Successful Qualitative Research
A Practical Guide for Beginners
Virginia Braun, Victoria Clarke
- 400 páginas
- English
- ePUB (apto para móviles)
- Disponible en iOS y Android
Successful Qualitative Research
A Practical Guide for Beginners
Virginia Braun, Victoria Clarke
Información del libro
*Shortlisted for the BPS Book Award 2014 in the Textbook Category* *Winner of the 2014 Distinguished Publication Award (DPA) from the Association for Women in Psychology (AWP)*
Successful Qualitative Research: A Practical Guide for Beginners is an accessible, practical textbook. It sidesteps detailed theoretical discussion in favor of providing a comprehensive overview of strategic tips and skills for starting and completing successful qualitative research. Uniquely, the authors provide a "patterns framework" to qualitative data analysis in this book, also known as "thematic analysis." The authors walk students through a basic thematic approach, and compare and contrast this with other approaches. This discussion of commonalities, explaining why and when each method should be used, and in the context of looking at patterns, will provide students with complete confidence for their qualitative research journey.
This textbook will be an essential textbook for undergraduates and postgraduates taking a course in qualitative research or using qualitative approaches in a research project.
Preguntas frecuentes
Información
SECTION
1
Successfully getting started in qualitative research
1 | Some very important starting information |
OVERVIEW
WHAT IS QUALITATIVE RESEARCH?
Quantitative | Qualitative |
Numbers used as data | Words – written and spoken language – (and images) used as data |
Seeks to identify relationships between variables, to explain or predict – with the aim of generalising the findings to a wider population | Seeks to understand and interpret more local meanings; recognises data as gathered in a context; sometimes produces knowledge that contributes to more general understandings |
Generates ‘shallow’ but broad data – not a lot of complex detail obtained from each participant, but lots of participants take part (to generate the necessary statistical power) | Generates ‘narrow’ but rich data, ‘thick descriptions’ – detailed and complex accounts from each participant; not many take part |
Seeks consensus, norms, or general patterns; often aims to reduce diversity of responses to an average response | Tends to seek patterns, but accommodates and explores difference and divergence within data |
Tends to be theory-testing, and deductive | Tends to be theory generating, and inductive (working up from the data) |
Values detachment and impartiality (objectivity) | Values personal involvement and partiality (subjectivity, reflexivity) |
Has a fixed method (harder to change focus once data collection has begun) | Method is less fixed (can accommodate a shift in focus in the same study) |
Can be completed quickly | Tends to take longer to complete because it is interpretative and there is no formula |
BOX 1.1 EXAMPLES OF SMALL Q QUALITATIVE RESEARCH
- A qualitative research project may be conducted in a realist, positivist way, where the values and assumptions of Big Q qualitative research are rejected.
- Qualitative methods can be used as a precursor for quantitative research. For example, in a study of the effects of the experiences of depression, US professors of psychiatry and nursing James Coyne and Margaret Calarco (1995) conducted two focus groups and thematically organised participants’ statements into eight categories, drawing on these to develop a survey, which they used to generate the data they analysed.
- It can be used alongside quantitative methods as part of a mixed methods design (see Mertens, 2005). In many mixed method designs, the qualitative component may be subsumed within a primarily quantitative, realist project, and it is rarely Big Q qualitative research. For instance, in food and farming researcher Charlotte Weatherall and colleagues’ (2003) study of UK consumer’s perceptions of food, farming and buying locally produced goods, the qualitative data from six focus groups were used to identify consumers priorities when buying food, perceptions of farming/food provision, and interest in local food production, and informed the development of a quantitative survey. The qualitative analysis was presented and interpreted alongside the quantitative results. The analysis described the content of what was said, assuming a direct relationship between what people say and what they believe (and do).
- Qualitative data might be converted to a numerical representation, and analysed quantitatively. For instance, public health researchers Mary Story and Patricia Faulkner (1990) collated a selection of episodes of 11 of the most popular US prime-time TV shows and coded the text of those programmes according to food references. The frequency of codes was compared, and was used to determine messages about food and eating presented during prime-time. Overall, they reported ‘pervasive’ (p. 740) references to food, the majority of which were related to low-nutritional-value snacks, and concluded that the shows and advertising promote poor nutritional practice. The typical method here is content analysis, where qualitative data are coded and analysed numerically, and there is debate about whether it is, or can be, a qualitative method. Many say no – for instance, The Sage Handbook of Qualitative Research (Denzin & Lincoln, 2005b) barely discusses it; we don’t consider it in this book because we want to focus on wholly qualitative methods. The quantitative focus in content analysis has been substantively critiqued (Mayring, 2004), and more interpretative forms developed – often referred to as qualitative content analysis (e.g. Hsieh & Shannon, 2005; Mayring, 2004), which is similar to thematic analysis.
QUALITATIVE RESEARCH AS A PARADIGM
- the use of qualitative data, and the analysis of words which are not reducible to numbers;
- the use of more ‘naturally’ occurring data collection methods that more closely resemble real life (compared to other possibilities, such as experiments) – this develops from the idea that we cannot make sense of data in isolation from context;
- an interest in meanings rather than reports and measures of behaviour or internal cognitions;
- the use of inductive, theory-generating research;
- a rejection of the natural sciences as a model of research, including the rejection of the idea of the objective (unbiased) scientist;
- the recognition that researchers bring their subjectivity (their views, perspectives, frameworks for making sense of the world; their politics, their passions) into the research process – this is seen as a strength rather than a weakness.