BASIC STEPS IN DATA ANALYSIS FOR QUALITATIVE RESEARCH

Steven Taylor & Robert Bogdan (1990) divide qualitative analysis into three phases: discovery,

coding, and discounting.

Discovery is basically for the purpose of seeing, in a general sense, what’s in your data.

1. Read and reread your data. Know it inside and out; get very familiar with it.

2. As you do so, you will notice that you have ideas and hunches. You’ll start noticing things that

repeat. Write down your ideas. Keep notes as you read; otherwise, you might lose some brilliant

thoughts.

3. Out of this process, you should start to notice some emerging themes - patterns that stand out, or

are subtle.

4. Construct typologies - schemes for classifying data - big, broad categories that have

subcategories underneath. These might lead to your findings. It’s a back-and-forth-process.

5. Review the literature, if applicable. Think about whether you have run across any concepts that

would be helpful or relate your work to the work others have done, i.e., how is your work similar or

different?

6. Develop a story line. What is the story your data tells? You may not tell everything, but think

about what you would like to know if you heard a teacher somewhere else had done an inquiry project

on the same area you did.

Coding is the process of marking all the data that fits with particular themes. This process enables

participants to pick examples (or vignettes) from the data that will best illustrate the story. As

participants code the data, they continually refine, change, or add to the categories. Coding is a process

that continues data analysis.

1. Develop categories.

2. Code all the data.

3. Sort the data.

4. See what’s unaccounted for.

5. Refine your analysis.

Discounting data may sound negative, but it is essentially an opportunity to consider the context in

which different kinds of data were collected and to notice what difference that context might make on

what was learned. It basically involves checking the emerging concepts and theoretical propositions

against possible bias and contamination.

1. Solicited or unsolicited data.

2. Observer’s influence on the setting.

3. Who was there?

4. Direct and indirect data.

5. Sources.

6. Your own assumptions and presuppositions.

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Matthew Miles and Michael Huberman (1994) state that qualitative data analysis consists of "three

concurrent flows of activity: data reduction, data display, and conclusion drawing/verification" (1994,

p. 10).

Data reduction refers to the process of selecting, and thus simplifying, the data that appears in written

field notes or transcriptions. The researcher has to make decisions on how to code the categories,

group, and organize them so that the conclusions can be reasonably drawn and verified.

Data display refers to ways of displaying the data, which include matrices, graphs, and charts

illustrating the patterns and findings from the data.

Conclusion drawing and verification refer to a process of developing an initial thought about patterns

and explanations from the findings, verifying them constantly by checking the data, and forming a new

matrix. It is through such process that the validity of the data is established and the meanings of

findings emerge.

These three stages of data analysis -- data reduction, data display, conclusion drawing and verification -

- form an interactive, cyclical process.

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Donald Freeman (1998)

Four elemental activities make up data analysis. These are naming, grouping, finding relationships, and

displaying.

Naming involves labeling the data in some way. These names are called codes; in qualitative research

they can come from three basic sources: from categories outside the data such as the setting, the

research question, previous research, and so on; from the data themselves; or they may be created by

the researcher.

Naming involves taking the data apart. Grouping involves reassembling the names you are giving to

parts of the data by collecting them into categories. As with codes, the categories can be grounded,

emerging from the data, or they can be a priori, from outside. Grouping the names you are giving to or

finding in the data begins to create a structure around the data.

To strengthen the structure, you need to find relationships in the data, to identify patterns among those

categories. As you name, group, and find relationships in the data, however, it is critically important to

look at what does not fit into the emerging structure of the analysis. These pieces that don’t fit are

called outliers and they can provide insights about your analysis; they can show you where the

interpretation is weak or incomplete, and how it needs to be redirected.

As the data analysis progresses, you will need to lay out what you are finding in order to see the

emerging whole of the interpretation. This step is called data display; the aim is to set out the patterns

and relationships you see among the categories. These displays make the interpretation concrete and

visible; they allow you to see how the parts connect into a whole.

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John V. Seidel (1998)

Analyzing qualitative data is essentially a simple process. It consists of three parts: Noticing,

Collecting, and Thinking about interesting things.

On a general level, noticing means making observations, writing field notes, tape recording interviews,

gathering documents, etc. When you do this you are producing a record of the things that you have

noticed. Once you have produced a record, you focus your attention on that record, and notice

interesting things in the record. As you notice things in the record you name, or “code,” them.

