Cross-Sectional Study | Definition & Examples

A cross-sectional study is an observational research design in which data is collected from multiple participants at a single point in time. It is the opposite of a longitudinal study, where data are collected from participants repeatedly over a period of time.

Cross-sectional studies are commonly used across a wide variety of fields, including psychology, economics, and medicine. They are particularly useful for determining the prevalence of a specific outcome or condition in a population.

What is a cross-sectional study?

Cross-sectional studies involve collecting data from a group of participants at a single point in time. They provide a “snapshot” of this sample that can tell researchers more about the overall population or help them formulate hypotheses.

Cross-sectional studies typically explore the prevalence of an outcome variable (how often it occurs). The “outcome” is the result or effect the researcher is interested in. This outcome might be a characteristic, medical condition, or behavior.

Cross-sectional studies can also be used to determine an outcome’s connection to “exposure” variables. The “exposures” are factors that might contribute to the outcome. In other words, the exposure variables are the “cause,” and the outcome variable is the “effect.”

A cross-sectional study is a type of observational research—data are collected without researcher interference. The opposite of observational research is experimental research, where researchers test hypotheses by manipulating independent variables and measuring their impact on dependent variables.

Types of cross-sectional studies

Cross-sectional studies can be classified as descriptive or analytical.

As its name suggests, a descriptive cross-sectional study describes the traits of a population at a point in time. In a descriptive study, you can measure the prevalence of an outcome in your sample (how frequently the outcome occurs).

An analytical cross-sectional study goes one step further and explores which factors contribute to an outcome. Researchers can use this information to calculate odds ratios, fit logistic regression models, and generate hypotheses.

Similar research designs

Cross-sectional studies are just one example of observational research. Other types include case-control studies and cohort studies.

In case-control studies, participants with a certain outcome or condition (the cases) are compared to a group of participants who are similar but do not have this outcome (the controls). These groups are usually compared retrospectively—the researcher selects both groups and then compares their histories, working backwards to determine factors that might have led to their different outcomes.

In cohort studies, participants are recruited based on their “exposure status” (whether they’ve experienced something of interest) and are followed over time to measure any effects of this exposure. Cohort studies can be prospective (data is actively collected after enrollment) or retrospective (researchers analyze data that have already been used for another study).

Cross-sectional study examples

The following examples illustrate the differences between descriptive cross-sectional studies (which describe the prevalence of an outcome in a population) and analytical cross-sectional studies (which identify risk factors associated with an outcome).

Consider how the previous example could be extended into an analytical study.

Strengths and weaknesses of cross-sectional studies

Like any research method, cross-sectional studies have benefits and disadvantages.

Strengths of cross-sectional studies

Some strengths of cross-sectional studies are outlined below.

• Fast and inexpensive: Because all data are collected at once, cross-sectional studies take very little time to run and do not cost as much as other types of research.
• Can often use existing datasets: Cross-sectional studies can be conducted retrospectively, using data that was collected for other work.
• Ethical: Because cross-sectional studies are observational and do not involve any variable manipulation (for example, giving one group a medication and a second group a placebo), ethical issues are not usually a concern.
• Good for hypothesis generation: Analytical cross-sectional studies allow researchers to generate hypotheses about what factors might be associated with an outcome.

Weaknesses of cross-sectional studies

• Cannot infer causality: Because data are collected at a single point in time, it’s difficult to establish a cause-and-effect relationship from a cross-sectional study.
• Susceptible to selection bias: Depending on your sampling technique (e.g., simple random sampling, cluster sampling, or stratified sampling), some people may be more likely to be included than others.
• Susceptible to response bias: Research participants often misreport information. Factors like recency bias (people’s tendency to favor recent information or experiences more heavily than older ones) or the availability heuristic (people’s tendency to believe easy-to-remember information is more likely) may influence findings.

Frequently asked questions about cross-sectional studies

What is the difference between a cross sectional study and a longitudinal study?

In a cross-sectional study, researchers collect data from individuals at a single point in time.

In a longitudinal study, researchers collect data from individuals repeatedly or continuously over an extended period of time (often years).

Cross-sectional studies are faster and less expensive to conduct than longitudinal studies. However, because they collect data at a single point in time, cross-sectional studies are not the best option for establishing cause-and-effect relationships.

A common practice is to conduct a cross-sectional study to generate hypotheses. You can then use this information to design a longitudinal study.

What is the difference between a case-control study and a cross-sectional study?

Case-control and cross-sectional studies differ in how participants are recruited and the types of questions they can answer.

In a case-control study, participants are recruited based on outcome status. Data are collected from two groups. The “case” group has an outcome of interest (e.g., a diabetes diagnosis), and the “control” group does not. These groups can be compared to understand what differences may have contributed to the outcome.

In a cross-sectional study, a sample of participants is recruited from a population without considering outcome status (often using random sampling). Data on outcomes and risk factors are then collected simultaneously from the sample. Cross-sectional studies are helpful for assessing the prevalence of an outcome.

What is the difference between a cross-sectional study and a cohort study?

In a cross-sectional study, researchers recruit a group of participants (often using random sampling), then measure exposure variables (e.g., risk factors—such as smoking) and outcomes (e.g., lung cancer). Cross-sectional studies are helpful for determining the prevalence of an outcome in a population.

Cohort studies instead recruit participants based on their exposure status. Cohort studies are longitudinal. They follow participants over time to observe the effect of this exposure (e.g., how many people who were exposed to asbestos go on to develop lung cancer). Cohort studies are helpful for establishing cause-and-effect relationships.

What is a repeated cross-sectional study?

In a repeated cross-sectional study, the same population is studied at multiple time points. At each time point, data are collected from a different sample of the population. 

A repeated cross-sectional study is a type of longitudinal study because data are collected repeatedly over a period of time. 

However, as the name suggests, it also resembles a cross-sectional study. Data are obtained from each group of participants at a single time point, and this process is repeated several times.

Repeated cross-sectional studies are helpful for studying changes in a population over time.