Qualitative Research Design Qualitative Research Design Purpose, Analysis, and Purpose, Analysis, and

CodingCoding

The purpose The purpose …of this case study (strategy of inquiry) will be to

_________________(understand? describe? develop? discover?) the _______________(central phenomenon being studied) for ________________ (the participants, such as the individual, groups, organization) at ________________(research site). At this stage in the research, ___________________(central phenomenon being studied) will be generally defined as _______________________(provide a general definition).

((John Creswell’s scripted purpose statement, 2003)

Design DecisionsDesign Decisions

Data – what kind of data Data – what kind of data How data will be gatheredHow data will be gathered Sampling -who, what, where Sampling -who, what, where How data will be analyzedHow data will be analyzed Unit of analysisUnit of analysis Will the plan develop credible, useful Will the plan develop credible, useful

information? Will it allow you to answer information? Will it allow you to answer your research questions?your research questions?

FlexibleFlexible

Design might change after you enter Design might change after you enter the fieldthe field

TriangulationTriangulation cross checking data & findings to cross checking data & findings to

strengthen validitystrengthen validity Data Triangulation – use multiple Data Triangulation – use multiple

data sources (participants)data sources (participants) Methods Triangulation Methods Triangulation Investigator TriangulationInvestigator Triangulation Theory TriangulationTheory Triangulation

To enhance validityTo enhance validity Disconfirming evidenceDisconfirming evidence

Look for negative or disconfirming Look for negative or disconfirming evidence. After tentative, preliminary evidence. After tentative, preliminary themes are established, sift through themes are established, sift through data for evidence that disputes, data for evidence that disputes, disconfirms, is inconsistent with disconfirms, is inconsistent with themes (categories). themes (categories).

To enhance validityTo enhance validity Thick, Rich DataThick, Rich Data

– Describe contextDescribe context– Bring reader to your story, contextBring reader to your story, context

Triangulate DataTriangulate Data Find Corroborative DataFind Corroborative Data Use Independent AnalystUse Independent Analyst Member ChecksMember Checks Reciprocity (Lather)Reciprocity (Lather)

DATA ANALYSIS DATA ANALYSIS

Process of analyzing dataProcess of analyzing data

Data comes as: transcripts, field Data comes as: transcripts, field notes, photographs, document notes, photographs, document summary forms…summary forms…

DATA ANALYSISDATA ANALYSISa process of making sense out of dataa process of making sense out of data

Working with dataWorking with data

Organizing dataOrganizing data

Breaking data into manageable unitsBreaking data into manageable units

Data analysis while in the fieldData analysis while in the field Make decisions that narrow studyMake decisions that narrow study Develop analytic questionsDevelop analytic questions Plan data collection based on what you find Plan data collection based on what you find Write Observer CommentsWrite Observer Comments Write memos about what you are learning Write memos about what you are learning Try out ideas and themes on participantsTry out ideas and themes on participants Play with metaphors, analogies & conceptsPlay with metaphors, analogies & concepts Use visual devices (Bogdan & Biklen)Use visual devices (Bogdan & Biklen)

Category ConstructionCategory Construction Categories are abstractions derived from Categories are abstractions derived from

the data, not the actual data. Concepts the data, not the actual data. Concepts that stand for phenomena. that stand for phenomena.

……conceptual elements that “cover” or conceptual elements that “cover” or span many individual examples of the span many individual examples of the categorycategory

……are often constructed through constant are often constructed through constant comparative method (continuous comparative method (continuous comparison of incidents, remarks, etc. with comparison of incidents, remarks, etc. with each other)each other)

Develop coding categoriesDevelop coding categories

search for themes, patterns, topicssearch for themes, patterns, topics

Discover what is importantDiscover what is important Discover what is to be learnedDiscover what is to be learned Deciding what you will tell othersDeciding what you will tell others

Unit of DataUnit of Dataany meaningful segment of dataany meaningful segment of data

Code 1

Sub-code 1 Sub-code 2 Sub-code 3

Unit of data must meet 2 Unit of data must meet 2 criteria:criteria:

1. It should be heuristic, i.e., unit should 1. It should be heuristic, i.e., unit should reveal information relevant to the study reveal information relevant to the study and stimulate the reader to think beyond and stimulate the reader to think beyond the particular bit of information.the particular bit of information.