As you notice and name things the next step is to collect and sort (arrange/classify) them. This process

is analogous to working on a jigsaw puzzle where you start by sorting the pieces of the puzzle. A

common strategy for solving the puzzle is to identify and sort puzzle pieces into groups (e.g., frame

pieces, tree pieces, house pieces, and sky pieces). Some of the puzzle pieces will easily fit into these

categories. Others will be more difficult to categorize. In any case, this sorting makes it easier to solve

the puzzle. When you identify pieces, you are noticing and “coding” them. When you sort the pieces

you are “collecting” them.

A useful definition of the QDA process, and one that seems to fit well with the jigsaw puzzle

analogy, comes from Jorgensen (1989). Analysis is a breaking up, separating, or disassembling

of research materials into pieces, parts, elements, or units. With facts broken down into

manageable pieces the researcher sorts and sifts them, searching for types, classes, sequences,

processes, patterns or wholes. The aim of this process is to assemble or reconstruct the data in

a meaningful or comprehensible fashion (Jorgensen, 1989: 107).

In the thinking process you examine the things that you have collected. Your goals are: 1) to make

some type of sense out of each collection, 2) look for patterns and relationships both within a

collection, and also across collections, and 3) to make general discoveries about the phenomena you are

researching. Returning to the jigsaw puzzle analogy, after you sort the puzzle pieces into groups you

inspect individual pieces to determine how they fit together and form smaller parts of the picture (e.g.,

the tree part or the house part). A similar process takes place in the analysis of qualitative data. You

compare and contrast each of the things you have noticed in order to discover similarities and

differences, build typologies, or find sequences and patterns.

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Generic steps for analyzing qualitative data (from John Creswell, 2003)

Step 1. Organize and prepare the data for analysis.

Step 2. Read through all the data. A first general step is to obtain a general sense of the information

and to reflect on its overall meaning. What general ideas are participant saying? What is the tone of the

ideas? What is the general impression of the overall depth, credibility and use of the information?

Step 3. Begin detail analysis with a coding process. Coding is the process of organising the material

into “chunks” before bringing meaning to those chunks. Itinvolves taking data or pictures, segmenting

sentences (or paragraphs) or images intocategories, and labelling those categories with a term, often a

term based in the actuallanguage of the participant (called an

in vivo term).

Step 4. Use the coding process to generate a description of the setting or people as well as categories

of themes for analysis. Description involves a detailed rendering of information about people, places or

events in a setting. Researchers can generate codes for this description. This analysis is useful in

designing detailed descriptions for case studies, ethnographies, and narrative research projects. Then,

use the coding to generate a small number of themes or categories, perhaps five to seven categories for

a research study. These themes are the ones that appear as major findings in qualitative studies and are

stated under separate headings in the findings sections of studies. They should display multiple

perspectives from individuals and be supported by diverse quotations and specific evidence.

Step 5. Advance how the description and themes will be represented in the qualitative narrative. The

most popular approach is to use a narrative passage to convey the findings of the analysis. This might

be a discussion that mentions a chronology of events, the detailed discussion of several themes

(complete with sub-themes, specific illustrations, multiple perspectives from individuals, and

quotations), or a discussion with interconnecting themes. Many qualitative researchers also use visuals,

figures or tables as adjuncts to the discussions.

Step 6. A final step in data analysis involves making an interpretation or meaning of the data. “What

were the lessons learned” captures the essence of this idea (Lincolnand Guba 1985). These lessons

could be the researcher’s personal interpretation, couched in the individual understanding that the

inquirer brings to the study from her or his own culture, history, and experiences. It could also be a

meaning derived from a comparison of the findings with information gleaned from the literature or

existing theories. In this way, authors suggest that the findings confirm past information or diverge

from it. It can also suggest new questions that need to be asked – questions raised by the data and

analysis that the inquirer had not foreseen earlier in the study

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According to Ian Dey (1993, 2005), the core of qualitative analysis lies on three related processes:

describing phenomena, classifying it and seeing how the concepts interconnect. Dey draws these as a

circular process (p. 31) to show that they interconnect each other. But because qualitative analysis is

iterative process, he also represents them by iterative spiral (p. 53).

The first step in qualitative analysis is to develop thorough and comprehensive description of the

phenomenon under study. Geerz (1973) and Denzin (1978) call this as ‘thick’ description, which

includes information about the context of an act, the intentions and meanings that organize action, and

its subsequent evolution. Thus description encompasses the contexts of action, the intentions of actor,

and the process in which action is embedded.

