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CHAPTER 20 Analyzing and Interpreting Data :pen:, Steps in analyzing…
CHAPTER 20
Analyzing and Interpreting Data
:pen:
The thinker, imaginer, and hypothesizer—that is, the qualitative researcher—is the data analyzer
Steps in analyzing qualitative Research Data
Definition and Purpose
Data analysis in qualitative research involves summarizing data dependably and accurately. The presentation of the findings of the study thus has an air of undeniability.
Data interpretation is an attempt by the researcher to find meaning in the data and to answer the “So what?” question in terms of the implications of the study’s findings.
A great deal of data analysis occurs before data collection is complete. Researchers think about and develop hunches about what they see and hear during data collection
An important step in the ongoing analysis of qualitative data is to reflect on two question
Is your research question still answerable and worth answering?
Are your data collection techniques catch-ing the kind of data you want and filtering out the data that you don’t want?
It is important to avoid premature actions based on early analysis and interpretation of data.
After fieldwork has been completed, the researcher must concentrate solely on the multistage process of organizing, categorizing, synthesizing, analyzing, and writing about the data. The researcher works to narrow a large set of issues and data into small and important groups of key data.
The time frame depends on the nature of the study, the amount of data to be analyzed, and the abilities of the researcher.
DATA ANALYSIS STRATEGIES
Identifying themes is a strategy that relies on the identification of ideas that have emerged from the review of literature and in the data collection.
Coding is the process of marking units of text with codes or labels as a way to indicate patterns and meaning in data. It involves the reduction of narrative data to a manageable form to allow sorting to occur.
Asking key questions is a strategy that involves the researcher asking questions such as “Who is centrally involved?” and “What major activities, events, or issues are relevant to the problem?” and seeking answers in the data
An organizational review helps the researcher understand the school or other organization as the larger setting.
Concept mapping allows the qualitative researcher to create a visual display of the major influences that have affected the study to allow for the identification of consistencies and inconsistencies between disparate groups
Analyzing antecedents and consequences allows the researcher to map the causes and effects that have emerged throughout the study.
Displaying findings involves using matrixes, charts, concept maps, graphs, and figures to encapsulate the findings of a study
Stating what’s missing from the study encourages the researcher to reflect and to identify any questions for which answers have not been provided.
Many computer programs are available to aid in analyzing qualitative data, but it is important for novice qualitative researchers to remember that computers do not analyze or code data; researchers do.
DATA INTERPRETATION STRATEGIES
ENSURING CREDIBILITY IN YOUR STUDY
To check the credibility (and trustworthiness) of their data, qualitative researchers should ask themselves the following six questions:
In what circumstances was an observation made or reported?
How reliable are those providing the data?
Are observations corroborated by others?
What motivations may have influenced a participant’s report?
Are the data based on one’s own observa-tion or on hearsay?
What biases may have influenced how an observation was made or reported?
Data interpretation is based heavily on the connections, common aspects, and linkages among the data pieces, categories, and patterns. Interpretation cannot be meaningfully accomplished unless the researcher knows the data in great detail
The aim of interpretation is to answer four questions: What is important in the data? Why is it important? What can be learned from it? So what?
Extending the analysis is a data interpretation strategy in which the researcher raises questions about the study, noting implications that may be drawn without actually drawing them.
Connecting findings with personal experience encourages the researcher to personalize interpretations based on intimate knowledge and understanding of the research setting
. Contextualizing the findings of the study in the related literature involves using the review of related literature to provide support for the findings of the study.
Turning to theory encourages researchers to link their findings to broader issues of the day and, in so doing, to search for increasing levels of abstraction and to move beyond a purely descriptive account.
Knowing when to say when means that the researcher refrains from offering an interpretation when he or she can offer only a wimpy interpretation
As a qualitative researcher, you should share your interpretations wisely and avoid being evangelical about them. Provide a clear link among data collection, analysis, and interpretation.
. Qualitative data analysis is a cyclical, iterative process of reviewing data for common topics or themes.
One approach to analysis is to follow three iterative steps: Reading/Memory
Reading/memoing is the process of writing notes in the field note margins and underlining sections or issues that seem important during the initial reading of narrative data.
Classifying small pieces of data into more general categories is the qualitative researcher’s way to make sense and find connections among the data.
Field notes and transcripts are broken down into small pieces of data, and these pieces are integrated into categories and often into more general patterns.
Describing involves developing thorough and comprehensive descriptions of the participants, the setting, and the phenomenon studied to convey the rich complexity of the research.