Lecture 2 - Thematic & Content

Question 1

What is qualitative research?

Answer 1

Qualitative research aims to describe and understand actual instances of human action and experience from the perspective of the participants who are living through a particular situation

Question 2

How does qualitative research differ from quantitative research?

Answer 2

Quantitative research is focused on making inferences to populations from a small sample – it covers breadth. Qualitative research is focused more on providing detailed experiences of a group – it covers depth

Question 3

What is content analysis (CA)?

Answer 3

Content analysis is a research method for making replicable and valid inferences from data to their context, with the purpose of providing knowledge, new insights, a representation of facts, and a practical guide to action

Question 4

What are the advantages of content analysis?

Answer 4

Advantages:

Low cost.
No need for specialized instruments or trained personnel.
Unobtrusive and can be done retrospectively.
Time-efficient for large datasets.

Question 5

What are the limitations of content analysis?

Answer 5

Simplifies complex realities into rigid categories.

Potential ethical concerns with using data without consent.

Imposes a potentially deceptive orderliness that can obscure deeper meanings.

Overall, lot of the data is discarded, broadly summarising the data rather then extracting all the information.

Question 6

What kind of Research Questions suit CA?

Answer 6

Best suited for questions that focus on identifying patterns or trends in large datasets, e.g.:

“What are the common themes in media representations of cats?”
“How are certain topics discussed in online forums?”

Question 7

What are the sources for CA?

Answer 7

Text, video, audio, social media, websites, news articles, photos, etc.

Question 8

What are the steps in CA?

Answer 8

Gather open-ended qualitative data.

Code data into categories to summarize and systematize.

Count frequencies or analyze patterns qualitatively/statistically.

Question 9

What is thematic analysis (TA)?

Answer 9

A method or tool for identifying, analyzing, and reporting patterns (themes) within data.

It minimally organizes and describes your dataset in detail and can help interpret the topic of research

Question 10

How are themes defined in thematic analysis?

Answer 10

A theme captures something important about the data in relation to the research question and represents some level of patterned response or meaning within the dataset.

Question 11

What is the thematic analysis process according to Braun & Clarke (2006)?

Answer 11

Furry Cats Taught The Dogs Rules…

Familiarization: Transcribe and thoroughly read data.

Coding: Identify interesting features and code systematically.

Theme Searching: Group codes into potential themes.

Theme Reviewing: Refine themes based on data and coherence.

Defining Themes: Develop clear definitions and names.

Report Writing: Relate themes to research questions and literature.

Question 12

What are the advantages of thematic analysis?

Answer 12

Accessible and easy to learn.

Less demanding than other forms of qualitative analysis; can summarize large datasets while retaining richness of data.

Can uncover unexpected insights and “tell a story.”

Question 13

What are the limitations of thematic analysis?

Answer 13

Sometimes misapplied due to its flexibility.

Simplicity can result in poor application.

Limited interpretative power without a theoretical framework.

Question 14

What is reflexivity?

Answer 14

Reflexivity in qualitative research means being aware of how your own beliefs, values, and experiences might influence the research process. This includes how you collect, analyze, and interpret data. It’s about recognizing that researchers are not completely objective but are part of the research context.

Question 15

Why is reflexivity important?

Answer 15

Enhances Credibility:
By acknowledging your influence, you make the research process more transparent, helping others trust your findings.

Minimizes Bias:
Reflexivity helps you identify and manage your biases so they don’t distort the data or its interpretation.

Improves Quality:
It ensures the research is rigorous by documenting how decisions were made and why certain interpretations were chosen.

Question 16

How to practice reflexivity in research?

Answer 16

Reflexive Journals:
Keep a diary of your thoughts, decisions, and assumptions throughout the study.

Audit Trails:
Record all stages of the research process, including how you selected data, coded it, and developed themes.

Peer Discussion:
Talk with colleagues or co-researchers to check whether your interpretations are reasonable or overly influenced by personal biases.

Question 17

How to ensure high quality data collection for both CA and TA

Answer 17

Clear Research Questions: Define specific, focused research questions that guide the data collection and analysis process.

Relevant Data Sources: Ensure data is representative of the population or topic being studied (e.g., social media posts, interviews, or transcripts).

Adequate Sampling: Choose a sample size that is large enough to capture meaningful patterns but manageable for analysis.

Ethical Considerations: Obtain consent (where applicable) and ensure anonymity to foster genuine responses and ethical compliance.

Question 18

How to ensure coding consistency for Content Analysis?

Answer 18

Use well-defined categories that are mutually exclusive and collectively exhaustive.

Create a codebook with clear definitions for each category.

Train coders to use the same approach, ensuring consistency (e.g., through intercoder reliability checks).

Question 19

How to ensure coding consistency for Thematic Analysis?

Answer 19

Start with data-driven codes (inductive approach) or theory-driven codes (deductive approach) aligned with research aims.

Review and refine codes to avoid duplication or overlap.

Question 20

How to ensure reliability checks in CA and TA

Answer 20

Intracoder Reliability: Regularly check that a single coder applies codes consistently over time.

Intercoder Reliability: Involve multiple coders and test consistency in how they apply codes or identify themes.

Conduct intercoder discussions to resolve disagreements and align understanding.

Question 21

How to maintain reflexivity and transparency for TA and CA?

Answer 21

Maintain audit trails that document all decisions, from data collection to coding and interpretation.
Keep a reflexive journal to record personal biases, assumptions, and decisions that might influence the analysis.

Question 22

How to ensure that reliable themes are extracted for TA?

Answer 22

Follow Braun & Clarke’s (2006) six steps methodically, ensuring that:
Themes are defined clearly.
Themes answer the research question rather than merely summarizing data.
Extracts from data are used to support themes effectively.
Include peer review or feedback to ensure themes are coherent and robust.
Ensure themes are meaningful and not merely descriptive (e.g., “scared” vs. “exams significantly impact future job prospects”).

Question 23

What to ensure instead of superficial coding in TA?

Answer 23

Avoid superficial coding; focus on both manifest (explicit) and latent (underlying) content to capture deeper meanings.

Question 24

What is Intracoder Reliability?

Answer 24

Measures the consistency of a single coder over time.

To reduce the likelihood of a coder interpreting the same data differently at different times.

Question 25

What is Intercoder Reliability?

Answer 25

Measures the consistency among multiple coders analyzing the same data.

To confirm that the coding process is objective and not overly influenced by personal bias or interpretation.

Question 26

What are the main differences between CA and TA?

Answer 26

Focus:
CA: Categorizes and counts content, focusing on frequency and patterns within data.
TA: Explores deeper meanings and identifies themes that capture patterns of response related to the research question.

Flexibility:
CA: Rigid; relies on predefined categories or codes.
TA: Flexible; themes emerge iteratively and require researcher judgment.

Applications:
CA: Best for analyzing large datasets systematically, such as media content or structured text.
TA: Suitable for exploring subjective experiences and providing rich, detailed insights.