1. Qualitative Research: Data Analysis and Interpretation
Educational Research
Chapter 18
Qualitative Research: Data Analysis and Interpretation
Gay, Mills, and Airasian

2. Topics Discussed in this Chapter
Data analysis
Characteristics of qualitative data
Analysis during and after data collection
Analytic strategies
Computerized analysis
Interpretation of results
Insights into interpreting
Strategies

3. Data Analysis
The purpose of data analysis is to bring order to the data
Characteristics of qualitative data
Thick, rich descriptions
Voluminous
Unorganized
Perspectives on analysis and interpretation
No single way to gain understanding of phenomena
Numerous ways to report data
Objective 1.1

4. Data Analysis Perspectives Researcher’s messages are not neutral
Researcher’s language creates reality
Researcher is related to what and who is being studied
Affect and cognition are inextricably linked
What is understood is not neat, linear, or fixed

5. Data Analysis During Data Collection
Data analysis is an ongoing process throughout the entire research project
Analysis begins with the very first interaction between the researcher and the participants
This is a very important perspective given the interpretive nature of the analysis and the emergent nature of qualitative research designs
Informal steps involve gathering data, examining data, comparing prior data to newer data, and developing new data to gain perspective
Objectives 3.1 and 3.2

6. Data Analysis After Data Collection
General guidelines and strategies but few specific rules
Common problems
Premature conclusions
Inexperience of the researcher
Self-reinforcement of the researcher’s own ideas without support from the data
Impulsive actions
Desire to finish quickly
Most problems are resolved by spending time “living” with the data
Objective 3.2

7. Data Analysis After Data Collection
Inductive nature of data analysis
Large amount of data to analyze
Progressively narrowing data into small groups of key data
Multi-staged process of organizing, categorizing, synthesizing, interpreting, and writing
Objective 3.2

8. Data Analysis After Data Collection
Iterative process focused on
Becoming familiar with the data and identifying potential themes
Examining the data in-depth to provide detailed descriptions of the setting, participants, and activities
Coding and categorizing data into themes
Interpreting and synthesizing data into general written conclusions
Objective 4.2

9. Data Analysis After Data Collection
Data management
Creating and organizing data collected during the study
Purposes
Organize and check data for completeness
Start the analytical and interpretive process
No meaningful analysis can be done without effective data management

10. Data Analysis After Data Collection
Data management (continued)
Suggestions
Write dates on all notes
Sequence all notes with labels
Label notes according to type
Make photocopies of all notes
Organize computer files into folders according to data types and stages of analysis
Make backup copies of files
Read through data to make sure it is legible and complete
Begin to note potential themes and patterns that emerge
Objective 6.1

11. Data Analysis After Data Collection
Three formal steps to analyze data
Reading and memoing
Describing the context and participants
Classifying and interpreting
Objective 4.2

12. Data Analysis After Data Collection
Reading and memoing
Reading field notes, transcripts, memos, and the observer’s comments
The purpose is to get an initial sense of the data
Suggestions
Read for several hours at a time
Make marginal notes of your impressions, thoughts, ideas, etc.
Objective 4.2

13. Data Analysis After Data Collection
Description
What is going on in the setting and among participants
Purposes
Provide a true picture of the setting and events to understand and appreciate the context
Separate and group pieces of data related to different aspects of the setting, events, and participants
Issues
The influence of context on participants’ actions and understanding
Objective 4.2

14. Data Analysis After Data Collection
Classifying and interpreting
The process of breaking down data into small units, determining the importance of these units, and putting pertinent units together in a general interpretive form
Use of coding and classifying schemes
Topic – A basic unit of information
Category – a classification of ideas or concepts
Pattern – a relationship across categories
Objective 4.2

15. Data Analysis Strategies
Eight strategies for starting data analysis
Identifying themes
A good place to start analyzing data
Listing themes or patterns you have seen emerge from the data
Coding data
Reducing the data to a manageable form
Guidelines
Read through all the data and attach working labels to blocks of text
Cut and paste these blocks of text to index cards to make it easier to organize the data in various ways
Group the index cards together based on similar labels
Re-visit each group of cards to be sure each card still fits
Objectives 6.1 and 6.3

16. Data Analysis Strategies
Eight strategies (continued)
Asking key questions
Working through a series of questions such as those proposed by Stringer (e.g., who is centrally involved, who has resources, how do things happen, etc.)
Doing an organizational review
Focus on the organization’s vision and mission, goals and objectives, structures, operations, problems, issues, and concerns
Concept mapping
Create a visual representation of the major influences that have affected the study
Objectives 6.1 and 6.3

17. Data Analysis Strategies
Eight strategies (continued)
Analyzing antecedents and consequences
Mapping causes and effects
Displaying findings
Represent findings in effective visual displays (e.g., graphs, charts, concept maps, etc.)
Stating what is missing
Identify what “pieces of the puzzle” are still missing
Objectives 6.1 and 6.3

18. Computerized Data Analysis
Software is readily available to assist with data analysis
Researchers must code the data
Manipulation of the data is enhanced
The effectiveness of this manipulation is dependent on the researcher’s ideas, thoughts, hunches, etc.
There is considerable debate as to whether data should be analyzed by hand or computer
Objectives 6.4 and 6.5

19. Interpretation
The purpose of the interpretation of qualitative analyses of data
Attempts to understand the meaning of the findings
Larger conceptual ideas
Consistent themes
Relationships to theory
Differentiating analysis and interpretation
Analysis involves making sense of what is in the data
Interpretation involves making sense of what the data mean
Objectives 5.1 and 7.1

20. Interpretation Insights into interpretation
Interpretation is reflective, integrative, and explanatory
Need to understand one’s own data to describe it
Integrated into report writing
Based heavily on connection, common aspects, and linkages among data, categories, and patterns
Interpretation makes explicit the conceptual basis of the categories and patterns
Objective 7.1

21. Interpretation Four guiding questions What is important in the data?
Why is it important?
What can be learned from it?
So what?
Objective 7.2

22. Interpretation Six strategies Extend the analysis
Note implications that might be drawn
Connect findings with personal experiences
The researcher knows the situation better than anyone else and can justify using his or her experiences and perspective
Seek advice from a “critical” friend
Seek the insights from a trusted colleague
Contextualize findings in the literature
Uncover external sources that support the findings
Objective 7.3

23. Interpretation Six strategies (continued) Turn to theory
Provides a way to link the findings to broader issues
Allows the researcher to search for increasing levels of abstraction
Provides a rationale for the work
Know when to say, “When!”
Don’t offer an interpretation with which you are not comfortable
Suggest what needs to be done
Objective 7.3

24. Credibility Issues
Six questions to help researchers check the quality of their data
Are the data based on your own observations or hearsay?
Is there corroboration by others of your observations?
In what circumstances was an observation made or reported?
Objective 7.4

25. Credibility Issues Six questions (continued)
How reliable are those providing data?
What motivations might have influenced a participant’s report?
What biases might have influenced how an observation was made or reported?
Objective 7.4