Introduction
Conversational Analysis in QInsights is a dynamic, researcher-guided tool designed for deep data exploration. It accommodates structured methods like inductive and deductive analysis but also serves as an accessible entry point for beginners. If you're unsure where to start, the QInsights AI assistant can help by suggesting preliminary questions to probe your data, making it easier to dive into analysis without needing extensive background knowledge in specific research methodologies.
How Conversational Analysis Works
1. Choose Your Starting Point
Are you a Novice? Start by asking the QInsights AI, "I am new to qualitative analysis, can you suggest some initial questions to explore my data?" This method is perfect for easing into qualitative research and effectively engaging with your data without prior experience.
Do you already have experience with qualitative analysis?
- Inductive Approach: Dive into your data and let the emerging patterns guide your analysis.
- Deductive Approach: Use established theories or hypotheses as a framework to examine and interpret your data.
- Advanced – Abductive Reasoning: Delve into this approach in the section below for deeper insights.
2. Engage in Dialogue with QInsights' AI-Assistant
Ask specific, focused questions based on your objectives or areas of interest.
Q will analyze your data and provide nuanced responses, offering insights or summarizing relevant portions of the dataset.
3. Iterate and Refine
Build on Q’s responses by asking follow-up questions to clarify or deepen your understanding.
Uncover patterns, relationships, or exceptions by steering the conversation in the direction most relevant to your analysis.
Novice Researcher
If you're new to qualitative analysis, you can ask Q, your AI assistant, to guide you. Start by asking something like:
"I am new to qualitative analysis. Can you suggest some initial questions to help me explore my data?"
If you didn’t add your research questions to the project summary during setup, you can include them directly in your prompt to get tailored suggestions that align with your study's goals.
Depending on the amount of data you have, you may want to start with a meaningful subset of your data (4 – 6 interviews) as suggested below for an inductive approach. When you ask a question across a large dataset, such as 30 interviews, the AI is likely to generate an answer that is broad or generalized. This is explained in more detail here.
As you engage with your AI assistant, you’ll probably start to develop a feel for how to ask questions that yield meaningful insights. Over time, you may find yourself becoming more confident and intuitive in framing your own queries empowering you to explore your data in ways you hadn’t considered before.
Inductive Approach: From the Descriptive to the Conceptual
The skepticism surrounding AI in qualitative research often stems from a lack of direct engagement with the technology. Many researchers remain on the sidelines, wary of trusting a tool they have not tried themselves. Yet, those who take the first step are often surprised at the quality and rigor they can achieve. QInsights delivers a practical solution to the pressures of time, budget, and client expectations. With minimal effort, you can achieve insights that impress—without sacrificing quality.
We invite skeptics and proponents alike to experience first-hand the capabilities of AI-powered analysis through QInsights. Discover how our approach not only maintains but enhances methodological rigor by making the researcher’s thought process more explicit than ever before.
Join us in redefining what qualitative analysis can be: more intuitive, more efficient, and, most importantly, more aligned with the needs and nuances of contemporary research. Try QInsights today, and see for yourself how we’re building the future of qualitative analysis—one question at a time.
Recommended Steps
Step 1: Start Small: Select a subset of your data to begin. This allows you to focus and learn what each respondent (let’s say you selected five) has said about your topic of interest.
Step 2: Ask Descriptive Questions: Begin with questions like, "What are the benefits mentioned?" "What are the challenges mentioned" or "What types of childhood experiences have been described?"
As the example question indicates, it's not necessary to specify "as mentioned by the respondents" when using Q, QInsights' AI-assistant. The responses you receive from Q are directly derived from the data you've uploaded, ensuring accuracy without the risk of fabricating data, often referred to as 'hallucination.' If your query is unrelated to your dataset, Q will rely on its general knowledge from training data. For now, just remember that Q is designed to provide reliable and data-true answers without hallucinating. For more details on asking questions that is not about your data, see below.
Step 3: Identify Similarities and Differences: Compare responses across participants to uncover patterns, trends, and variations. You can also make use of the variables entered during project setup (e.g., gender, age, educational level) to compare and contrast respondent groups.
For Word or PDF files, activate these variables under filter settings, allowing your AI-assistant to differentiate respondents based on selected criteria like gender or educational level. Ask questions like, “Are there gender differences regarding the benefits mentioned?” For Excel files, input variables directly in your prompt without needing activation in the settings.
Step 4: Group and Label: Ask the AI assistant to group similar responses and provide higher-order labels to describe them. For instance:
- A list of benefits might be grouped into "Professional Development" or "Emotional Well-Being."
- Childhood experiences could be classified as "Positive Reinforcements" or "Adverse Events."
