View/Open - ARAN - National University of Ireland, Galway

3.9 Data Analysis
116
Chapter 3 Research Framework
There is a lack of clarity in the literature regarding the way in which data analysis for
case study research should be undertaken (Li and Seale, 2007;Yin, 2009; Parkhe,
1993). In this section, different approaches to data analysis will be discussed and a
rationale for the data analysis approach selected for use in this study will be
presented. Firstly, qualitative data analysis approaches will be discussed and this will
be followed by a discussion on quantitative data analysis approaches. Research
rigour and ethics will subsequently be discussed.
3.9.1 Qualitative Data Analysis
There is no agreed method to analyse qualitative data (Burns and Grove, 2011;
Holloway and Wheeler, 2010; Parahoo, 2006). However, some methodologies use
particular frameworks. Colazzi’s framework is typically associated with
phenomenology (Moustakas, 1994; Polit and Beck, 2008) while the constant
comparative method of analysis tends to be used in grounded theory (Glaser and
Straus, 1967). The analysis of case study data is the least well defined (Li and Seale,
2007; Parkhe, 2003; Yin, 2009,). However, regardless of the methodological
approach used by the researcher, the goal of qualitative data analysis is to richly
illuminate the experiences of the participants (Speziale and Carpenter, 2003).
Morse and Field (1996) suggest that there are four cognitive processes integral to all
qualitative data analysis methods. These processes are comprehending, synthesising
(decontextualising), theorising and recontextualising, and they tend to occur
sequentially. Comprehending is about making sense of the data and then coding the
data. Coding involves assigning a label which capturesthe essence or meaning of a
portion of the data (Saldana, 2009). These codes are then typically grouped together
based on the similarity of the content of the coded data, and these grouped codes are
called categories. Comprehending also involves reading the data until no new
insights emerge (also referred to as data saturation). Synthesising is about being able
to confidently describe the data. This is done in two ways. Firstly using inter-
participant analysis, which involves comparing transcripts from several participants,
and secondly analysing and sorting categories across the data based on their
commonalities. Theorising is the process of constructing alternative theories or