This is a slide show of chapter 18 from Educational Research: Competencies for Analysis and Applications. Primarily intended for instructor use in the classroom, it is also available for students’ study use or to review as an advance organizer before class lectures or discussions.

1. 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