Step 5: Relate and Contextualize: Once certain concepts and sub-concepts are identified, explore their relationships. For example:
- How does attitude X influence user behaviour?
- What influence did experience X, Y and Z have on the development of a certain leadership style?
The example below illustrates that such questions can also be asked across different documents.
Inductive analysis allows you to discover new patterns, concepts, and relationships through an exploratory process, which can later inform a broader, more structured deductive analysis.
Deductive Approach: From the Conceptual to the Descriptive
When starting deductively, the researcher begins with existing concepts or hypotheses and examines whether and how they appear in the data. This approach moves from abstract ideas to detailed, contextual descriptions.
Recommended Steps
Step 1: Define Starting Concepts or Hypotheses: Begin with a clear framework of what you want to explore. Ask questions and validate:
- Do all respondents mention this specific concept?
- Does a hypothesized relationship between variables (e.g., gender and leadership style) exist in the data?
- Here are six leadership styles (define them). Which of them are described / have been mentioned by the respondents?
Example Prompt
Here is a list of 7 different leadership styles:
Autocratic Leadership: A style where the leader makes decisions unilaterally, without much input from team members. / Democratic Leadership: Involves team members in the decision-making process, promoting collaboration and participation. / Transformational Leadership: Focuses on inspiring and motivating followers to achieve their full potential and embrace change. / Transactional Leadership: Based on a system of rewards and punishments, where compliance is expected in exchange for rewards. / Servant Leadership: Prioritizes the needs of the team and helps members develop and perform as highly as possible. / Laissez-Faire Leadership: A hands-off approach where leaders provide minimal direction and allow team members to make decisions. / Situational Leadership: Adapts leadership style based on the maturity and capability of team members and the specific situation.
Which of them can you identify in the interviews?
Step 2: Drill Down: Explore specific examples and details to better understand and explain your findings.
Step 3: Group-Level Analysis: Use variables entered during project setup (e.g., gender, age, educational level) as filters to investigate patterns. Or asked questions based on your variables:
- Is leadership style related to different age groups?
Deductive analysis allows you to test predefined ideas or theories within your data while retaining the flexibility to refine or expand them based on evidence.
Deductive analysis allows you to test predefined ideas or theories within your data while retaining the flexibility to refine or expand them based on evidence.
Abductive Analysis – Finding the Unexpected
Abductive analysis is a methodological approach that blends elements of both inductive and deductive reasoning. It focuses on generating the most plausible explanations for observed patterns or phenomena in data. The term originates from the work of Charles Sanders Peirce, who described abduction as a form of logical inference aimed at forming hypotheses to explain surprising or puzzling observations.
Abductive analysis is particularly useful in qualitative research when:
- Unexpected findings arise during the study.
- The researcher aims to bridge empirical data and theoretical insights.
- There is a need for a flexible, iterative approach to understanding complex phenomena.
Abductive analysis starts when you encounter something unexpected, unexplained, or puzzling in the data. You can then think of plausible hypotheses or explanations for shedding light on these anomalies – these then become your theories to be further explored and tested. In the process of abductive reasoning, you oscillate between data and theory, using the data to inspire new ideas and theories to refine your understanding.
Abductive Analysis is like Detective Work
Abductive reasoning is often compared to the work of a detective because both processes involve piecing together incomplete information to arrive at the most plausible explanation. Here's how they align:
Abduction starts with an observation or a surprising fact and seeks the best explanation for it. For example: "Why is this window broken?" → Possible hypothesis: "It was a burglary."
Unlike deductive reasoning (which guarantees conclusions) or inductive reasoning (which generalizes), abduction selects the likeliest explanation, given the evidence. As new evidence is uncovered, hypotheses are revised or replaced to better fit the facts.
Father Brown, the television series based on G.K. Chesterton's stories, is an excellent example of abductive reasoning in action. Father Brown’s method of solving mysteries beautifully illustrates how this type of reasoning works, as he consistently relies on observation, intuition, and a deep understanding of human nature to form plausible explanations for crimes.
Father Brown starts by noticing details others might overlook. His keen attention to small, seemingly unrelated clues is the foundation of his reasoning: a misplaced object, an unusual tone of voice, or a reaction from a suspect might catch his attention as something worth investigating. He doesn’t jump to conclusions but instead considers various possible explanations for the observed facts.
His hypotheses are often guided by his profound understanding of people's motivations, emotions, and moral struggles. For instance, he might hypothesize that a murder wasn’t motivated by greed but by a deeper personal conflict or guilt. By weighing the evidence, Father Brown identifies the most likely explanation. As new evidence comes to light, Father Brown adjusts his hypotheses. He frequently engages the suspects or witnesses in conversation, using their reactions to refine his understanding of the crime.
