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Educational Research

                 Chapter 18
   Qualitative Research: Data Analysis and
                Interpretation

           Gay, Mills, and Airasian
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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

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Data analysis chapter 18 from the companion website for educational research

  • 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