Before we go into initial data analysis, lets review the general steps in most qualitative research:

******GENERAL STEPS IN QUALITATIVE RESEARCH******

1. Observe events/ask questions with open-ended answers,

2. Record/log what is said and/or done

3. Interpret (personal reactions, write emergent speculations or hypotheses, monitor methods) OR Look for Regularities (analyze for patterns)

4. Return to observe, or ask more questions of people5. [recurring cycles of 2-4--iteration]

6. Formal theorizing [emerges out of speculations and hypotheses]

7. Draw conclusionsTHE STEPS THATINVOLVE DATAANALYSIS

DATA ANALYSIS USUALLY MEANS

1.REVIEWING 2. SORTING, 3. CLASSIFYING OR CATEGORIZING, 4. ANALYZING AND THEN 5. SYNTHESIZING YOUR DATA

IN SOME SYSTEMATIC WAY THAT LEADS TO CONCLUSIONS ABOUT THE RESEARCH QUESTIONS.

However, this process of reviewing, sorting, classifying, analyzing and synthesizing is not done haphazardly!!!!

you must identify – ahead of time how will you go about this.

first data must be ‘transcribed’. This means it must be put in a form that is ‘reviewable’. Audiotapes are often used in focus groups. However, after the group someone – usually you, although it is better to pay someone for this tedious task – must put them in written form.

1. REVIEWING • make sure that the data is separated, based on the methods (or

sources) that you used. For example, all of my observation ‘field notes’ are kept together in one pile, my in-depth interviews with clients in another, and my transcription of my video/audio tape of staff ‘focus groups’ in a third pile.

• then all data, from all sources and all methods are reviewed by the evaluator or some others hired by the evaluator. During this first reading, you are not looking for answers to the research questions. All you are doing is ‘sensitizing yourself to the data’. This is known as ‘immersion’. THIS PROCESS OF IMMERSING ONESELF IN THE DATA CAN TAKES WEEKS!

• This is a good time to take personal notes!!!! As you are reviewing the data (step 6), note anything- ANYTHING- that jumps out at you or that you think is interesting.

2. SORT, AND 3. CLASSIFY USUALLY MEANS THESEPhases of Initial Analysis:

1. Code Various Units--can have multiple codes for a unit (codes are not mutually exclusive, at least at first, but work towards exhaustive coding, at least eventually). Theory--preexisting or emergent--influences coding, but can push self to code broadly, thus less bound by theory. May need to code same data several times--computers definitely help.

2. Develop Categories, Subcategories, Superordinate Categories. These require definitions. Categories and definitions will probably need to be revised many times as you continue analysis. Keep track of the revisions and reasons for revisions of categories and definitions in theoretical notes (revisions occur because data indicates that previous definitions were not sufficient).

3. Give Examples of Categories in your theoretical notes, indexed specifically to pages, line numbers, etc. of field notes/video data/other sources of data. Include exemplars--best examples that represent the core of the category. Also include outliers--poor examples, but nevertheless examples of the category, as they define the limits of the category.

4. Linkages Between Categories need to be specified, and also note the kinds of linkages involved. Consider these kinds of linkages, among others:

• Set the data aside AGAIN . Go back to it. Later. Review again, according

to piles. Except this time you are looking for specific answers to the research questions. Here is where you begin to take ‘theory notes’.

• When I analyze I begin to interpret the meaning of the data!!!

• In reviewing the data this time (step 8), YOU ARE LOOKING FOR COMMONALITIES OR THEMES WITHIN THE PILES.

• TAKE NOTES. In analyzing and note –taking, you are looking for the strength of an assertion. For example, if your research question is “how relevant are the activities in “X” program?”, you might find that most ‘staff’ see the activities as ‘highly relevant’. This gives strength and credibility to the notion that the activities are highly relevant. Whereas, if only half of the staff see the activities as relevant, this would indicate that there is ‘mixed support among staff’ for the relevancy of activities. Additionally, for example, you might note that staff responses contain subtle criticisms of each other! That would tell you, perhaps, that there are some conflicts among staff that may be interpersonal or around commitment to how the program is being implemented.

You would do this for each method or data source.

4. ANALYZING-AFTER I SET UP MY CATEGORIES AND HAVE SORTED THE DATA

After you have done this for each data source, then begin to compare your notes for each source. Look for commonalities (strength of support for the research question-one way or another) or disagreements (lack of strength of support).

