To undertake a robust analysis of the collected data the researcher has completed qualitative content analysis. Engagement in qualitative content analysis has been described by Lansdorf (2011, p154) as “the employment of a systematic classification
process of coding and identifying themes to interpret the content of the data”. The data analysis has been completed in accordance with guidelines provided by Bryman and Bell (2007); Forman and Damschroder (2007); Miles, Huberman and Saldana (2014) and Robson and McCartan (2016).
Data was transcribed manually following completion of each semi-structured interview to allow for a thorough examination of the data. This was to ensure that no item was omitted for analysis and to keep intact the interviewees and interviewers word (Bryman and Bell, 2007). The manual transcription of data was time consuming and generated large amounts of data for analysis. This resulted in a strategic change to the project. The researcher increased the duration of time allocated for data analysis from four to five hours to transcribe of every hour of audio data collected. It is evident that the issue of data transcription was not to be taken lightly (Bryman and Bell, 2007).
Following guidelines outlined in Forman and Damschroder (2007) and Miles, Huberman and Saldana (2014) the qualitative content analysis involved immersion, condensation and presentation of the transcribed data. Whilst the manual method of data analysis has been considered a basic method of data management, it is considered to be of practical value. It allows for the creation of a first level of coding and was a useful preliminary data analysis method (Coffey and Atkinson, 1996). It allowed for data relating to one particular subject or theme to be positioned alongside interview questions to allow for identification of common themes and to undertake pattern matching.
Manual data analysis has been considered an effective method of reducing data to manageable levels. It is also considered to be an appropriate method to use for data analysis when dealing with small volumes of data. Key words and phrases related to general themes and categories identified by the parent and child coding system in relation to each research objective were identified. The data has been analysed by creating a parent and child coding system identified by Forman and Damschroder (2007) as pertinent to the analysis of qualitative data. Parent codes represent the specific research objective whilst child codes were generated to represent key themes relating to each research objective.
Initial themes were generated following the completion of the initial literature review and used to generate an initial codebook. The research involves an iterative codebook development process where the codebook has been verified and modified following completion of the qualitative data analysis of semi-structured interviews, documentary analysis and fixed online survey. All extracted transcribed data and data extracted from the documentary analysis has been analysed and allocated a code in accordance with the development of codebook.
The author assigned a basic numeric identification system to identify the interviewees to ensure confidentiality and anonymity. Interview transcripts were reread on three occasions to ensure that data was codified accurately and to fully immerse the researcher in the collected data (Forman and Damschroder, 2007). A resultant matrix displaying the key words and themes extracted from interview responses by interview respondent and analysed has been provided. The matrices are displayed in chapters 6 to 9 and relate to each research objective.
The data display has been organised to create “an organised, compressed assembly of information that allows conclusion drawing and action (Miles, Huberman and Saldana, 2014, p12). Code reports have been arranged using the method of assigning interviewee and document reference numbers that relate to each research objective. This is to allow the researcher to identify patterns and key themes relating to the research objective.
The code report identifies the interviewee or document reference, key text relating to the objective, associated parent and child code and memo created by the author. It clearly distinguishes the extracted text from the memo created by the researcher and provides a summary description of the themes and patterns emerging from the data. It is considered vital to draw a distinction between the raw data and the interpretation of the data (Forman and Damschroder, 2009). An example of the data display is shown in Table 4 below.
Table 4. Example of Data Display Table. RO1. Investigate the evolution of use of heritage assets as a vehicle for successful urban regeneration.
RESPONDENT RESPONSE MEMO / INTERPRETATION PARENT
CODE
CHILD CODE I think occupancy is the obvious answer so the fact that people have wanted to buy
occupy or invest in a building and put their business there all comes back to occupancy and repopulation. A lot of these buildings will be derelict and suffer from problems of perception and it is about repopulating and changing peoples’ perceptions through the regeneration of that.
Occupancy, repopulation and changing people’s perceptions are key measures of success
SUC1 OCC1
Albert Mill, Manchester because it was finished at the time the market collapsed. Whilst the building might have been enveloped, it was not a success in that whilst the building had been saved it did not function because it was not occupied.
We have managed to find a design lead solution for it in this four-year period and it is a very successful project on all of those levels that we talked about before as it has been profitable and required no public sector funding. Anything over and above that, I can’t think of anything that I have admired.
Heritage scheme did not require public sector funding. Direct evidence of project viability of heritage projects. Design led solution.
SUC1 SUC1
VIA1 DES1
The researcher has created a summary table that displays a summary of responses from the respondent in relation to each child code relating to the respective research objective. The creation of a summary table allows the researcher to identify patterns, key themes and negative case analysis from the analysed data. It assists the researcher to draw conclusions in relation to each research objective. The researcher, in accordance with mixed methods research, has additionally adopted the use of the quantitative method of descriptive statistics (Mason, 2002). The use of descriptive statistic to record the number of occasions that a subject or topic has been described in order to understand the frequency and importance of the topic.
To utilise the data display to draw conclusions, the researcher has followed guidance from Miles, Huberman and Saldana, (2014) and Forman and Damschroder (2007). The researcher undertook an initial scan of the data to understand emerging patterns and to identify contrasts and comparisons in the data. Each qualitative data analysis chapter contains an explicit narrative relating to an explanation of initial conclusions that have been verified following completion of rechecking the collected data.
Rival explanations or negative cases have also been identified within the data to identify a divergence or convergence from initial findings. The research produces descriptive summaries of the displayed data to knit together the data (Miles, Huberman and Saldana, 2014). The synthesised data analysis will be used to complete the objectives of the research by way of providing evidence relating to the key elements to be included in the theoretical framework.