Father Brown exemplifies abductive reasoning, because he doesn’t focus solely on physical evidence; he considers psychological, emotional, and moral factors to create a complete picture. He remains open to changing his theories as new insights emerge, a key aspect of abductive reasoning. Rather than seeking certainty, he seeks the most plausible explanation for the evidence at hand. His ability to think outside conventional logic mirrors the creative aspect of abduction.
Example of an Abductive Analysis
Imagine you are analyzing interviews about workplace satisfaction, and you find that many employees express satisfaction despite working under highly stressful conditions. This unexpected finding prompts abductive reasoning. You might hypothesize that employees' satisfaction stems from a strong sense of team support or meaningfulness in their work, even under stress. To refine this hypothesis, you revisit the data to look for supporting evidence and consult existing theories about workplace dynamics to shape your understanding further. When using QInsights, you engage in a dialogue with Q to explore ideas, test assumptions, and refine your understanding.
At this stage, it’s the perfect moment to tap into the creative strengths of AI. Use Q to brainstorm a range of possible explanations, even those you may not have considered yourself. The AI can help you explore diverse angles—cultural factors, leadership styles, or even less obvious workplace dynamics—that might explain the surprising satisfaction under stress. By combining its capacity to generate ideas with your critical thinking, you can refine hypotheses and uncover insights that might otherwise remain hidden. This collaborative process highlights how AI can amplify your creativity while keeping you firmly in control of the analysis.
How Abductive Analysis Works in QInsights
You might start looking for unexpected patterns, contradictions, or outliers purposefully; or you stumble across them when exploring your data. In both cases, the findings become the foundation for abductive reasoning.
Step 1: Ask Exploratory Questions
Use Q to probe unexpected observations further and ask questions like:
- Why might respondents with similar experiences express contrasting emotions?
- What factors could explain this unexpected behavior or response?
Step 2: Generate Hypotheses
Based on the responses, let Q help you generate plausible explanations for your observations. These hypotheses are grounded in the data but informed by your own expertise and existing theoretical knowledge.
Step 3: Iterate and Refine
Abductive Analysis is an iterative process. Move between the data and emerging hypotheses, asking follow-up questions to clarify, refine, or challenge your initial ideas.
Step 4: Validate with Data
Test your hypotheses by exploring whether they apply consistently across other subsets of your data or groups of respondents.
In practice, abductive analysis is often used in grounded theory, ethnography, and interpretive research, where the goal is not just to describe but to explain and make sense of social or cultural phenomena. It enables you to move beyond description and into the realm of meaning-making, fostering deep insights and rich theoretical contributions to your research.
Abductive analysis encourages you to develop new theories or explanations from your data by focusing on surprising or puzzling findings, allowing for creative and insightful interpretations that may not be initially evident.
Tips for Communicating with Q, Your AI Assistant
Querying All Data vs. Subsets: Striking the Right Balance in Analysis
When you ask a question across a large dataset, such as 30 interviews, the AI is likely to generate an answer that is broad or generalized. This happens because:
Summarization Bias: LLMs are optimized to distil vast amounts of information into concise responses, often blending multiple points together. This can make it harder to discern specific patterns or themes.
Complexity Dilution: When dealing with large datasets, the AI tends to prioritize comprehensiveness over depth. As a result, nuances or unique perspectives might be overshadowed by dominant or recurring themes.
Pattern Obfuscation: The sheer volume of data can lead to overly synthesized responses, which may mask the diversity and richness of individual data points, making it harder to see distinct patterns.
Asking a Question to a Meaningful Subset
When you narrow your query to a targeted subset of interviews that reflect specific sample criteria, the AI can provide more focused and contextually relevant insights. This approach benefits from:
Relevance: The AI can tailor its response to the subset, aligning with the specific characteristics or themes relevant to your criteria.
Depth: With a smaller dataset, the AI can explore nuances and provide more detailed observations rather than broad generalizations.
Pattern Recognition: By focusing on a subset, patterns and themes become more apparent, as the diversity within the subset is less overwhelming compared to the entire dataset.
This is How You Do It In QInsights
- Make use of variables in QInsights and use them as filters (e.g., demographics, contexts, or topics) to create subsets that align with your research questions.
- Engage in a dialogic process—ask broad questions to get a sense of the data and then follow up with more specific, targeted questions to uncover deeper insights.
This approach aligns with the strength of LLMs while preserving the integrity and richness of your qualitative data analysis.
Prompting
Below you will find a list of prompts for various purposes. Choose the approach that best suits your project’s needs. The examples below are designed to inspire and guide you in crafting your own tailored questions for deeper and more effective analysis.
Follow-up prompts
Follow-up prompts allow you to deepen your understanding of specific topics or explore nuances in the data. Here are examples to inspire your tailored questions:
- I would like to explore more about [Topic X]. Please provide more detailed insights on the following: [Specific question or area].