Classification is the second process in qualitative data analysis. Without classifying the data, we have

no way of knowing what it is that we are analyzing. We also cannot make meaningful comparisons

between different bits of data. So, classifying the data is an integral part of the analysis. Moreover, the

conceptual foundations upon which interpretation and explanation are based lay on it.

Description and classification are not ends in themselves but must serve an overriding purpose, which

is to produce an account for analysis. For that purpose we need to make connections among building

block of concepts of our analysis. Here Day offers graphic representation as a useful tool in analyzing

concepts and their connections.

DATA ANALYSIS FROM ACTION RESEARCH

Herbert Altrichter, Peter Posch and Bridget Somekh (1993)

The constructive stage of analysis:

• ‘Reading’ data

Data are ‘read’ (closely scrutinised) in order to recall the events and experiences that they represent:

What was done? What was said? What really happened?

• Selecting data

Important factors are separated from unimportant ones; similar factors are grouped; complex details are

sorted and (where possible) simplified.

• Presenting data

The selected data are presented in a form that is easy to take in at a glance. This can be in the form of a

written outline or a diagram.

• Interpreting data and drawing conclusions

Relationships are explained and a practical theory (or model) constructed to fit the situation which has

been researched. This theory or model should relate to the research focus.

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Marion MacLean and Marian Mohr (1999) offer several strategies to help teachers navigate data:

Categorize and sort. Sorting data into categories is a way of identifying potential themes that will

organize findings. Recording key quotes or observation details on index cards, for example, allows the

teacher-researcher to “shuffle” data into different categories in an effort to understand “what’s going

on?”

Order. Analysis can be facilitated by ordering data in various ways: chronologically, by frequency, or

by importance, for example. Chronological ordering of one student’s data, for example, might show

development of a particular capacity over time; ordering data by frequency might yield insight into the

time of day certain behaviors occur.

Identify and acknowledge assumptions. Teacher research groups are an ideal setting for identifying and

exploring assumptions the researcher brings to the process. Unacknowledged assumptions may leave

the researcher vulnerable to seeing only what she expects to see. For example, an unacknowledged

assumption that students read better in silence and isolation might leave the researcher blind to findings

that suggest more interactive reading strategies are effective for some students.

Pay attention to surprises and unexpected results. The identification of assumptions and possible biases

leaves the teacher-researcher more receptive to surprises that may come from the data and involves

paying attention to data that doesn’t seem to fit with other data. Surprises can lead to new areas of

inquiry or deeper understanding of the area of investigation.

Talk with students and others about what they think. Students are a tremendous yet often untapped

resource for understanding what’s going on in the classroom. In addition to involving students in data

collection, student insight can be valuable for data analysis as well, for they can confirm or disconfirm

initial analyses, as well as provide alternative analysis. The research group should play the same

function as researchers work to organize and focus data. In addition, talking with interested others

about analysis of data is an opportunity to speak findings out loud and listen for moments that lack

clarity.

State theories. Data analysis should lead to the articulation of a teacher’s theory about what is going on

in the classroom. Plenty of research offers theories on the way things work in schools, but analysis

frequently generalizes findings across settings so that the theories that emerge are too abstract to apply

to particular classrooms. The benefit of findings that emerge from teacher research is the generation

and articulation of a personal theory of how things work or how they might be changed to enhance

classroom practice.

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Anne Burns (1999)

Stage 1. Assembling the data: Collect the data that you have collected over the period of research, scan

it in a general, and note down thoughts, ideas or impressions as they occur during this initial

examination. At this stage, broad patterns or trends should begin to show up which can be compared

and contrasted to see what fits together.

Stage 2. Coding the data: once there has been some overall examination of the data, categories or codes

can be developed to identify patterns more specifically. Coding is a process of attempting to reduce the

large amount of data that may be collected to more manageable categories of concepts, themes or types.

Stage 3. Comparing the data: once the data have been categorized in some way, comparison can be

made to see whether themes or patterns are repeated or developed across different data gathering

techniques. You may notice hierarchies or sequences of data or identify relationships and connections

between different sources of data. At this stage you may also be able to map frequencies of

occurrences, behaviors or responses. Tables may be created using simple descriptive techniques to note

frequency counts or percentages. The main aim at this stage is to describe and display the data rather

than to interpret or explain them.

Stage 4. Building interpretations: this is the point where you move beyond describing, categorizing,

coding and comparing to make some sense of the meaning of the data. This stage demands a certain

amount of creative thinking as it is concerned with articulating underlying concepts and developing

theories about why particular patterns of behaviors, interactions or attitudes have emerged.

