Cross-sectional Study- Definition and Examples

Examples of Cross-sectional Studies

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.

National Survey on Drug Use and Health (NSDUH)

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.

The Great Migration study

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.

Framingham Heart Study

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.

Types of Cross-sectional Studies

There are three types of cross-sectional designs: simple, stratified, and clustered.

Simple design

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.

Stratified design

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.

Clustered design

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.

The Research Methods used in Cross-sectional Studies

Some of the research methods used in cross-sectional studies are:

• Case questionnaires
• Interviews
• Psychological tests.

There are three types of cross-sectional studies in terms of research:

1. Point-in-time cross-sectional studies
2. Directly observed cross-sectional studies
3. Survey research.

Point-in-time cross-sectional studies

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.

Directly observed cross-sectional studies

Indirectly 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

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.

Survey design

The two main elements of a survey are the questionnaire and the sampling technique.

Questionnaire

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.

What Problems Can Occur in Cross-sectional Studies?

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.

Cross-sectional study advantages

• 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.

Cross-sectional Study Disadvantages

• 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.