What is a longitudinal study?

A longitudinal study is a type of correlational research in which researchers repeatedly observe the same variables in the same subjects over time—anywhere from a few weeks to several decades. Researchers periodically collect data without manipulating the study environment.

Longitudinal studies are common in epidemiology, economics, and medicine, and they’re also used across the social sciences—including to study customer trends. A company might run a tracking study, surveying a target audience at consistent intervals to measure changes in brand awareness, customer satisfaction, and consumer opinions, or to analyze the impact of an advertising campaign.

One of the longest-running examples is the Harvard Study of Adult Development, which has collected data on the mental and physical health of a group of Boston men since 1938.

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Types of longitudinal studies

There are two types of longitudinal studies: cohort and panel studies.

Panel study

A panel study involves collecting data on a fixed set of variables at regular but distant intervals. Researchers follow a group or groups of people over time. Panel studies are designed for quantitative analysis but also work for qualitative analysis.

A panel study might research the causes and effects of age-related changes. Researchers may measure a group’s health markers over time—blood pressure, blood cholesterol, and mental acuity—then compare the scores to understand how age correlates with each measure.

Cohort study

A cohort study gathers information from a group of people with something in common, such as a specific trait or experience of the same event. Researchers observe the group’s behaviors and other details over time. Unlike panel studies, cohort studies let you pick a different group to test.

A drug manufacturer might study the effects of a new drug on a group of users over a period. A drinks company might research consumers with common characteristics—regular purchasers of sugar-free sodas, say—to understand trends within its target market.

Benefits of longitudinal research

If you want to study the relationship between variables and the causal factors behind certain outcomes, a longitudinal approach is a strong choice.

Its benefits over other research methods include the following:

Insights over time

It shows how and why things change over time.

Better information

Researchers can better establish sequences of events and identify trends.

No recall bias

Participants won’t have recall bias in a prospective longitudinal study. Recall bias is an error that occurs when respondents don’t wholly or accurately recall the details of their actions, attitudes, or behaviors.

New data

Because variables can change during the study, researchers can discover new relationships or data points worth further investigation.

Small groups

Longitudinal studies don’t need a large group of participants.

Potential pitfalls

The challenges and potential pitfalls of longitudinal studies include the following:

Expensive

A longitudinal survey takes a long time, involves multiple data collections, and requires complex processes, making it more expensive than other research methods.

Unpredictability

Because they take a long time, longitudinal studies are unpredictable. Unexpected events can change the variables, making earlier data less valuable.

Slow insights

It can take researchers a long time to uncover insights from the study, since it involves multiple observations.

Dropouts

Participants can drop out of the study, shrinking the dataset and making it harder to draw valid conclusions.

Overly specific data

If you study a smaller group to reduce costs, the results will be less generalizable to larger populations than a study with a larger group.

Despite these pitfalls, a well-designed longitudinal study can still deliver significant value by uncovering long-term patterns and relationships.

Longitudinal study designs

Longitudinal studies can take three forms: repeated cross-sectional, prospective, and retrospective.

Repeated cross-sectional studies

In repeated cross-sectional studies, participants change across sampling periods. For example, as part of a brand awareness survey, you ask different people from the same customer population about their brand preferences.

Prospective studies

A prospective study involves real-time data collection, following the same participants over a period. Prospective longitudinal studies can be cohort studies, where participants share characteristics or experiences, or panel studies, where you choose the population sample randomly.

Retrospective studies

Retrospective studies collect data on events that participants have already experienced. Researchers examine historical information to identify patterns that led to an outcome established at the start of the study. Retrospective studies are the most time- and cost-efficient of the three.

How to perform a longitudinal study

When developing a longitudinal study plan, decide whether to collect your own data or use data from other sources. Each choice has benefits and drawbacks.

Using data from other sources

You can freely access data from many previous longitudinal studies, especially those conducted by governments and research institutes. For example, anyone can access data from the 1970 British Cohort Study on the UK Data Service website.

Using existing data saves the time and money you’d spend gathering it yourself. However, it’s more restrictive: you’re limited to the variables the original researchers investigated, and they may have aggregated the data, obscuring some details.

If you can’t find data or longitudinal research that applies to your study, the only option is to collect it yourself.

Collecting your own data

Collecting your own data enhances its relevance, integrity, reliability, and verifiability. Your data collection methods depend on the type of longitudinal study you want to perform—a retrospective study collects historical data, while a prospective study collects real-time data.

Whichever you choose, an effective and versatile data collection tool improves both the speed and the accuracy of the information you collect—and it’s the best way to ensure your data stays relevant and reliable.