RESEARCH METHOD COHEN ok

144 VALIDITY AND RELIABILITY
this is exposed most clearly in data triangulation,
as it is presumed that a multiple data source
(concurrent validity) is superior to a single data
source or instrument. The assumption that a single
unit can always be measured more than once
violates the interactionist principles of emergence,
fluidity, uniqueness and specificity (Denzin 1997:
320). Further, Patton (1980) suggests that even
having multiple data sources, particularly of
qualitative data, does not ensure consistency or
replication. Fielding and Fielding (1986) hold that
methodological triangulation does not necessarily
increase validity, reduce bias or bring objectivity
to research.
With regard to investigator triangulation,
Lincoln and Guba (1985: 307) contend that
it is erroneous to assume that one investigator will
corroborate another, nor is this defensible, particularly
in qualitative, reflexive inquiry. They extend
their concern to include theory and methodological
triangulation, arguing that the search for
theory and methodological triangulation is epistemologically
incoherent and empirically empty (see
also Patton 1980). No two theories, it is argued,
will ever yield a sufficiently complete explanation
of the phenomenon being researched. These criticisms
are trenchant, but they have been answered
equally trenchantly by Denzin (1997).
Ensuring validity
It is very easy to slip into invalidity; it is both
insidious and pernicious as it can enter at every
stage of a piece of research. The attempt to build
out invalidity is essential if the researcher is to
be able to have confidence in the elements of the
research plan, data acquisition, data processing
analysis, interpretation and its ensuing judgement
(see http://www.routledge.com/textbooks/
9780415368780 – Chapter 6, file 6.3. ppt).
At the design stage, threats to validity can be
minimized by:
choosing an appropriate time scale
ensuring that there are adequate resources for
the required research to be undertaken
selecting an appropriate methodology for
answering the research questions
selecting appropriate instrumentation for
gathering the type of data required
using an appropriate sample (e.g. one which is
representative, not too small or too large)
demonstrating internal, external, content,
concurrent and construct validity and ‘operationalizing’
the constructs fairly
ensuring reliability in terms of stability
(consistency, equivalence, split-half analysis
of test material)
selecting appropriate foci to answer the
research questions
devising and using appropriate instruments:
for example, to catch accurate, representative,
relevant and comprehensive data (King et al.
1987); ensuring that readability levels are
appropriate; avoiding any ambiguity of
instructions, terms and questions; using
instruments that will catch the complexity
of issues; avoiding leading questions; ensuring
that the level of test is appropriate – e.g.
neither too easy nor too difficult; avoiding
test items with little discriminability; avoiding
making the instruments too short or too long;
avoiding too many or too few items for each
issue
avoiding a biased choice of researcher or
research team (e.g. insiders or outsiders as
researchers).
There are several areas where invalidity or bias
might creep into the research at the stage of data
gathering; these can be minimized by:
reducing the Hawthorne effect (see the
accompanying web site: http://www.routledge.
com/textbooks/9780415368780 – Chapter 6,
file 6.1.doc)
minimizing reactivity effects: respondents
behaving differently when subjected to scrutiny
or being placed in new situations, for example
the interview situation – we distort people’s
lives in the way we go about studying them
(Lave and Kvale 1995: 226)
trying to avoid dropout rates among respondents
taking steps to avoid non-return of questionnaires