Cross-sectional studies are defined as a type of observational research that analyzes data from variables collected at a given time in a population sample or in a predefined subset.
This type of study is also known as a cross-sectional analysis, cross-sectional study, or prevalence study. Although cross-sectional research does not involve conducting experiments, researchers often use it to understand results in the physical and social sciences and in many business sectors.
In this article we analyze what is and what is not a cross-sectional study. We will review examples of cross-sectional studies and explain the types of cross-sectional research that can be conducted. We’ll also look at the benefits that make this research useful for your work.
Characteristics of Cross-Sectional Studies
Some of the fundamental characteristics of a cross-sectional study are:
Researchers can conduct a cross-sectional study with the same set of variables over a given period.
Similar research may look at the same variable of interest, but each study looks at a new set of subjects.
Cross-sectional analysis evaluates topics during a single instance with a defined start and end point, unlike longitudinal studies, in which variables can change during extensive research.
Cross-sectional studies allow the researcher to observe an independent variable as the focus of the cross-sectional study and one or more dependent variables.
Do you want a suitable metaphor for a cross-sectional study? Think of a snapshot of a group of people at an event, for example, a family reunion. The people in that extended family are used to determine what is happening in real time, in the moment. All people have at least one variable in common – being relatives – and multiple variables that they do not share. From that starting point, all kinds of observations and analyses can be made. Therefore, this type of research “takes the pulse” of population data at any given time.
This type of research can also be used to map the predominant variables that exist at a specific point; for example, cross-sectional data on past drinking habits and a current diagnosis of liver failure.
How to conduct a Cross-Sectional Study
To conduct a cross-sectional study, you can rely on data gathered by another source or collect your own. Governments often make cross-cutting datasets available to the public online.
Among the most prominent examples are the censuses of several countries, such as the United States or France, which offer a transversal snapshot of the country’s residents on important measures. International organizations such as the World Health Organization or the World Bank also offer access to cross-cutting datasets on their websites.
However, these datasets are often aggregated at the regional level, which may impede the investigation of certain research issues. It will also be limited to the variables that the original researchers decided to study.
If you want to choose the variables of your study and analyze the data at the individual level, you can collect your own data using research methods such as surveys. It is important to carefully design the questions and choose the sample.
Examples of Cross-Sectional Studies
The data collected in a cross-sectional study refer to subjects or participants who are similar in all variables except the one being examined. This variable remains constant throughout the cross-sectional study. Unlike a longitudinal study, in which variables can change throughout the research. Consider these examples for clarity:
In retail, cross-sectional research can be conducted on men and women in a specific age range to reveal similarities and differences in gender-related spending trends.
In business, researchers can conduct a cross-sectional study to understand how people of different socioeconomic status in a geographic segment respond to a change in an offering.
Healthcare scientists can use cross-sectional research to understand how U.S. children ages 2 to 12 are prone to calcium deficiency.
A cross-sectional study at school is particularly useful for understanding how students who scored within a particular grade range in the same preliminary courses perform with a new curriculum.
The definition of cross-sectional study in psychology is research that involves different groups of people who do not share the same variable of interest (such as the variable you are focusing on), but who do share other relevant variables. These could include age range, gender identity, socioeconomic status, etc.
Cross-sectional research allows scholars and strategists to quickly collect actionable data that helps make decisions and deliver products or services.
Types of Cross-Sectional Studies
When conducting a cross-sectional research study, you will participate in one or both types of research: descriptive or analytical. Read their descriptions to see how they can be applied to your work.
A cross-sectional study can be fully descriptive. A cross-sectional descriptive study evaluates the frequency, amplitude or severity of the variable of interest in a specific demographic group. Think of the retail example we mentioned earlier. In that example of a cross-sectional study, researchers make focused observations to identify spending trends. They can use those results to develop products and services and market existing offerings. They’re not necessarily looking at why those gender trends occur in the first place.
Cross-sectional analytical research investigates the association between two related or unrelated parameters. However, this methodology is not entirely infallible, because the external variables and results are simultaneous, and its studies are also infallible. For example, to validate whether coal miners can develop bronchitis, only the variables of a mine are taken into account. What it does not take into account is that the predisposition to bronchitis could be hereditary, or that this health condition could be present in coal workers before their employment in the mine. Other medical research has shown that coal mining is harmful to the lungs, but he doesn’t want those assumptions to skew his current study.
In a real-life cross-sectional study, researchers typically use both descriptive and analytical research methods.
Advantages of a Cross-Sectional Study
Curious to know if cross-sectional research is the right approach for your next study? Surveys are an effective and revealing way to collect data. Check out some of the fundamental advantages of conducting research online using a cross-sectional study and see if it fits your needs.
Relatively quick to perform.
Researchers can collect all the variables at once.
Multiple outcomes can be investigated at once.
The prevalence of all factors can be measured.
Suitable for descriptive analysis.
Researchers can use it as a springboard for other research.
If you’re looking for an approach that studies subjects and variables over time, you might prefer a longitudinal study. In addition, you could continue your cross-sectional research with a longitudinal study.
Disadvantages of a Cross-Sectional Study
It is difficult to establish cause-and-effect relationships using cross-sectional studies, as they represent only a single measurement of alleged cause and effect.
Since cross-sectional studies only study a single moment in time, they cannot be used to analyze behavior over a period of time or establish long-term trends.
The timing of the transverse snapshot may not be representative of the behavior of the group as a whole. For example, imagine that you are studying the impact of psychotherapy on an illness such as depression. If the depressed individuals in your sample started therapy shortly before data collection, then it might appear that the therapy causes depression even though it is effective in the long run.
It’s easy to confuse the two research methods, so we’ve broken it down here:
Cross-sectional studies vs. Longitudinal
Although both are quantitative research methods, there are some differences when we compare and contrast cross-sectional and longitudinal studies.
In cross-sectional studies, researchers collect the variables at a given time. Longitudinal studies span several sessions and the variables may change.
Researchers prefer cross-sectional studies to find commonalities among variables. Even so, they use longitudinal studies due to their nature, to further dissect the research of the cross-sectional study.
More examples of Cross-Sectional Studies
Now that you better understand what cross-sectional research is and how to conduct your studies, let’s look at two examples in more detail:
Cross-sectional study example 1: Gender and telephone sales
Phone companies rely on advanced and innovative features to drive sales. Research conducted by a phone manufacturer across the target demographic validates the expected adoption rate and potential phone sales. In a cross-sectional study, researchers enroll men and women from different regions and age ranges for research. If the results show that Asian women wouldn’t buy the phone because it’s bulky, the mobile phone company can tweak the design to make it less bulky. You can also develop and market a smaller phone to appeal to a wider group of women.
Cross-Sectional Study Example 2: Men and Cancer
Another example of a cross-sectional study would be a medical study examining the prevalence of cancer among a defined population. The researcher can assess people of different ages, ethnicities, geographic locations, and social backgrounds. If a significant number of men in a certain age group are more likely to suffer from the disease, the researcher may conduct more studies to understand their reasons. In this case, it is best to use a longitudinal study to study the same participants over time.
Descriptive Studies vs. Analytical Studies
Cross-sectional studies can be used for both analytical and descriptive purposes:
An analytical study tries to answer how or why a certain result can occur.
A descriptive study only summarizes this result using descriptive statistics.
For example, you are studying childhood obesity. A descriptive study could look at the prevalence of obesity in children, while an analytical study could examine exercise and eating habits, as well as obesity levels, to explain why some children are much more likely to be obese than others.
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