Imagine a familiar moment in qualitative work. You have transcripts in front of you. Some passages feel important, others less so. You sense themes forming, but the usual step would be to start tagging. Traditional coding treats meaning as something that sits in neat little buckets. In reality, the path to understanding is rarely straight. Meaning shifts. Contradictions appear. Explanations take shape over time.

Conversational analysis takes this reality seriously. Instead of tagging first, you begin with pointed questions about the text itself. You let meaning surface through dialogue. The back-and-forth becomes your method.

Conversational Analysis in QInsights

Conversational Analysis in QInsights is a simple way to explore interview and text data by engaging with our AI assistant Q. It works for people who are new to qualitative work and also for those with experience.

You begin by choosing your starting point. Some people like to explore without any theory. This is called inductive. Others begin with a framework. This is called deductive. Inductive work means you let the data speak. You notice what appears. You follow patterns. Deductive work means you test ideas or concepts you already have. Both paths benefit from conversation.

When you have a place to begin, you ask focused questions. Q responds by pointing to relevant parts of your material. You can validate any answer by clicking on a document name below the response. Q shows short highlights.

You then ask again. You refine. You compare. You look for patterns that matter to your study. This cycle continues until you feel you have a coherent view. If you are completely new to qualitative analysis, QInsights offers Guided Analysis as the simplest entry.

Starting with the data

Inductive work begins with what is said in the data. You describe. Then you move toward more abstract ideas.

You can take a small slice of your data. Study what each person says about your topic. Ask descriptive questions. For example:

• What benefits and challenges are mentioned.

• What experiences are described.

Q answers from the data you uploaded. You do not need to add phrases like as mentioned by the respondents. Q already knows the source.

You then compare answers across participants. Look for shared patterns and exceptions. Use variables like gender or age if you added them when setting up your project. Q can check if certain groups talk about things differently.

Next you might ask Q to group similar ideas and propose higher level labels. A set of benefits might cluster into professional development or emotional wellbeing. Childhood stories might form clusters like positive reinforcements or adverse events.

Then you relate concepts. For example, you might study how a certain attitude shapes behaviour or how several early life experiences connect with later leadership style. You can span multiple documents. To synthesie a dialog, you can ask Q to create a talble that provides an overview for everthing that was mentioned in the chat. This step helps you move from description to explanation.

Inductive analysis supports discovery. You explore. You notice new ideas. Later you may continue with a more structured deductive phase.

Deductive analysis

Deductive work begins with concepts or hypotheses that you want to test. You ask Q to check if they show up in your data and how.

You define the concepts. Then you ask Q to identify them. For example, you can provide a list of leadership styles and ask which ones appear in the interviews. Q checks your documents and returns findings linked to the data.

You then drill down. Study examples. Describe how the concept appears. This builds depth and context.

You can also study group differences. If you enter meta data, you can check whether certain ideas are more prevalent in certain groups. For example, you can explore whether a leadership style is present among younger or older participants.

The rhythm of conversation

Most of the work is a simple loop.

You ask.
Q answers from your data.
If not clear from the response, you check the source.
You ask follow-up questions.

As you move, patterns start to settle. You gain clarity. And you keep control of the reasoning. The tool does not decide what matters. You do.

Conversational Analysis rewards curiosity. It treats analysis as a living exchange. You learn by talking to your data and pushing your thinking further. The work becomes active, not mechanical. This approach fits real qualitative work. It keeps context, nuance, and judgment in the hands of the researcher. And it brings speed without losing depth.