Can thematic analysis be used for questionnaires?

Yes, thematic analysis can be a valuable tool for exploring qualitative data obtained through questionnaires.

Considerations When Using Thematic Analysis with Questionnaires

1. Questionnaire Design:

Virginia Braun highlights the potential of surveys for gathering rich and complex data, noting that they can be a useful tool for qualitative research, even for sensitive topics.

Braun emphasizes that the open-ended format of qualitative surveys allows for unexpected responses and participant-driven narratives.

This aligns with the principles of reflective thematic analysis, which seeks to uncover the meanings and patterns that emerge from the data, rather than imposing predetermined categories.

To get data suitable for thematic analysis, questionnaires must include open-ended questions that encourage detailed responses.

Open-ended questions in questionnaires invite participants to express their thoughts, experiences, and perspectives in their own words, providing rich textual data suitable for thematic analysis.

When designing such questions, they should be clear, concise, and focused on the research question.

Can thematic analysis be used with closed questions?

Closed-ended questions, such as multiple-choice or Likert scale questions, might not provide the depth of information needed for the nuanced interpretation that characterizes thematic analysis.

By their nature, these questions tend to produce data that lends itself more to ‘bucket themes,’ focusing on summarizing responses within predetermined categories rather than uncovering complex, emergent patterns of meaning.

One of the key considerations in thematic analysis is understanding the context that shapes participants’ responses.

Closed-ended questions, especially in questionnaires, often lack the contextual richness that emerges from open-ended questions or interviews.

However, there are potential applications for thematic analysis in conjunction with closed questions:

• Analyzing open-ended responses within a primarily quantitative questionnaire: If a questionnaire includes a mix of closed-ended and open-ended questions, thematic analysis could be applied to the open-ended responses to explore the nuances of participants’ perspectives within the broader quantitative data set.
  • Content analysis might be more suitable for analyzing responses to closed-ended questions, where the emphasis is on categorizing and counting responses rather than interpreting deeper meanings.
• Supplementing quantitative analysis with thematic insights: Researchers could consider a mixed methods approach that integrates thematic analysis of closed-ended questions with qualitative analysis of open-ended responses or follow-up interviews, allowing for a more comprehensive understanding of the data.
  • Thematic analysis of open-ended responses could provide a qualitative layer of understanding to complement the quantitative analysis of closed-ended questions.
• Exploratory phase of research: In the initial stages of research, closed-ended questions could be used to gather broad insights and identify potential areas for further exploration.
  • Subsequent qualitative methods, like interviews, could then be used to delve deeper into these areas, with thematic analysis applied to the qualitative data.

2. Data Volume:

The richness of the qualitative data obtained from the questionnaire directly impacts the quality of the thematic analysis.

If the responses are brief or superficial, it might be challenging to develop robust themes.

Just as with other qualitative data collection methods, researchers need to ensure data saturation when using thematic analysis for questionnaires.

Data saturation means that collecting more data does not yield new themes or insights.

Piloting questionnaires is essential for ensuring they generate rich data for thematic analysis.

Through piloting, researchers can test whether questions elicit detailed responses, identify unclear language, assess question flow, and refine prompts.

This process helps optimize the questionnaire before full implementation, ensuring sufficient data depth for meaningful analysis.

Questionnaires can generate a large volume of data, potentially making manual analysis time-consuming.

Researchers might consider using software to assist with data management and coding. Some popular software options for qualitative data analysis include ATLAS.ti, Dedoose, MAXQDA, and NVivo.

Contextual Understanding:

While questionnaires provide insights into participants’ perspectives, they might offer less contextual detail compared to interviews.

Context refers to the social, cultural, historical, and political factors that shape participants’ experiences and perspectives.

This context can include the setting where data was collected, the participant’s demographic characteristics, the power dynamics between the researcher and participant, and broader societal influences.

For example, participants might be more reserved in a hospital setting, and demographic characteristics like race, gender, and socioeconomic status can significantly influence their experiences.

This presents a challenge for researchers using with thematic analysis, as context is crucial for understanding the nuances of participants’ responses and developing comprehensive interpretations.

Unlike interviews, where researchers can probe for clarification or explore emerging ideas, questionnaires offer limited opportunity for dynamic interaction.

This may require researchers to be particularly attentive to the nuances of written language and potential ambiguities in participant responses.

While questionnaires may not capture context as naturally as interviews, researchers can proactively incorporate contextual questions within the questionnaire design:

• Demographic data, such as age, gender, ethnicity, socioeconomic status, and educational background, can provide valuable insights into participants’ social positions and how these might influence their perspectives
• Questions about the setting or circumstances surrounding the topic can also provide context. For example, if the questionnaire explores work-life balance, questions about work demands, family responsibilities, and social support systems could illuminate the context shaping participants’ responses.
• Including open-ended questions that encourage participants to elaborate on their experiences and provide their own contextual details is crucial.

It is important to acknowledge the potential for researcher bias in thematic analysis.

Reflexivity involves critically examining your own assumptions and how they might influence the interpretation of the data

Which approach to thematic analysis is most appropriate for questionnaire data: deductive or inductive?

The choice between a deductive or inductive approach depends on the research aims and the nature of the questionnaire.

Deductive Thematic Analysis:

A thematic analysis deductive approach is suitable when the questionnaire is designed to test existing theories or hypotheses, or if the researcher has pre-defined themes based on a literature review or previous research.

In this approach, the researcher starts with a theoretical framework and uses it to guide the coding and theme development process.

For example, a study investigating factors influencing people’s decisions to undergo genetic testing might use pre-existing theories about health decision-making as a starting point for their analysis.

Inductive Thematic Analysis:

An inductive approach (e.g. reflexive thematic analysis) is more appropriate when the aim is to explore the data openly and allow themes to emerge from the responses.

This approach is particularly useful when investigating an under-researched area or working with participants whose views on the topic are not well-known.

In this approach, the researcher starts with the data and lets the themes emerge through a process of coding and theme development.

For example, a researcher might use an inductive approach to analyze responses to a questionnaire exploring men’s experiences of body hair removal.