What Is Longitudinal Data?

What Is Longitudinal Data?

Let me explain what longitudinal data is—it's data collected through repeated observations of the same subjects over an extended time frame, and it's particularly useful for measuring change.

You collect this data via longitudinal studies, where you follow the same sample over time. This is fundamentally different from cross-sectional data, which involves sampling different subjects—like individuals, firms, countries, or regions—at each point in time. With cross-sectional data, you always draw a new random sample each time.

You'll find longitudinal data used widely in the social sciences, including by economists, political scientists, and sociologists.

Understanding Longitudinal Data

As an analyst, you often want to know how things change over time. In a typical cross-sectional sample, if you measure a variable today and then again a year from now, you're likely sampling different people each time. To really grasp how things evolve for the same people, you need to track them and follow up in future waves—that's where longitudinal data comes in.

I see longitudinal data used a lot in economic and financial studies because it offers advantages over repeated cross-sectional data. For instance, since it measures how long events last, you can use it to check if the same individuals stay unemployed during a recession or if different people are cycling in and out. This helps identify the factors that most influence unemployment.

What Is a Longitudinal Study?

A longitudinal study means making repeated observations of the same variables—such as people—over long periods, using longitudinal data. It's often observational, but it can also be a longitudinal randomized experiment. You might also hear it called a longitudinal survey or panel study.

Example of a Longitudinal Study

In a longitudinal study, researchers repeatedly examine the same individuals to spot any changes over time. The advantage here is that you can detect developments or changes in the target population's characteristics at both group and individual levels.

Take, for example, a study to understand similarities or differences between identical twins raised together versus those raised apart. The study looks at several variables, but the constant is that all participants have identical twins.

Applications of Longitudinal Data

You can use longitudinal analysis to calculate a portfolio's value at risk (VaR) with the historic simulation method. This involves simulating how the current portfolio's value would have fluctuated in previous periods, based on historical asset fluctuations, giving an estimate of the maximum likely loss ahead.

Longitudinal data also applies in event studies to analyze factors driving abnormal stock returns over time, or how stock prices react to mergers and earnings announcements. It's useful for measuring poverty and income inequality by tracking individual households. Since standardized test scores in schools are longitudinal, they help assess teacher effectiveness and other factors impacting student performance.

Social scientists rely on it to understand causation from past events and their later outcomes—like how a new law affects crime statistics, or a natural disaster influences births and deaths years later.

What Is the Difference Between Longitudinal Data and Panel Data?

People sometimes call longitudinal data panel data, but there's a subtle difference. Longitudinal data refers to repetitive measurements over time, which could involve the same units or not. Panel data is a specific type of longitudinal data where the observed units are always the same.

Is a Longitudinal Study Qualitative or Quantitative?

Longitudinal studies are primarily qualitative because the researcher observes and records changes in variables over an extended period. However, they can gather quantitative data too, depending on your research context.

Is There a Drawback to Longitudinal Studies?

Yes, a major drawback is the significant time required to collect all the necessary data before you can start identifying patterns.

The Bottom Line

To wrap this up, longitudinal data is collected sequentially through repeated observations of the same subjects over time. It's valuable for measuring change and is applied in finance to track company profitability, risk, and the effects of economic shocks.