1. Analyzing Qualitative Data

2.  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.  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.  Part of a continuum  Typological  Inductive  Interpretive  Political  Polyvocal

5.  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.  Identify typologies to be analyzed  Selection should be fairly obvious ▪ Predetermined

7.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  Create a master outline expressing relationships within and among domains  Provides an opportunity to refine the analysis done to this point

24.  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.  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.  Read the data for a sense of the whole

27.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  Reread the entire data set and code the data based on your generalizations  Going back to the original complete data set

40.  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.  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.  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.  One kind of analysis that fits within the assumptions of the poststructuralist paradigm

44.  Read the data for a sense of the whole

45.  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.  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.  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.  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.  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.  Write revised stories that represent each voice to be included  Revise your drafts, taking into account the comments and concerns of your participants

51.  Assists in the sorting and organization of data  Can be an efficient alternative to doing the same work by hand ▪ But cannot perform the “mind-work” that humans can  List of advantages and disadvantages on page 208

52.  Hatch, J. A. (2002). Doing Qualitative Research in Education Settings. Albany, New York: State University of New York Press