Analysis

Data Analysis

Analysis is a key part of any research project and involves scrutinizing, editing, coding, and theorizing the data collected. Data here means the raw transcripts of interviews. The goal of any analysis is to develop useful findings that can form the bases of any recommendation from the research.

Confidentiality

All data from the project was anonymised and stored in a secure place throughout the project. The Delphi and semi structured interviews gave us a large amount of data, so we used a software programme NVivo 10 to store and work on it. This helped us to manage the process of analysis effectively, whilst keeping the data and our interpretation of the data in one place.

The approach we used:

We took a collaborative approach to analysing the data. All members of the team were involved in all stages of the analysis. This is in contrast to more traditional approaches to data analysis where it is undertaken solely by the academics. The research aimed to be emancipatory, therefore embedding the core values of service user and carer led research into all stages of the research process was important for the validity of the project.

When did data analysis take place?

Data analysis took place after each stage of the action research. This meant that we could take the findings from the Delphi and use them to shape the semi- structured interview and narrative questions. The findings from the semi-structured interview in turn shaped the dissemination events.

How did we do the analysis?

For the Delphi data analysis, a group of researchers met on a weekly basis. We looked at a small number of Delphi questionnaires and thought about the key messages from the data (see diagram 1). We developed a coding structure from this which we continued to talk about at each meeting. We added new codes through the course of the data analysis process. Initially we each looked at what people had said (transcripts) on our own and then talked about them as a group to see if we agreed on the coding. Examples of how we coded the data are shown in Diagrams 2 & 3.

diagram 3

Our learning from doing the Delphi analysis

• Service users and carers add value to data analysis because they have lived the experience. The assumption is made that this is the only thing that adds value, but we found that the strength was in bringing multiple perspectives to the process.
• Discussing the data together can lead to a new understanding of the data.
• It was important to take a systematic approach to ensure that our approach to the data analysis was rigorous and trustworthy.

Coding the semi structured interviews

We started to use a coding structure similar to the Delphi, but it took too long to complete a single interview. So we coded participant’s answers to the semi structured interviews according to a set of questions or prompts that would help us answer our main research question. For an example of our coding see Diagram 4.

Taking this approach meant that we could keep to our project timetable; it recognises that analysis is time consuming and researchers have other life commitments. The process became more inclusive and researchers who had not previously been involved became engaged in the analysis process. We used a mapping process and worked together as a group of researchers to identify the key themes and sub themes from the semi structured and narrative interviews. These themes are presented in section 6.6.