Advantages and disadvantages of cross-sectional studies

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Cross-sectional studies are surveys that collate and present information on health-related issues such as diseases, risk factors and health care resources and personnel.

A cross-sectional survey may be used to measure the prevalence, burden or cost of a disease or the number of doctors, nurses or hospital beds available in a specific region. Whatever a cross-sectional study is examining, it’s examining it within the context of a predefined population sample and at a particular point in time. Like all research methods, cross-sectional studies have both advantages and disadvantages.


As cross-sectional studies don’t take long to complete, and don’t require any follow-up actions they can be very cost-effective. However, such surveys collect data from a large number of subjects dispersed across a large geographical area. This can make cross-sectional studies costly in comparison to research methods that involve fewer people and locations.


With cross-sectional research, you can collate data on many different variables in just the one study. This enables you to examine whether several variables are associated with one another without having to conduct multiple studies. The downside is you can only make associations between variables; you can’t actually prove that one variable is causing changes in the other variables. This is less of a problem if you have the time and resources to follow up the cross-sectional study with a different type of study, such as a cohort study, that is able to examine more closely the issue of causality amongst the variables.


The advantage of cross-sectional surveys focusing on one particular point in time is that they provide you with a snapshot of the situation at a specific moment in time. You can look at several studies conducted on the same subject and in the same location but at different times to compare different snapshots in time. The disadvantage of a study being time bound in this way is that it has difficulty in determining the correct chronological sequence of events. It cannot tell which variable out of several started changing first.


Although the cross-sectional study isn’t based on a hypothesis as such, its findings on potential associations between variables are able to generate hypotheses to base future studies upon. However, a cross-sectional survey in itself has difficulty in disproving rival studies’ hypotheses as it can neither confirm nor disprove the existence of causal relationships between specific variables.