Hatch Chapter 4

1. Analyzing Qualitative Data Hatch Chapter 4

2. Data Analysis • A systematic search for meaning • A way to process qualitative data so that what has been learned can be communicated to others • Organizing and interrogating data in ways that allow researchers to see patterns, identify themes, discover relationships, develop explanations, make interpretations, mount critiques, or generate theories • Asking questions of the data

3. Starting and stopping • Start soon after data collection has begun • Allows researchers to shape the direction of future data collection based on what they are actually finding or not finding • Keep analyzing until you have answered your research questions

4. 5 Models of data analysis • Part of a continuum • Typological • Inductive • Interpretive • Political • Polyvocal

5. Typological analysis • Dividing the overall data set into groups or categories based on predetermined typologies • Generated from theory, common sense, and/or research objectives • Good for interview studies and processing artifact data • Not recommended for observational studies • Advantage • Efficiency • Because categories are predetermined • Disadvantage • Potentially blinds the researcher to other important dimensions in the data

6. Typological: step 1 • Identify typologies to be analyzed • Selection should be fairly obvious • Predetermined

7. Typological: step 2 • Read the data, marking entries related to your typologies • Read through the data completely with one typology in mind • Does this information relate to my typology? • Mark that portion of the data so that you can go back to it later for closer examination

8. Typological: step 3 • Read entries by typology, recording the main ideas in each entry on a summary sheet • This time only the data within the typology of interest will be read • A summary sheet should be created for each informant • Write a brief statement of the main idea of the excerpt on the summary sheet • Not the step to be interpret for significance

9. Typological: step 4 • Look for patterns, relationships, themes within typologies • What broad statements can be made that meaningfully bring all of these data together? • Patterns are regularities • Similarity (things happen the same way) • Difference (they happen in predictably different ways) • Frequency (they happen often or seldom) • Sequence (they happen in a certain order) • Correspondence (they happen in relation to other activities or events) • Causation (one appears to cause another) • Relationships are links • Strict inclusion (X is a kind of Y) • Rationale (X is a reason for doing Y) • Cause-effect (X is a result of Y) • Means-end (X is a way to do Y) • Themes are integrating concepts • What broad statements can be made that meaningfully bring all of the data together?

10. Typological: step 5 • Read data, coding entries according to patterns identified and keeping a record of what entries go with what elements of your pattern • Make a simultaneous record of where elements related to the category are found in the data

11. Typological: step 6 • Decide if patterns are supported by the data, and search data for nonexamples of your patterns • Decide if the evidence is strong enough to support your case, or • Ask if there is evidence upon which other cases, even competing cases, can be made

12. Typological: step 7 • Look for relationships among the patterns identified • Step back from individual analyses that have been completed and look for connections across what has been found • Making visual representations of categories can help

13. Typological: step 8 • Write your patterns as one-sentence generalizations • Generalization: expresses a relationship between two or more concepts • Making yourself construct sentences forces you to organize your thinking into a form that can be understood by yourself and others • Gives closure to your analyses

14. Typological: step 9 • Select data excerpts that support your generalizations • Go back to the data to select powerful examples that can be used to make your generalizations come alive for your readers

15. Inductive analysis • Similar to Typological Analysis, except categories are not predetermined • Begins with particular pieces of evidence, then pulls them together into a meaningful whole • Works well with studies that emphasize the discovery of cultural meaning from large data sets that include observational data (postpositivist and constructivist) • Works less well for studies that focus on answering narrowly defined questions or that rely on interview data almost exclusively • Advantages • Its power to get meaning from complex data that have been gathered with a broad focus in mind • Provides a systematic approach for processing large amounts of data

16. Inductive: step 1 • Read the data and identify frames of analysis • What will be my frames of analysis? • Frames of analysis: levels of specificity within which data will be examined • No analysis yet; put rough parameters on how you will start looking closely at the data • Must begin with a solid sense of what is included in the data set • The data will be read over and over

