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PLANNING NATURALISTIC RESEARCH 183
and fitness for purpose. It is not to say that
‘anything goes’ but ‘use what is appropriate’ is
sound advice. Mason (2002: 33–4) advocates the
integration of methods, for several reasons:
to explore different elements or parts of a
phenomenon, ensuring that the researcher
knows how they interrelate
to answer different research questions
to answer the same research question but in
different ways and from different perspectives
to give greater or lesser depth and breadth to
analysis
to triangulate (corroborate) by seeking
different data about the same phenomenon.
Mason (2002: 35) argues that integration can
take many forms. She suggests that it is necessary
for researchers to consider whether the data are
to complement each other, to be combined,
grouped and aggregated, and to contribute to
an overall picture. She also argues that it is
important for the data to complement each other
ontologically, to be ontologically consistent, i.e.
whether they are ‘based on similar, complementary
or comparable assumptions about the nature of
social entities and phenomena’. Added to this
Mason (2002: 36) suggests that integration must
be in an epistemological sense, i.e. where the data
emanate from the same, or at least complementary,
epistemologies, whether they are based on ‘similar,
complementary or comparable assumptions about
what can legitimately constitute knowledge of
evidence’. Finally Mason (2002: 36) argues
that integration must occur at the level of
explanation. By this she means that the data from
different courses and methods must be able to
be combined into a coherent, convincing and
relevant explanation and argument.
Stage 8: Data collection outside the field
In order to make comparisons and to suggest
explanations for phenomena, researchers might
find it useful to go beyond the confines of the
groups in which they occur. That this is a thorny
issue is indicated in the following example. Two
students are arguing very violently and physically
in a school. At one level it is simply a fight
between two people. However, this is a common
occurrence between these two students as they
are neighbours outside school and they don’t
enjoy positive amicable relations as their families
are frequently feuding. The two households have
been placed next door to each other by the local
authority because if has taken a decision to keep
together families who are very poor at paying
for local housing rent (i.e. a ‘sink’ estate). The
local authority has taken this decision because of a
government policy to keep together disadvantaged
groups so that targeted action and interventions
can be more effective, thus meeting the needs of
whole communities as well as individuals.
The issue here is: how far out of a micro-situation
does the researcher need to go to understand
that micro-situation This is an imprecise matter
but it is not insignificant in educational research:
for example, it underpinned: (a) the celebrated
work by Bowles and Gintis (1976) on schooling in
capitalist America, in which the authors suggested
that the hidden curricula of schools were preparing
students for differential occupational futures that
perpetuated an inegalitarian capitalist system,
(b) research on the self-fulfilling prophecy (Hurn
1978), (c) work by Pollard (1985: 110) on the
social world of the primary school, where everyday
interactions in school were preparing students
for the individualism, competition, achievement
orientation, hierarchies and self-reliance that
characterize mass private consumption in wider
society, (d) Delamont’s (1981) advocacy that
educationists should study similar but different
institutions to schools (e.g. hospitals and other
‘total’ institutions) in order to make the familiar
strange (see also Erickson 1973).
Stage 9: Data analysis
Although we devote two chapters specifically
to qualitative data analysis later in this book
(Chapters 22 and 23), there are some preliminary
remarks that we make here, by way of
fidelity to the eleven-stage process of qualitative
research outlined earlier in the chapter. Data
analysis involves organizing, accounting for, and
Chapter 7