Use, analysis and presentation of qualitative data

The uses of qualitative data are broad and varied and have been discussed throughout the chapter. Qualitative findings may be published in peer reviewed journals, in non-peer reviewed journals, and in reports for funders and decision-makers. However, the raw data obtained from interviews and focus groups (transcripts of what was said), and observations (field notes on what was observed by the researcher) must first be analysed.

General considerations

No general consensus exists amongst qualitative researchers concerning the process of data analysis. Rather, there are a variety of approaches to analysis and interpretation. These reflect the particular theoretical perspectives or field within which the researcher is working. It could be argued that this is another way in which qualitative research methods significantly differ from quantitative approaches. In the latter, there exists really only one route from data to conclusions, and this is statistical analysis, although there are different statistical approaches available, depending upon the size, distribution, and type of data.

In contrast, the methods available for qualitative analysis vary considerably. However, many of the qualitative methods textbooks do attempt to identify some general features that are common to the analytical phase of qualitative research; these include the following:

Some form of review of all the information to gain an initial sense of the data, these ideas might then be fed back to the informants for verification purposes.
The process of organising the data into some manageable form. This is often described as 'reducing the data', and usually involves developing codes or categories. However, as will be argued below, this process can be potentially problematic if the desire of the researcher is to maintain the unique richness of qualitative forms of data.
Interpreting the data
Presenting it in some form, e.g. tables, prose, or diagrams.

Having identified these broad stages, it should nevertheless be stated that the process of qualitative analysis is not a linear but rather continuous and iterative (12). That is, an emergent analytical process which moves backwards and forwards from the data to analytical concepts, refining and synthesising the latter as more data becomes available. As has been consistently asserted above, the theoretical approach that informs a piece of qualitative research will essentially determine the process by which the data is to be analysed.

Most qualitative analysis involves induction, that is, interpreting the data in order to derive some theoretical framework or working hypothesis, proposition, or `essence' of the social processes under investigation. Findings are inducted from the data, to generate a theory from the concepts inherent within the data.

It is possible to use a deductive approach with qualitative data: for example, if one charts the frequency with which concept appears within the data as a means of summarising the content, or if a framework approach is used to organise each line of text. Such approaches are often called simple content analysis and may be used when analysing free-text entries in questionnaires, for example. It may be argued however that deductive approaches do not maximise the value available from qualitative data and that inductive approaches are more likely to reveal new theories and progress understanding about the field.

Steps in analysis

1) Managing Data: The process of indexing/coding/labelling the data

The process of coding is an essential first step in managing the analytical process. During coding, elements of the data that are conceived of as sharing some perceived commonality are indexed and linked. Codes can be used to simplify or reduce transcript data to manageable levels, the purpose being to achieve a simple conceptual schema. This process usually involves the exclusive index coding of segments of data text (“line by line coding”) in order to be able to eventually retrieve segments sharing a common code. Alternatively, coding can be used as a method to open up the data, thus enabling the researcher to think or conceptualise beyond the data itself. This allows for more in-depth analysis. The in-depth analysis can be undertaken in several ways.

2) Main Approaches to Analysing Qualitative Data

In this section three main approaches to qualitative data analysis are discussed. In practice, qualitative researchers may incorporate elements of grounded theory, constant-comparison approaches, and even analytical induction elements when analysing the data. Moreover, there are additional approaches to analysing data that are not discussed here, such as interpretive phenomenological, narrative, and discourse analysis.

Thematic analysis

This method involves the identification and reporting of patterns – called themes – which are retrieved from the primary qualitative data. Thematic analysis has been described as an accessible form of qualitative analysis as it does not require development of theory (see “grounded theory” below for contrast). A “step by step” guide to undertaking thematic analysis can be found in a paper by Braun and Clarke (13).

