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Transcript of Analyzing Qualitative Data. A systematic search for meaning A way to process qualitative data so...
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
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
5 Models of data analysis
Part of a continuum Typological Inductive Interpretive Political Polyvocal
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
Typological: step 1
Identify typologies to be analyzed Selection should be fairly obvious▪ Predetermined
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
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
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?
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
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
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
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
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
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
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
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)
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
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
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
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
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
Inductive: step 8
Create a master outline expressing relationships within and among domains Provides an opportunity to refine the
analysis done to this point
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
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
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
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
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
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?
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
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
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
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
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
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
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
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
Political: step 5
Reread the entire data set and code the data based on your generalizations Going back to the original complete data
set
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
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
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
Polyvocal analysis
One kind of analysis that fits within the assumptions of the poststructuralist paradigm
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
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
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
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
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
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
Computer-assisted analysis
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