Aim and research questions
Section 3: Sorting and analysing the interview data
Introduction
As described above there were four different types of data analysis applied to the study. The first was policy analysis which is described in full in Chapter 4. Within South Ayrshire observation of the mental health needs assessment including local strategy and policy documents created a large amount of data in note form and analyses of data from these processes are presented in Chapter 5. Section 3 describes the process adopted for sorting and analysing data using interviews with frontline professional staff as an illustration.
Coding and sorting data from the transcripts
ATLAS.ti (ATLAS.ti, 2006) was used to code and sort the data following a two day training programme. Pre-set headings drawn from interview questions were generally not used for coding data with the exception of some specific details requested in the interview. These details were the number of years respondents had been in practice, geographical areas they worked in and the population they worked with but they were used as background information for understanding respondents’ contexts rather than providing main factors for analysis. Themes and sub-themes were allowed to emerge from the respondent’s speech and named as “codes”. Codes were then assigned to chunks of data described as “quotes”.
The coding process
The process embarked on to code and sort the data aimed to achieve a set of stable codes which could then be investigated to identify meaning across the sample of respondents. I began by reading each transcript closely while listening to the recording of the interview to check for mistakes and mis-hearings. I did this as soon as possible after receiving the transcript to help with recall in case there had been problems with recording which happened with two interviews. Once I had checked 4transcripts I entered them into ATLAS.ti and spent some time learning how to use the software.
I read through each of the 4 transcripts again once they were entered into ATLAS.ti and began assigning codes such as Aim of first enquiry, First reaction to Tom, Exploration of Tom’s circumstances, Inequalities=access, Problems addressing mental health and so
on. Around 70 codes were identified from the first 4 transcripts. A coded transcript in ATLAS.ti is given in Figure 3.1
Figure 3.1 Extract from a coded transcript in ATLAS.ti
Quotes were stored under their given code headings. An extract from a list of codes is given as an illustration in Figure 3.2
Figure 3.2 Extract from the list of codes in ATLAS.ti:
Codes only became static at the stage of the final analysis and until then they were constantly reviewed, split and merged as new quotes were added and as analysis progressed. New codes were constantly being created from splitting and merging existing codes but only one completely new code was created from the final 4transcripts suggesting that data saturation was achieved.
Throughout the analysis I moved back and forward through transcripts in a process Glaser and Strauss (1967) described as constant comparison, adding quotes to codes and constantly questioning previous decisions. Major changes were usually recorded in memos in ATLAS.ti to help me to remember and understand the decision trail. Decisions about coding quotes sometimes changed as new thoughts and ideas emerged from my reading and understanding of the transcripts. The issue of bereavement offers one example of this. I had not included a code for this as an issue until Transcript 8 and felt sure I must have missed references to Tom’s bereavement in previous transcripts. ATLAS.ti allowed me to search all transcripts easily for the term “bereavement” and associated terms such as “grief” and “death” but I found that to my surprise it had not been mentioned as an issue for Tom until that point.
I added transcripts to ATLAS.ti and coded them throughout the data collection period and as time allowed over an 18 month period. Occasionally I had to re-learn aspects of ATLAS.ti if too much time had elapsed between sessions. By the time initial coding was complete I was very familiar with all the transcripts and could often remember who had said what without having to spend much time searching. I continued to allow the respondents’ speech to define the names of codes rather than impose specific questions at this stage although I did occasionally search for particular responses that I might have expected to see or wanted to find out about such as bereavement as described above, deprivation or use of voluntary organisations. The final number of codes at the end of this stage was 73. I believed them now to be stable as I had investigated the transcripts thoroughly including using constant comparison and questioning my decision-making throughout the process. I was also satisfied that data saturation had occurred.
Extracting meaning
The three headline topics of Core Roles, Tom and Inequalities were created to divide up the codes and provide manageable chunks of linked data to analyse although some codes did not fit neatly into one or other of the categories. I continued to move some data between the topics as the analysis progressed. Taking quotes within each code heading in turn I began to draw out their meaning or meanings by summarising or paraphrasing the points made in each quote and identifying and bringing together themes. I carried this out separately for Generalists and Mental Health Specialists to provide an opportunity to identify where their results differed and where they merged. It was particularly useful to keep them separate when exploring practical issues such as their core roles. In the final stage of analysis there were some issues where it was useful to understand the perspectives of the two groups separately and there were other issues where their combined perspective provided additional insight into the issue being explored. Themes were further developed by continuing the process of constant comparison, splitting and linking to make new links within and between codes and create new combined codes and sub-headings. I could then use these to build a comprehensive picture of the respondents’ collective views to gain additional insight into the topics under scrutiny.
It should be acknowledged here that meanings are extracted in relation to the questions being asked by the research. For example a detailed linguistic analysis would identify a different perspective from individual quotes than I might in searching for coherence across quotes. The very personal analytical process that takes place in qualitative research to identify meaning is rooted in the researcher’s knowledge and experience and while the internal process would be difficult to describe the basis and outcomes of
the process are explained throughout the thesis and the end product presented in the results section. An illustration of the process of meaning making from coding to analysis is given in Appendix 8.
Summary of methods and processes
Interpretive policy analysis was chosen as a useful framework for exploring all dimensions of policy perspectives and interpretations of inequalities in health and inequalities in mental health. Identifying four communities of meaning helped to focus the research questions towards relevant sources of data and document analysis and qualitative methods of semi-structured interviews and observation were employed to gather the data. A vignette was used in interviews with frontline staff in order to explore the approaches used to a patient displaying a set of vague symptoms which might or might not signal existence of mental health problems.
A study site was found that offered the opportunity of a geographical context whose profile included urban, rural, deprived and affluent areas. Personnel within the chosen primary care organisation were helpful, accommodating and interested and they ensured easy access to meetings and individual respondents. Delays in establishing the needs assessment process appeared to have potential to threaten early data collection plans but the problem was overcome with only a short extension required for the data collection period.
Document, observation and interview data analyses were guided by professional advice from professional texts and supported by university standards and supervisors and peer researcher checks. ATLAS.ti software was used for sorting and analysing data and the decision trails for the analyses were set out in this Chapter and the following four chapters of results.