Challenges with Qualitative Data Analysis

Many students that I interact with recite a common liturgy of qualitative data analysis challenges. You’ll often hear of contextual variability, researcher bias, lack of generalizability, and subjective interpretation. Here, I’ll inspect 6 unique challenges associated with qualitative data analysis.

A Quick Aside…

There are 4 simple steps to follow when your professor insists on a qualitative data analysis. They’re:

1. Gather all the relevant feedback (collect data).
2. Code the comments.
3. Run your queries, and finally
4. Report your findings.

Qualitative data analysis investigates people’s perceptions / feelings about a situation, event, or business trends. It’s therefore susceptible to these challenges:

Challenge #1: Choosing a Method

Please add “…and getting started” before I forget. Thanks to the overwhelming variety of qualitative research methodologies, students sometimes find it hard to decide on a single study design.

Challenge #2: Identifying the Research Problem

Most students confuse this part with challenge #1 above, although they’re closely related. Methodology takes instruction from the research question and due to uncountable choices, it’s hard to pick the most appropriate thesis statement. The two elements greatly inform research data collection and analysis frameworks / models.

Challenge #3: Reliability and Validity

It’s challenging to maintain research data findings’ validity or reliability with qualitative data analysis. This is mainly because qualitative processes aren’t as standardized as their quantitative counterparts. Personal biases, for example, are likely to skew qualitative research data results.

Challenge #4: Time-intensive, with Tons of Data to Analyze!

By default, qualitative studies collect tons of relevant research information. Data analysis therefore becomes a resource-intensive, time-consuming, tiresome, and nerve-wracking experience.

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Challenge #5: The Intricate Nature of Qualitative Research

Yes, qualitative research is difficult to conduct. It’s also less open to interpretation, and even less likely for the analyzed data to be generalized to whole populations. With qualitative data analysis, students always complain of accuracy and consistency challenges.

Challenge #6: Avoiding Bias

There are many contributory limitations that make avoiding qualitative data analysis bias such a headache, including:

• Potential bias in study responses.
• Possible small sizes of the sampled population.
• Self-selection bias, Or
• Potentially faulty, non-pointed questions by the student researcher.

Is it Hard to Analyze Quantitative Data?

Since quantitative data analysis deals with things a student can reduce to numbers, it’s relatively easier than qualitative data analysis. The best way to understand the challenges of analyzing quantitative data is by studying the 3 uses of quantitative analyses.