Stage 5. Reporting the outcomes: the final stage involves presenting an account of the research for

others. A major consideration is to ensure that the report sets out the major processes of the research

and that the finding and outcome are well supported with examples from the data.

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Mary Brown (1999)

Analysis of research data consists of four general stages:

A. Processing the evidence

Editing

1. Check that you have all the questionnaires, interview schedules, etc., and that all coded values are

entered for all items.

2. Check that each response has been interpreted uniformly (treated with the same criteria).

Coding

1. Classify evidence and place the data into net categories so that patterns may be coherently

established.

2. Set up coding frames.

Sampling: Conceptual and theoretical

1. Become immersed in the data by comparing and contrasting findings and by ordering themes and

components.

2. Examine the data creatively and reflexively. Develop grounded theories.

B. Mapping the data

Noting the frequency of recurrence of issues, themes, and units: It is now important to get some grasp

of the frequency of occurrence of specific units or themes. It is also important to try to chart of map

these relationships to determine clusters of topics within themes, categories, or components. During

mapping, we are not concerned with inference, but with organizing and describing the collected data.

Percentages, frequencies, and simple descriptive tables will suffice.

C. Interpreting the evidence

Interpreting data: Interpretation of the data occurs when we move beyond description and try to make

some statement about what various responses mean and to suggest relationships among data. Finally,

we build a model of the research data by trying to get the larger picture in focus by assembling the

various indicators and themes into a more self-explanatory set of relationships.

Building a model: Theories result from continuously looking at the collected data, posing questions and

seeing how these hang together. An incubation period is often needed before ideas and theories begin

to surface.

D. Presenting the results

Reporting evidence; drawing conclusions.

First, summarize the problem studied and present summary tables of the main findings. Then interpret

what those findings mean within the context of the study. Finally, the report should describe how the

actions taken have improved or not improved the problem and pose new lines of research or new

proposals for curriculum inquiry.

The most essential factor in analysis is the reflexivity of the researcher; that is, the ability of the

researcher to think and reflect critically about what has been gathered and what is still required.

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Gerald Pine (2009)

Conducting teacher action research

1. Triangulate the data. Study the research question from at least three separate pieces of data and three

points of view.

2. Sift through and put into order everything you have collected, making notes as you go. As you

examine the data, continually compare the data that were collected earlier in the study with data

collected later in the study.

3. Design a systematic approach to analyze your data. Categorize and clarify the data and determine

how to arrange the data findings, organizing the data chronologically, by importance, and by frequency

(how often an incident occurs), for example. Develop charts, columns, outlines, and ways of counting

occurrences. Coding your findings will help categorize the data. You can make up different categories

that fit the teaching situation(s) or use categories developed by another researcher.

4. Review your information after it is coded to determine if there is a frequency of certain phenomena

or powerful, unusual comments, events, or behaviors that particularly interest you.

5. Let the data influence you. Do not be afraid to let the data influence what you are learning as you go

deeper with your analysis. Look for what doesn’t fit the assumptions or theories of other researchers,

and note what stands out or goes against the grain.

6. Examine and study your data several times. New ideas will occur to you with a fresh perspective.

Speculate. Identify repetitive words, phrases, ideas, beliefs, or values, as well as similarities and

differences. Identify points that occur more frequently and are more powerful. Look for themes and

conceptual and attitudinal patterns to emerge. Key words and phrases can trigger themes. Determine

these themes by scanning the data, not by relying on your preconceived ideas of what you think the

categories are. Narrow the themes down to something manageable.

7. Write continuously to document actions and ideas as they take place. Writing can reveal meaning

and significance to you in the act of writing itself. As you proceed through the action research process,

make notes. Jot down what you are seeing, what questions are emerging, and what you are learning.

Keep notes on those new ideas that are unanticipated.

8. Create a visual representation for what you have collected. Look for patterns related to time and

sequence as well as patterns related to differences in other factors. A grid, an idea map, a chart, or some

visual metaphor—these are all possibilities to help make sense of the data and display a powerful

presentation of your ideas. Map out your data; draw it all on one page.

9. Abstract and distill. State the core of your findings as if you had to summarize and encapsulate the

essence of your study in an abstract of 50 words or less. What matters most in these data?

10. Consult with and involve your students. Ask your students what they think about what you are

observing and writing about. They may offer new ideas about their learning or validate what you are

finding.

