Cross-sectional studies pros and cons (with definition)

By Indeed Editorial Team

Published 3 June 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

A cross-sectional study is a method of research which involves collecting data at a specific point in time. Researchers frequently use this type of study as it can provide valuable information about a population at a given point in time. When you plan a cross-sectional study, it's important to consider the benefits and challenges of this type of study. In this article, we define what a cross-sectional study is, look at an example of one and explore the advantages and disadvantages of this type of research.

Related: What are the different types of research methodology?

What is a cross-sectional study?

A cross-sectional study is a type of research study that collects data from a group of people simultaneously at a specific point in time. Cross-sectional studies can provide information, such as the characteristics of a specific population, the prevalence of a data point like a disease and the relationships between variables. Methods of data collection for cross-sectional studies include surveys, interviews, focus groups and observations.

Cross-sectional studies are an important tool for researchers who want to gain a better understanding of a particular population. As such, researchers commonly use them in a variety of fields, such as health, psychology, sociology and economics. This type of study does have its limitations and there are inherent cross-sectional studies pros and cons.

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Cross-sectional studies pros and cons

The following are some cross-sectional studies pros and cons:


Here are some advantages of cross-sectional studies:

A convenient way to collect large amounts of data

Researchers can conduct cross-sectional studies quickly, which makes them convenient for researchers. This is because they can collect data from a large number of people simultaneously. Researchers often use cross-sectional studies when there isn't a lot of time or when researchers want to study a large population.

Relatively inexpensive

Cross-sectional research is frequently less expensive than other types of study. This is partly due to them not requiring any follow-up data collection. Many cross-sectional studies use questionnaires, which researchers can distribute to a large population electronically or in person, at little cost.

Identify relationships between variables

Cross-sectional studies can help to explain how different variables relate to each other. Researchers could use a cross-sectional study to examine the relationship between two variables, such as sedentary work and heart disease. The researchers can make hypotheses from any relationships they uncover between different variables.

Easier experimental control groups

It's relatively easy to create experimental control groups when conducting a cross-sectional study. This is because researchers can easily divide people into groups based on the characteristics that they're studying. For example, if researchers want to study the association between sedentary work and heart disease, they can easily create a control group of people who do not have sedentary work to compare against a group of people whose work is sedentary.

Identify risk factors for a disease

Researchers can use cross-sectional studies to identify risk factors for health conditions. For example, if a cross-sectional study showed that there was a relationship between sedentary work and heart disease, this would suggest that sedentary work is a risk factor for heart disease. This type of information is important as they can use it to design interventions to reduce the risk of developing a particular health condition.

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Study multiple factors and outcomes

Cross-sectional studies enable researchers to study multiple factors and outcomes simultaneously. This can be beneficial when trying to understand complex phenomena that many different factors may influence. For example, a cross-sectional study of attitudes towards environmentalism could consider a range of factors, including age, education, income and political affiliation.

Representative samples allow for generalisations

Researchers can use cross-sectional studies to make generalisations about a population because they use a representative sample of the population. This enables researchers to then generalise the results from the data collected to the wider population and make inferences that are relevant to large groups of people. For example, a cross-sectional study of university students showed that a high proportion of students had anxiety, researchers could then generalise this to the wider population of university students.

Work well as a preliminary step before further research

Researchers can use a cross-sectional study as a preliminary step before conducting further research. This is because cross-sectional studies can identify relationships between variables, which helps researchers generate hypotheses for further research. For example, if they found a relationship between a rare disease and a particular exposure in a cross-sectional study, they could conduct further studies to investigate this relationship.

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The disadvantages of a cross-sectional study include:

Can't infer causality

Cross-sectional studies can't determine cause-and-effect relationships. They can show a relationship between two variables, but it's not possible to say that one variable caused another. For example, a cross-sectional study might show that there is a relationship between a disease and a particular exposure, but it can't show that the particular exposure caused the disease.

Require a large sample size for accuracy

To accurately represent a population, cross-sectional studies require a large sample size. To accurately represent an entire population, such as a country, the sample size would be very large. Such a large sample can be difficult to achieve and samples that don't accurately represent a population can make the study less reliable.

May not include certain groups

It can be difficult to include all the relevant groups in a population in a cross-sectional study. You may exclude groups, such as the homeless, prisoners, people with mental illness and people who are difficult to reach. This can make the results of the study less reliable as it doesn't examine an accurate representation of the population.

Related: What is a biased sample? (Examples and tips to avoid it)

Susceptible to non-response selection bias

Cross-sectional studies that rely on people to respond to surveys or questionnaires are particularly prone to non-response bias. This is when people who don't respond to the survey or questionnaire are different from those who respond. When a certain section of society is more likely to participate in the study than others, it creates an over-representation of certain groups, which can make the results of the study less reliable.

Susceptible to information biases

Recall bias is another type of bias that can occur in cross-sectional studies. Participants may not accurately recall information or they may not answer the questions truthfully. Social desirability bias, where people want to give an answer that makes them look good, is another form of bias that can be present. Biases such as these can undermine the results of a cross-sectional study.

Make it difficult to interpret identified relationships

The results of cross-sectional studies can be difficult to interpret as they only identify relationships between variables, not cause and effect. This creates room to interpret the relationships between variables inaccurately. To accurately assess a cross-sectional study's findings, researchers often conduct further research.

Not suitable for study over a period of time

Cross-sectional studies only measure data at a single point in time. This means that they're not suitable for studying behaviour over a period of time. If you want to study behaviour over time, it's better to use a longitudinal study.

Not suitable for studying rare diseases

A reliable study of rare diseases requires a substantial sample size, which researchers are unlikely to get with a cross-sectional study. If the disease is very rare, there may not be any occurrences of it in the population in the study. This makes it impossible to conduct a cross-sectional study of the disease.

Example of a cross-sectional study

An example of a cross-sectional study is researchers studying the occurrence of a common disease across different age groups. To do this, they collect data from people of differing ages and record the occurrence of the disease within each age group. This would show the prevalence of the disease in each age group at a specific point in time and allow researchers to see any trends, such as an increase or decrease in the occurrence of disease with age.

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