The framework method is becoming an increasingly popular approach to the management and analysis of qualitative data. It is appropriate for use in research teams even when not all members have previous experience of conducting qualitative research. The key terms used in this analysis are thoroughly explained by Gale et.al (2013), as follows:
- analytical framework - a set of codes organised into categories that have been jointly developed by researchers involved in the analysis, and can be used to manage and organise the data. The framework provides a new structure for the data (rather than the full original accounts given by participants), which is helpful to summarize/reduce the data in a way that can support answering the research questions;
- categories - during the analysis process, codes are grouped into clusters around similar and interrelated ideas or concepts. Categories and codes are usually arranged in a tree diagram structure in the analytical framework. While codes are closely and explicitly linked to the raw data, developing categories is a way to start the process of data abstraction;
- charting - entering summarized data into the framework method matrix;
- code - a descriptive or conceptual label that is assigned to excerpts of raw data in a process called ‘coding’;
- data - qualitative data usually needs to be in the textual form before the analysis. These texts can either be elicited texts (written specifically for the research), or pre-existing texts, such as meeting minutes, policy documents, or can be produced by transcribing an interview or focus group data, or creating ‘field’ notes while conducting the participant observation or observing objects or social situations;
- indexing - the systematic application of codes from the agreed analytical framework to the whole dataset;
- matrix - a spreadsheet containing numerous cells into which summarised data are entered by codes (columns) and cases (rows);
- themes - interpretive concepts or propositions that describe or explain aspects of data, which are the final output of the analysis of the whole dataset. Themes are articulated and developed by examining data categories through the comparison between and within cases. Usually a number of categories would fall under each theme or sub-theme;
- transcript - a written verbatim (word-for-word) account of a verbal interaction, such as an interview or conversation.
Like thematic analysis and content analysis, this approach identifies commonalities and differences in qualitative data before focusing on relationships between different parts of the data, thereby seeking to draw descriptive and/or explanatory conclusions clustered around themes. Its defining feature is the matrix output: rows (cases), columns (codes) and ‘cells’ of summarised data, providing a structure into which the researcher can systematically reduce the data in order to analyse it by case and by code (Richie & Lewis, 2003). Most often a ‘case’ is an individual interviewee, but this can be adapted to other units of analysis, such as predefined groups or organisations. While in-depth analyses of key themes can take place across the whole data set, the views of each research participant remain connected to other aspects of their account within the matrix so that the context of the individual’s views is not lost. Comparing and contrasting data is vital to the qualitative analysis, and the ability to compare with ease data across cases, as well as within individual cases, is built into the structure and process of the framework method.
The framework method provides clear steps to follow and produces highly structured outputs of summarised data. It is therefore useful where multiple researchers are working on a project, particularly in multi-disciplinary research teams, where not all the members have experience of the qualitative data analysis, and for managing large data sets, where obtaining a holistic, descriptive overview of the entire data set is desirable. However, caution is recommended before selecting the method as it is not a suitable tool for analysing all the types of qualitative data or for answering all the qualitative research questions, nor is it an ‘easy’ version of the qualitative research for quantitative researchers. Importantly, the framework method cannot accommodate highly heterogeneous data, i.e. data must cover similar topics or key issues so that it is possible to categorize it. Individual interviewees may, of course, have very different views or experiences in relation to each topic, which can then be compared and contrasted. The framework method is most commonly used for the thematic analysis of semi-structured interview transcripts, although it could be adapted for other types of textual data, including documents, such as meeting minutes or diaries or field notes from observations. The framework method, however, is not aligned with a particular epistemological, philosophical, or theoretical approach. It is rather a flexible tool that can be adapted for use with many qualitative approaches that aim to generate themes.
The development of themes is a common feature of a qualitative data analysis, involving the systematic search for patterns to generate full descriptions capable of shedding light on the phenomenon under investigation. In particular, many qualitative approaches use the ‘constant comparative method’, developed as part of the grounded theory, which involves making systematic comparisons across cases to refine each theme. Unlike the grounded theory, the framework method is not necessarily concerned with generating a social theory, but can greatly facilitate constant comparative techniques through the review of data across the matrix.
The framework method can be adapted for use with deductive, inductive, or combined types of the qualitative analysis. However, there are some research questions where analysing data by case and theme is not appropriate, and so the framework method should be avoided. For instance, depending on the research question, life history data might be better analysed using the narrative analysis, and documentary data using the discourse analysis (Hodges et al., 2008).
Gale et al. (2013) go further and provide a detailed explanation of seven stages of the framework method procedure:
- transcription – a good quality audio recording, and ideally a verbatim(word for word) transcription of the interview is needed. Transcripts should have large margins and adequate line spacing for later coding and note taking. The process of the transcription is a good opportunity to become immersed in the data, and is to be strongly encouraged for new researchers.
- familiarisation with the interview – becoming familiar with the whole interview using the audio recording and/or transcript and any contextual or reflective notes that were recorded by the interviewer is a vital stage in the interpretation. It can also be helpful to re-listen to the whole audio recording or its parts. In multi-disciplinary or large research projects, those involved in analysing the data may be different from those who conducted or transcribed the interviews, which makes this stage particularly important. One margin can be used to record any analytical notes, thoughts or impressions.
