5 Challenges in Conducting Qualitative Research 

Anyone who has ever looked at a spreadsheet understands how impersonal it may feel. What do numbers say about a person’s motivations, beliefs, and thoughts? While statistics are important for identifying corporate patterns and inefficiencies, they don’t always convey the whole picture. 

Why does this product appeal to the buyer more than the others? What makes them want to use this hashtag on social media? What are employees’ thoughts on the new supply chain process? 

Businesses frequently use qualitative research to answer more intimate questions about human experience. 

What is Qualitative Research? 

Qualitative research assists entrepreneurs and existing businesses in understanding the numerous aspects that influence consumer behavior. Because most companies gather and evaluate quantitative data, they aren’t always aware of how their target markets feel or what they desire. When researchers can watch a small group of customers in a comfortable setting, ask questions, and let them talk, it helps them. 

The research approach used differs by industry and the sort of company requirements. Many businesses use a combination of strategies to get the information they need to make better decisions. While both quantitative and qualitative research approaches are helpful, they each have their own set of limitations. 

Here, let’s look at five challenges in conducting qualitative research.  

1- Limited Sample Size: 

To guarantee that the results are accurate, businesses must seek a large enough group of participants. A sample size of 15-20 people is insufficient to provide a realistic picture of customer perceptions of a product. If a big enough sample size cannot be found, the data obtained may be inadequate. 

Two suggestions for sample size: 

  • As a rule, if new participants continue to provide you with meaningful, fresh insights, you’ll need additional participants. 
  • Don’t specify a set number of participants initially; instead, be flexible. 

2- Sampling Bias 

According to Wikipedia, “In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population are less likely to be included than others.” 

To put it another way, your qualitative sample will never comprise a representative sample of all the visitors to your website. 

For instance- employees may be biased in internal qualitative investigations. Workers, for example, may provide a standard answer that their coworkers agree with rather than their genuine feelings. This may have a detrimental impact on the study’s outcome. 

3- Self-Selection Bias 

Businesses that rely on volunteers to give answers are concerned that the responses will not represent the entire group. It’s preferable if the firm chooses people at random for research projects, especially if they’re workers. This, however, shifts the process from qualitative to quantitative. 

This is precisely the issue at hand. It is your decision whether to engage in a research project. The biases of sampling and self-selection are interconnected and restrict the use of qualitative data. 

4- Artificial Scenario 

It’s unusual to study customers in stores, form a focus group, or inquire about workers’ job experiences. This artificiality may influence the findings because it is outside the norm of expected behavior and relationships. 

In this scenario, the purpose is so narrow that you won’t learn anything else helpful from this research. The participant could have a lot more to say, but you won’t know unless you ask them. 

5- Observer-Expectancy Effect   

Assume you’re conducting a survey and acting as a research observer in the research room. You take a stroll around the room, observing the individuals. 

Do you believe you won’t have an impact on the outcome? 

It is well recognized that a researcher’s opinions or expectations cause them to influence experiment participants inadvertently. The observer-expectancy effect is what it’s termed. 

In Conclusion-  

As you can see, qualitative data poses several challenges. Marketers, however, can do exceptionally well if they combine this data with quantitative data to develop solid A/B test hypotheses. 

Don’t make changes to your brand based on a limited number of qualitative comments. Instead, use all accessible data sources to expand your conversion optimization framework and get more out of your testing efforts. 

And if you are stuck somewhere, you can always reach out to us at SG Analytics. Our market research consulting services can give you the best possible results based on the research.  

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