For social or market researchers who have years or decades of experience with quantitative data analysis, AI qualitative data analysis can feel intimidating. Researchers often specialize in quant or qual because the skill sets are quite different. However, you’ll soon see that qualitative data analysis has much in common with quantitative data analysis, especially if your experience is with advanced statistics like factor analysis or cluster analysis. Let’s find out why!

The qualitative side of factor and cluster analysis

When conducting a factor analysis, statistical software determines which answers tend to co-occur. For example, correlations will show that someone who indicates their job title is CEO or President probably also indicates they have a high income. Correlations will also show that someone who indicates they love bargains probably seeks out coupons and BOGOs. Across 100 questions, it’s impossible for a researcher to identify all the meaningful correlations among two, three, or ten variables.  

That’s why we love when statistical software steps in with factor and cluster analysis. This software makes it much faster and easier to identify significant correlations and categorize 100 variables and 500 possible answers into several solutions, each one with 4, 5, 6, 7, 8, or 15 buckets. None of these solutions are inherently truth but as you review each set of buckets, you’ll generate a subjective opinion that one solution feels more valid and reliable than others. That solution will have:

• Fewer variables and answers that don’t seem to logically belong to the bucket they’ve been assigned to.

• Fewer buckets that look like a random collection of answers.

• More buckets that feel like cohesive, meaningful, nameable constructs.

After reviewing the various solutions, you’ll choose just one to name and claim. Despite the extensive amount of quantitative statistical analysis taking place, you can see that the final process is very qualitative.

The quantitative side of AI qualitative coding

Thirty years ago, before automated and generative AI tools simplified and sped up the process in a meaningful way, people did all the work manually. Over days and weeks, researchers would read paper questionnaires and transcripts to identify potential trends in the words and phrases and then determine whether there were broader connections and patterns among them. As technology improved, it became much easier and faster to search for concepts and assign qualitative codes (e.g., gender, anger, pricing) and quantitative codes (e.g., 1=Female, 7.2=Anger.High, 32=Pricing). Today, with the assistance of AI qualitative coding, creating a coding structure with multi-level netting and extracting descriptive themes from open end comments has turned months, weeks, and days into mere minutes.

A researcher with lots of qualitative experience might prefer to review and analyze the qualitative codes manually to generate possible theories and choose the best one. Similarly, someone with lots of quantitative experience might see envision the quantitative codes serving as a dataset for running a factor analysis. Whether you see the process from a qualitative or quantitative point of view, the better method of identifying a theory is the one that feels right to you and results in valid and actionable results.  

Putting a human into the coding

Whether generated by AI or by human, having a set of codes and themes, named or not, does not mean the qualitative analysis is done. The next step is to review and understand the codes to identify a theory or result that is the most meaningful, strategic, and actionable.  

• Which solution connects in a logical way with an existing consumer, personality, social, or economic theory?  

• What aspects of the existing theory are currently missing from the analysis? Are those aspects still in the data or were they never a part of the data - why?  

• What aspects of potential solution are different from the existing theory? Is the theory wrong? Is the solution wrong? What are the known or unknown caveats?

• Do the results warrant development of a new theory that will require additional research?

AI and statistical software don’t have the personal, social, emotional, psychological, and cultural understanding of the human experience - yet. What makes sense statistically will still need some tweaking to flush out the full depth of the human experience. That’s what expert market and social researchers bring to the table.

Summary

AI is no longer the wave of the future. It is today and it is in everything we do. From pattern recognition, theory development, logical reasoning, consumer behavior, and more, your skills as a quantitative researcher transfer in meaningful ways to the qualitative world. Consider AI qualitative coding as a slightly different way to conduct a factor or cluster analysis, a technique you already have fun with!

If you’d like to learn more about our AI tools, check out our AI qualitative coding tool, ascribe, and read Voxco’s answers to ESOMAR’S 20 Questions to Help Buyers of AI-Based Services. When you’re ready to speed up your qualitative data analysis with a one-stop tool, please get in touch with one of our survey experts.

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