Educational Research
Chapter 18
Qualitative Research: Data Analysis and
Interpretation
Gay, Mills, and Airasian
Topics Discussed in this Chapter
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Data analysis
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Characteristics of qualitative data
Analysis during and after data collection
Analytic strategies
Computerized analysis
Interpretation of results
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Insights into interpreting
Strategies
Data Analysis
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The purpose of data analysis is to bring order
to the data
Characteristics of qualitative data
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Thick, rich descriptions
Voluminous
Unorganized
Perspectives on analysis and interpretation
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No single way to gain understanding of
phenomena
Numerous ways to report data
Objective 1.1
Data Analysis
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Perspectives
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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
Data Analysis During Data Collection
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Data analysis is an ongoing process
throughout the entire research project
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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
Data Analysis After Data Collection
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General guidelines and strategies but few
specific rules
Common problems
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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
Data Analysis After Data Collection
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Inductive nature of data analysis
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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
Data Analysis After Data Collection
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Iterative process focused on
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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
Data Analysis After Data Collection
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Data management
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Creating and organizing data collected
during the study
Purposes
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Organize and check data for completeness
Start the analytical and interpretive process
No meaningful analysis can be done
without effective data management
Data Analysis After Data Collection
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Data management (continued)
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Suggestions
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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
Data Analysis After Data Collection
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Three formal steps to analyze data
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Reading and memoing
Describing the context and participants
Classifying and interpreting
Objective 4.2
Data Analysis After Data Collection
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Reading and memoing
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Reading field notes, transcripts, memos,
and the observer’s comments
The purpose is to get an initial sense of the
data
Suggestions
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Read for several hours at a time
Make marginal notes of your impressions,
thoughts, ideas, etc.
Objective 4.2
Data Analysis After Data Collection
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Description
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What is going on in the setting and among
participants
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Purposes
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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
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The influence of context on participants’ actions and
understanding
Objective 4.2
Data Analysis After Data Collection
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Classifying and interpreting
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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
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Topic – A basic unit of information
Category – a classification of ideas or concepts
Pattern – a relationship across categories
Objective 4.2
Data Analysis Strategies
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Eight strategies for starting data analysis
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Identifying themes
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A good place to start analyzing data
Listing themes or patterns you have seen emerge from
the data
Coding data
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Reducing the data to a manageable form
Guidelines
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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
Data Analysis Strategies
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Eight strategies (continued)
 Asking key questions
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Doing an organizational review
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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.)
Focus on the organization’s vision and mission, goals and
objectives, structures, operations, problems, issues, and
concerns
Concept mapping
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Create a visual representation of the major influences
that have affected the study
Objectives 6.1 and 6.3
Data Analysis Strategies
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Eight strategies (continued)
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Analyzing antecedents and consequences
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Displaying findings
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Mapping causes and effects
Represent findings in effective visual displays (e.g.,
graphs, charts, concept maps, etc.)
Stating what is missing
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Identify what “pieces of the puzzle” are still missing
Objectives 6.1 and 6.3
Computerized Data Analysis
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Software is readily available to assist with
data analysis
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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
Interpretation
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The purpose of the interpretation of
qualitative analyses of data
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Attempts to understand the meaning of the
findings
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Larger conceptual ideas
Consistent themes
Relationships to theory
Differentiating analysis and interpretation
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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
Interpretation
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Insights into interpretation
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Interpretation is reflective, integrative, and
explanatory
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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
Interpretation
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Four guiding questions
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What is important in the data?
Why is it important?
What can be learned from it?
So what?
Objective 7.2
Interpretation
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Six strategies
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Extend the analysis
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Connect findings with personal experiences
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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
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Note implications that might be drawn
Seek the insights from a trusted colleague
Contextualize findings in the literature
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Uncover external sources that support the findings
Objective 7.3
Interpretation
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Six strategies (continued)
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Turn to theory
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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!”
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Don’t offer an interpretation with which you are not
comfortable
Suggest what needs to be done
Objective 7.3
Credibility Issues
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Six questions to help researchers check
the quality of their data
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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
Credibility Issues
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Six questions (continued)
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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