RESEARCH METHOD COHEN ok

86 PLANNING EDUCATIONAL RESEARCH
Box 3.2
continued
Model Purposes Foci Key terms Characteristics
Testing and
assessment
To measure
achievement and
potential
To diagnose strengths
and weaknesses
To assess
performance and
abilities
Academic and nonacademic,
cognitive,
affective and
psychomotor
domains – low-order to
high-order
Performance,
achievement, potential,
abilities
Personality
characteristics
Reliability
Validity
Criterion-referencing
Norm-referencing
Domain-referencing
Item-response
Formative
Summative
Diagnostic
Standardization
Moderation
Materials designed to
provide scores that can
be aggregated
Enables individuals and
groups to be compared
In-depth diagnosis
Measures performance
end, considering models of research might be
useful (Morrison 1993).
Data analysis
The prepared researcher will need to consider
how the data will be analysed. This is very
important, as it has a specific bearing on the form
of the instrumentation. For example, a researcher
will need to plan the layout and structure of a
questionnaire survey very carefully in order to assist
data entry for computer reading and analysis; an
inappropriate layout may obstruct data entry and
subsequent analysis by computer. The planning of
data analysis will need to consider:
What needs to be done with the data when
they have been collected How will they be
processed and analysed
How will the results of the analysis be verified,
cross-checked and validated
Decisions will need to be taken with regard to
the statistical tests that will be used in data
analysis as this will affect the layout of research
items (for example in a questionnaire), and the
computer packages that are available for processing
quantitative and qualitative data, e.g. SPSS and
N-Vivo respectively. For statistical processing the
researcher will need to ascertain the level of data
being processed – nominal, ordinal, interval or
ratio (discussed in Chapter 24). Part Five addresses
issues of data analysis and which statistics to use:
the choice is not arbitrary (Siegel 1956; Cohen
and Holliday 1996; Hopkins et al. 1996). For
qualitative data analysis the researchers have at
their disposal a range of techniques, for example:
coding and content analysis of field
notes (Miles and Huberman 1984)
cognitive mapping (Jones 1987; Morrison
1993)
seeking patterning of responses
looking for causal pathways and connections
(Miles and Huberman 1984)
presenting cross-site analysis (Miles and
Huberman 1984)
case studies
personal constructs
narrative accounts
action research analysis
analytic induction (Denzin 1970b)
constant comparison and grounded theory
(Glaser and Strauss 1967)
discourse analysis (Stillar 1998)
biographies and life histories (Atkinson 1998).
The criteria for deciding which forms of data
analysis to undertake are governed both by fitness
for purpose and legitimacy – the form of data analysis
must be appropriate for the kinds of data gathered.
For example, it would be inappropriate to use
certain statistics with certain kinds of numerical
data (e.g. using means on nominal data), or to use
causal pathways on unrelated cross-site analysis.