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

Educational Research

Chapter 18Qualitative 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

understandingObjective 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

fitsObjectives 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 studyObjectives 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 meanObjectives 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

findingsObjective 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