Qualitative data collection, quality, analysis and interpretation Flashcards
identify common methods used to collect data in qualitative research
1) one-on-one interview
2) group interview
3) observation - concealed or not
> reactivity-distortion changes the participants bhvr bc they know they are being observed. also ethical concerns with this. observer can be biased and make judgements
> some ppl do things differently than they say they do it
4) documents, artifacts and records
5) media, newspapers, movies,
describe the dimensions used in establishing trustworthiness of qualitative data and identify methods of enhancing qualitative data quality
Rigor- how trustworthy is the data collected.
> the basic way to ensure rigor is: methodical research design, data collection, interpretation and communication.
The goals researcher seek to achieve are:
1) to account for the method and the data, which must be independent so that another researcher can analyze the same data in the same way and make the same conclusion.
2) to produce a credible and reasoned explanation of the phenomenon under study
Rigor is judged by unique criteria:
1) credibility
2) auditability
3) fittingness
Qualitative is not linear…
Its cyclical, transformative, reciprocal, and interative
> interviews common
> sort and sift through data and retrieve similar info, themes, phrases, and differences between subgroups
> isolate patterns, commonalities, processes and differences> elaborate on a small set of generalizations that cover the consistencies discerned in the data base
> confront those generalizations with a formalized body of knowledge in the form of constructs or theories
> can generalize in qualitative
> data collection is more flexible– can ask and reask Q’s
Goodness of Fit
must all fit together:
>purpose > design > research question(s) > conceptual and operational definitions > data collection method
Critique of qualitative study
> is the data collection focused on the human experience?
> does the researcher describe data collection strategies? what are they? how are they collecting the data?
> was the data of sufficient depth and richness?
– want actual quotes, verbatim
Creditability
> refers to accuracy, validity and soundness of data.
> faithful description of human experience
> truth of the findings as judged by participants and others within the discipline
> the authors credentials
> “thick description”– verbatim quotes from participants
> to ensure:
1) prolonged engagement- longer interviews
2) persistent observation> spend time with participants to check for discrepancies in responses
3) peer debriefing> with experts in field to help generate questions. helps with creditability by improve trustworthiness. more than one expert. or go to expert to look over data
4) member checks> get the participants to review the themes and narratives to determine whether the researchers accurately describe their experiences
5) triangulation> the cross-checking and verification of data through the use of different information sources, such as a variety of data sources, investigators, theoretical models, and research methods.
> detailed field notes: want ex’s and Q’s they are asking participants
> ex. they started out with 500 labels to begin and ended up with 3 themes. explain how they ended up with the 3 themes.
> was there a critical reflection? records of thoughts along the way? journal?
Auditability
> characteristic of a qualitative study
> whether another researcher is able to follow the “decision trail” used by the researcher
> from the research question, through the raw data and the various steps of analysis to the interpretation of findings.
Fittingness/Transferability
the degree to which the study findings are applicable outside the study situation and the degree to which the results are meaningful to individuals involved in the research.
> how well the findings fit into context outside the study situation and whether the data fit the findings
conformability
findings that reflect implementation of creditability, auditability, and transferability standards
Data analysis methods:
4
1) phenomenology
2) ethnography
3) grounded theory
4) case studies
Phenomenology Analysis
> immersion in the data: listen to recordings: read and reread transcripts
> extract significant statements
> determine relationship among themes (statements)
> describe phenomena and themes
> synthesize themes into a consistent description of phenomenon under study
“The worst experience”: The experience of grandparents of a grandchild who has cancer…
Ethnography Analysis
> immerse in data
> identify patterns and themes
> take cultural inventory
> interpret the findings
> compare findings to the literature
“how do nurses on Barker 2 handle stress….”
Grounded Theory Analysis
> examine each line of data line by line
> divide data into discrete parts
> compare data for similarities and differences
> compare data with other data collected, continuously–consistent comparative method
> cluster into categories
> develop categories
> determine relationships among categories
” keep Vigil over the PT: A grounded theory over nurse anesthesia practice.”
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
Data reduction steps:
1) ongoing process as data collected
2) process of selecting, focusing, simplifying, abstracting and transforming the data
3) organizing into meaningful clusters (themes or structured meaning units)
4) thematic analysis = process and recognizing and recovering the emergent themes
