Qualitative Research Data Analysis and Interpretation Teacher name

Qualitative Research: Data Analysis and Interpretation Teacher name: Mahvish Fatima Kashif

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

Cont. � 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

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

Cont. � 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

Cont. � 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

Cont. � 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

Cont. � 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

Cont. � Three formal steps to analyze data ◦ Reading and memoing ◦ Describing the context and participants ◦ Classifying and interpreting

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.

Description ◦ What is going on in the setting and among participants �Purposes �Provide a true picture of the setting and events to understand 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

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

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

Cont. ◦ 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

Cont. ◦ 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

Computerized Data Analysis is readily available to assist with data analysis � Software ◦ 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

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

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

Four guiding questions ◦ ◦ What is important in the data? Why is it important? What can be learned from it? So what?

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

Cont. ◦ 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

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?

Cont. ◦ 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?

Thank You