17. Inductive: step 2 • Create domains based on semantic relationships discovered within frames of analysis • Domains are categories organized around relationships that can be expressed semantically • Develop a set of categories of meaning or domains that reflect relationships represented in the data • Strict inclusion (X is a kind of Y) • Spatial (X is a place in Y) • Cause-effect (X is a result of Y) • Rationale (X is a reason for doing Y) • Location for action (X is a place for doing Y) • Function (X is used for Y) • Means-end (X is a way to do Y) • Sequence (X is a step in Y) • Attribution (X is a characteristic of Y)

18. Inductive: step 3 • Identify salient domains, assign them a code, and put others aside • Narrow the focus of your analysis • “Data reduction” • Assign a Roman numeral to each domain and a capital letter to each included term • Could this relationship be linked to other domains discovered in the data? • More questions to ask yourself on page 168

19. Inductive: step 4 • Reread data, refining salient domains and keeping a record of where relationships are found in the data • Read the data with specific domains in mind • Make a record of where they are located

20. Inductive: step 5 • Decide if your domains are supported by the data and search data for examples that do not fit with or run counter to the relationships in your domain • Up until now, domains have been hypothetical and tentative • Deductive reasoning is fully employed to decide if the hypothetical categories identified hold up • Search for counterevidence • Questions to ask yourself on page 170

21. Inductive: step 6 • Complete an analysis within domains • Looking within the domains identified for complexity, richness, and depth • Study the data that have been organized into domains in ways that allow the discovery of new links, new relationships, and new domains • In search for other possible ways to organize what’s there • Going much deeper into the data by looking beneath the surface of included terms for richer representations

22. Inductive: step 7 • Search for themes across domains • Look for connections or themes among them • Systematic comparison • How does this all fit together? • What’s the same or different about these domains? • Make a “data display” • Visual formats that present information graphically or systemically • Write a summary statement • More analytic questions on page 173

23. Inductive: step 8 • Create a master outline expressing relationships within and among domains • Provides an opportunity to refine the analysis done to this point

24. Inductive: step 9 • Select data excerpts to support elements in your outline • Powerful or prescient quotes should be starred in the data and on the domain sheets

25. Interpretive analysis • Giving meaning to data • Generating explanations for what’s going on within them • Making inferences, developing insights, attaching significance, refining understandings, drawing conclusions, and extrapolating lessons • Situates the researcher as an active player in the research • Researchers will usually do typological or inductive analysis prior to this model • Fits most comfortably within the constructivist paradigm

26. Interpretive: step 1 • Read the data for a sense of the whole

27. Interpretive: step 2 • Review impressions previously recorded in research journals and/or bracketed protocols, and record these in memos • The object is to get a handle on which impressions might lead to more careful examination • Will lead to the identification of relationships among impressions and the formation of new impressions • Memos can take many forms • At this point they should be written in tentative, hypothetical language with complete sentences and paragraphs

28. Interpretive: step 3 • Read the data, identify impressions, and record impressions in memos • Systematically make and record your interpretations of what is happening within the social contexts captured in your data • Discover new impressions that may develop into interpretations that bring meaning to your data • Analytic questions on page 184 • The product of steps 2 & 3 are sets of memos that form the raw material on which more formal interpretations can be based

29. Interpretive: step 4 • Study memos and salient interpretations • Read through the entire set of memos • Organize the memos according to how they relate to one another and how they connect to the issues you want to address in your research • Begin to get a sense of the big picture you will be drawing for your reader

30. Interpretive: step 5 • Reread data, coding places where interpretations are supported or challenged • Search for places that relate directly to the interpretations in your memos • A deductive activity • What are all the places in the data where my interpretations are addressed?

