Data 101: What is Data and How Do I Use it in My Research?

With the phrase “data-driven” being thrown around constantly in academic, business, and policy circles, you probably know data is important.

But why?

This post will take you through what constitutes data, why it’s important, and how to use different types of data in your research.

First, what is data?

According to Merriam-Webster, data is, “factual information such as measurement or statistics used as a basis for reasoning, discussion, or calculation.” In the context of research, however, data is any information that has been collected—either by yourself or other researchers—that can either help validate or disprove your research.

Generally, most data can be classified into two types: qualitative data and quantitative data:

Quantitative Data: Quantitative data is what people most frequently think of when they hear the word “data.” It refers to any information that can be quantified —i.e., given a numerical value. It is worth noting that even linguistic analyses, the counting of words or phrases, can be considered quantitative because a number is assigned to it.

How to use Quantitative Data:

1. Hypothesis-testing: If you have specific hypotheses in mind (e.g., you hypothesize people with blue eyes like cheddar more than swiss) you can test them by collecting either existing data (by going on google scholar, jstor, or other data sources) or creating new data (asking people with blue eyes and people with other eye colors what type of cheese they prefer).

2. Furthering qualitative theorizing: You may have observed certain themes through qualitative data, and now you want to see if these insights hold true empirically. For example, you may have interviewed people about how the pandemic impacted them, and now you want to collect quantitative data about their stress levels.

Qualitative Data: Qualitative data is descriptive and observational, usually focusing on the use of words to describe conceptual findings. Examples of qualitative data include themes and insights generated through observing people’s behavior, interviewing people, and administering open-ended questionnaires. Qualitative data can be an insightful source of data on its own or a great complement to quantitative data.

How to use Qualitative Data:

1. When you want to further explain quantitative results: Sometimes, p-values will only get you so far. In order to help ground your quantitative data more holistically (e.g., stress levels during the pandemic), you can use qualitative data (e.g. quotes and excerpts of why people are stressed), to help you better explain your quantitative findings.

2. When you want to explore a completely novel concept: If you are investigating a relatively new phenomenon (say, the economic impact of NFTs), you should first explore it by collecting qualitative data. Excerpts of interviews (e.g. of NFT users) with related past research (e.g. the economic impact of blockchain) can help you ground your theories. This is known as the grounded theory approach, which is the use of qualitative data to create a new theory.

Now you know that data is more than just p-values, and that there are many ways to use it! For more information on how to collect and analyze your data, be on the lookout for my next post coming in a few weeks.

-Joani, CRI mentor