- coding – after familiarisation, the researcher carefully reads the transcript line by line, applying a paraphrase or label (a ‘code’) that describes what they have interpreted in the passage as important. In more inductive studies, at this stage ‘open coding’ takes place, i.e. coding anything that might be relevant from as many different perspectives as possible. Codes could refer to substantive things (e.g. particular behaviours, incidents or structures), values (e.g. those that inform or underpin certain statements), emotions (e.g. sorrow, frustration, love), and more impressionistic/methodological elements (e.g. the interviewee found something difficult to explain, the interviewee became emotional, the interviewee felt uncomfortable) (Saldaña, 2009). In purely deductive studies, the codes may have been pre-defined (e.g. by an existing theory or specific areas of interest to the project) and therefore this stage may not be strictly necessary, and one can just move straight onto indexing, although it is generally helpful even if a broadly deductive approach is taken to do some open coding on at least a few of the transcripts to ensure important aspects of the data are not missed. Coding aims to classify all of the data so that it can be compared systematically with other parts of the data set. At least two researchers (or at least one from each discipline or speciality in a multi-disciplinary research team) should independently code the first few transcripts, if possible. It is vital in inductive coding to look out for the unexpected, and not just to code in a literal, descriptive way. So, the involvement of people from different perspectives can greatly aid in this. As well as getting a holistic impression of what was said, coding line-by-line can often alert the researcher to consider that which may ordinarily remain invisible because it is not clearly expressed or does not ‘fit’ with the rest of the account. In this way, the developing analysis is challenged, whereas to reconcile and explain anomalies in the data can make the analysis stronger. Coding can also be done digitally using CAQDAS, which is a useful way to keep track of new codes automatically. However, some researchers prefer to do the early stages of coding using paper and pen, and only start to use CAQDAS once they reach Stage 5.
- developing a working analytical framework – after coding the first few transcripts, all researchers involved should meet to compare the labels they have applied, and agree on a set of codes to apply to all subsequent transcripts. Codes can be grouped together into categories (using a tree diagram, if helpful), which are then clearly defined. This forms a working analytical framework. It is likely that several iterations of the analytical framework will be required before no additional codes emerge. It is always worth having an ‘other’ code under each category to avoid ignoring data that does not fit; the analytical framework is never final until the last transcript has been coded.
- applying the analytical framework – the working analytical framework is then applied by indexing subsequent transcripts using the existing categories and codes. Each code is usually assigned a number or abbreviations for easy identification (and so the full names of the codes do not have to be written out each time), and written directly onto the transcripts. The Computer Assisted Qualitative Data Analysis Software (CAQDAS) is particularly useful at this stage because it can speed up the process and ensure that, at later stages, data is easily retrievable. It is worth noting that, unlike the software for statistical analyses, which actually carries out the calculations with the correct instruction, putting the data into a qualitative analysis software package does not analyse the data; it is simply an effective way of storing and organising the data so that they are accessible for the analysis process.
- charting data into the framework matrix - qualitative data are voluminous (an hour of interview can generate 15–30 pages of text), and being able to manage and summarize (reduce) data is a vital aspect of the analysis process. A spreadsheet is used to generate a matrix, and the data are charted into the matrix. Charting involves summarizing the data by category from each transcript. Good charting requires an ability to strike a balance between reducing the data on the one hand, and retaining the original meanings and feel of the interviewee’s words on the other. The chart should include references to interesting or illustrative quotations. These can be tagged automatically if you are using CAQDAS to manage your data, or otherwise a capital ‘Q’, an (anonymized) transcript number, page and line reference will suffice. It is helpful in multi-disciplinary teams to compare and contrast styles of summarizing in the early stages of the analysis process to ensure consistency within the team. Any abbreviations used should be agreed by the team. Once members of the team are familiar with the analytical framework, and well practised at coding and charting, on average it will take about half a day per hour-long transcript to reach this stage. In the early stages, it takes much longer.
- interpreting the data – it is useful throughout the research to have a separate notebook or computer file to note down impressions, ideas and early interpretations of the data. It may be worth breaking off at any stage to explore an interesting idea, concept or potential theme by writing an analytic memo for subsequent discussion with other members of the research team. Gradually, characteristics of, and differences between the data are identified, perhaps generating typologies, questioning theoretical concepts (either prior concepts or ones emerging from the data) or mapping connections between categories to explore relationships and/or causality. If the data are rich enough, the findings generated through this process can go beyond the description of particular cases to the explanation of, for example, the reasons for the emergence of a phenomenon, predicting how an organisation or other social actors are likely to instigate or response to a situation, or identifying areas that are not functioning well within an organisation or system. It is worth noting that this stage often takes longer than anticipated, and that any project plan should ensure that sufficient time is allocated to meetings and individual researcher time to conduct the interpretation and writing up of findings.
While the framework method is amenable to the participation of non-experts in data analysis, it is critical to the successful use of the method that an experienced qualitative researcher leads the project. The qualitative lead would ideally be joined by other researchers with at least some prior training in or experience of qualitative analysis. The responsibilities of the lead qualitative researcher are: to contribute to the study design, project timelines and resource planning; to mentor junior qualitative researchers; to facilitate analysis meetings in a way that encourages critical and reflexive engagement with the data and other team members; and finally to lead the write-up of the study.