Hatch Chapter 4

Hatch Chapter 4Analyzing Qualitative Data

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 1

Read the data for a sense of the whole

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


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


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


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?


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


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


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 1

Read the data for a sense of the whole

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

Text taken directly from:

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

Hatch Chapter 4

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