the DI model is developmental in that it emphasizes antecedent life
experiences that represent fundamental inputs at critical stages in a person’s
development. To develop the model, Owens and Schoenfeldt (1979) reported a
procedure in which an initial pool of 2000 items was generated representing a
wide range of behavioural and personal history categories. Following further
statistical analyses, a 118-item short form was developed composed of those
items which best accounted for variation in response. Empirical support for the
DI model has been found in certain stable factor structures and scoring keys for
biodata items over long periods of time (Lautenschlager & Shaffer, 1987; Brown,
1978), as well as its usefulness in the interpretation of concurrent measurement
and field studies (Mumford & Owens, 1984). Unfortunately, such evidence does
not yield great insight into how particular developmental experiences influence
behaviour. In other words, the level of understanding of why biodata inventories
predict future performance has not kept pace with the development of different
types of biodata items.
In an introduction to a special issue on biodata in the Human Resource
Management Review journal, Stokes (1999) stressed that ‘the role of life history
in determining the success of individuals in different jobs has not yet been
adequately understood’. Although a decade has passed, Breaugh (2009) posits
that this concern remains. That is, at present we still do not have a sound
understanding of why biodata predicts employee behaviour.
Constructing a biodata instrument
Biodata items
Hard vs soft
A notable distinction is between ‘hard’ and ‘soft’ items. The former represents
historical and verifiable information about an individual, whereas the latter are of
more abstract nature and cover value judgments, aspirations, motivations,
attitudes and expectations.
Some writers have argued that only an individual’s historical experiences, events
or situations that are verifiable should be classified as biographical information.
Gunter et al. (1993) contests that biodata items that are historical and verifiable
may result in a narrow, yet representative set of data about the individual, while
the enlarged classification may be quite unrepresentative.
There is considerable disagreement among researchers regarding the defining
attributes of biodata items. This is problematic because if biodata is seen as
including such things as interests, personality, skills and values, it becomes
difficult to distinguish biodata measures from other measures.
Although research comparisons of the two have generally shown that hard items
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