There are different types of research studies used in different fields of science. They differ in methodology, when they are done, and how they are used for statistical analysis. In this article, we’ll discuss the cross-sectional study and give you some examples.
A cross-sectional study is an observational study that involves a sample from a population or a representative subset. In other words, cross-sectional studies do not follow individuals over time. The researchers collect data from individuals at a specific point in time.
The cross-sectional study applies the cross-sectional study design in which single research is done to a group of subjects at a given time.
To answer this question, we need to understand the point of time when scientists do their research. The timing details are essential in differentiating differents research study methods.
For cross-sectional studies, researchers usually collect data at three different points during the study. These are:
- At the very beginning of a study, when they are recruiting subjects for their research.
- During the experimental phase
- At the end of the study, after they have finished collecting all their data.
Some situations that necessitate cross-sectional studies are as follows:
- Scientists need to conduct cross-sectional studies when they are collecting their data at different points in time. For example, they can’t do that if they are conducting longitudinal studies or cohort studies.
- When there’s a large population to study, it becomes practically impossible to track individuals’ data over time. In that case, too, scientists conduct cross-sectional studies.
- It is also possible that scientists want to study different groups of individuals. In cohort or longitudinal studies, it might not be possible to do that because they have to track individuals for a long period of time. In that case, too, cross-sectional studies are used.
Some specific examples of cross-sectional studies include:
- The National Survey on Drug Use and Health (NSDUH)
- The Great Migration study
- The Framingham Heart Study.
This survey assesses the prevalence, patterns, and consequences of alcohol, tobacco, and illegal drug use among individuals aged 12 years or older in the United States.
NSDUH data are used to monitor the prevalence of these behaviors, determine trends in the underlying causes of drug use, and develop programs to help reduce abuse of these substances.
During this survey, researchers interviewed about 70,000 people. NSDUH uses a sampling technique called clustered area probability proportionate to size (APPROACH), which ensures that certain demographic groups are well represented in the results.
Sociologist E.R. Lewis in Chicago conducted this survey during the 1930s to study the migration of African Americans from the Southern US to the Northern States.
This survey was critical in African-American integration. It showed that black Americans who migrated during this period were more likely to be unemployed than their southern counterparts.
The migration study used a simple random sampling technique, which allowed researchers to describe the larger population.
The Framingham Heart Study began in 1948. Researchers enrolled 5,209 men and women from Framingham, Massachusetts, to study their cardiovascular health. The researchers conducted intensive follow-up examinations roughly every two years to track their health.
The Framingham Heart Study found that risk factors such as smoking, obesity, hypertension, and diabetes are related to cardiovascular disease. Researchers also found that subjects who have not exhibited risk factors are unlikely to develop heart problems.
General cross-sectional study examples are:
- In a study on the relationship between age and blood pressure, researchers measure the participants’ blood pressure.
- In a biostatistics class at a university, researchers ask students to complete a survey on their knowledge of the material presented in class.
- In a community health center, researchers interviewed 100 women to measure their education and family income.
- In a school district, researchers ask 100 eighth-graders to rate their level of happiness on a scale of 1-10.
There are three types of cross-sectional designs: simple, stratified and clustered.
It is a kind of cross-sectional design that takes into account the confounding effects.
An example of a simple design is the five-year trend study on the percentage of smokers over time. This kind of study tells us if there’s an increase or decrease in the smoking rate over five years.
It is a kind of cross-sectional design that allows stratification by including subgroups in the sample. This design works best for populations that lack homogeneity in various aspects.
For example, if studying different age groups, the study would include only age-stratified subgroups.
The sample is drawn from clusters. Cluster sampling is used under some abnormal conditions. During selection, the assumption is that the subjects in the sets are homogenous regarding the characteristic being measured.
For example, children in the same school would be homogenous with respect to some features such as age, sex, etc. Homogeneity enables researchers to generalize the study in a particular group, making work easier.
Some of the research methods used in cross-sectional studies are:
- Case questionnaires
- Psychological tests.
There are three types of cross-sectional studies in terms of research:
- Point-in-time cross-sectional studies
- Directly observed cross-sectional studies
- Survey research.
Point-in-time cross-sectional studies are the simplest forms of these studies. Researchers observe subjects at a single point in time. For example, they may observe people crossing the street at a specific traffic light throughout one day.
These cross-sectional studies are mainly used to determine whether or not something is related to a specific factor. For example, researchers may want to know if there’s a relationship between drinking and traffic accidents.
To find out, they could observe drivers’ behavior at a specific intersection. The researchers would determine how many drivers were drinking and engaging in other risky behaviors like texting or speeding.
In directly observed cross-sectional studies, researchers observe subjects at a single point in time. The difference between this type of study and the one mentioned above is that researchers also directly observe the subject.
Researchers may follow people around throughout a single day to record their behavior. The sampling technique is dictated by the research question and the study population.
Survey research is perhaps the most common type of cross-sectional study. Researchers approach subjects and ask them about their behavior, opinions, beliefs, and the like. The information that researchers collect from surveys is used to make conclusions about a larger group of people.
