Data collection and analysis

Data analysis, in short involves “organising, accounting for, and explaining the data….noting patterns, themes categories and regularities”. (Cohen et al, 2003, p147). In this case, the data collected may be considered as categorical, as opposed to quantifiable (Saunders 2007) – that is we are not measuring data numerically, but classifying data into sets/subject areas that address the research question.

(Briggs, et al, 2012, p341) acknowledge that ‘the collection and analysis of quantitative data could be central to your research or it may be intended to complement other, qualitative methods’. Additionally ‘some audiences particularly value the apparent objectivity of numerical information, while with others you may be more successful in conveying ideas through qualitative data, such as choice quotations from your participants’ (ibid, p342). For this particular research, the qualitative data

from interviews and documentary analysis formed the main data, whilst quantitative data from questionnaires helped in developing themes in the research findings. As Hayes (2006, p3) notes in characterising the case study approach, ‘data are qualitative rather than qualitative. This does not mean that numbers are unimportant but that they are relatively insignificant’. Whilst not considered entirely insignificant, quantitative data was considered secondary to qualitative data in light of the interpretive approach to this particular thesis.

Data reduction (Miles and Huberman 1994) is a key stage of analysis in that it allows us to select, collate and summarise information, which may well emerge as patterns that reflect the literature. Frequency distribution (Somekh and Lewin, 2007) can be used to describe the frequency of categories, and interpreting the data and giving it meaning will draw conclusions. In presenting and reporting the data, some data display in terms of pictorial means may aid conceptual interpretation (ibid). Similarly, ‘Descriptive methods can also be used to explore the data and to confirm that it is worth continuing with further data analysis’ (Somekh and Lewin, 2007, p225). A more statistical approach and coding of data helped interpret the results to a greater effect, drawing upon advanced research methods recently studied, and subsequent use of computer- based tools such as Minitab software. Whilst SPSS had been used in the Advanced Research Methods modules, Minitab was deemed suitable for this particular research as it was able to perform the same functions as SPSS whilst being more accessible to the author.

In drawing upon the data set for some initial results, it was thought useful to generate some descriptive statistics to present as preliminary findings. As Briggs et al, (2012) suggest:

Before tackling the relationship between variables, it is always a good idea to start by looking at individual variables, and generating some descriptive statistics…..Descriptive information such as the gender of our respondents or their age is useful in providing us with important statistics that may help us to answer our research questions of provide vignettes about our participants. When looking at one variable at a time, the term we use is univariate analysis.

Briggs et al (2012, p345) In terms of the questionnaire, some application of these principles, and the resulting pictorial displays for quantitative data are applied below in Table 3.2.

In studying the graphic displays, results regarding normal distribution were considered. In subsequently undertaking the Anderson Darling test and looking for a ‘P value greater than 0.05’ (i.e normal distribution) some initial findings were able to be either strengthened or dismissed.

Table 3.2. Applied presentation of quantitative data

Individual variables descriptive statistics

Question Description Presentation suggestions

Q1. Male/female Grouped categories

(nominal)

Bar chart

Q2. Length of service Frequency Histogram (distribution curve) Mean, mode, median

Q3. Role Grouped categories

(nominal)

Bar chart Q3a Role preference Frequency of role vs rank Histogram Q4 a, b, c, d and e Likert /frequency

distribution

Histogram for each

Q5 HEA Grouped categories Bar chart

Q 6, 1-6 Likert/frequency distribution Histograms Q7 Cross tab Q8 1-4 Likert/frequency distribution Histogram Q9 Likert/frequency distribution Histogram

Q10 Grouped categories Bar chart

Q10 a-e Grouped categories Bar chart

Whilst not pretending that these are causal relationships it was interesting to see if there is a correlation between certain variables that would allow some hypothesis regarding outcomes. These themes will be outlined in the findings and analysis section of the resulting thesis.

Analysis of interviews and qualitative data

In using an interpretative approach, ‘the aim is to emerge with the meanings that are being constructed by the participants (including you) in the situation’ (Thomas, 2011, p198, italics added).

The essence of interpretative enquiry is that ‘you let the ideas (the theory) emerge from your immersion in a situation rather than going in with fixed ideas about what is happening’ (Thomas, 2011, p202). In this respect, in collating the data from interviews, some initial constructs (Thomas, 2011) were identified, and then these used to construct a table of responses. The constant comparative method (ibid) was used in this respect to further refine and theme the findings of the study. An example of these constructs in relation to interview transcripts is shown in Appendix C.

Having outlined the key data-collection tools proposed for the thesis, it is thought useful at this stage to reflect on possible limitations of the research design.