For example, you might find that most staff see the activities as highly relevant and most clients see them as highly irrelevant. Then you know there is a strong disagreement between clients and staff about the activities. HOWEVER, lets say that in your observations, you note that the activities – based on your lit review and observations – really do not fit the program objectives and that staff seem to be doing activities that they like or are convenient for them, rather than meeting the needs of the client. Then you might conclude that the activities are not meeting the needs of the clients and program objectives. ON THE OTHER HAND, lets say your observation (field notes) indicate that the activities are relevant and the staff is really trying, but the clients seem disinterested, you might conclude that EITHER THE CLIENTS DO NOT UNDERSTAND HOW THE ACTIVITIES FIT WITH THE PROGRAM OR THAT THE CLIENTS ARE NOT BEING ENGAGED ON THEIR LEVEL. SEE HOW THIS WORKS?

THEN YOU BEGIN DRAWING AND WRITING YOUR CONCLUSIONS.

5. SYNTHESIZING

• But what do I look for when I begin to look for themes, commonalities, categories (steps 8 & 9)?

• Below is a list of different methods for classifying and reviewing data (steps 8 & 9). This list does not go into detail!!!

No matter what the methodology, all data analysis is

1.REVIEWING 2. SORTING, 3. CLASSIFYING OR CATEGORIZING,

4. ANALYZING AND THEN 5. SYNTHESIZING YOUR DATA

1. Typology - a classification system, taken from patterns, themes, or other kinds of groups of data. (Patton pp. 393,398) John Lofland & Lyn Lofland Ideally, categories should be mutually exclusive and exhaustive if possible, often they aren't.Basically a list of categories. example: L acts, activities, meanings, participation, relationships, settings (in the third edition they have ten units interfaced by three aspects--see page 114--and each cell in this matrix might be related to one of seven topics--see chapter seven).

for example, Activities: categories might be 1. highly relevant to consumer. Moderately relevant. 3. meaningless

 2. Taxonomy (See Domain Analysis - often used together, especially

developing taxonomy from a single domain.) James SpradleyA sophisticated typology with multiple levels of concepts. Higher levels are inclusive of lower levels. Superordinate and subordinate categories. In other words there are sub-categories within categories. For example within the category “highly relevant activities” , you might see, subcategories of : “Staff-centered” and “consumer centered”. It is up to the reseracher and the data to determine the categories.

 

3. Constant Comparison/Grounded Theory (widely used, developed in late 60's) Anselm Strauss. This method is less strucutred that the first two. Essentially the researcher reviews and reviews the data with no preconceived ideas and lets the categories and patterms “come to her”.

• Look at document, such as field notes • Look for indicators of categories in events and behavior - name them and code

them on document • Compare codes to find consistencies and differences • Consistencies between codes (similar meanings or pointing to a basic idea)

reveals categories. So need to categorize specific events • We used to cut apart copies of field notes, now use computers. (Any good word

processor can do this. • Write Memos on the comparisons and emerging categories • Eventually category saturates when no new codes related to it are formed • Eventually certain categories become more central focus - axial categories and

perhaps even core category. •  

4. Analytic Induction (One of oldest methods, a very good one) F. Znaniecki, Howard Becker, Jack Katz. This method uses an initial observation to forma tentative hypothesis, then looks for confirmation of the hypothesis in other data.A. Look at the first event or major event and develop a hypothesis or hypothetical statement of what happened. B. Then look at another similar event and see if it fits the hypothesis. If it doesn't, revise hypothesis.C. Begin looking for exceptions to hypothesis, when find it, revise hypothesis to fit all examples encountered. Eventually will develop a hypotheses that accounts for all observed cases.

 5. Logical Analysis/Matrix Analysis An outline of generalized causation, logical reasoning process, etc.Use flow charts, diagrams, etc. to pictorially represent these, as well as written descriptions. Matthew Miles and Huberman gives hundreds of varieties in their huge book Qualitative Data Analysis, 2nd ed. The PLM is an example of this method.

 6. Quasi-statistics (count the # of times something is mentioned in field notes as very rough estimate of frequency) Howard BeckerOften enumeration is used to provide evidence for categories created or to determine if observations are contaminated. (from LeCompte and Preissle).this often involves reporting the frequency of occurrence in data. For example, “5 out of 7 members of the focus group reported that the MSW program had ‘changed their professional lives in signifantly positive ways’”.