- Extract a quote that supports [Topic X].
- Give me an example quote from [Respondent Name(s)] that supports [Topic X].
- What are the differences between the respondents?
- Let’s focus on the similarities now. Which respondents expressed similar perspectives or had similar experiences regarding [Topic X]?
- How do respondents’ views on [Topic X] evolve over the course of the interview? Please summarize any changes in perspective.
- Can you identify underlying motivations or reasons behind respondents' views on [Topic X]?
- What additional nuances did respondents share about [Topic X] that might not have been fully explored?
- How do respondents’ emotions or tone change when discussing [Topic X], and what does this suggest about their perspectives?
- Are there any contradictions or tensions between different responses?
Prompting for Overview Tables
When comparing multiple responses, requesting an overview table is helpful for visualizing variations and commonalities in the data. You can customize the table to suit your analysis:
Create a table with respondent names in the columns across the top, and the [various perspectives/experiences/opinions on Topic X] in the rows.
- In the cells of the table, indicate with an X if [Topic X] was mentioned.
- In the cells of the table, include a supporting quote if [Topic X] is applicable to the respondent.
- In the cells of the table, [specify what you want to see, e.g., themes, keywords, or sentiment].
Analytic Questions
Analytic questions help move from description to interpretation, exploring relationships, patterns, and dependencies in the data. Below are different types of analytic questions and examples:
Relational Questions: Highlight connections between elements
- What are the relationships between participant attitudes and their stated values?
- How does trust influence decision-making across respondents?
Comparative Questions: Focus on similarities and differences
- How do participants from different groups perceive Topic X?
- What are the similarities and differences in participants' approaches to Challenge Y?
Correlative Questions: Explore associations
- How does the frequency of emotional language relate to levels of satisfaction?
- Is there a link between years of experience and leadership style?
Pattern-Seeking Questions: Examines causes and effects or influencing factors.
- What patterns emerge in respondents' views on Topic X across demographics?
- Do respondents consistently mention certain benefits when discussing [specific topic]?
Causal Questions: Examines causes and effects or influencing factors
- What factors seem to influence respondents' attitudes toward Z?
- What drives participants to adopt Strategy Y?
Conceptual Linkage Questions: Identifies conceptual or thematic relationships
- What links exist between the concepts of collaboration and innovation?
- I’ve noticed a relationship between [Aspect A] and [Aspect B]. Verify if this exists across the dataset and describe how it is expressed.
Dependency Questions: Focus on hierarchies or dependencies
- How do participants’ experiences depend on external factors such as resource availability?
- Which respondents’ perspectives shift based on specific variables (e.g., age, gender, or education)?
Validating Your Synthesis
You can also validate a synthesis that you have written. Open a new chat and paste your write-up into the entry field, using the following prompt:
- Here is my synthesis on [Topic]. Please check the accuracy of the data, review the flow of reasoning, and ensure that nothing important is missing. Correct any spelling errors, improve sentence structure, and include a quote from each respondent if not already provided.
The result can serve as a building block for your report, so be sure to save it with an appropriate name in the project archive.
Relating Findings to Theory
Another option is to relate your findings to existing theories. If the theory you're working with is well-established, Q is likely familiar with it. However, it's a good idea to verify this first by asking:
- Are you familiar with [Theory X]? If so, provide a detailed description, including its key concepts.
If Q is unfamiliar, provide context:
- Here is a brief description of [Theory X] and its important concepts: [Insert Description]. Based on this theory, how can it help explain [specific findings or relationships]?
Identifying relationships
You can allow Q to take more initiative by asking it to identify relationships in the data. While it’s important to be aware that the results may reflect patterns from its training data, this approach can often reveal new insights or highlight connections you might not have noticed. It can be an inspiring way to explore your data with fresh perspectives—just be sure to reflect critically on the findings as you incorporate them.
To maintain context, start by summarizing the themes and patterns you and Q have already identified in previous chats. This way, Q continues from the established discussion instead of starting a completely new analysis. Once you've provided the summary, you can ask:
- What relationships or connections can you identify between the different themes or patterns in the data? How do these relate to the overall research question [define research question]?
In Summary
Through this blog, you've learned how to effectively harness QInsights for qualitative data analysis, providing tools to uncover actionable insights from your data. Whether you're identifying customer preferences, testing market hypotheses, or exploring unexpected trends, Q empowers you to dive deeper into the "why" behind the data. By leveraging its capabilities for inductive, deductive, and abductive reasoning, you can creatively explore connections, validate findings, and refine your understanding with confidence. With QInsights, you gain a powerful ally to navigate complex data, helping you make informed, strategic decisions that drive results.