Chapter 8 Flashcards - Data Analysis in Qualitative Research

Question 1

Purpose of Data Analysis

Answer 1

Organize and interrogate data generated via interviews, observations, visuals, etc. Allows evaluators to see patterns, identify themes discover relationships and make interpretations

Question 2

How is qualitative data analysis different from quantitative?

Answer 2

Researchers generate non-numerical data and wish to analyze

Question 3

When considering the “goals” of data analysis what is key?

Answer 3

The results they seek for data analysis should support their research question

Question 4

Goals of data analysis include? Hint: Three T’s

Answer 4

1. Taxonomy
   * A system that classifies multifaceted and complex phenomena (separating data into classes based on characteristics that are common)
2. 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
3. Theory
   * Develop interlocking causal variables to explain aspect of personal, social or physical realities

Question 5

Purpose of Taxonomy in Data Analysis? Simplest way of understanding taxonomy?

Answer 5

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

Question 6

Purpose of Themes in Data Analysis

Answer 6

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

Question 7

Example in the textbook about Themes

Answer 7

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

Question 8

Purpose of Theory in Data Analysis

Answer 8

Making a theory by interlocking causal variables that explain some sort of physical, social or personal reality

Question 9

What is Inductive and Deductive Analysis?

Answer 9

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)

Question 10

What is abductive data analysis?

Answer 10

• Another form similar to deductive and inductive
• Inferential process of creating theories and hypothesis based on surprising research

Question 11

Why is abductive data analysis a hybrid of deductive and inductive?

Answer 11

Uses existing theories (deductive) while finding new insights from the data (inductive)

Question 12

Qualitative data analysis is fundamentally distinctive from quantitative through these three ideas…?

Answer 12

Qualitative analysis is: Immediate, Ongoing, Spiral

Question 13

What is Immediate Data Analysis?

Answer 13

• Data analysis begins immediately as part of development of research
• Investigator is primary data instrument which means analysis begins when thinking of research begins

Question 14

What is Ongoing Data Analysis?

Answer 14

• 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

Question 15

What is spiral data anaysis

Answer 15

• Researchers flow through data analysis in analytical circles
• Embrace spiral nature of data analysis

Question 16

Common steps used in analysis approaches for strategies of inquiry?

Answer 16

1. Organize and prepare the data
2. Read or look at all the data
3. Start coding all the data
4. Generate descriptions or themes
5. Decide how the findings will be represented
6. Interpret the findings

Question 17

What is key to Organizing and Preparing the data for analysis?

Answer 17

• 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

Question 18

What is key to reading or looking at all the data in data analysis?

Answer 18

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

Question 19

What is key to coding all the data in data analysis?

Answer 19

• 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

Question 20

What is “In-vivo” in data analysis

Answer 20

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

Question 21

What do coding strategies depend on?

Answer 21

a) Type of data
b) Types of coding categories of interest

Question 22

Types of coding categories of interest include? What do they mean?

Answer 22

Themes that arise from codes due to maybe being an expected result, unusual or even surprising

Question 23

2 Key points of data analysis direct researchers towards aspects of data. These are?

Answer 23

1. Types of results from their analysis - Taxonomy, themes, and theories
2. Important to identify what is being coded (analyzed) - Conceptual codes, relationship codes, participant perspective codes, participant characteristic codes, setting codes

Question 24

What are conceptual codes?

Answer 24

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

Question 25

What are relationship codes?

Answer 25

Links between concepts

organizing and labeling two phrases under the same theme

Question 26

What are participant perspective codes?

Answer 26

Direction of participants about a particular experience

Question 27

What are participant characteristic codes?

Answer 27

Descriptive characteristics of the participants

Question 28

What are setting codes?

Answer 28

Characteristics of the setting in which data is generated

Question 29

What is open coding?

Answer 29

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.

Question 30

What is categorization matrix?

Answer 30

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

Question 31

What is qualitative data analysis software (QDAS)?

Answer 31

Might or might not be used to code data
* Allows researcher to stay organized throughout QDA process

Question 32

What is generating descriptions and themes when it comes to the steps for data analysis?

Answer 32

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

Question 33

What is this picture representing in the common steps of data analysis?

Answer 33

In the generating descriptions or themes part themes can be made and those can have subthemes

Question 34

What is “Decide how the findings will be represented” in the common steps of data analysis?

Answer 34

• How is the data going to be shown?
• Published through journal articles; might not reach intended audience however

Question 35

What is “Interpret the findings” in the common steps of data analysis?

Answer 35

Interpreting findings of the data analyzed and seeing if it differs from researcher’ theoretical inclinations, knowledge of literature, etc.