File Name: qualitative research theory method and practice silverman .zip
Rapley, T. Forthcoming Some Pragmatics of Data Analysis. Silverman ed. London: Sage. Initially I focus on four routinely cited approaches to qualitative data analysis in order to explore their similarities and differences. I then describe some of the pragmatic issues you might need to consider alongside some of qualities or states of mind you might seek to cultivate.
You look at various journal articles, and often see the same key phrases again and again. Above all, whatever you read, you realise that it is de rigeur to have some kind of tag. You need the right kind of label in your methods section, ideally one that positions you as competent, so that your work can be nicely categorised. The practices of good or even adequate qualitative data analysis can never be adequately summed up by using a neat tag. They can also never be summed up by a list of specific steps or procedures that have been undertaken.
Above all, you need to develop a working, hands-on, empirical, tacit knowledge of analysis. Familiarize 1. Read single 1. Initial coding yourself with yourself with transcript note initial and memo writing dataset note dataset note comments and ideas line-by-line initial themes or initial comments coding, compare concepts and ideas 2. Generate initial new codes with themes transform old, evaluate, 2. Generate 2. Create initial list of 2. Focused coding themes from data dataset themes and memo writing and interview select and then topic guide 3.
Search for 4. Cluster themes code key issues, themes collate order the list of keep comparing, 3. Indexing apply similar codes into themes into write notes to thematic potential themes, connected areas refine ideas framework, label gather all data for data with number potential theme 5. Collect new or term with Superordinate data via 4. Review themes themes and sub- theoretical 4. Sort data by Check if themes themes sampling theme or concept work in relation to strategically and summarise dataset, check for 6.
Continue to accounts develop 5. Refine themes 7. Develop them, generate categories until no explanatory propositions, look new issues accounts look for complexity, emerge for patterns, associations associations, 5.
Grounded theory is a broad church. I need to add a health warning here. The table is a heuristic device. Following the phases in a step-wise way will not ensure you are conducting the analysis in the ways the authors intended, as it does not render the specific practical action and reasoning that you are meant to employ. If you like the look of any of them, go and read about them. As you will see, in there most basic terms they all share some family resemblances, in that they seek to move from the particular to the abstract.
By that I mean they all start with a close inspection of a sample of data about a specific issue. This close inspection is used to discover, explore and generate an increasingly refined conceptual description of the phenomena. The resulting conceptual description therefore emerges from, is based on, or is grounded in the data about the phenomena.
We can learn some quite useful lessons from looking at them as whole, rather than as totally distinct approaches. If yes, use that label.
If no, create a new one. If it fits somewhat, you may want to modify your understanding of that label to include this. It might be useful to give some key examples, to write a sentence or two that explains what you are try to get at, what sort of things should go together under specific labels. Ask yourself whether the data and ideas collected under this label is coherent, ask yourself what are the key properties and dimensions of all the data collected under that label.
This will often mean shifting from more verbatim, descriptive, labels to more conceptual, abstract and analytic labels o Keep evaluating, adjusting, altering and modifying your labels and labelling practices. The above fundamentals should be read as quite general statements about the analytic process, rather than as a step-wise guide about how to conduct analysis.
They seem to be embedded, to various degrees, in the writing and lived practices of a whole range of traditions. Interestingly, they are all quite accessible, mundane and, above all, quite doable. Despite their apparent family resemblances, even a brief reading of the table, without any knowledge of the actual analytic methods, shows that there are some telling differences.
You should note that each approach has its own analytic- language. Each approach also has its own specific norms and rules of application. For example, both IPA and Framework assume that the dataset is made up of some kind of recorded interviews. These tables enable you to divide the data into topics, you then begin to finely label that data, and then refine those labels to more abstract, over-arching, labels.
In the case of Constructivist Grounded Theory and grounded theory as a whole the focus is on developing substantive theory of a particular process, situation, or inter actions. Central to this style of work is constantly noting and developing your ideas in memos, encouraging you to shift your thinking from just this moment of data to more abstract reasoning. The iterative process of rounds of data collection, coding and memo-writing, is meant to encourage conceptual development, as new rounds of fieldwork are undertaken to explore, and ultimately confirm or discard, analytic ideas.
Unlike the other approaches, thematic analysis, although referenced widely, suffers as it has no coherent groups of academics claiming, defining and shaping its trajectory. So, the specific analytic etiquette of doing thematic analysis seems to vary broadly between authors3.
Some observations on aspects of a qualitative analytic attitude I now want to focus on some descriptions of some aspect of qualitative analytic practice and reasoning.
