Director, Account Service
“Big Qual,” or large-scale qualitative research, is an online platform where 50 to up to 1,000 participants answer questions, react to stimuli, and explain their thoughts to a qualitative researcher. Big qual combines the strengths of quantitative and qualitative research methods to uncover consumer insights.
How does Big Qual Work?
Large-scale qualitative sessions fuse qualitative knowledge with the benefit of a quantitative scale. The qualitative researcher develops a series of questions that can be changed at any point during the study. The moderator then leads the participants through the activities and encourages interaction.
Large-scale qualitative sessions fuse qualitative knowledge with the benefit of a quantitative scale. The qualitative researcher develops a series of questions that can be changed at any point during the study. During the study, the moderator leads the participants through the activities and encourages interaction. The participants answer the qualitative and quantitative questions when the moderator releases them. After answering, respondents are sometimes shown other respondent’s answers and asked if they agree, disagree, or can’t decide. Additionally, MDRG sometimes incorporates online metaphor elicitation into our big qual studies for System 1 insights.
If the moderator begins to uncover insights beyond the initial queries, then he or she can concentrate on those aspects more. Since big qual occurs in real-time, the moderator can easily change his or her strategy. During the big qual study, qualitative data is structured using natural language processing (AI). This allows the program to determine the most populous answers for analysis. The program also allows analysis by demographic and attitudinal segmentation.
To encourage interactivity, most large-scale qualitative research platforms, such as Remesh or Groupsolver, require the participants and the moderator to be present at the same time. However, other platforms allow participants to log on throughout a 3 to 5-day time span. Text analytics platforms, like Ascribe, help the moderators analyze open-ended answers once the large-scale qualitative session is completed.
Advantages of Large-Scale Qualitative Research:
- Agility: Large-scale qualitative research projects can be launched quickly unless the sample target is difficult to find. As the results are available in real-time, analysis can occur quicker. Most platforms also provide a summary report shortly after the session ends.
- Reach: Since big qual occurs entirely online, chosen participants can be located internationally. The study can occur simultaneously in multiple languages or countries depending on the research needs.
- Statistical Significance: Sample sizes for big qual studies are larger than typical qualitative studies, therefore, the results are more statistically reliable.
- Interactive: Since the research occurs in real-time, the moderator interacts with all of the participants. The qualitative researcher can adapt the exercises to the conversations occurring. The interactivity and flexibility provided in big qual allows for deeper consumer insights and answering complex research questions.
Challenges of Large-Scale Qualitative Research:
- Recruitment: The cost to recruit the number of required participants is higher than a traditional quantitative survey or other qualitative methods.
- Sample: The sample for big qual projects cannot be very detailed, otherwise not enough participants will be found.
- Moderation: The market research firm must have a strong moderator who can pick up on changes and adapt their questions, along with ensuring participation from all.
Is Big Qual Ready for Primetime?
To make it short and simple: yes.
MDRG is ready to use large-scale qualitative research (Big Qual) with our clients. Drawing upon partnerships and experience, we discovered solutions to the common big qual challenges. The four advantages of large-scale qualitative research listed above are only the tip of the iceberg. Participants are more comfortable sharing opinions anonymously online. The online setting also removes group bias as participants cannot see other answers until they answer. Additionally, AI advancements helped text coding become more accurate, producing results in a shorter amount of time.
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