Within the context of a cross-sectional study, information is collected on the entire study population at a single point of time. The goal of collecting this data is to examine the relationship of a specific target point, such as a disease, and other variables of interest within the population group.
That allows this type of study to provide an overall snapshot of the characteristics, frequency, or occurrence of the targeted data point, at any given time, within the population group being studied.
Using this methodology, it becomes possible to assess the burden of the population when they encounter the targeted data point being studied. That makes it a useful option when determining the allocation of resources to the population should the incident occur.
There are two types of cross-sectional studies: descriptive and analytical. Descriptive studies are used to assess distribution and frequency. Analytical studies are used to investigate associations.
Here are the advantages and disadvantages of cross-sectional studies to consider.
List of the Advantages of a Cross-Sectional Study
1. It is an affordable study method.
Cross-sectional studies are much cheaper to perform than other options that are available to researchers. That is because there is no follow-up required with this type of research. Once the information is collected from the entire study group, it can be analyzed because only that single time reference is being considered. That allows for useful information to be obtained without a potentially risky initial investment.
2. It provides good controls over the measurement process.
Like any other study, a cross-sectional study is only as good as the measurement processes which are implemented to collect information. Because there are zero long-term considerations involved with this type of study, researchers have a better control over the ascertainment process. The data obtained in the study can be easily measured and applied to population groups because controls are easier to implement.
3. It offers a completeness with key data points.
Although any study type can miss key data points, the risks of doing so within a cross-sectional study are much less. The structure of this study type is what leads it toward this advantage. Researchers are able to maximize the completeness of their key data points because they are looking at an entire population group in one specific time point. That leads to fewer mistakes or variables because data isn’t being collected multiple times. All the variables are collected only once.
4. It provides better precision in the sampling process.
When researchers look at an entire population, they sample certain groups, areas, or individuals, then correlate the data from the subgroups to everyone else. These stratification samples can lead to a rate of error within the research because certain variables within each subgroup may have local influences that do not apply to other subgroups. With cross-sectional studies, the entire population is considered at once, which forces researchers to consider all local influences at the time data is being gathered. That means a lower error rate is achieved within the data because there is a higher level of control involved.
5. It allows anyone to analyze the data to draw conclusions.
The information that is obtained through cross-sectional studies is suitable for a secondary data analysis. That means researchers can collect the data for their own purposes, then another set of researchers can use the same data for a different purpose. That allows the information collected about a general population group to be have ongoing usefulness, which maximizes the investment value of the collected data points.
6. It provides researchers access to multiple outcomes and exposures.
Cross-sectional studies permit researchers an opportunity to study multiple outcomes and exposures simultaneously. That allows multiple variables to be accessed simultaneously, which increases the accuracy of an assessment on the burdens of a data point within the specific population group. When there are higher levels of accuracy, then resource allocation is more accurate, which reduces the risks of falling through the cracks for some people within a population group.
7. It offers information for a descriptive analysis.
Cross-sectional studies are useful when a general hypothesis must be generated for situations facing a population group. The research provides better descriptions for the data points which occur, making it possible for the information to lead toward possible solutions that may not have been previously considered.
8. It provides a foundation for future research opportunities.
Although cross-sectional studies do not look at the reasons why certain events happen in a population group, it can provide a foundation for future studies to look at this issue. This type of research is designed to discover clues about population groups that can then help other study types be able to determine why an illness occurs or how different data points are preferred over others.
List of the Disadvantages of a Cross-Sectional Study
1. It is only effective when it represents the entire population.
Proper cross-sectional studies must be representative of an entire population being studied. If no such representation exists, then the findings from the research will not have validity. Some researchers may be hesitant to reach out to certain groups, such as the homeless, people in prison, or homebound individuals, which would throw off the generalizations that could be made about the population group because the information would be incomplete.
2. It requires a larger sample size to provide accuracy.
Because the entire population group is being studied at once, a larger sample size is typically required in cross-sectional studies compared to other study types. If a small sample is taken, then the risk of error dramatically increases because the results could be due to chance or coincidence alone. Because a larger sample size is required, there are cost considerations that researchers must take into account as well.
3. It allows bias to affect results.
Receiving a non-response when conducting a cross-sectional study can result in bias when outcomes are being measured. It becomes quite problematic if the characteristics of those who do not respond are different than those who do respond within the context of the generalized population group being studied. Attempting to draw conclusions from this type of data is almost useless because the bias eliminates one entire subgroup from the research. Information misclassification can lead to bias within this type of study as well.
4 It offers no control over choice or purpose.
When the information from a cross-sectional study is being used for secondary data analysis, the bias of a researcher may influence the data without the secondary studies realizing it. There is no control over how the data is collected when accessing it in a secondary way. For that reason, information about the method of information collection, the purpose of collecting the data, and the choices made must be included during a transfer to secondary data analysis for the information to be useful.
5. It does not offer data about casual relationships.
Cross-sectional studies are designed to provide correlated data that can be used to draw conclusions about population groups. If casual relationships are present within the population, then this type of study cannot provide any information about that relationship. It can only let researchers see that the relationship is there for some reason. Two different data points are examined simultaneously with equal weight, even if the relationship may not be weighted when applied to the population.
6. It requires a defined population group to be successful.
Unless the population group is large enough, with proper definitions in place, then the information collected through this study type may not be reliable. This disadvantage is often present when the information points are examining rare exposures or outcomes within the population group. Without clear definitions in these circumstances, inappropriate conclusions could be drawn from the collected data, which may encourage a response that is not required within the population group.
7. It is unable to measure incidence.
Cross-sectional studies look at the information that is being collected. It does look at why the specific data points occur in the population. That can limit the availability of an outcome for researchers because they are not always able to determine why certain events occur within the population. It only measures incidence, not what triggers the data in the first place.
The advantages and disadvantages of cross-sectional studies should be carefully considered when determining which study type to pursue. Although it benefits from a massive simultaneous collection effort of data points within a specific population, there are short-term incidents and recall bias issues that can affect results.