Potential ideas can emerge from any quarter — from your prior and ongoing reading, your knowledge of the field, from engagements with your data, from conversations with colleagues, and from life beyond academia - and from any phase in the life-cycle of the project.
Whatever you do, remember to write them down! You also need to listen to and value your intuition and hunches. At some points you follow up a hunch, you go back over the data, and re-look at your archive your project related transcripts, texts, fieldnotes, labelling practices, notes to self, memos, journal articles, books etcetera with a sense of joy as you feel you might be on to something. Sometimes, this ends in frustration, as your idea does not hold water; either the interviewee did not say something that radically different, or your idea echoes something already well-developed in the literature.
Sometimes the idea only comes to fruition much later in the project, or acts as a spark that instigates a new trajectory of thought. Centrally, try and cultivate a sense of creative, even playful engagement with your archive. If you spot a potential pattern, then search your archive to see if this is coherent.
On labelling There is something very interesting about adding some sort of label to your data archive. By that I mean, you make some analytic choices about which lines, chunks or sections of data to highlight. In highlighting some things as belonging to a particular label, you begin inductively to create a local coding schema, a specific way to see and understand the phenomena. Just underline, mark it in someway or add a note about whatever interests you.
These initial engagements with your data-archive, what Layder calls pre- coding, are vital, and enable you to start exploring some of the potential in your archive. Give yourself time to reflect and ponder. On your initial systematic engagements At some point, you need to start engaging in a more systematic, albeit tentative and preliminary, style of labelling.
You are really doing this for three reasons: 1. It forces you to engage, word-by-word, line-by-line, section-by-section with the detail, to ask specific questions about and off your data. Reading in and for detail is essential practice. You can then easily gather all the data you have collected under a specific label and use this, alongside your related notes, to review the issue you are exploring, to help you to establish connections, commonalties and any overarching orders.
When starting to systematically label, I would always suggest working with a paper and pen over a computer. Computers can overly constrain the options you have for marking-up a text, whereas with paper and pen you can scrawl all over the text.
You will often find that initial marked-up text will be very messy, both in a practical sense - in that things will be underlined, crossed out, commented on, drawn on - as well as in a conceptual sense - in that you may have very large number of disparate, sometimes even competing or contradictory, labels.
At the start all you are trying to do is to establish the possible dimensions of the phenomena so just try and make sense about what each word, line and section is about. Highlighting and labelling practices are always provisional and over the life of the project you will engage in continually modifying, refining and sometimes re- labelling whole chunks of texts as your understanding shifts.
This initial tight focus can really help you concentrate on working with the data, and help avoid importing too many a priori pre- suppositions about what you think should be going on there. That is not say that you cannot draw on your prior reading, knowledge or experiences from the field. In thinking about and designing your interview schedule, setting up interviews or observations, or collecting documents or recordings you will already be making and forming certain analytic ideas.
However, these ideas should never wholly overshadow or be the sole direction to your sense making as you engage with your data-archive. They are tools which you can draw on to enable and enhance engagement with your archive.
Above all, follow the data. When it comes to the early phases of acts of labelling and highlighting, you can learn a lot from disciplines like conversation analysis and discourse analysis.
I Internet communication as a tool for qualitative research 95 Annette N. Holstein and Jaber F. Together with Sara Delamont he edits the journal Qualitative Research. He was co-editor of The Handbook of Ethnography. In her research on literacy she applied ethnomethodology and conversation analysis to instances of 'talk around text'. Her recent publications include Inventing 18 Who cares about 'experience'? Missing issues in qualitative research pain medicine: From the laboratory to the clinic , Rutgers University Press David Silverman and Quelle medecine voulons-nous?
Lesson 1. Quantitative Research, Hypotheses, and Variables. Arxiu en format PDF. Catalan english Spanish. The proposed teaching and assessment methodology that appear in the guide may be subject to changes as a result of the restrictions to face-to-face class attendance imposed by the health authorities.
Qualitative research relies on data obtained by the researcher from first-hand observation, interviews, questionnaires, focus groups, participant-observation, recordings made in natural settings, documents, and artifacts. The data are generally nonnumerical. Qualitative methods include ethnography , grounded theory , discourse analysis , and interpretative phenomenological analysis. Qualitative research has been informed by several strands of philosophical thought and examines aspects of human life, including culture, expression, beliefs, morality, life stress, and imagination.
The system can't perform the operation now. Try again later. Citations per year.
with David Silverman). Joanne Meredith is a Lecturer in Psychology at the University of Salford, Manchester. (UK). She specialises in using interactional methods.Reply