Please enable JavaScript.
Coggle requires JavaScript to display documents.
ANALYSING AND INTERPRETING DATA, image, image, image, image, image, image,…
ANALYSING AND INTERPRETING DATA
STEPS IN ANALYZING QUALITATIVE RESEARCH DATA
READING/MEMOING
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.
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/memoing, describing what is going on in the setting, and classifying research data.
DESCRIBING
Describing involves developing thorough and comprehensive descriptions of the participants, the setting, and the phenomenon studied to convey the rich complexity of the research.
CLASSIFYING
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.
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.
An important step in the ongoing analysis of qualitative data is to reflect on two questions:
a. Is your research question still answerable and worth answering?
b. Are your data collection techniques catching the kind of data you want and filtering out the data that you don’t want?
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.
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.
DATA INTERPRETATION STRATEGIES
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.
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 observation or on hearsay?
What biases may have influenced how an
observation was made or reported?
