4 Cross-Sectional Study Examples (With Traits and Types)

Updated February 3, 2023

Cross-sectional studies can provide researchers with valuable data about a population. There are many ways people in various industries can use cross-sectional studies to collect data and analyze the results to find new information about a group. Learning more about these studies can help you improve your research skills, which can strengthen your career qualifications.

In this article, we define what cross-sectional studies are, describe their common characteristics and benefits, and list four examples of how various industries use them.

What is a cross-sectional study?

A cross-sectional study is an observational analysis that evaluates and compares different groups at a point in time. Researchers use cross-sectional studies to observe multiple variables with one group that accurately represents a sample of the population. Cross-sectional studies have a defined starting and stopping point, and researchers can examine what happens during that time to find conclusions. The primary purpose of cross-sectional studies is to describe and evaluate the characteristics of a population.

Because of this, researchers keep one variable in these studies the same, which is the independent variable. This means the participants in a cross-sectional study have a variable that connects them, such as living in the same community. There are many ways researchers can use this type of study, including:

Studying health in a population
Evaluating business and retail industries
Studying how socioeconomics affect a community
Providing evidence for future, more in-depth studies

Characteristics of cross-sectional studies

Because cross-section studies aim to investigate how prevalent specific traits or events are within a population, they often use a similar structure. Some of the most common characteristics of these studies are:

Researchers use them to analyze traits or characteristics of a population.
They provide valuable information about what the population does or experiences.
The studies monitor the population for only one point in time or a short period, not over longer spans.
They use constant variables that researchers don't influence.
They allow researchers to evaluate several population traits.

4 examples of cross-sectional studies

Here are some examples of how various industries may use cross-sectional studies:

1. Medical

Cross-sectional studies are popular for looking at the health of a population, including disease, disability or lifestyle choices. Researchers might use a cross-sectional study to understand why women older than 40 are more prone to a certain disease. Another example could be to determine how many people in a community smoke tobacco. Through this, researchers can analyze the demographics of people who smoke in an area to see how components like age, gender or social status affect those results.

2. Mental health

Psychologists can use cross-sectional studies to analyze mental health within a community. For example, they could send out a survey that asks the participants whether they have a history of mental illness and whether they've sought counseling services to help with their mental health. Results from this type of study are important because psychologists can use that information to create programs for those who want and need help.

3. Retail

Marketers or sales specialists can use cross-sectional studies to analyze shopping trends. From the results, they can determine what strategies they can implement to promote customer satisfaction. Another example might be business owners sending out a survey asking which stores people in a certain community like to shop at most frequently. Depending on the results, corporations could decide to build more stores that people most often visit to increase sales.

4. Education

A school board can conduct cross-sectional studies to gather results about students in different school districts. For example, they could compare the grades of students whose parents have similar income levels. Teachers also could send out a survey to their students asking what subjects they need more help with in class. Based on these results, teachers could create lesson plans that cater to their students' needs.

Benefits of conducting a cross-sectional study

There are many benefits of cross-sectional studies, including:

Provide a quick, easy and inexpensive way to obtain information

Researchers can perform cross-sectional studies in easy and inexpensive ways, such as mail-in surveys. This allows researchers to collect a lot of data in a short amount of time. These surveys can be cheaper than other popular types of studies. Although some cross-sectional studies can involve physical observation, surveys or questionnaires can be a valuable way to collect data from a population or community.

Study different variables simultaneously

Cross-sectional studies examine a population and allow researchers to make judgments about the group, which means researchers often can analyze and study different variables in the group at the same time. Variables of a population could include gender, age, income or level of education, for example. Cross-sectional studies typically have a subject or concept researchers want to address, such as blood sugar levels within a community, and they can analyze the different variables of a group to gather information about that subject and try to find connections and correlations within the data.

Use the results for further studies

Although cross-sectional studies provide researchers with a lot of information concerning a group, these types of studies often promote new tests. Because of this, researchers can use the results of cross-sectional studies to conduct further, more complex studies. Cross-sectional studies are valuable for starting a series of other tests to learn more about a population.

For example, a researcher might perform a cross-sectional study to measure how people with diabetes respond to going on a diet that's low in carbohydrates. This analysis might indicate younger people in the population respond more positively to the diet, which can allow you to do further studies into that subset of the population to analyze which types of food have the largest impact on their weight loss.

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Types of cross-sectional studies

There are three types of cross-sectional studies researchers use to conduct a test:

Descriptive cross-sectional studies

Although most cross-sectional studies are descriptive, some focus solely on describing the population and how the study affects it. For example, if a researcher wants to study heart disease within a particular community, they can use a descriptive cross-sectional study. To do this, they focus on describing how heart disease affects the group, which could mean determining the age group or gender that heart disease affects the most. Researchers in these studies often are more interested in describing the characteristics of the group, instead of finding the cause of heart disease.

Analytical cross-sectional studies

Analytical cross-sectional studies refer to when researchers compare outcomes between related and unrelated criteria. They do this to see whether they can determine new information based on the criteria. For example, researchers may want to determine whether smoke and fire cause lung disease in firefighters. This can create inaccurate results, though, since factors other than inhaling smoke can cause lung disease, such as genetics or lifestyle choices.

Repeated cross-section studies

Repeated cross-section studies are when researchers complete a follow-up test to gather more data about their original test. This study uses the same population, but they typically conduct the following test on a different group of people. If the researchers complete this process multiple times, they can collect cross-sectional data about the population.

Researchers can gather a large amount of data from the first study, but they may want to conduct the same test on new participants in six or 12 months to see how the results change or vary. This can help them account for more variables and reduce the effects of randomness in the study.