Optimizing Qualitative Data Analysis (QDA) using ResTech

Jan 27, 2025, Ushma Kapadia

The digital age has brought exciting changes to the domain of Qualitative Research, particularly Content. Online platforms now let researchers collect and analyze data in new ways. They can run online focus groups, send out surveys and analyze social media posts. These changes have expanded what Qualitative Research can do, in terms of insight-generation.

Much is being said about the wider applications of research technology (ResTech) to Qualitative Research. QDA is poised for redefinition, with increasing adoption of generative AI (GenAI) models like ChatGPT, Claude, Gemini, Perplexity etc. Traditional ways of conducting QDA bring rigor to content analysis, which the best of AI cannot match. Yet, practitioners continue to test and push GenAI’s limits, to apply Human Intelligence and narrow the rigor gap.

 

Qualitative Research tools have claimed a place in the ResTech landscape

Using AI in Qualitative Research is now essential. AI can process large amounts of textual data, quickly and accurately. Given this escalation of interest about all things ResTech, we thought it was time to step back and review the online Qualitative Research platforms' landscape available to research practitioners today. In our assessment, this is a broad classification of options available to Qualitative practitioners looking for QDA tools:

·         Full-stack. Typically, these are platforms that cover pre-reporting phases of a qualitative project. In that sense, they are a ‘single window’ for recruitment, fieldwork scheduling, conducting fieldwork, storing video and audio files of interviews/ FGDs conducted on the platform, transcribing and analyzing such interviews/ FGDs. These are great for virtual fieldwork, be it IDIs/ FGDs/ Diaries. They offer features like video calls, live chat, interactive whiteboards, tools for turning audio recordings into text, organizing survey answers, GenAI-assisted analysis and displaying data with charts and graphs.

·         Purpose-built. On the other hand, certain online platforms are built to execute specific Qualitative methodologies eg.


Source: ResTecher.com

These tools are methodology-first, in the sense that they can be used only when data is collected using certain devices and/ or methodologies. Typically, they collect data using wearables or specific devices. For instance, EyeTracking tools capture consumer reactions using eyeglasses that monitor path and intensity of consumers’ eye movement.

Thus, the ResTech landscape for Qualitative Research offers many options, ranging from simple to very advanced features. Researchers can pick tools based on their needs and skills. This is true whether they are just starting with AI or looking for complex methods for analysis.

For beginners, there are many easy-to-use platforms/ tools available. These tools make the process of thematic analysis simpler. Many have drag-and-drop features. This allows you to easily code data, find themes, and visualize results without knowing a lot about Coding.

Experienced researchers can use advanced tools, which can manage large amounts of data. They can do sentiment analysis and find connections among seemingly diverse data points. Advanced tools often use machine learning, which means they learn and improve over time, providing more accurate and useful analysis.

 

How to evaluate among Qualitative ResTech tools

Navigating qualitative data analysis (QDA) software can get tricky. The unprecedented rise of ResTech tools has made picking the ‘right’ one a challenging task. Whether you are a skilled researcher or a beginner, it's important to look at different software options, to find the best match for your qualitative research projects. When evaluating QDA software, you could consider:

·        


Purpose. Choosing the best online platform for your qualitative research needs starts with knowing your research question and goals. If your requirement is simply organizing data in usable files, even Excel is an option. Instead, some tools are great for automated coding; whereas others do well with multimedia data (audio and video), visualization or sentiment analysis. Similarly, if your research aims to calculate Net Promoter Score (NPS) from customer feedback, you should look for QDA software that can analyze open-ended responses. This will help you understand why customers feel satisfied or dissatisfied, giving you richer insights, rather than just a simple score.

·      Learning curve. Using these platforms properly requires specific skills. At the least, researchers must know how to collect data online, since all ResTech tools are built for digitized data. Your learning curve of a platform will vary, depending on whether you are a novice, a dabbler or a seasoned AI user. If you plan to use online surveys or polls, look for platforms offering survey-design (eg. different question types), reporting and visualization. If you are doing observational research, like looking at social media conversations, check out platforms that provide social media monitoring and sentiment analysis.

·       Features: If you are a somewhat experienced ResTech user, it is likely that you are actively seeking specific features in your next ResTech tool eg. Automated coding, Theme visualization, Sentiment Analysis, Data visualization, Mixed-methods analysis, Team collaboration, Text analytics. For instance, a researcher looking to conduct brainstorming sessions online could use a platform offering ‘Breakout Rooms’ as a feature. This allows for smaller group discussions, engages participants better. Similarly, researchers seeking quick overviews of important findings and trends could use platforms offering smart reporting features that can automatically analyze consumer responses.

·       Data privacy. Researchers also need to think about ethics, protect privacy while keeping data high-quality. Those using GenAI models should be aware that they can prohibit usage of their data, in training those models.

“When I started, I started with ChatGPT’s paid version. I just want to stress for anybody that's new to it, data privacy is a huge concern. So you just want to be sure that you're using a tool that at least promises that this information is not being used to train their LLM.” – Doug Keith, Founder, Future Research Consulting

·       Timing. When choosing a tool, we would not recommend picking it up at the last moment, just before a report is due. That can cause last-minute panic and frustration. Instead, try exploring different features of 2-3 platforms, over multiple projects. This allows you enough time to test various features of the platforms, across enough projects; to give you the confidence to you’re your choice.

“Just start experimenting with it. Don't wait until 11 o'clock the night before the reports are due to say, I'm going to use this thing. It could be a little frustrating.” – Doug Keith, Founder, Future Research Consulting

·       Customer support. Also, don't forget how important customer service is when picking an online platform. Look for platforms that serve your customer support requirements i.e. time zone, availability on weekends, geography/ languages that customer support is offered in. They should be ready to assist you and solve any technical problems you might face during your research process.

·       Budget. Certain tools are flexibly priced – allowing for both, pay-per-use and subscription. If you are familiar with ResTech, you can find free and open-source options, as well as more advanced commercial software. Check out different choices and try free trials, before you decide.

“Don’t commit right away to any one platform… instead, experiment with a couple of different platforms. I experimented with 6 and now I've gone through 10 or 12. Once you decide on a platform, ask for a demo, have them show you how to use it. Because they will show you things that you wouldn't think of.” – Dean Stephens, Founder, Happy Talk Research

·       User-friendliness. It’s also important to gauge how easy-to-use the software is. This is especially true for researchers who are new to ResTech and AI tools. Many options have user-friendly designs, with simple drag-and-drop features. This makes it easier to do complex analyses, even with little tech experience.

  

In conclusion, thanks to advances in AI, researchers can indeed work more efficiently and accurately. This is a change that’s here to stay; so the earlier that researchers adapt, the faster they would thrive. Seasoned practitioners are already learning to combine their own human intelligence with GenAI; to sharpen their insight-generation skillsets and build efficiencies in the QDA process. The journey of adoption can be started with simple steps, enabled by tool-trials and running sample data in small quantities. Ultimately, such exploration will help researchers choose QDA tools best suited to their and their team’s needs. To explore how flowres.io can help your QDA needs, reach us at https://calendly.com/maniish-flowres/flowres-demo?month=2025-01. 


Ushma Kapadia
Jan 27, 2025