Descriptive and Analytical Research: What’s the Difference?

Research is loosely translated as a search for knowledge. It’s a scientific and systematic approach to seeking information about a chosen topic. Therefore, research is an art of scientific investigation. It aims to get more information. These include analytical and descriptive research.

These aren’t the only types of research. There’s also essential and applied research (Amrhein, Trafimow, & Greenland, 2019).

For now, we’ll focus on descriptive and analytical research. We’ll talk about what they mean and how they’re different.

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Descriptive vs. Analytical Research

Both descriptive and analytical research serve a key role in statistics and data analysis. The difference is in what they look at.

Omair (2015) describes it as a question of what vs. why.

Descriptive research asks “what?” It describes something. Meanwhile, analytical research asks “why?” We try to find out how something came to be.

Descriptive research classifies, describes, compares, and measures data. Meanwhile, analytical research focuses on cause and effect.

For example, take numbers on the changing trade deficits between the United States and the rest of the world in 2015-2018. This is descriptive research. For example, you may talk about the mean or average trade deficit. Meanwhile, analytical research measures something different. Instead, you’d look at why and how the trade deficit has changed (McLeod, Payne & Evert, 2016). Special statistics and statistical controls help ensure the results are meaningful.

For example, analytical research can explore why the value of the Japanese Yen has fallen. This is because analytical research can look at questions of “how” and “why.”

Comparing Examples

Our research focuses on helping disabled people. So, let’s share some examples of research questions on disability.

Descriptive	Analytical
How many disabled people face social isolation?	What causes social isolation in disabled people?
What is the unemployment rate for disabled people?	Why do disabled people have a harder time finding work?
How many siblings of people with Down syndrome have positive experiences?	Why do so many siblings of people with Down syndrome have positive experiences?

Both descriptive and analytical research must be done carefully. For example, not every survey counts as research. Thus, experts must follow best practices to make sure the data is good.

Importance of Analytical Research

Analytical research brings together subtle details to create more provable assumptions.

Thus, analytical research tells us why something is true. Researching why something happens isn’t easy. You need critical thinking skills and careful assessment of the facts. For example, people might use analytical research to find the missing link in a study (Valcárcel, 2017). It offers new ideas about your data. Thus, it helps prove or disprove hypotheses.

This type of data helps establish the relevance of an idea or confirm a hypothesis. It helps identify a claim and find out whether it is true or false (Omair, 2015).

Students, psychologists, marketers, and more find analytical research useful. In a company, it helps figure out which ad campaigns work best. Meanwhile, in medicine, it finds out whether a given treatment works well.

Thus, analytical research can save lives, save money, and help people meet their goals.

Inferential and Descriptive Statistics

There are two main types of statistics: inferential and descriptive. Each type offers different methods with different goals.

The descriptive approach seeks to describe what’s happening. Meanwhile, inferential statistics offers more. Researchers use it to explore findings within a given sample group. Then, they see if it applies to a larger group.

Inferential statistics uses complex math. It lets us infer trends about a population using a smaller sample of the group (Amrhein et al., 2019). Also, it helps us look at the relationships between variables in a sample. Then you can make inferences about the larger population. To do this, you need a careful research design. The sample has to be representative of the bigger population. Also, the study must control the influence of other variables. Thus, predictions and conclusions are more likely to be correct.

Descriptive statistics involves collecting numbers to show different features of a population. It lets us see things like the center and spread of the data. But it doesn’t lead to any generalized insights. This is because it measures numbers like the mode, mean, and standard deviation. Often, it’s hard to generalize from these.

Meanwhile, inferential statistics uses some of the same numbers. But it includes more (Valcárcel, 2017; Amrhein, Trafimow & Greenland, 2019). For example, it also uses the standard deviation and mean. But there’s a different focus. People can generalize, but can’t be exact. Instead, they estimate the parameters with different degrees of probability.

Conclusion

Descriptive and analytical research both play important roles. The first shows what the data looks like while the second examines cause and effect.

Analytical research helps in many fields of study. For example, these include psychology, marketing, medicine, and other areas. Often, it’s used because it provides more definitive information in answering research questions.

Our team uses both these types of research. We run surveys on disability, health, and more. To see what we’ve found, check out our survey results.

by Rob Akins, edited and illustrated by Jenna Breunig

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References

Amrhein, V., Trafimow, D., & Greenland, S. (2019). Inferential statistics as descriptive statistics: There is no replication crisis if we don’t expect replication. The American Statistician, 73(sup1), 262-270.

McLeod, M. S., Payne, G. T., & Evert, R. E. (2016). Organizational ethics research: A systematic review of methods and analytical techniques. Journal of Business Ethics, 134(3), 429-443.

Omair, A. (2015). Selecting the appropriate study design for your research: Descriptive study designs. Journal of Health Specialties, 3(3), 153.

Valcárcel, M. (2017). Usefulness of analytical research: rethinking analytical R&D&T strategies. Analytical chemistry, 89(21), 11167-11172.