Data Types, Online Survey, Research, Survey Examples

What is a Cross-Sectional Survey and Why Should I Use One?

Are you in retail, healthcare, education, psychology, or marketing? Looking for a fast way to collect a large amount of data? Then you may want to consider a cross-sectional survey. In this blog, we’ll look at what they are, how to use them, the pros and cons, and more.


What is a Cross-Sectional Survey?

A cross-sectional survey is a type of observational research that analyzes data across a sample population at a specific point in time. This survey type is also known as a cross-sectional study, transverse study, prevalence study, or cross-sectional analysis. Although cross-sectional surveys don’t involve conducting experiments, researchers often use one to understand outcomes in a variety of industries, selecting participants based on particular variables of interest.

This type of survey can be used to uncover characteristics that exist among participants, but cannot be used to determine cause-and-effect relationships between different variables. For example, educators studying students’ attitudes toward virtual learning may select groups of students of differing ages and ask questions about online learning at a specific point in time. In doing so, any differences in attitudes about this type of learning can presumably be attributed to age, rather than something that occurred over time.

As you’ll see in our examples of cross-sectional surveys throughout this blog, they’re used across a number of industries, including:

  • Retail
  • Healthcare
  • Education
  • Psychology
  • Marketing

Characteristics of Cross-Sectional Survey Design

Let’s look a little closer at the specific characteristics of a cross-sectional survey.

  • Surveys are conducted with the same set of variables at a single point in time.
  • Researchers can look at various characteristics at once (age, gender, race, etc).
  • They’re often used to look at the defining characteristics of a population at a given point in time.
  • Additional surveys may look at the same variable of interest, but each observes a new set of participants.

Think of a cross-sectional survey of a snapshot of people at a particular point in time. Consider a cross-sectional healthcare study conducted by a hospital about COVID-19 patients. All the participants have one thing in common: they are all over 70 years old. But, there are multiple variables they don’t share (gender, weight, race, immunocompromised, etc).

From that point, researchers could make observations and analyses. For example, the survey may uncover that the majority of patients over 70 who checked in with COVID were overweight and immunocompromised. While this type of study cannot explicitly demonstrate cause and effect, it can provide a quick look at some existing correlations at a particular point in time. 

Types of Cross-Sectional Surveys

Cross-sectional surveys take on one or two types of research: descriptive and/or analytical. 

  • Descriptive research looks at how frequently, widely, or severely the variable being studied occurs throughout a specific population. For example, a retail survey may uncover that more men of a specific age group prefer shopping online versus in-store than women in the same age group. Of course, it won’t uncover why or span a lengthy period of time. 
  • Analytical research looks at the link between two related or unrelated variables. This methodology does have its challenges, however, because outside variables and outcomes are simultaneous. For example, to validate whether smokers are more likely to develop lung cancer, the survey would only look at those specific variables. It wouldn’t take into consideration family history, predisposition, occupation, and so on.

Pros and Cons of Cross-Sectional Surveys

Three key factors contribute to the popularity of cross-sectional surveys:

They’re Inexpensive and Fast

Because cross-sectional surveys only collect data at a specific point in time, and not over an extended period of time, they are relatively quick to conduct. Having a set-time frame also makes them less expensive than more in-depth surveys with longer timeframes of study.

They Provide Data on Multiple Variables

Researchers can collect data on a number of different variables. For example, a marketer may uncover how differences in gender, age, educational status, and income, for example, might correlate with participants’ interest in their product. 

They Often Prompt Additional Research 

These surveys can act as a catalyst that prompts further research. For example, a healthcare study about whether a particular behavior may be connected to a particular illness provides clues that will serve as the basis for more in-depth follow-up surveys and studies. Researchers can use the results to gain approval on conducting additional research.

Of course, no one method of surveying is not without its flaws. A few disadvantages of cross-sectional surveys include:

Inability to Determine Cause and Effect

While researchers can make inferences about participants and outcomes, they cannot determine conclusions about causation. For example, in a psychology study, just because more teens aged 13-15 identify as being depressed than teens 16-18, the cross-sectional study can’t provide a definite “why.”

Existence of Cohort Differences

Responses from participants in a cross-sectional study can be impacted by cohort differences. A cohort is a group of individuals who experience the same event at the same time. So, participants born during the same period may share important historical experiences which have shaped their worldview. On the flip side, people in that group who are born in a given geographic region may share experiences limited solely to their physical location. Read more about generational differences in surveys.

Potential for Survey Bias

Surveys or questionnaires about certain aspects of people’s lives may not always result in accurate reporting, and there is usually no mechanism for verifying this information. Check out our blog on how to avoid survey bias in order to collect the most accurate responses.

Cross-Sectional vs. Longitudinal Studies

Lastly, you may be wondering how cross-sectional research differs from longitudinal studies. That’s an easy one: Longitudinal studies involve multiple variables over an extended period. Because of this, they take longer to conduct and often involve more money and resources. Another challenge with longitudinal studies is that because they take place over time, some participants may eventually drop out (known as selective attrition). This, of course, results in survey bias which can impact the validity of the study. However, longitudinal studies provide more in-depth information, so they’re great for following up on cross-sectional study findings. Cross-sectional studies also help inform the types of questions to ask, further shaping the longitudinal study.


Cross-sectional surveys are a great way for organizations in a number of industries to collect a large amount of data quickly and inexpensively. And, they make a great starting point for any organization conducting research before using a longitudinal study. Ready to conduct your cross-sectional survey? Start now with SurveyLegend. Our surveys are easy to use, easy on the eyes, highly secure, and it’s free to sign up!

Have you conducted a cross-sectional study? What industry are you in? Did you find the results valuable? We’d love to hear from you in the comments!

Frequently Asked Questions (FAQs)

What is the definition of a cross-sectional survey?

A type of observational research that analyzes data across a sample population at a specific point in time. The survey may be descriptive or analytical in nature.

What is the definition of a longitudinal survey?

A type of research that involves multiple variables over an extended period of time.

Which industries can benefit from cross-sectional surveys?

They’re in use across a number of industries, including retail, healthcare, education, psychology, and marketing.

About the Author
Born entrepreneur, passionate leader, motivator, great love for UI & UX design, strong believer in "less is more”. Big advocate of bootstrapping. BS in Logistics Service Management. I don't create company environments, I create family and team environments.