Is a type of research where individuals of different ages are compared at one time?

Longitudinal studies differ from one-off, or cross-sectional, studies. The main difference is that cross-sectional studies interview a fresh sample of people each time they are carried out, whereas longitudinal studies follow the same sample of people over time.

Features of longitudinal vs cross-sectional studies

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Some cross-sectional studies take place regularly, each time including a large number of repeat questions. For example, the British Social Attitudes Survey is a repeat cross-sectional study that has been carried out nearly every year since 1983. It provides excellent data about how Britain’s attitudes and values have changed (or not changed) over time.

Repeating the same questions in each round allows researchers to look at how society as a whole has changed over time. But because the questions are asked of a new sample every time, these studies can only reveal change at an aggregate level – they can shed little light on who has changed, or how or why.

For example, the 2015 British Social Attitudes (PDF) survey found that 66 per cent of people thought that “it’s everybody’s duty to vote” in a general election, down from 76 per cent in 1987.

What do these findings tell us? The data clearly show us that, overall, fewer people now than in the late-1980s think that citizens have a duty to vote. We can look at the characteristics of those who do or don’t agree with this view, and how the profile of these groups had changed over time. We can also examine how the likelihood of thinking that voting is a duty has changed among different population groups (for example, different age groups or ethnicities).

These sorts of calculations would provide some very helpful insights. But there are many things that this kind of cross-sectional data cannot tell us, but which longitudinal data would help us to address. For example:

• Which individuals changed their views about voting over the period? What are their characteristics? Do some people switch, and then switch back again?
• The British Social Attitudes findings show us the net or aggregate change over time – the difference between the proportion who thought voting was a duty in 1987 and the equivalent proportion now. But this tells us nothing about change at an individual level. For example, what proportion became more inclined to think voting is a duty and what proportion became less inclined? It is important to bear in mind that if these two proportions are similar, they could cancel one another out at a net level and make it appear that very little has changed over all, even though substantial numbers of people have changed their views.
• What factors best explain the transition away from thinking voting is a duty?

Sometimes data from longitudinal studies is analysed cross-sectionally. This means that the researcher is just focusing on the information collected at one round of the study, and not linking that information to data from earlier or later rounds.

What Is a Cross-Sectional Study?

A cross-sectional study looks at data at a single point in time. The participants in this type of study are selected based on particular variables of interest. Cross-sectional studies are often used in developmental psychology, but this method is also used in many other areas, including social science and education.

Cross-sectional studies are observational in nature and are known as descriptive research, not causal or relational, meaning that you can't use them to determine the cause of something, such as a disease. Researchers record the information that is present in a population, but they do not manipulate variables.

This type of research can be used to describe characteristics that exist in a community, but not to determine cause-and-effect relationships between different variables. This method is often used to make inferences about possible relationships or to gather preliminary data to support further research and experimentation.

Example: Researchers studying developmental psychology might select groups of people who are different ages but investigate them at one point in time. By doing this, any differences among the age groups can be attributed to age differences rather than something that happened over time.

Defining Characteristics of Cross-Sectional Studies

Some of the key characteristics of a cross-sectional study include:

• The study takes place at a single point in time
• It does not involve manipulating variables
• It allows researchers to look at numerous characteristics at once (age, income, gender, etc.)
• It's often used to look at the prevailing characteristics in a given population
• It can provide information about what is happening in a current population

Think of a cross-sectional study as a snapshot of a particular group of people at a given point in time. Unlike longitudinal studies, which look at a group of people over an extended period, cross-sectional studies are used to describe what is happening at the present moment.This type of research is frequently used to determine the prevailing characteristics in a population at a certain point in time. For example, a cross-sectional study might be used to determine if exposure to specific risk factors might correlate with particular outcomes.

A researcher might collect cross-sectional data on past smoking habits and current diagnoses of lung cancer, for example. While this type of study cannot demonstrate cause and effect, it can provide a quick look at correlations that may exist at a particular point.

For example, researchers may find that people who reported engaging in certain health behaviors were also more likely to be diagnosed with specific ailments. While a cross-sectional study cannot prove for certain that these behaviors caused the condition, such studies can point to a relationship worth investigating further.