11. Take a break away from the study. Sometimes, it helps to take a break from the research process to

clear your mind and give yourself a rest. Coming back to the process with a refreshed outlook will

often lead to new understandings and perspectives.

12. Confer with colleagues—with your critical friends group. Share your findings with your research

group, your critical friends. Discuss the research approach you used. Explain the data interpretations.

Do they see the same things? Consider their different interpretations and use them to clarify, broaden,

and otherwise validate the findings.

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According to Donna Kalmbach & Kevin Carr (2010), data analysis involves two moments: ongoing

data analysis and data interpretation.

Ongoing data analysis is the term we use to refer to the process of analyzing, synthesizing,

deconstructing and contextualizing data as it is gathered throughout the AR process.

To analyze means to take apart, to break down or dissect. We divide the data into categories, columns,

or other regularized spaces. We separate pieces of the data, making it simpler to study. To analyze is to

narrow our gaze; it is pulling in the subjects, problems, and questions with the micro lens of inquiry.

Key questions for analysis include:

What seems to be happening in this data? What is not happening in this data? What is repeated in this

data (words, behaviors, attitudes, occurrences)? What is surprising, perplexing, disturbing in the data?

To synthesize is the act of putting back together again, of creating wholeness, or integrating pieces to

form a sense of harmony or unity. To synthesize the data means pulling away from the parts (analysis)

and seeing the data set as a whole.

Key questions for synthesis include:

What patterns emerge across the landscape of the data? What is the classroom context for these

patterns? Where are the contradictions, paradoxes, and dilemmas in the data? What does not seem to fit

in the landscape?

To deconstruct is to check assumptions, to consider what personal and social context frames our

analysis and synthesis. To deconstruct data is to check under, around, behind, and over our conclusions.

Key questions for deconstruction include:

Where have categories of either/or interpretations been made? How can these either/or conclusions be

reconstructed using a different lens? What are the limitations of the analysis and synthesis? What

assumptions are being made in the analysis and synthesis? What values and beliefs do these

assumptions rely upon?

The cultural, social, and political context of who we are as teacher-researchers creates the lens through

which we interpret our data. We like the idea of “situated knowledge” (Haraway, 1996). Situated

knowledge means that knowledge (and thus our interpretations of data) is related to specific location,

historical and sociopolitical context. Who we are as teacher-researchers matters. Where we’ve grown

up, our ethnic identification, our social class, all matter. Our religious beliefs, our values based on

gender, our understanding of “good” and “bad” teachers all matter.

Key questions in considering context include:

How do the interpretations reflect your beliefs/values of what “good teaching” is, “good students” are,

and “good curriculum” should be? How do the interpretations mirror values and beliefs you hold as a

teacher-researcher given your ethnic, gender, and social standings, and other labels that may be used to

define you? How are the interpretations limited by these same labels?

Once you have completed data collection and ongoing analysis it is time for the teacher-researcher to

bring together the whole of the data, including ongoing analysis work. We refer to this process as data

interpretation. At this point in the process, the teacher-researcher has crate(s), file box(es), and/or

notebook(s) of organized data.

The five steps of the data interpretation process are generally as follows:

1. Getting started—The initial step in interpreting is to simply play with the data, inter-acting with it in

a dynamic, fluid way. Revisit, reflect, and reread collected data. Then, create appropriate mind maps,

timelines, and charts;

2. Expanding your interpretation: adding raw data to the interpretation. This step should result in a

greatly enhanced mind map, timeline, or chart. Add data to the mind map, timeline, and/or chart that

supports what you have learned. Draw connections, invent codes or symbols that help represent your

thoughts as they are forming. Be creative;

3. Applying interpretative layers: adding the perspectives of others. Seek another perspective on your

data by sharing your analytic memos or other synthesis statements with your critical colleague,

cooperating teacher, student teaching supervisor, university instructor, and/or another specialist in the

area;

4. Returning to the questions: applying questions to your interpretation. If you are working with a

chart, return to your chart column “What I still wonder about my re-search topic/question.” Add to this

column now that you have applied interpretative layers to your data. If you are working with a mind

map or timeline, ask the question, “What do I still wonder about my research topic/question?” Expand

your mind map or timeline with these thoughts, additional questions and/or partial understandings you

have about your research topic; and

5. Drafting synthesis statements: summarizing what you know. Identify from your work what seem to

be the major themes—we use the term categories—of your study. Create a table listing your categories.

Draft synthesis statements for each category. Each synthesis statement should be very succinct yet

include what you learned, how you know what you learned and what other voices say about what you

learned.