Broadly speaking, there are two different types of data: Quantitative and Qualitative.
Quantitative data provides the big picture: it is often data driven, statistics-based, and can be assigned numerical value. Qualitative data fills in the details: it is often composed of impressions, opinions, and views — seeking to describe a subject rather than measure it.
Depending on your data type, you will use different tools and methods to make sense of your findings. Sometimes, there is no need to choose between them, as quantitative and qualitative data analysis can be used either alone or together which is called mixed methods research. While neither quantitative nor qualitative data is inherently better or worse, a researcher must carefully consider the strengths and weaknesses of each data type when designing their survey. The type of data a researcher collects should depend on what they are trying to measure in their research, as well as the resources available to them. Likewise, you should structure your subsequent analysis approach to suit the type(s) of data you have collected.
The table below demonstrates some of the pros and cons of quantitative and qualitative data analysis. Using data science techniques, we can analyze any information quantitatively in multiple ways. Here we will keep our focus on simply looking at how to analyze survey data.
| Quantitative | Qualitative |
|---|---|
| Data Source: Closed-ended questions, questions which require users to input a number, and multiple choice questions. | Data Source: Open-ended questions, descriptive questions, multiple choice questions. |
| Pros: Quantitative analyses are numeric and generally used for statistical testing and inference. Often less difficult (relative to qualitative data) to analyze. | Pros: Allows for discovery and a range of responses that go beyond the imagination of the researcher. |
| Cons: Scope of responses are limited by the researcher’s initial hypotheses and therefore less focused on each individual’s lived experience. May not truly capture the phenomena being studied as participants experience it. | Cons: Often more difficult (relative to quantitative data) to analyze, more likely to go unanswered, are generally more taxing on the participant to answer. |
A mix of both quantitative and qualitative data can enable your survey to yield powerful and meaningful analysis. A well-designed survey often utilizes a mix of data types in their questions in order to reap the benefits of both quantitative and qualitative data. This is referred to as a “mixed-methods” design. For further explanation, you can check out this short video clip from Scribbr on mixed methods analysis, which allows you to combine the best of both these data types.