Framework

This approach to analysis has been developed over time by the National Centre for Social Research (12). The term 'framework' derives from the 'thematic framework' which is the central component of this approach to data management and interpretative analysis. The thematic framework is utilised to classify and organise data according to key themes, concepts and emergent categories. Each research study requires its own distinctive thematic framework comprising of a series of main themes, subdivided by a succession of related sub-themes or topics. These categories evolve and are refined (as an iterative process) through the researcher's familiarisation with the raw data and the subsequent cross-sectional labelling. Once the researcher judges that they have a comprehensive list of main and sub-themes, each is then 'charted' or displayed in its own matrix. The response of each research subject is then allocated a row with each column representing a separate subtopic. The final stage of this data management component of 'framework' involves summarising or synthesising the original data from each case (subject) within the appropriate parts of the thematic framework. Gale et al. describe the steps involved when taking a Framework approach (14).

Analytical Induction (AI)

In analytical induction (AI) or `deviant case analysis', each section of the transcript (in the case of interviews or focus group discussions) or notes of an observation is not assigned a single code in a 'final and arbitrary interpretative act' but is merely the first stage in the process of analysis (15). Initially these codes will be generalised but they become progressively more elaborate as more data are examined. Once coding is completed, systematic comparisons are made within and between the labelled transcript data. In AI, generalisability of the final conclusions is achieved by focusing on the `deviant' or contradictory indexed items that emerge at this stage. An attempt is made to modify the initial analytical themes in order to embrace these deviant cases. This procedure is essential to guard against selective attention to data in order to provide a more systematic means of extending analytic thinking. There are clear merits to this deviant case approach applied to interview data analysis, particularly in its drawing attention to the importance of contradictions as being indicative of an important dynamic at work rather than some aberrant occurrence or utterance that cannot be fitted into a code.

Grounded theory

In grounded theory, a set of ideas (the “theory”) is generated from the concepts and constructs retrieved from the coding stages. However, the theory remains grounded in the data, and is obtained from analysis of the codes and “memos” noted during the coding process, which come together to create an overall theory explaining the phenomenon under investigation.

Constant-comparative approach

This is a method often employed as part of grounded theory and involves comparing newly acquired data with the dataset already collected. In this way, each new “unit” of data (e.g. a new interview transcript) is considered in terms of how it changes the developing theory and what it adds to the emerging theory. By using constant-comparative methods it is possible to identify when theoretical saturation is reached as the additional data add little to the established findings. The simultaneous collection of data and analysis is an important feature of qualitative research and its iterative nature therefore allows the researcher to optimise the selection of participants based on features that may be of interested given the emerging findings.

Presentation

The challenges faced in qualitative research reporting do differ somewhat from those faced by quantitative researchers, and this primarily relates to the different forms of data that are being analysed and the interpretative approach to analysis. This requires the following concerns to be addressed in the final report:

A discussion in the report of the potential transferability of the qualitative findings to other settings.
There needs to be a discussion of the methods utilised and the reasons why they were appropriate to the object under investigation.
It needs to be demonstrated that the conclusions drawn within the study are consistent with the evidence. The interpretative analysis needs to be presented in a transparent way so that the reader can follow the processes leading to the conclusions.
Presenting the depth and richness of qualitative data is a challenge as they cannot be set-out in a neat series of graphs as would be typically found within quantitative research reports. Nevertheless, the imaginative use of diagrams and other schematics to illustrate the analytical process and findings can be a very useful way of simplifying the complexity of the iterative process of the gradual refinement of analytical categories.
Qualitative methods are used precisely because of their potential to investigate and explain complex and diverse social phenomena, and therefore a report or presentation which focuses only on one element of the findings will be misleading. Any apparent contradictions or inconsistencies that emerged need to be reported upon in as much detail as the recurrent themes found within the study.
Including verbatim quotes from the research subjects is a very useful way of illustrating the main themes that emerged from the study and in demonstrating the reliability of the conclusions. However, this can be overdone, resulting in an overlong narrative which distracts from the main findings.

Moreover, guidelines for best practice in reporting qualitative research have been produced (16).