What is qualitative research?
Qualitative research is exploratory, providing understanding of opinions and motivations. Qualitative data is largely descriptive in nature, meaning it’s used to analyze the quality of a subject rather than measure the quantity of a parameter.
This is to say, you would use qualitative data to understand why students are unhappy with course content as opposed to how many students were satisfied or dissatisfied.
Qualitative data is also known as categorical data, as it can be grouped and segmented based on categories. These categories usually arise from your data as the most frequent feedback, responses or language used by participants.
For example, you could have positive, neutral and negative responses for an employee satisfaction survey.
Choosing between quantitative and qualitative data collection
Choosing between quantitative and qualitative data is dependent upon your goals. I.e. what are trying to achieve with your research?
If your aim is to build a demographic database of regional or national residents, then you’ll likely be running a fully quantitative survey.
But if you’re conducted a 360-degree review of an employee, you’re going to need to use more open-ended questions. Meaning there will be a qualitative aspect to your research.
Whether you choose to collect on qualitative or quantitative data will impact the way you frame your research questions.
Qualitative questions encourage participants to explore a subject in detail, whilst quantitative questions require a definitive answer. So, what choice you make will impact the construction of your research project.
How to get qualitative data
Qualitative research requires a much smaller sample for data to be collected from. This is because you’ll be collecting more in-depth responses from each participant and will have plenty to work with when it comes to your analysis.
With methods such as record keeping, there isn’t even a need for a respondent pool, only access to previous research and texts on the subject matter.
Qualitative research methods
There are a few common methods by which you can collect qualitative data:
- Case studies
- Secondary research (record keeping)
- Expert opinions
- Focus groups
- Online surveys (mobile, kiosk, desktop)
- Paper surveys
- Observational studies
As qualitative research is exploratory, most of these research methods are framed conversationally to obtain as much open-ended data as possible.
Unlike quantitative data, it is less concerned with who, what and when and more concerned with why and how. These types of questions allow us to understand the behaviours, motivations and feelings of participants.
Qualitative data analysis
The most time-consuming element of qualitative research methods is undoubtedly the data analysis. Because the responses are richer and more in-depth than those of its quantitative counterparts, a lot of effort will go into categorizing responses and sifting through every word for meaning.
There are two main approaches to analyzing qualitative data: inductive and deductive.
A deductive approach would have the researcher begin with an initial hypothesis or expectation for the data set. The results you collect are then used to either prove or disprove those expectations.
An example of this would be if you’d surveyed a sample previously. Then, you’d likely have a set of expectations for how that group is likely to answer questions.
An inductive approach does not require a theory or any expectations to begin research and is the more commonly used method for qualitative data analysis. Instead you’d collect data from your sample, then work to identify patterns and trends within that data set.
Your next step will be based entirely upon the conclusions your draw from those patterns. Whether it’s to write a theory for further testing, evaluate the performance of an employee or inform a marketing strategy.
Analyzing open-ended data
If you’re taking a deductive approach, your analysis process is fairly simple. Measure your data against the parameters you set in your hypothesis or against the questions you asked.
However, if you’re using inductive reasoning, picking a method of analysis can be overwhelming. So, our suggestion is to use ‘Bucketing’.
1. Review your data
Read through your responses for a general understanding.
2. Identify patterns and trends
Begin categorizing responses into ‘buckets’ based on reoccurring trends and patterns.
Every response should be in at least one bucket, as they may represent multiple points of view.
These buckets could be as simple as ‘Positive comments’, ‘Neutral Comments’ and ‘Negative comments’.
3. Sub-categorize buckets
Feel free to sub-categorize your buckets based on themes or smaller patterns. This step is optional but can help develop your understanding of the data.
4. Review the buckets
Take another look at your buckets to see if any can be combined or split into separate categories.
5. Summarize the major trends
Explore the correlations and contrasts in each bucket. A good way to start is to identify your participants’ most used words and phrases, from which you can draw conclusions.
Strengths of qualitative research
Data is inferential
The kind of language your respondent uses can often tell you as much about how they feel or what think as what they said. If you learn to pick up on these linguistic nuances, they can inform more than your research.
For instance, open feedback in a customer satisfaction survey can help you build customer profiles.
Where quantitative data is numerical in nature, qualitative data provides more in-depth and meaningful feedback. You can gain more of an understanding of a participant’s reasoning and beliefs.
Data collection requires less resources
Because your respondent pool is smaller, you’ll require less time and investment (if you’re incentivizing participants) to conduct your research.
Weaknesses of qualitative research
Analysis is time consuming
As the type of data you collect for a qualitative survey is typically long-form, the results will take more time and effort to analyze.
Smaller respondent pool
Qualitative surveys only need a small sample size, so it can be more difficult to generalize your results for a wider population.
In fact, it’s not very likely your results will be representative of anything other than the group your interviewed.
Face-to-face interviews and focus groups require researchers to know when to continue a line of questioning and when to move on.
There is a fine line between gaining more context to answers and collecting superfluous feedback.
In an online survey, the way you frame questions is particularly important, as there is no researcher present to provide further context.
Data interpretation also needs a high level of skill and experience to remain objective.
Qualitative surveys are fantastic for collecting experiential data and feedback.
In an ideal world, you would incorporate both quantitative and qualitative research methods into your project. Enabling you to collect data for statistical analysis, from which you can draw generalized conclusions, and open-ended responses to provide context to your data set.