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, it should be
evident from the discussion that the researcher has adhered
to the processes inherent in the methodology (Table 3).
Interviews are by far the most common method of
data collection and are mainly either semi-structured or
unstructured (Holloway and Wheeler, 2002). If a semistructured
interview format is selected it should be evident
how the themes or questions were derived. In unstructured
interviews the initial opening question should be presented
and clearly linked to the purpose of the study. Interviews
are more frequently conducted face to face, but online or
telephone interviews are also used. They can be undertaken
with individuals or groups, such as focus groups, and can be
one-off or multiple. The rationale for each of these decisions
should be clearly presented.
Although traditionally associated with grounded theory,
‘data saturation’ is often referred to by some qualitative
researchers as a point where they claim no new information
will arise from further sampling. Thorne and Darbyshire
(2005) suggest that some researchers use the concept of
data saturation as a convenient stopping point, and it may
be pertinent to assess whether the study being evaluated,
particularly if it is a small-scale descriptive study, could have
achieved this.
Data analysis
In qualitative research the process by which data analysis is
undertaken is fundamental to determining the credibility
of the findings. Essentially it involves the transformation of
raw data into a final description, narrative, or themes and
categories. There is considerable variation in how this is
undertaken, depending on the research question and the
approach taken (Vishnevsky and Beanlands, 2004).
Some researchers use generic data analysis tools whereas
others use less structured and more creative approaches.
What is important is that the process is described in
sufficient detail to enable the reader to judge whether the
final outcome is rooted in the data generated (Holloway
and Wheeler, 2002). The researcher should demonstrate
understanding of concurrent data collection and analysis,
the processes of organizing and retrieving data, as well
as the steps in coding and thematic analysis. In addition,
verification strategies, if used, should be presented. Examples
include use of an expert panel or member checking
(verifying with participants).
Several computer-assisted packages are available to assist
the qualitative researcher during analysis, e.g. NUD*IST
(Non-numerical Unstructured Data Indexing, Searching
and Theorising), Ethnograph and NVivo (Robson, 2002).
There are inherent advantages to these packages in terms
of handling large amounts of data and assisting with coding
and organizing the material. However, the rationale for how
and why a particular tool was chosen should be evident.
Although data analysis is central to qualitative research, it
is often poorly delineated in research publications. Very few
offer sufficient detail to determine the emergence of the
findings from the raw data, with the result that readers are
asked to ‘accept’ what they see. According to Thorne and
Darbyshire (2005), the obligation to show the data that led
to the findings is a reasonable one.
Rigour (trustworthiness)
Unlike the quantitative (positivist) paradigm that seeks to
examine objective, meas