Data Analysis in Qualitative Studies

Question 1

Qualitative Data Analysis

Answer 1

Is used when researchers generate non-numerical data.
Goal should stem from research question or study purpose.
Analysis is immediate, ongoing, and sprial.

Question 2

Immediate

Answer 2

Data analysis begins as part of the development of the research.
Meaning-making begins at the start of the investigation.

Question 3

Why is it important to reflect on your past experiences and personal connections to a study topic/participant?

Answer 3

Because researcher is a key instrument in qualitative research.

Question 4

Researcher as key instrument

Answer 4

The researcher collects the data.
-personal contact, personal experience and engagement, and actively engaging in the life of the participant
Requires a commitment to extensive time in the field.
-gaining entry, gaining access, building rapport
All parts of the research process filtered through the lens of the researcher.
-researcher’s personal experiences, characteristics, pre-existing knowledge, etc. inform the research question.
Impossible to not be involved or separate yourself from the research.

Question 5

Researcher Reflexivity

Answer 5

Researcher cannot be separated from the research.
Qualitative researchers “position themselves” by reflecting on their biases, values, experiences, and background to consider how these may shape the research.
Consists of two parts…
1) Reflecting on one’s experiences with the phenomenon being explored
2) Considering how one’s experiences shape the interpretation

Question 6

At what points during research process might it be important for qualitative researchers to be reflexive?

Answer 6

Throughout the process
Look back and think if things are occurring because you are being bias or not.

Question 7

Ongoing

Answer 7

Researcher reflexivity should be continuous.
Data analysis doesn’t take place at one moment.
Researchers engage in data analysis throughout the entire research process.

Question 8

Spiral

Answer 8

Data analysis is not a fixed linear approach.
Researchers flow through the data analysis process in analytic circles, often returning to earlier steps in the analysis as new insights are created.
-these analytic circles create a spiral from the beginning of data generation through to final account of findings.
The data analysis spirals
-data collection and analysis concurrent and ongoing processes
Data analysis is an ongoing process of meaning making
-analysis conducted concurrently with gathering data
-involves continual reflection and making interpretations
Data analysis “steps” are a guide, but not necessarily discrete
-researchers can move between steps, return to earlier steps, etc. to build on

Question 9

What do researchers have to decide in data analysis?

Answer 9

Need to decide if they will utilize inductive or deductive processes or combination of both

Question 10

Inductive analysis

Answer 10

Patterns, themes, and categories of analysis come from the data.
Emerge out of the data rather than being imposed on them prior to data collection and analysis

Question 11

Deductive analysis

Answer 11

Themes are provided in advance of analysis and the researcher makes the data fit themes in pre-existing framework

Question 12

Steps in Qualitative Data Analysis

Answer 12

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

Question 13

Step 1: Organize and prepare the data

Answer 13

Transcribe interviews.
Type field notes.
Scan images.
Create files.

Question 14

Step 2: Read or look at all the data

Answer 14

Read and memo
Read and re-read text
Avoid jumping to step 3 too quickly
Gain general sense of the data and reflect on overall meaning.
Immersion in entire database and get overall sense of information as a whole before breaking it into parts.
Making margin notes (general thoughts, short phrases, ideas, key concepts).

Question 15

Step 3: Start coding all the data

Answer 15

Systematically organize and reduce the data into meaningful segments/chunks/categories, and assign names for segments.
Often includes in vivo terms (terms mentioned by participants).
Some methods for coding might include using sticky notes, highlighters, electronic comments, spreadsheets, visual grouping.

Question 16

Step 4: Generate descriptions or themes

Answer 16

Researchers look for organizing frameworks that tell the story of their data in a way that best informs their research question.
Common categories might be grouped, isolated data points might be discarded, unique elements might be highlighted.
Types and numbers of themes can vary widely.
Descriptions might be particularly important to allow the reader direct entry into the research (researcher needs to tell story that best represents data).

Question 17

Step 5: Decide how the findings will be represented

Answer 17

Many ways to represent findings of a qualitative study.
Journal articles
-common in academia, require peer-review, many researchers are trained to write.
Journal articles can be limiting.
-page limits, might miss intended audience, written outcome might not correspond with the research.
Can present findings differently using visual abstract, ads, magazines, websites, etc.

Question 18

Step 6: Interpret the findings

Answer 18

Make an interpretation by making sense of the data and going beyond the themes to the larger meanings.
Multiple interpretations of findings are okay, expected, and valued.
Researcher as key instrument.
-strive for perspectival subjectivity (want to avoid biased subjectivity)
Looking at how the themes are interrelated, themes related to pre-existing knowledge, etc.

Question 19

Role of the researcher

Answer 19

Qualitative researchers are an integral part of the research process.
The researchers values, history, and characteristics are inescapable elements of the research that shape their interpretation.

Question 20

Helpful hints for qualitative data analysis

Answer 20

Slow down
Consider looking for commonalities and differences as a way to get to know your data.
Remember that you only have a piece, not the whole.
Be aware of challenges.
-multiple researchers (dominant voices, difference assumptions and perspectives).
-there are systematic ways to analyze qualitative data, but we need to remember to rely on our brains and not just specific techniques