Advantages of Cross-Sectional Studies

Cross-sectional studies are popular because they have several benefits that are useful to researchers.

Inexpensive and Fast

Cross-sectional studies typically allow researchers to collect a great deal of information quickly. Data is often obtained inexpensively using self-report surveys. Researchers are then able to amass large amounts of information from a large pool of participants.

For example, a university might post a short online survey about library usage habits among biology majors, and the responses would be recorded in a database automatically for later analysis. This is a simple, inexpensive way to encourage participation and gather data across a wide swath of individuals who fit certain criteria.

Can Assess Multiple Variables

Researchers can collect data on a few different variables to see how they affect a certain condition. For example, differences in sex, age, educational status, and income might correlate with voting tendencies or give market researchers clues about purchasing habits.

Might Prompt Further Study 

Although researchers can't use cross-sectional studies to determine causal relationships, these studies can provide useful springboards to further research. For example, when looking at a public health issue, such as whether a particular behavior might be linked to a particular illness, researchers might utilize a cross-sectional study to look for clues that can spur further experimental studies.

For example, researchers might be interested in learning how exercise influences cognitive health as people age. They might collect data from different age groups on how much exercise they get and how well they perform on cognitive tests. Conducting such a study can give researchers clues about the types of exercise that might be most beneficial to the elderly and inspire further experimental research on the subject.

Challenges of Cross-Sectional Studies

No method of research is perfect. Cross-sectional studies also have potential drawbacks.

Difficulties in Determining Causal Effects

Researchers can't always be sure that the conditions a cross-sectional study measures are the result of a particular factor's influence. In many cases, the differences among individuals could be attributed to variation among the study subjects. In this way, cause-and-effect relationships are more difficult to determine in a cross-sectional study than they are in a longitudinal study. This type of research simply doesn't allow for conclusions about causation.

For example, a study conducted some 20 years ago queried thousands of women about their consumption of diet soft drinks. The results of the study, published in the medical journal Stroke, associated diet soft drink intake with stroke risk that was greater than that of those who did not consume such beverages. In other words, those who drank lots of diet soda were more prone to strokes. However, correlation does not equal causation. The increased stroke risk might arise from any number of factors that tend to occur among those who drink diet beverages. For example, people who consume sugar-free drinks might be more likely to be overweight or diabetic than those who drink the regular versions. Therefore, they might be at greater risk of stroke—regardless of what they drink.

Cohort Differences

Groups can be affected by cohort differences that arise from the particular experiences of a group of people. For example, individuals born during the same period might witness the same important historical events, but their geographic regions, religious affiliations, political beliefs, and other factors might affect how they perceive such events.

Report Biases

Surveys and questionnaires about certain aspects of people's lives might not always result in accurate reporting. For example, respondents might not disclose certain behaviors or beliefs out of embarrassment, fear, or other limiting perception. Typically, no mechanism for verifying this information exists.

Cross-Sectional vs. Longitudinal Studies

Cross-sectional research differs from longitudinal studies in several important ways. The key difference is that a cross-sectional study is designed to look at a variable at a particular point in time. A longitudinal study evaluates multiple measures over an extended period to detect trends and changes.

Cross-Sectional Study

• Evaluates variable at single point in time

• Participants less likely to drop out

• Uses new participant(s) with each study

Longitudinal Study

• Measures variable over time

• Requires more resources

• More expensive

• Subject to selective attrition

• Follows same participants over time

Longitudinal studies tend to require more resources; these are often more expensive than those used by cross-sectional studies. They are also more likely to be influenced by what is known as selective attrition, which means that some individuals are more likely to drop out of a study than others. Because a longitudinal study occurs over a span of time, researchers can lose track of subjects. Individuals might lose interest, move to another city, change their minds about participating, etc. This can influence the validity of the study.

One of the advantages of cross-sectional studies is that data is collected all at once, so participants are less likely to quit the study before data is fully collected.

A Word From Verywell

Cross-sectional studies can be useful research tools in many areas of health research. By learning about what is going on in a specific population, researchers can improve their understanding of relationships among certain variables and develop additional studies that explore these conditions in greater depth.

By Kendra Cherry
Kendra Cherry, MS, is an author and educational consultant focused on helping students learn about psychology.

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