RESEARCH METHOD COHEN ok

570 MULTIDIMENSIONAL MEASUREMENT
variables with factor loadings as follows were selected
for inclusion in their respective factors: > 0.51
(factor 1), > 0.44 (factor 2), > 0.55 (factor 3),
> 0.44 (factor 4), and > 0.64 (factor 5). The
factors are named, respectively: Leadership skills in
school management; Parent and teacher partnerships
in school development; Promoting staff development by
creativity and consultation; Respect for, and confidence
in, the senior management team; andEncouraging staff
development through participation in decision-making.
(See http://www.routledge.com/textbooks/
9780415368780 – Chapter 25, file SPSS Manual
25.1.)
Having presented the data for the factor analysis
the researcher would then comment on what
it showed, fitting the research that was being
conducted.
Factor analysis is based on certain assumptions
which should be maintained in order to serve
fidelity to this technique, for example:
The data must be interval and ratio.
The sample size should be no fewer than around
150 persons. 1
There should be at least 5 cases for each
variable (Pallant (2001: 153) suggests 10 cases
for each variable).
The relationships between the variables should
be linear.
Outliers should be removed.
The data must be capable of being factored.
To achieve this, several of the correlations
should be of 0.3 or greater, the Bartlett test
of sphericity (SPSS calculates this at the
press of a button) should be significant at
the 0.05 level or better, and the Kaiser-
Meyer-Olkin measure of sampling adequacy
(calculated automatically by SPSS) should be
at 0.6 or above.
Factor analysis: an example
Factor analysis, we said earlier, is a way of
determining the nature of underlying patterns
among a large number of variables. It is particularly
appropriate in research where investigators aim to
impose an ‘orderly simplification’ (Child 1970)
upon a number of interrelated measures. We
illustrate the use of factor analysis in a study of
occupational stress among teachers (McCormick
and Solman 1992).
Despite a decade or so of sustained research, the
concept of occupational stress still causes difficulties
for researchers intent upon obtaining objective
measures in such fields as the physiological and
the behavioural, because of the wide range of individual
differences. Moreover, subjective measures
such as self-reports, by their very nature, raise questions
about the external validation of respondents’
revelations. This latter difficulty notwithstanding,
McCormick and Solman (1992) chose the
methodology of self-report as the way into the
problem, dichotomizing it into, first, the teacher’s
view of self, and second, the external world as it is
seen to impinge upon the occupation of teaching.
Stress, according to the researchers, is considered
as ‘an unpleasant and unwelcome emotion’ whose
negative effect for many is ‘associated with illness
of varying degree’ (McCormick and Solman
1992). They began their study on the basis of the
following premises:
Occupational stress is an undesirable and
negative response to occupational experiences.
To be responsible for one’s own occupational
stress can indicate a personal failing.
Drawing on attribution theory, McCormick and
Solman (1992) consider that the idea of blame is
akeyelementinaframeworkfortheexploration
of occupational stress. The notion of blame for
occupational stress, they assert, fits in well with
tenets of attribution theory, particularly in terms
of attribution of responsibility having a selfserving
bias. 2 Taken in concert with organizational
facets of schools, the researchers hypothesized
that teachers would ‘externalize responsibility
for their stress increasingly to increasingly
distant and identifiable domains’ (McCormick and
Solman 1992). Their selection of dependent and
independent variables in the research followed
directly from this major hypothesis.
McCormick and Solman (1992) developed a
questionnaire instrument that included 32 items
to do with occupational satisfaction. These were