AI is Transforming Qualitative Research Coding

Coding transforms raw data into meaningful insights, the cornerstone of any research endeavor. As the research field progresses, we find ourselves at the cusp of a transformative era, where the integration of Generative AI into research practices is changing the game. How exactly does this technology work? Does it change current research practices? And what does it mean for researchers and insights professionals? Let’s dive in.

The importance of coding in qualitative research

When it comes to valuable data, we know that qualitative data has a wealth of insights. But when you ask open-ended questions, you’re left with hundreds or even thousands of responses that are all over the place. How do you make sense of it all? With coding.

Coding is the essential link connecting researchers and the insights buried within mountains of qualitative data. It's the process of categorizing, organizing, and extracting meaning from open-ended responses. Through coding, researchers can identify patterns, themes, and trends, ultimately providing a coherent narrative for the data.

Traditionally, coding has been a manual task, demanding researchers to meticulously sift through large volumes of text to identify and categorize relevant information. While effective, this manual approach has drawbacks—it's time-consuming, susceptible to human bias, and lacks scalability. This article explores how machine learning technology can be fitted to traditional coding methodologies in order to drive the efficiency, objectivity, and scalability of qualitative research.

Two principal manual coding methodologies

Before we look into how AI enters the picture, it’s important to understand traditional methodologies. AI doesn’t create new ideas; it works based on how we train it.

There are two principal methodologies for coding: inductive and deductive coding.

Inductive Coding

In this approach, researchers derive codes and themes directly from the data itself. Grounded theory, a popular framework in qualitative research, embodies this approach. It starts with open coding, applying initial codes, then axial coding to identify the connections between codes, and then selective coding, where researchers create clusters of similarly themed codes. This method offers a comprehensive framework to code large datasets and allows for the emergence of unexpected insights. 

Deductive Coding

In contrast, deductive coding starts with a predefined codebook. Researchers apply pre-established codes to the data based on a priori theories or hypotheses. While it offers a more structured approach, it may limit the discovery of novel insights.

Emergence of Generative AI in coding

There’s been some automation in coding for a long time, but it’s mostly relied on term frequency. This process involves pulling specific words that appear often and then coding based on those words. While this approach can help code a large amount of data much quicker than manual methods, it relies on keywords alone, neglecting nuance and meaning.

In contrast, Generative AI can evaluate the meaning of the entire text to derive comprehensive and nuanced codes. It is trained on vast amounts of text data - hundreds of billions of words - enabling it to predict relevant words and phrases related to a text input. In an application like ChatGPT, a user submits a prompt, and the model can generate a human-like response that feels relevant. In the context of coding, the predictive nature of Generative AI makes it well-suited to identifying potential themes in a body of text. 

Looking under the hood of Remesh AI

As we start embracing AI in our research practices, it’s essential to have complete transparency about how AI is used and what’s happening under the hood. This is how we can maintain the integrity of our research while utilizing innovation to improve our processes and insights.

At Remesh, we’re proud to be a pioneer in integrating Generative AI into qualitative research coding through our new feature, Auto Code. By blending the strengths of AI algorithms with proprietary models, our platform is shaving hours off of the researcher’s plate. Our process is loosely inspired by grounded theory for inductive coding, which allows for the emergence of unexpected insights. It also requires several handoffs between Remesh’s proprietary algorithm and the Generative AI models in order to achieve the right level of nuance and accuracy in the codes.

In the first step, Remesh prompts the Generative AI models to consider both the question and the response and asks it to output many descriptive codes for each response. Meta parameters like temperature are set to ensure nuanced responses.

The Generative AI models are powerful, but they are not always consistent. They often provide slightly different codes with essentially the same meaning to two different responses. To handle this, Remesh runs all of the codes that it gets back from Generative AI through its own proprietary algorithm that can map all of the codes by semantic similarity, which outputs groupings of the codes.

Some of those groupings contain just one code, and some are very large. Remesh has designed a process to assign all the singletons to an existing cluster and break large, multi-theme clusters into unique groups. Once the system has the right set and size of clusters, it asks Generative AI to create a unique name for each. Those names become the codes that are shown to the researcher on Remesh.

All of the codes are then assigned to a larger category grouping, using a similar process to the one used to assign codes to responses. The insights professional can review all of the codes and categories - editing, adding, and organizing as needed to ensure accuracy. This keeps the researcher in the driver’s seat while still utilizing AI’s powerful capabilities to make their existing processes faster and better.

While Remesh's platform assists in coding, human researchers remain integral to the process. The AI serves as a powerful tool, accelerating coding tasks and reducing the risk of errors, but the researcher's expertise and context are invaluable in refining and interpreting the results.

The future potential of AI-assisted coding

As we look ahead, the potential of AI-assisted coding is promising. The technology can drastically expedite coding tasks, making them accessible to a broader audience. With the assistance of AI, researchers can focus more on analyzing nuanced data cuts, deepening insights, and providing real-time business context. However, AI is not a cure-all; the symbiotic relationship between human researchers and AI tools is paramount for accurate and meaningful results.

The evolving role of the researcher

In this AI-driven era, the role of the researchers must evolve. Researchers are the stewards of data, leveraging AI tools to extract more profound insights efficiently. They are responsible for interpreting AI-generated codes and ensuring accuracy. They may need to validate and refine the codes to better tell the story of the data, especially given their familiarity with the questions and nuances of the dataset. Additionally, researchers have the invaluable context that AI lacks. The researcher understands the stakeholder needs and historical context that is needed to make recommendations for next steps. AI is a powerful ally, but the human touch turns data into actionable knowledge.