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Chapter 15: Qualitative Data Analysis
Terms in this set (29)
Data Management Characteristics
-Qualitative data analysis occurs concurrently with data collection rather than sequentially, as in quantitative research
-The researcher is simultaneously gathering data, managing a growing bulk of collected data, and interpreting the meaning of data
Goals of Description
-Become familiar with data.
>Read and reread notes and transcripts
>Recall observations and experiences.
>Listen to audiotapes.
-Become immersed in data.
Types of Descriptive Analysis
*bracketing- question own assumptions you have about the data
-Researcher explores personal feelings and experiences that may influence study and integrates this understanding into study.
-Requires conscious awareness of self
-Used in some phenomenological research to help researcher avoid misinterpreting phenomenon as it is being experienced by participants
-Bracketing is suspending or laying aside what researcher knows about experience being studied
-Analysis focuses on reducing large volume of acquired data to facilitate examination.
-Researcher begins to attach meaning to elements of data.
-Researcher discovers classes of things, persons, events, and properties.
-Way of indexing or identifying categories in data
-Codes may be placed in data at time of data collection, when entering data into computer, and during later examination of data.
-Data segments can then be retrieved by coding category.
-Are equivalent to summary tables used in quantitative studies
-Allow researcher to convey succinctly main ideas of study
-Codes used to organize the display
Types of Data Analysis
Coding used for?
Coding, used earlier for description, also can be used to expand, transform, and reconceptualize data, providing opportunities for more diverse analyses
-Used to record insights or ideas related to notes, transcripts, or codes.
-Moves researcher toward theorizing and is conceptual rather than factual.
-May link data or use specific piece of data as an example of conceptual idea.
Interpretation of Qualitative Results
-The researcher offers his or her interpretation of what is going on
-The focus is on understanding and explaining beyond that which can be stated with certainty
-May focus on usefulness of findings for clinical practice.
-Researcher develops hunches about relationships that can be used to formulate tentative propositions
Rigor in Qualitative Research
-Rigor needs to be defined differently in qualitative research because desired outcome is different
-Evaluation of rigor is based, in part, on logic of emerging theory and clarity with which it sheds light on phenomenon studied
Characteristics of Rigor
-Scrupulous adherence to a philosophical perspective
-Thoroughness in collecting data
-Consideration of all data in subjective theory development phase
Causes for Lack of Rigor
-Inconsistency in adhering to philosophy of approach being used
-Poorly developed methods
-Insufficient time spent collecting data
-Failure to give careful consideration to all data
Description of Decision Trails
-Strategies by which other researchers, using the same data, can follow logic of original researcher and arrive at same conclusions
-Requires researcher to establish rules for categorizing data, arriving at ratings, or making judgments
Requirements for Decision Trails
-A record is kept of all decision rules used in data analysis to support the study's conclusions and emerging theory
-All raw data are stored and available for review, if requested
Data Reduction Process
-Ongoing process as data is collected
-Process of selecting, focusing, simplifying, abstracting, and transforming the data
-Organized into meaningful clusters (themes or structured meaning units)
-Thematic analysis: process of recognizing and recovering the emergent themes
-Memos are kept to help organize data, write personal notes to self
-Data is coded—given a tag or label according to theme/category
-Codebook used to organize code into lists
-Researcher immerses self in the data during this stage, often for weeks or months!
-An organized, compressed assembly of information that permits conclusion drawing and action
-Graphs, flow charts, matrixes, model
Conclusion Drawing and Verification
-The challenge for the researcher is to stay open to new ideas, themes, and concepts as they appear
-Conclusion drawing is the description of the relationship between the themes
-Verification occurs as the data is collected
1. Note patterns, themes
2. See plausibility
3. Clustering coherence
4. Make metaphors
7. Partition variables
8. Subsume particulars
10. Note relationships
11. Intervening variables
12. Chain of evidence
Truth of findings as judged by participants and others within the discipline
Accountability as judged by the adequacy of information leading the reader from the research question and raw data through various steps of analysis to the interpretation of findings
Faithfulness to the everyday reality of the participants, described in enough detail so that others in the discipline can evaluate the importance for their own practice, research, and theory development
Findings that reflect implementation of credibility, auditability, and fittingness standards
-Immersion in the data—read and reread
-Extract significant statements
-Determine relationship among themes
-Describe phenomena and themes
-Synthesize themes into a consistent description of phenomenon
-Immerse in the data
-Identify patterns and themes
-Take cultural inventory
-Compare findings to the literature
Case Study Analysis
-Identify unit of analysis
-Code continuously as data is collected
-Find commonalities, themes
-Analyze field notes
-Review and identify patterns and connections
-Is the method of analysis clear?
-Is it appropriate for the study?
-Can you follow the analysis step by step?
-Is there evidence that the interpretation accurately reflected what was said?
-Are credibility, auditability, fittingness, and trustworthiness accounted for?
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