31. Interpretive: step 6 • Write a draft summary • It will not include an extensive data display or context description but will be focused on communicating the explanations, insights, conclusions, lessons, or understandings you have down from your analysis • A “story” that others can understand • Provides a test for logical consistency of your thinking and expose any gaps in your argument that might exist • Don’t write in shorthand

32. Interpretive: step 7 • Review interpretations with participants • “Member check” • Invite them to a working session • Participants should have the chance to consider and give their reactions to the interpretations included in the summary just written • Can also show copies of memos and even research protocols

33. Interpretive: step 8 • Write a revised summary and identify excerpts that support interpretations • Communicate the understandings you have constructed, clarify what they mean in the contexts of your study, and represent what is captured in your data • Identify a collection of possible quotes that will help convince your readers that your interpretations are well founded

34. Political analysis • Provides a framework that builds in analytic integrity so that findings are grounded in data while acknowledging the political nature of the real world and the research act • Designed to accommodate the critical/feminist paradigm • Advantage • It can be modified for analyzing virtually any type of observation, interview, or unobtrusive data collected in these kinds of studies

35. Political: step 1 • Read the data for a sense of the whole and review entries previously recorded in research journals and/or bracketed in protocols • The object of the reading is to see the forest—the trees will not go away

36. Political: step 2 • Write a self-reflexive statement explicating your ideological positionings and identifying ideological issues you see in the context under investigations • Gives the researcher a chance to spell out what you believe and where you stand on issues related to your study • Write out your best guesses about the ideological issues that are salient to the context you are studying • Important to do both these in writing, in paragraph form

37. Political: step 3 • Read the data, marking places where the issues related to your ideological concerns are evident • Where are all the places in the data that include information related to the ideological issued identified? • Deductive thinking—finding examples that fit your issues

38. Political: step 4 • Study places marked in the data, then write generalizations that represent potential relationships between your ideological concerns and the data • Sets of generalizations related to each of your issues • Discover the connections between what you thought you might find and what is there • Then develop written generalizations that express the relationships discovered within each issues

39. Political: step 5 • Reread the entire data set and code the data based on your generalizations • Going back to the original complete data set

40. Political: step 6 • Decide if your generalizations are supported by the data and write a draft summary • If they hold up against all the data you have so far • Product of this step will be a draft summary that reports the final versions of your generalizations organized as a narrative • Take this back to the participants of your study • Written for them, the primary audience

41. Political: step 7 • Negotiate meanings with participants, addressing the issues of consciousness raising, emancipation, and resistance • Summaries will be designed to expose the dimensions of oppression experienced by the individuals being studied • Raise their consciousness about what is going on around them, and benefits, and why

42. Political: step 8 • Write a revised summary and identify excerpts that support generalizations • Revise your summary to include what you learned from the negotiations in the previous steps

43. Polyvocal analysis • One kind of analysis that fits within the assumptions of the poststructuralist paradigm

44. Polyvocal: step 1 • Read the data for a sense of the whole

45. Polyvocal: step 2 • Identify all of the voices contributing to the data, including your own • You will have structured your data collection around your objective to capture particular voices • The objective is to identify all possible voices • Later you will decide which voices to include your final report • Essential that you count your own voice • Already should have decided who to talk to, what to ask, what will be recorded, what will be analyzed, and what will be included

46. Polyvocal: step 3 • Read the data, marking places where particular voices are heard • Assign some sort of identifier to each voice, read the data, making decisions about whose voice is represented in each data excerpt and mark the data • Product: separate sets of data divided by voices

47. Polyvocal: step 4 • Study the data related to each voice, decide which voices will be included in your report, and write a narrative telling the story of each selected voice • Ask the data to tell you what each voice you have identified has to say about your research focus • Entries related to particular voices should be processed at this time • Make a decision about which voices should be included in the final report • Important criteria for inclusion: the contribution of each voice’s story to revealing different perspectives on the topic of study • Must be sufficient support in your data to construct a story for each voice you select • Draft an initial version of the story you plan to tell for each voice • Develop and discover a plot that links the data together

48. Polyvocal: step 5 • Read the entire data set, searching for data that refine or alter your stories • Do not expect everything to fit together in a tidy package

49. Polyvocal: step 6 • Whenever possible, take the stories back to those who contributed them so that they can clarify, refine, or change their stories • This step builds on ethical and methodological concerns • Improves the balance of power in the construction and ownership of stories • Improves the quality of the stories that have been drafted

50. Polyvocal: step 7 • Write revised stories that represent each voice to be included • Revise your drafts, taking into account the comments and concerns of your participants