RESEARCH METHOD COHEN ok

PLANNING NATURALISTIC RESEARCH 177
(Morse 1994: 228), those who completely fulfil
asetofselectioncriteriaandthosewhofilla
selection of those criteria respectively.
Lincoln and Guba (1985: 201–2) suggest
an important difference between conventional
and naturalistic research designs. In the former
the intention is to focus on similarities and
to be able to make generalizations, whereas
in the latter the objective is informational,
to provide such a wealth of detail that the
uniqueness and individuality of each case can
be represented. To the charge that naturalistic
inquiry, thereby, cannot yield generalizations
because of sampling flaws, the writers argue that
this is necessarily though trivially true. In a word,
it is unimportant.
Patton (1980: 184) takes a slightly more cavalier
approach to sampling, suggesting that ‘there are
no rules for sample size in qualitative inquiry’,
with the size of the sample depending on what one
wishes to know, the purposes of the research, what
will be useful and credible, and what can be done
within the resources available, e.g. time, money,
people, support – important considerations for the
novice researcher.
Ezzy (2002: 74) underlines the notion of
‘theoretical sampling’ from Glaser and Strauss
(1967) in his comment that, unlike other forms
of research, qualitative inquiries may not always
commence with the full knowledge of whom to
sample, but that the sample is determined on
an ongoing, emergent basis. Theoretical sampling
starts with data and then, having reviewed these,
the researcher decides where to go next in
order to develop the emerging theory (Glaser and
Strauss 1967: 45). We discuss this more fully in
Chapter 23.
Individuals and groups are selected for
their potential – or hoped for – ability to offer
new insights into the emerging theory, i.e.
they are chosen on the basis of their
significant contribution to theory generation and
development. As the theory develops, so the
researcher decides whom to approach to request
their participation. Theoretical sampling does not
claim to know the population characteristics or
to represent known populations in advance, and
sample size is not defined in advance; sampling
is only concluded when theoretical saturation
(discussed below) is reached.
Ezzy (2002: 74–5) gives as an example
of theoretical sampling his own work on
unemployment where he developed a theory
that levels of distress experienced by unemployed
people were influenced by their levels of financial
distress. He interviewed unemployed low-income
and high-income groups with and without debt,
to determine their levels of distress. He reported
that levels of distress were not caused so much by
absolute levels of income but levels of income in
relation to levels of debt.
In the educational field one could imagine
theoretical sampling in an example thus:
interviewing teachers about their morale might
give rise to a theory that teacher morale is
negatively affected by disruptive student behaviour
in schools. This might suggest the need to sample
teachers working with many disruptive students
in difficult schools, as a ‘critical case sampling’.
However, the study finds that some of the teachers
working in these circumstances have high morale,
not least because they have come to expect
disruptive behaviour from students with so many
problems, and so are not surprised or threatened by
it, and because the staff in these schools provide
tremendous support for each other in difficult
circumstances – they all know what it is like to
have to work with challenging students.
So the study decides to focus on teachers
working in schools with far fewer disruptive
students. The researcher discovers that it is these
teachers who experience far lower morale, and
hypothesizes that this is because this latter group
of teachers has higher expectations of student
behaviour, such that having only one or two
students who do not conform to these expectations
deflates staff morale significantly, and because
disruptive behaviour is regarded in these schools as
teacher weakness, and there is little or no mutual
support. The researcher’s theory, then, is refined,
to suggest that teacher morale is affected more by
teacher expectations than by disruptive behaviour,
so the researcher adopts a ‘maximum variation
sampling’ of teachers in a range of schools,
Chapter 7