2. It should be “the smallest piece of 2. It should be “the smallest piece of information about something that can information about something that can stand by itself –i.e., it must be stand by itself –i.e., it must be interpretable in the absence of any interpretable in the absence of any additional information other than a broad additional information other than a broad understanding of the context in which the understanding of the context in which the inquiry is carried out.” Lincoln & Gubainquiry is carried out.” Lincoln & Guba

Guidelines to Determine Efficacy of Categories Guidelines to Determine Efficacy of Categories Derived from Constant Comparative Method Derived from Constant Comparative Method

(Merriam)(Merriam)Categories should:Categories should:1.1. reflect purpose of researchreflect purpose of research2.2. be exhaustive (find place for all relevant data)be exhaustive (find place for all relevant data)3.3. be mutually exclusive. (if same data fit in more be mutually exclusive. (if same data fit in more

than 1 category, more conceptual work may than 1 category, more conceptual work may need to be done to refine categories)need to be done to refine categories)

4.4. be sensitizing (capture meaning)be sensitizing (capture meaning)5.5. be conceptually congruent. (Level of abstraction be conceptually congruent. (Level of abstraction

should characterize categories at same level. should characterize categories at same level. May need to form subcategories.)May need to form subcategories.)

Categories and BinsCategories and Bins

Put a unit of data into a category or Put a unit of data into a category or binbin

Guidelines for Creating CategoriesGuidelines for Creating Categories 1. The number of people who mention something 1. The number of people who mention something

or the frequency with which something arises in or the frequency with which something arises in the data indicates an important dimension.the data indicates an important dimension.

2. One’s audience may determine what is 2. One’s audience may determine what is important…some categories may appear to important…some categories may appear to various audiences as more or less credible.various audiences as more or less credible.

3. Some categories will stand out because of their 3. Some categories will stand out because of their uniqueness and should be retained.uniqueness and should be retained.

4. Certain categories may reveal “areas of inquiry 4. Certain categories may reveal “areas of inquiry not otherwise recognized” or “provide a unique not otherwise recognized” or “provide a unique leverage on an otherwise common problem.” leverage on an otherwise common problem.”

(Lincoln & Guba)(Lincoln & Guba)

Creating categoriesCreating categories Develop a chart or table to display Develop a chart or table to display

categories. See how well the parts fit categories. See how well the parts fit togethertogether

Too many categories may mean Too many categories may mean analysis is stuck in concrete analysis is stuck in concrete description. description.

CodingCoding

Coding is a procedure that Coding is a procedure that disaggregates the data, breaks it disaggregates the data, breaks it down into manageable segments and down into manageable segments and identifies or names those segments. identifies or names those segments. (Schwandt)(Schwandt)

Practice CodingPractice Coding We will begin coding by practicing We will begin coding by practicing

with at least one of the sources of with at least one of the sources of data you have collected for your field data you have collected for your field project (e.g., interview transcript or project (e.g., interview transcript or observation write-up with analytical observation write-up with analytical memo)memo)

Making Sense of Your Field Research Data Analysis with Research Groups: Step 1

Step 1: Work independently. Read observation field notes, interview transcripts and document summary forms. What are you learning from the data? Look for emerging insights, themes and patterns in data. How is the data helping to answer your research questions? What are you finding? What part, if any, do role groups or programs play in your study? Read everything again.

Data Analysis with Research Groups: Step 2

Step 2: Convene group. Research groups meet to discuss the data. Each group member must report what he or she is finding in data gathered (see step 1). After each member has presented, the group will consider how the themes are similar or different depending on data source. Group must generate and agree on a tentative list of themes or codes. If the group still has a lot more data to collect, approach the codes as very tentative and finalize later in week, perhaps in a virtual meeting. Everyone needs a copy of the code list.

Data Analysis with Research Groups: Step 3

Step 3: Work independently. Next step will be to code all data gathered using the agreed upon coding scheme (code list).

To do the actual coding, the group may decide to use atlas.ti software. Alternatively, groups may want to bring colored markers and post-its to class for hands-on coding, or coding can be done electronically with a word processing program.

Data Analysis with Research Groups: Step 4

Step 4: Convene group. Next the group will reconvene to discuss how individual coding was similar or different, and decide what is best representation of the data. Once that is set, carefully examine again all the data and themes. It is at this stage that you will look for patterns and move from themes and codes to findings.

FindingsFindings

Now that all the data have been coded, how do the data triangulate? What are the findings? Debate alternative interpretations. How will you address unique cases or outliers? Determine the story that your study must tell.

When writing up findings: Use thick rich When writing up findings: Use thick rich data. Remember context mattersdata. Remember context matters

ConclusionsConclusions Based on the findings, what do you

conclude? How does your study inform the

guiding research questions? How does your study add to our

knowledge or understanding of education?