Rationale for selecting an IPA approach to data analysis

Before committing to use of the IPA approach, a range of potential methodologies were considered that could have informed the approach adopted for data analysis. These comprised grounded theory, content analysis, discourse analysis, narrative analysis, IPA, and thematic analysis. Table 12 presents a brief description and critique of each data analysis methodology, in addition to a rationale for discounting or adopting the same.

155 Table 12: Rationale for selecting an IPA approach to data analysis

Method Description Critique Rationale for discounting/adopting the

methodology Grounded

Theory (GT)

Grounded Theory (GT; Glaser & Strauss, 1967) is a systematic methodology involving the generation or discovery of a theory for a process or an action (Creswell, 2013). The researcher seeks to develop a theory in a 'bottom up' approach. GT requires initial or emerging theory to be tested against data that is systematically collected (Mertens, 2015).

In GT, the researcher needs to set aside theoretical ideas or notions so that the analytic theory can emerge. The researcher often faces the difficulty of determining when categories are saturated or when the theory is sufficiently detailed (Creswell, 2013).

Classic GT requires the researcher to engage in ‘data saturation’ whereby the researcher, having analysed the first round of data, conducts further interviews to address questions arising from previous analysis. As this study utilised a case study approach to data collection, this option of re-entering the field several times after original data collection was not available. In addition, this study sought to triangulate several data types such as interviews, observations, and large-scale survey results. As GT relies primarily on inductively coded interviews or focus groups, GT was therefore ruled out as the most appropriate data analysis methodology for this study.

Content Analysis (CA)

Content Analysis (CA) was first introduced by Lasswell and Casey (1946) and is the analysis of texts of various types including writing, images, recordings and cultural artefacts. It tends to code and analyse qualitative data numerically, often providing frequency counts (Braun & Clarke, 2013; Wilkinson, 2000).

CA has been substantively critiqued (Mayring, 2004) whereby there is strong debate over whether it is, or can be, considered a qualitative method. Braun and Clarke (2013) and The Sage Handbook of

Qualitative Research (Denzin & Lincoln,

2005) scarcely refer to this methodology, in light of this reason.

As outlined, the themes in CA are often quantified and the unit of analysis tends to be a word or phrase. However, in this study, the researcher deemed it best not to quantify the themes. Rather, the unit of analysis in each case comprised a teacher, an SNA and two pupils for comparison. Therefore, CA was not considered to be the best data analysis approach for this study.

156 Discourse Analysis

(DA)

Discourse Analysis (DA) focuses on understanding the meaning of participants’ language (Mertens, 2015). The researcher seeks to read ‘between the lines’ of the participant to determine deeper meanings to qualities of language includes colloquialisms, images, and rules of turn taking.

A range of critics of DA query whether DA actually produces valid knowledge, with criticisms coming from the fields of philosophy, applied linguistics and critical linguistics (Haig, 2004). In addition, this method often does not produce analyses for use in applied research (Braun & Clarke, 2013).

As this study was not examining language, per se, as a means of constructing meaning, DA was not considered to be the optimum choice of methodology for data analysis.

Narrative Analysis (NA)

Narrative Analysis (NA) uses field texts, such as stories and autobiographies, in addition to observations, documents and pictures, as the units of analysis. Using this approach, researchers collect descriptions of events or happenings. Creswell (2013) outlines how narrative analysis seeks to shed light on the identities of individuals and is often shaped by the researcher into a chronology.

Critics argue that NA is a challenging approach to use as it can be difficult to uncover the multi-layered context of one’s life (Edel, 1984). In addition, the researcher must be reflective as to how to retell the individual’s story (Creswell, 2013).

As this study was not considering an individual’s story, but rather, the larger context of the classroom (including nonverbal communications between SNAs, teachers and pupils), it was deemed inappropriate to rely on purely narrative communications in this study.

Interpretative Phenomenological Analysis (IPA)

The aim of IPA is to explore in detail how participants make sense of their personal and social world (Smith & Osborn, 2007). The approach involves detailed examination of the participant’s life-world, whereby it attempts to explore personal experience and an individual’s personal perception or account of that experience so as to gain an understanding of the phenomenon in question (Smith et al., 2009). Larkin et al. (2006) describe IPA as a broadly ‘contextualist’ approach because of its focus on persons-in- context.

Critics argue that at times, it can be difficult to find individuals who have all experienced the same phenomenon. In addition, the researcher must decide how his/her personal understandings will be introduced into the study, whereby they will impact on the interpretation of the individuals’ experience (Creswell, 2013).

IPA was considered to be a good fit between the research aims and objectives of this study as it seeks to examine individual’s personal experiences of the SNA scheme within mainstream primary schools. In addition, the flexibility of approach when using this methodology for data analysis was deemed positive, whereby it would allow both within case and cross-case analyses of individual themes, with due regard for the similarities and differences across cases (Smith & Osborn, 2007). In addition, the approach would ensure that the individual voice could be central to the final write-up (Smith et al., 2009).

157 Thematic Analysis

(T.A)

Thematic Analysis (T.A) is a method for identifying themes and patterns of meaning across a dataset in relation to a research question. Braun and Clarke (2013) outline how this is possibly the most widely used qualitative method of data analysis. Various forms of this approach include inductive T.A, theoretical T.A, experiential TA and constructionist T.A (Braun & Clarke, 2013).

T.A is perceived by some qualitative researchers as lacking the substance of other ‘branded’ and theoretically driven approaches, such as IPA and GT (Braun & Clarke, 2013). In addition, analyses can often consist simply of descriptions of participants’ concerns, with a lack of more interpretative analysis.

Although T.A presents as a flexible analytical approach, it was deemed that the potential lack of concrete guidance for higher level, more interpretive analysis of the data could render the analysis to lack the depth of other approaches. In addition, Braun and Clarke (2013) outline how the ‘voices’ of individual participants can get lost in the analysis, particularly when working with larger datasets. As both the individual experience and the persons-in- context were deemed paramount to this research, it was decided to reject this analytical approach in favour of IPA.

158

3.8.2.1 Summary rationale for selecting an IPA approach.

Reflecting on all approaches for qualitative data analysis, the researcher debated between use of T.A and IPA. Although T.A presents as a flexible analytical approach, it was deemed that the potential lack of concrete guidance for higher level, more interpretive data analysis could render the analysis to lack necessary depth. In addition, Braun and Clarke (2013) outline how the ‘voices’ of individual participants can get lost in T.A, particularly when working with larger datasets. As both the individual experience and the persons-in-context were deemed paramount to this research, it was decided to reject T.A in favour of IPA. In summary, it was decided that a case study approach for data collection would be adopted for this study, whereby the researcher would seek to explore and understand complex situations in real world settings. This was then combined with an IPA approach to data analysis (Smith et al., 2009), considered to be the most appropriate methodology to align with the key tenets of the research project. In this way, both within case and cross-case analysis could occur, with due regard for the voices of the individual experience in context (Smith et al., 2009).