[Host's Note: a brief description of "Preference for Numerical
Information" is appended below. ...Rick]
Thanking you.
Madhu Viswanathan
Univ. of Illinois
Dept. of Bus. Admin.
email: mviswana@uiuc.edu
Excerpts from the paper (JAP, 1993)
Brief Description of the Preference for Numerical Information
Construct.
`Preference for numerical information' (PNI) is a construct that
taps a proclivity toward using numerical information and engaging in
thinking involving numerical information, and studies its relationship
with several constructs. Many authors have documented the importance of
numerical information in several spheres of everyday life (cf., Paolos,
1988; Steen, 1990). While past research has focused on ability in using
numerical information on the one hand, and on attitudes toward domains
involving numerical information such as statistics and mathematics on the
other, a basic attitude or proclivity toward numerical information has not
been isolated and studied. The importance of studying attitude toward
numerical information as a separate and distinct domain is emphasized
here, and the construct is argued to have applications in several
settings. Examples of Applications and Future Research Ideas in
Occupational Settings.
PNI may also be useful in occupational settings as an input in
hiring, training, and counseling decisions and in assessing biases in job
analyses and performance appraisals. Steen (1990) points out the
widespread use of numerical information in the workplace with the advent
of information technology, such as assembly line workers using information
about statistical process control, bank clerks working with investment
risks, or doctors assessing comparative risks of procedures. With the
widespread availability of calculators, computers, and decision support
systems, several jobs may involve the usage of large amounts of numerical
information in decision making while requiring a minimum level of
mathematical ability. Attitude toward numerical information rather than
ability may be central to the nature of several jobs and, consequently,
measures such as PNI may be of importance here whereas measures of ability
have been used in the past. Further, PNI may influence individuals=B9
learning of numerical skills in jobs which require them, as well as
individuals=B9 inclination to apply or use these skills in job-related
situations. Therefore, PNI could be used to supplement measures of
numerical ability in occupational settings. The Dictionary of
Occupational Titles (cf., Miller, Treiman, Cain, and Roos, 1980; Cain and
Green, 1983) describes worker functions in terms of the relationship of
jobs to data (such as numerical information). These worker functions such
as synthesizing, coordinating, analyzing, compiling, computing, copying,
and comparing data (cf., Miller et al., 1980) may involve the use of
numerical information while requiring different levels of mathematical
ability. PNI could provide diagnostic information for jobs involving
numerical data that could be used in hiring, counseling, and training
decisions. PNI could be used to supplement information from numerical
aptitude tests such as the General Aptitude Test Battery (cf., Miller et
al., 1980) and ratings of temperaments and interests. Future research
should focus on the relationship between PNI and occupational types such
as those available from the Dictionary of Occupational Titles (Miller et
al., 1980) or Holland=B9s (1985) vocational types. PNI could also be used
to study whether attitude rather than ability leads to better performance
in jobs where interest in numerical information rather than some form of
mathematical ability may be important. (In this connection, whereas
gender differences may exist in tests of mathematical ability, there was
no evidence of gender differences in PNI in this study.)
Job analysis procedures involve the use of qualitative information
as well as quantitative information such as quantitative ratings on job
characteristics (cf., McCormick, Jeanneret, and Mecham, 1972). Similarly,
performance evaluation could be based on both qualitative information
(such as visual observation of employees) and quantitative information
(such as quantitative data from Computerized Performance Monitoring
systems (cf., Nussbaum and duRivage, 1986)). Biases in job analysis and
performance evaluation are important issues in occupational settings (cf.,
Hahn and Dipboye, 1988; Thompson and Thompson, 1985). A basic preference
for numerical information could be an important source of bias in the
usage of qualitative versus quantitative data during job analyses and
performance evaluations. Hence, the relationship between PNI and bias
toward quantitative versus qualitative data in job analyses and
performance evaluations could be examined in specific occupational
settings. Further, the PNI scale could be used to develop weighting
schemes for correcting for bias in job analyses and performance
evaluations and to develop training programs to reduce such bias. Future
research should assess the relationship between PNI and bias in using
qualitative versus quantitative data in job analyses and performance
evaluations.
--madhu <mviswana@ux1.cso.uiuc.edu>
Learning-org -- An Internet Dialog on Learning Organizations For info: <rkarash@karash.com> -or- <http://world.std.com/~lo/>