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Qualitative Research: Data Analysis and Interpretation

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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

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