Chapter 8 Flashcards - Data Analysis in Qualitative Research
Purpose of Data Analysis
Organize and interrogate data generated via interviews, observations, visuals, etc. Allows evaluators to see patterns, identify themes discover relationships and make interpretations
How is qualitative data analysis different from quantitative?
Researchers generate non-numerical data and wish to analyze
When considering the “goals” of data analysis what is key?
The results they seek for data analysis should support their research question
Goals of data analysis include? Hint: Three T’s
- Taxonomy
* A system that classifies multifaceted and complex phenomena (separating data into classes based on characteristics that are common) - Themes
* Characterizes responses of participants and gives insight into essential components of their experience - i.e. Three themes why someone with an ACL injury chooses to come back to sport - Theory
* Develop interlocking causal variables to explain aspect of personal, social or physical realities
Purpose of Taxonomy in Data Analysis? Simplest way of understanding taxonomy?
Increase clarity in defining and comparing complex phenomena
* Simplest way to understand it is A way of understanding and classifying things according to similarities and differences
Divides into manageable chunks
Purpose of Themes in Data Analysis
Looks to characterize experiences of individual participants by general insights from whole data
What were concepts that were recurring amongst the participant when looking at that specific subject of inquiry
Example in the textbook about Themes
Semi-structured interviews of people who had an ACL injury and some common themes that arised from wanting to return to sport: fear, lifestyle priorities, differences in personality
Purpose of Theory in Data Analysis
Making a theory by interlocking causal variables that explain some sort of physical, social or personal reality
What is Inductive and Deductive Analysis?
Inductive: Exploratory, data driven approach to identify taxonomies, themes or theory
Deductive: Top down, theory-based approach going from theory, taxonomy or themes that exist by which researchers code the data (analyze the data)
What is abductive data analysis?
- Another form similar to deductive and inductive
- Inferential process of creating theories and hypothesis based on surprising research
Why is abductive data analysis a hybrid of deductive and inductive?
Uses existing theories (deductive) while finding new insights from the data (inductive)
Qualitative data analysis is fundamentally distinctive from quantitative through these three ideas…?
Qualitative analysis is: Immediate, Ongoing, Spiral
What is Immediate Data Analysis?
- Data analysis begins immediately as part of development of research
- Investigator is primary data instrument which means analysis begins when thinking of research begins
What is Ongoing Data Analysis?
- Ongoing; data analysis does not take place at one moment
- Researchers engage in it throughout the process
- New info challenges previous interpretations as generated from new participants
What is spiral data anaysis
- Researchers flow through data analysis in analytical circles
- Embrace spiral nature of data analysis
Common steps used in analysis approaches for strategies of inquiry?
- Organize and prepare the data
- Read or look at all the data
- Start coding all the data
- Generate descriptions or themes
- Decide how the findings will be represented
- Interpret the findings
What is key to Organizing and Preparing the data for analysis?
- Transcribing - taking oral data and reproducing it faithfully as possible
- Fullest and richest data obtained from transcribing verbatim
Must take into account context of verbatim; even the umms and hmms that someone says could provide insight to how they feel
What is key to reading or looking at all the data in data analysis?
Going through the data multiple times so that nothing beneficial is skipped.
This is an important step when you’re analyzing data because it results in richer and insightful final interpretations
Researcher can focus on larger picture than can get missed
What is key to coding all the data in data analysis?
- Organizing data into different categories and this is done by looking at the data and seeing any codes (common phrases that were mentioned time and time again). Generating themes by systematically going through the codes and assessing the categories they fall into.
Example: Participant talks about being interested in products with ingredients grown naturally
Codes (common phrases mentioned time and time again): Natural, Locally grown
- These codes are put into categories
What is “In-vivo” in data analysis
Refers to coding the data and this is words or phrases used by the participant that the researcher singles out
Purpose: Prevents researchers from imposing their own framework
What do coding strategies depend on?
a) Type of data
b) Types of coding categories of interest
Types of coding categories of interest include? What do they mean?
Themes that arise from codes due to maybe being an expected result, unusual or even surprising
2 Key points of data analysis direct researchers towards aspects of data. These are?
- Types of results from their analysis - Taxonomy, themes, and theories
- Important to identify what is being coded (analyzed) - Conceptual codes, relationship codes, participant perspective codes, participant characteristic codes, setting codes
What are conceptual codes?
Essential components of a conceptual domain
The main essential codes (something important that has been mentioned multiple times) that link back to the main research question or purpose or noticing reccuring themes or overarching concepts
What are relationship codes?
Links between concepts
organizing and labeling two phrases under the same theme
What are participant perspective codes?
Direction of participants about a particular experience
What are participant characteristic codes?
Descriptive characteristics of the participants
What are setting codes?
Characteristics of the setting in which data is generated
What is open coding?
Inductive data analysis
* Writing notes and headings in a text as it is being read
* Goal is to describe all aspects of the content
* Coded content grouped into higher order themes
Essentially you read through everything and generate themes along the way while trying to effectively explain it; inductive - observations lead to theories, themes, etc.
What is categorization matrix?
Deductive content analysis
* Existing categories used that were developed from previous theory and research
* Content of text coded by using categorization matrix as a guide
What is qualitative data analysis software (QDAS)?
Might or might not be used to code data
* Allows researcher to stay organized throughout QDA process
What is generating descriptions and themes when it comes to the steps for data analysis?
Once data has been coded into categories => generate descriptions or themes that best represent the data.
* Organizing frameworks that tell the story which best informs their research question
* Themes created need to resonate with the feelings of the participants in the study
What is this picture representing in the common steps of data analysis?
In the generating descriptions or themes part themes can be made and those can have subthemes
What is “Decide how the findings will be represented” in the common steps of data analysis?
- How is the data going to be shown?
- Published through journal articles; might not reach intended audience however
What is “Interpret the findings” in the common steps of data analysis?
Interpreting findings of the data analyzed and seeing if it differs from researcher’ theoretical inclinations, knowledge of literature, etc.
