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What type of data do I need? Quantitative or Qualitative?

Catch up on the most important differences between these types of data to decide what approach fits best for your research

When it comes to designing your study, you’ll need to make another important decision: Do you want quantitative or qualitative data, or a combination of both? It’s worth looking into the differences, advantages and disadvantages of each before you make your decision. 


Quantitative data are numeric and are typically obtained en masse using tests or questionnaires delivered to a large number of people. Results are usually reported in the form of statistical analyses, which are then used to draw statistical inferences. The greatest advantage of quantitative data is that it allows the capture of a diversity of responses, at scale. Hopefully, this best practice guide will help shed light on some of the most important statistical concepts! If you’re really keen on improving your understanding of how to conduct empirical research and use statistics, we highly recommend the free online course by Daniel Läkens on Coursera called “Improving Your Statistical Inferences”. 


Qualitative data are non-numeric. Usually exploratory in nature and aim to explain a phenomenon in terms of ‘how’ and ‘why’. Methods can be anything from open-ended questions and interviews to diary studies. Qualitative data can be analyzed and interpreted in a variety of ways, for example, by letting independent raters assign numerical values to the collected documents, or by coding words and extracting themes until saturation. 

Qualitative data can have some advantages over quantitative data. For example, qualitative research may allow researchers to gain unique insights into participants’ feelings, thoughts, and behaviours, thereby detecting areas and issues that may otherwise be missed in a purely quantitative approach. Qualitative data can also be complementary to quantitative data, especially when there are contradictory or ambiguous results. In conjunction, they can hint at potential (causal) relationships, and thus point to future research directions more accurately than a solely quantitative measure could. 

In conclusion, when deciding which type of data to collect, you should refer back to your research question and identify the type of information needed to answer the question.


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