If a researcher surveys 100 students on presidential opinion, the information is used to make conclusions about the country’s population. The views of the majority of citizens go unconsidered. This method allows for a lot of biases, especially in matters where people hold highly diverse opinions.
Unlike the point-in-time cross-sectional studies, surveys can be used to make inferences about the group of people at any moment in time.
The two main elements of a survey are the questionnaire and the sampling technique.
The questionnaire is the set of questions that researchers ask subjects during a survey. Researchers should try to make their questionnaires short and easy to answer, especially if they are talking to children.
The sampling technique
There are two important elements of the sampling technique for survey research:
- The sample size
- The sampling technique.
The sample size is the number of people from a given population that researchers survey. The sample size has a massive impact on the ability of the findings to describe or represent the larger population.
The sampling technique describes how researchers select subjects for their studies. There are two main types of sampling techniques:
- Probability-based sampling techniques, in which researchers select subjects so that specific characteristics of the population are well represented.
- Non-probability-based sampling techniques, in which researchers use judgment or other criteria to select subjects for their study.
Several sampling techniques fall under the non-probability-based category, including quota, purposive, and snowball sampling.
Quota sampling- researchers divide their target population into homogenous groups (quotas). They then select a specific number of people from each group to survey. This type of sampling is used when the population size and characteristics are already well-known.
Purposive sampling– researchers select subjects they judge the most appropriate for their study based on specific criteria. For example, researchers may want to poll a certain number of workers from each industry in a particular location.
Snowball sampling– researchers begin with a small group of people they know and then ask every new person to recommend another. Researchers keep repeating this process until they’ve gathered the desired number of subjects.
This type of sampling is useful when researchers don’t have a clear sample in mind but still want to survey a specific set of people.
The major problem with all types of cross-sectional studies is that they cannot establish causation. Since these studies do not follow subjects over time, it is impossible to know which variables directly cause the outcomes researchers to observe.
For example, in the Framingham Heart Study, researchers found that obesity was related to an increased risk of cardiovascular disease. However, we can’t know if obesity directly causes cardiovascular disease. Obese individuals might have other risk factors that are the real cause of cardiovascular issues.
Another problem with cross-sectional studies is that they can produce biased results if researchers do not carefully select their sample. Sometimes the sample of people used in a study does not accurately represent the population. In such cases, researchers will have a hard time making inferences about that group as a whole.
- It allows you to identify disorders that are not evident. For example, some mental health disorders are only identified through cross-sectional surveys.
- You can acquire baseline data for use in future longitudinal studies. For example, you might run a cross-sectional survey on the physical activity levels of adults and children. This information can be used as a baseline in a longitudinal study to track changes in activity levels over time.
- A cross-sectional study is efficient. It allows you to gather data that can help you decide what other types of studies are needed.
- A cross-sectional study is cheap. It requires less time and funds than other types of studies.
- A cross-sectional study is suitable for screening. It can determine if further, more in-depth research needs to be done or not.
- You cannot acquire longitudinal data. The lack of continuity in cross-sectional studies limits them to non-longitudinal data.
- Due to a lack of follow-up information, you can’t use cross-sectional studies to analyze causal relationships between different variables.
- Cross-sectional studies do not use a standard data collection method, so the quality and accuracy of data can vary between studies.
- A cross-sectional study cannot be used to make inferences about individuals. For example, you can’t say that people who are non-smokers will never develop lung cancer.
- Cross-sectional studies can produce biased results if the sample selected does not accurately represent the population. For example, only college graduates may be surveyed, so the data might not accurately portray the opinions of all Americans.
- It isn’t easy to control external factors that could affect the measurements in a cross-sectional study.
- A cross-sectional study does not allow you to make conclusions about the cause and effect relationships between your variables.
- The cross-sectional study results cannot be applied to other groups since the study only assesses a particular group at a given time.
- A cross-sectional study does not measure change over time. For example, changes in blood pressure measurements would be studied in a longitudinal study and not a cross-sectional study.
It is important to distinguish between cross-sectional and longitudinal studies. While the two types of research are similar, they differ concerning the timing of the data collection.
- In a cross-sectional study, data is collected at a given point in time. In a longitudinal study, the data is collected over a period of time.
- Longitudinal studies can track change over time, while cross-sectional studies cannot.
- Cross-sectional studies are more commonly used in social sciences, education, and health. Longitudinal studies are usually done in life sciences.
- The outcome of cross-sectional studies may be affected by external factors that are out of the control of researchers. Longitudinal studies allow researchers to control for extraneous variables and observe changes over time.
- To conduct a cross-sectional study, the variable of interest should be evident in the population to be studied. In a longitudinal study, the researcher must wait for the outcome (dependent variable).
A cross-sectional study is a type of research that gathers data from individuals at one point in time. You can’t use this study to analyze causal relationships between different variables or changes over time. However, it’s excellent for screening and making decisions about what other types of studies are needed.
Cross-sectional studies are typically used in social sciences, education, health care fields. Longitudinal studies are usually done in life sciences like biology and medicine. The choice between the two depends on your field (and which questions you’re trying to answer). It also depends on how much control you need over external factors affecting your measurements.