We consider firstly the general population sample since it both
validates the method which we will subsequently use for the medical
student samples, and also helps calibrate the axes.
The general population sample
The questionnaire was completed by 1044 subjects, 49.2% of whom were
male, and 46% aged over 42. Multidimensional scaling used the INDSCAL
method, with the four groups comprising older and younger males and
older and younger females. The stress plot indicated that there were
two major underlying dimensions in the data, as Holland's typology
would suggest (see also Prediger [53,35]). The locations of the different careers are shown in figure 2.
There is good evidence for Holland's RIASEC typology, and the letters
R, I, A, S, E and C have been placed on the graph to clarify
interpretation. Pilot and Engineer are typical of Realistic careers,
Biologist of Investigative careers, Artist and Museum Curator of
Artistic careers, Social Worker, Counsellor and Teacher of Social
careers, Personnel Director and Lawyer of Enterprising careers, and
Accountant and Computer Programmer of Conventional careers. For this
non-medical group of subjects, the four medical careers are all placed
in the top half of the figure, with surgeon and anaesthetist closest to
Investigative, and Psychiatrist closest to Social.
Of some importance, given the arbitrariness of the
Holland hexagon to rotation in conventional MDS, is that the INDSCAL
analysis clearly sets one axis as running from R to S, with the other
axis orthogonal to that, running from I and A to C and E. These are
similar to the Things-People and Ideas-Data dimensions shown in figure 1.
The medical student samples
Sample sizes for the medical student studies were 1135, 2032 and
2973 for the students in the 1981, 1986 and 1991 cohorts (and these
samples consisted of all applicants in the 1981 and 1986 cohorts, and all entrants in
the 1991 cohort), and were 330, 376 and 1437 for the final-year
students in the 1981, 1986 and 1991 cohort studies. The INDSCAL
analyses were restricted to those subjects for whom complete career
information was available; this consisted of 538 applicants and 312
final-year students in the 1981 cohort, 1118 applicants and 301
final-year students in the 1986 cohort, and 1638 entrants and 1437
final-year students in the 1991 cohort. See 1 for details of the breakdown of samples by sex and maturity.
The dimensionality of the medical student samples was assessed by
carrying out a standard multi-dimensional scaling analysis (i.e. MDS,
not INDSCAL), separately for the combined applicant data and the
combined final-year data from the three cohorts. The stress formula
attempts to quantify the discrepancies between the fitted distances in
the model and the observed dissimilarities among the career ratings,
with larger values indicating poorer fit. It is obviously the case that
the more dimensions are extracted, the better the fit of the model and,
hence, the lower the stress value. However, it is also the case that a
greater number of dimensions complicates interpretation and may lead to
overfitted and unstable solutions. The stress levels with 1,2,3,4,5,
and 6 dimensions were .352, .174, .112, .077, .059 and .048 for
applicants, and .390, .174, .112, .082, .064 and .052 for final-year
students. For the final-year students, it is clear that two dimensions
are necessary, and that there is little advantage of adding extra
dimensions. The applicant data are slightly less clear and although
there is still no doubt that at least two dimensions are necessary
there is a suggestion that a third dimension may be of value.
Subsequent scrutiny of models with three dimensions suggested that the
third dimension was contributed almost entirely by one or two
specialities such as forensic medicine, which have a high public and
media profile, but which form only a small proportion of medical
personnel. It was, therefore, felt to be safe to extract two
dimensions, particularly since Holland's typology provided an a priori expectation that there would be two dimensions.
INDSCAL analyses
Separate analyses were carried out for the
applicant and final-year data in each of the three cohorts. In each
case, data were broken down into sub-groups according to sex
(male-female) and age (mature at entry to medical school, i.e. >21
yrs old; or typical post-school entry, at ≤ 21 years old). INDSCAL
analyses can clarify the underlying dimensions within data as long as
the sub-groups are likely to vary along those dimensions. It should be
noted that many studies have found sex differences in medical career
interest (e.g. [54]), and younger students are also likely to have different attitudes towards careers than their non-mature counter-parts [55].
Figures 3,4 and 5 show the group plots of the different specialities in applicants to medical school, and figures 6, 7 and 8
show the group plots for the specialities in final-year medical
students. In order to help interpret these plots, and for reasons which
will become clearer later, we have joined together the data points for
Surgery, Hospital Medicine, Psychiatry, Public Health, Administrative
Medicine and Laboratory Medicine. For the applicants, it is now clear
that these specialities are arranged approximately in the form of a
hexagon, with Surgery at the extreme left and Administrative Medicine
at the bottom right-hand corner. The pattern shown in the final-year
students is similar, Surgery still being at the left-hand side, and
Administrative Medicine at the bottom right. Although there are some
minor differences between the three cohorts, the broad picture is of
overall similarity in the structure of the maps.
The maps shown in figures 3 to 8 are, in INDSCAL terminology, group spaces [44,45].
They are, however, composed of several different sources, broken down
by age and sex. Maps can also be produced of 'source space' which shows
how the groups differ in their relative weighting of the two extracted
dimensions. Figure 9
shows the source spaces for the applicant and final-year student data
in the three cohorts. The vertical axis represents the relative
importance of the Things-People dimension, whereas the horizontal
dimension shows the importance of the Data-Ideas dimension. It should
be noted that these axes do not mean that, say, People are
more important than Things, but that the Things-People dimension is
more differentiated than the Data-Ideas dimension (just as, say, in a
map of Italy or Chile, there is far more north-south differentiation
than east-west, as they are long-thin countries). In each of the six
analyses, the male subjects put more emphasis on the Things-People
dimension whereas the female subjects put more emphasis upon the
Data-Ideas dimension (and hence the male subjects tend to be in the top
left corner and the female subjects in the bottom-right). In the 1981
cohort there is also a suggestion that younger subjects put more
emphasis on the Things-People dimension, and older subjects on the
Data-Ideas dimension for differentiating careers, but the effect is
smaller in the 1986 cohort, and barely visible in the 1991 cohort,
suggesting a possible change in the way these groups perceive medical
careers. In interpreting these analyses it should be noted that
although the absolute size of the various groups was more than adequate
for the INDSCAL analyses, the group weights for the mature candidates
(male and female) in the 1981 and 1986 finalyear data were based on
very small samples, ranging between 5 and 17 participants, and may,
therefore, be somewhat unstable.
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