From first cycle to second cycle qualitative coding: "Seeing a whole"


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From first cycle to second cycle qualitative coding: "Seeing a whole"

  1. 1. From first cycle to second cycle qualitative coding “Seeing the whole” Heather Ford & Isis Hjorth | Advanced Qualitative Analysis | Oxford Internet Institute | Hilary Term 2014 This work is licensed under a Creative Commons Attribution 4.0 International License.
  2. 2. Goals for today 1. Discussion of the readings 2. Focusing strategies and theory development 3. Voluntary analysis surgery (1 hour)
  3. 3. “Seeing a whole” (Richards) This week’s readings provide tangible strategies for advanced stages of analysis. •Miles & Huberman (1994: 245-263): 13 specific tactics for drawing conclusions, including: Clustering, Making metaphors. •Richards (2009:171-189) provide 6 techniques for seeing and testing synthesis and patterns: coding and category handling; modeling; writing; typologies; matrices; case studies. ** See table 9.1 (p. 173) for a synthesis** •Saldaña (2013: chapters 5, 6) outlines ‘second order coding’ strategies and shows how to move from codes to categories/themes/concepts to theory. --> These strategies can help you advance your coding schemes, ultimately allowing for a more rigorous analysis. 3
  4. 4. "The majority of projects arrive at a good conclusion by steady steps through analysis processes rather than a grand moment of discovery. Arrival will be confirmed by growing confidence that you really know what’s going on. It happens, in other words, over time, through thinking and working with the data.” (Richards, 2009:143) you are getting there  4
  5. 5. Saldaña, 2013: 12
  6. 6. 2nd cycle coding The process that enables you to move from multiple codes in the 1st cycle/s of coding to a few major themes/categories/concepts or at least one theory/narrative. Saldaña, 2013: 12
  7. 7. 2nd cycle coding methods • • 1st cycle: In Vivo, process and initial coding 2nd cycle coding: • • Focused coding: finding thematic/conceptual similarity; • Theoretical coding: discovering the central/core category that identifies the primary research theme; • More in Saldaña, 2013, ch.5 Axial coding: relations between a category’s properties and dimensions;
  8. 8. Other focusing strategies 8
  9. 9. Clustering Clustering is a good strategy for handling and re-categorizing early codes and coding structures/schemes. Clustering information is an inherent human quality. Example given in Miles & Huberman (1994:248-50) may serve to inspire ways to reorganize your current coding schemes. --> relates to Richards' (2009) concept of 'coding and category handling' 9
  10. 10. Clustering Example provided by Miles & Huberman (1994): “We thrive in information-thick worlds because of our marvellous and everyday capacities to select, edit, single out, structure, highlight, group, pair, merge, harmonize, synthesize, focus, organize, condense, reduce, boil down, choose, categorize, catalog, classify, refine, abstract, scan, look into, idealize, isolate, discriminate, distinguish, screen, sort, pick over, group, pigeonhole, integrate, blend, average, filter, lump, skip, smooth, chunk, inspect, approximate, cluster, aggregate, outline, summarize, itemize, review, dip into, flip through, browse, glance into, leaf through, skim, list, glean, synopsize, winnow wheat from chaff, and separate the sheep from the goats.” (Tufte, 1990:50 – in Miles & Huberman, 1994:248) 10
  11. 11. Clustering Clustered in < 10 minutes (Miles & Huberman) Information-Processing Capacities SCANNING/EXPLORING scan, skim, look into, pick over, inspect, dip into, flip through, browse, glance into, leaf through, glean, ORDERING/REVIEWING itemize, list, review, SELECTING select, edit, single out, choose, screen, filter, skip 11
  12. 12. Clustering Clustered in < 10 minutes (Miles & Huberman) Information-Processing Capacities SCANNING/EXPLORING scan, skim, look into, pick over, inspect, dip into, flip through,browse, glance into, leaf through, glean, ORDERING/REVIEWING itemize, list, review, SELECTING select, edit, single out, choose, screen, filter, skip 12
  13. 13. The “top 10” list • extract just 10 quotes or short passages from your data that strike you as most vivid/representational of your study; • • print each on a separate page; arrange them in various orders: chronologically, hierarchically, telescopically, episodically, narratively, from the smallest detail to the bigger picture etc
  14. 14. • • Themajor study’s “trinity” The 3 codes/categories/themes/concepts that stand out. Steps: 1. Write each on a separate piece of paper and arrange them in a triangle; 2. Which is the apex or dominant item and why? In what ways does this apex influence and affect or interrelate with the other codes etc? 3. Explore other three-way combinations.
  15. 15. Codeweaving • Codeweaving is the actual integration of key code words and phrases into narrative form to see how the puzzle pieces fit together. • Steps: 1. 2. 3. Codeweave primary codes/categories/themes into as few sentences as possible; Write several variations to investigate how the items interrelate, suggest causation, indicate process or work holistically to create a broader theme. Search for evidence in your data to prove & disprove your statements and revise.