 

7. Metaphorical Analysis (usually used in later stages of analysis) Michael Patton, Nick SmithTry on various metaphors and see how well they fit what is observed. Can also ask participant for metaphors and listen for spontaneous metaphors. "Hallway as a highway." Like highway in many ways: traffic, intersections, teachers as police, etc.Best to check validity of metaphor with participants - "member check". In other words, applying a metaphor to patterns that emerge in the data comes in the last stages of analysis; INTERPRETATION

 

8. Hermeneutical Analysis (hermeneutics = making sense of a written text) Max Van Manen

Not looking for objective meaning of text, but meaning of text for people in situation. Try to bracket self out in analysis - tell their story, not yours. Use their words, less interpretive than other approaches.

Different layers of interpretation of text. Knowledge is constructed – we construct meaning of text (from background and current situation - Social construction because of influence of others - symbolic interactionism)

Use context - time and place of writing - to understand. What was cultural situation? Historical context. Meaning resides in author intent/purpose, context, and the encounter between author and reader - find themes and relate to dialectical context. (Some say authorial intent is impossible to ascertain.)

Videotape - probably needs to be secondary level of analysis. Get with another person who is using another method and analyze their field notes.

 

11. Discourse analysis (linguistic analysis of ongoing flow of communication) James GeeUsually use tapes so they can be played and replayed. Several people discussing, not individual person specifically. Find patterns of questions, who dominates time and how, other patterns of interaction.

 12. Semiotics (science of signs and symbols, such as body language) Peter ManningDetermine how the meanings of signs and symbols is constructed. Assume meaning is not inherent in those, meaning comes from relationships with other things. Sometimes presented with a postmodernist emphasis.

13. Narrative Analysis (study the individual's speech) Catherine ReismanOverlaps with other approaches. (Is it distinctive?) Discourse analysis looks at interaction, narrative is more individual)The story is what a person shares about self. What you choose to tell frames how you will be perceived. Always compare ideas about self. Tend to avoid revealing negatives about self. Might study autobiographies and compare them. Elements of analyses area. context-situation b. core plot in the story told about self c. basic actions

d. Theme of story

Narrative analysis could involve study of literature or diaries or folklore.

all three of these analyze language and speech and how each shapes meaning.. Narrative might focus more on written documents.

 

14. Content Analysis (not very good with video and only qualitative in development of categories - primarily quantitative) (Might be considered a specific form of typological analysis) R. P. Weber

Look at documents, text, or speech to see what themes emerge. What do people talk about the most? See how themes relate to each other. Find latent emphases, political view of newspaper writer, which is implicit or look at surface level - overt emphasis.Theory driven - theory determines what you look for. Rules are specified for data analysis.

Standard rules of content analysis include:– How big a chunk of data is analyzed at a time (a line, a sentence, a

phrase, a paragraph?) Must state and stay with it. – What are units of meaning?, the categories used. Categories must be: – Inclusive (all examples fit a category) – Mutually exclusive – Defined precisely: what are properties – All data fits some category (exhaustive)

Also note context. Start by reading all way through, then specify rules. Could have emergent theory, but usually theory-driven. After determine categories, do the counting - how often do categories occur. Most of literature emphasizes the quantitative aspects.

Originated with analyzing news articles for bias - counting things in print. Very print oriented - can it be adapted for visual and verbal?

• The list might be a little confusing. The important thing to note is all methods of data analysis share these steps:

1.REVIEWING

2. SORTING, 3. CLASSIFYING OR CATEGORIZING, 4. ANALYZING AND THEN 5. SYNTHESIZING YOUR DATA

These steps in data analysis are part of the larger general steps in qualitative research methodology.

Before we go into initial data analysis, lets review the general steps in most qualitative research:

******GENERAL STEPS IN QUALITATIVE RESEARCH******

1. Observe events/ask questions with open-ended answers,

2. Record/log what is said and/or done

3. Interpret (personal reactions, write emergent speculations or hypotheses, monitor methods) OR Look for Regularities (analyze for patterns)

4. Return to observe, or ask more questions of people5. [recurring cycles of 2-4--iteration]

6. Formal theorizing [emerges out of speculations and hypotheses]

7. Draw conclusionsTHE STEPS THATINVOLVE DATAANALYSIS

• Now review the example I wrote for qualitative section 3.