  16. 16. Exercise • Choose 3 codes (or develop new categories from codes) that stand out from your current coding scheme. Write each on a separate piece of paper. • Ask yourself: Which is the apex or dominant item and why? In what ways does this apex influence and affect or interrelate with the other codes etc? • Explore other three-way combinations. • Report back. Which story does your coding strategy convey? How can it be improved? Which of the strategies in today’s readings are most appropriate for your project at this stage? 16
  17. 17. Metaphors Why are metaphors powerful tools in qualitative analysis? Cognitive linguistics tell us that our cognitive apparatus is fundamentally metaphorical, and central to the development of thought. Lakoff & Johnson (1980) e.g. argue that we perceive and act in accordance with metaphors. That metaphors are matters of thought and not of language. 17
  18. 18. Metaphors Metaphors as analytical tools: According to Miles & Huberman (1994) metaphors can serve as a) data-reducing devices; b) pattern-making devices; c) decentering devices; and d) can connect findings to theory. 18
  19. 19. Metaphors Metaphors as a) data-reducing devices: “They are data-reducing devices, taking several particulars and making a single generality of them. For instance the "scapegoat" metaphor pulls together into one package facts about group norms treatment of deviants, social rituals, and social rationalizations. This ability is not to be sneezed at." (Miles & Huberman, 1994:250-52) 19
  20. 20. Metaphors Metaphors as b) pattern-making devices: “For example, in the school improvement study, we found at one site that the remedial learning room was something like an "oasis" for the pupils [...] (A teacher used the word spontaneously, and we began to see the pattern.) The metaphor "oasis" pulls together separate bits of information: The larger school is harsh (like a desert); not only can students rest in the remedial room, but they also can get sustenance (learning); some resources are very abundant there (like water in an oasis); and so on.” (Miles and Huberman, 1994:252) 20
  21. 21. Metaphors Metaphors as c) decentering devices: “..metaphors will not let you simply describe or denote a phenomenon, you have to move up a notch to a more inferential or analytical level.” (Miles and Huberman, 1994:252) 21
  22. 22. Metaphors Metaphors as d) a means of connecting findings to theory: “The metaphor is halfway from the empirical facts to the conceptual significance of those facts; it gets you up and over the particulars en route to the basic social processes that give meaning to those particulars. [...] In doing that, you're shifting from facts to processes, and those processes are likely to account for the phenomena being studied at the most inferential level.” (Miles and Huberman, 1994:252) 22
  23. 23. Metaphors Example of good use of metaphors: Meyer, E.T. (2011). Splashes and Ripples: Synthesizing the Evidence on the Impact of Digital Resources. London: JISC. Available online:http :// •"The title of this report, Splashes and Ripples, reflects the nature of impacts in the heritage sector. As we will see in this report, some digital resources have made a considerable splash, both in the UK and elsewhere, and their impacts are fairly easy to see. Other resources, however, have resulted in smaller ripples in the water, and uncovering the nature of their impacts can take a bit more digging. By and large, however, the water is anything but becalmed – the projects we will see here are succeeding in big and small ways to influence research, teaching, learning, and the wider public." (p. 5) •"Clearly, some of the efforts to enhance the impact of the digital resources described here are having a splash, whereas others are simply resulting in small ripples in the pond. Even ripples, however, can contribute to change over time. In order to increase the likelihood of splashes – the larger impacts which resonate more widely – the evidence in this report suggests a number of approaches." (p. 49) 23
  24. 24. Metaphors exercise In groups or pairs, discuss and identify metaphors emerging from your data. How may these aid you in moving on in your analytical work? When doing so, take into account the different ways metaphors can be used (c.f. Miles & Huberman). Report back in plenum. Quotes for inspiration: •“Use metaphors and analogies and pursue them. Sometimes chase them to the point of absurdity. When the metaphor no longer fits, you will find ideas flowing. Pay particular attention to metaphors used by participants in your research; run with them and explore their implications. Carefully withdraw from the metaphor, reflecting on its messages, when it no longer is in contact with the situation.” (Richards, 2009: 179) – But remember: •“Know when to stop pressing the metaphor for its juice. When the oasis starts to have camels, camel drivers, a bazaar, and a howling sandstorm, you know you're forcing things. Use it as long as it's fruitful, and don’t overmetaphorize. Remember that the two things compared in a metaphor always have differences.” 24
  25. 25. From coding to theorizing
  26. 26. Elements of a theory 3 main characteristics: 1. 2. 3. predicts and controls action through an if-then logic; explains how and/or why something happens by stating its cause(s); provides insights and guidance for improving social life.
  27. 27. Elements of a theory • Many theories are provisional therefore language should be tentative; • • “Theory is in the eye of the beholder” (p114) “A theory is not so much a story as much as it is a proverb. It is a condensed lesson of wisdom we formulate from our experiences that we pass along to other generations.” (p250)
  28. 28. Theories as “categories of categories” “categories of categories” Look for possible structures... •Taxonomy: categories of equal importance; •Hierarchy: from most to least (frequency, importance, impact etc); •Overlap: share some features while retaining some unique properties; •Sequential order: progresses in a linear way; •Concurrency: two or more categories operate simultaneously to influence and affect a third; •Domino effects: categories cascade forward in multiple pathways; •Networks: categories interact and interplay in complex pathways to suggest interrelationship
  29. 29. Categories and analytic memos as sources for theory • Memo-writing to complete the sentence: “The theory constructed from this study is...” • • Give it time to ‘brew and steep’ Or try a key assertion (Erickson, 1986): a summative and datasupported statement about the particulars of a research study rather than a generalizable and transferrable meanings e.g. “Quality high school theatre and speech experiences can not only significantly influence but even accelerate adolescent development and provide residual, positive, lifelong impacts throughout adulthood” (p5) i.e. you don’t always have to develop theory
  30. 30. A good reporting checklist •Clearly explain the steps you followed during the analysis process (transparency). •Develop a good plot that will allow you to present an interesting story that encompasses plausible answers to your research questions. •Illustrate with verbatim quotes from data, but do not expect data to speak for themselves. •Make sure any quotes have been stripped of identifiable elements if applicable. •Cover all different perspectives and exceptions: a rich account. •Acknowledge limitations and convince the reader by discussing other possible explanations. •Relate findings back to literature.
  31. 31. Workshop summary & next steps • Today we learned techniques for second cycle coding, focusing and theory development; • Your next steps are to keep working with the data: moving from coding to focusing and back again towards synthesis. • And to write an engaging account of your findings and process! 31