In a longitudinal study, the researcher aims to examine the same individuals to monitor and detect changes over time.

A type of correlational research, a longitudinal study aims to observe and collect data on several variables without influencing them.

Longitudinal studies are most commonly used in medicine, epidemiology and economics; however, they can also be used in other fields like social sciences.

What is the length of a longitudinal study?

Typically, there is no specific length requirement for a longitudinal study as long as the aim is being fulfilled. The span of the study can vary considerably, from a few weeks to as long as a few decades. In most cases, the study is conducted for at least a year.

The Harvard Study of Adult Development is one of the longest longitudinal studies collecting mental and physical health data of males from Boston for over 80 years.

The differences between a longitudinal study and a cross-sectional study

A longitudinal study and a cross-sectional study are entirely on the opposite end of the spectrum. In the case of a longitudinal study, observations are collected from the same participant over a time period; however, in the case of a cross-sectional study, observations are drawn from different samples or cross-sections of the population in a given observation period which helps to provide a snapshot of the cross-section at the specific moment.

Both of these types of studies find extensive usefulness in research. Cross-sectional studies are less time consuming and therefore costs less to conduct. In addition, a cross-sectional study can help discover correlations that can be investigated in a longitudinal study.

Examples of cross-sectional vs longitudinal study

Suppose researchers are looking into the relationship between the use of tobacco and stomach cancer. In that case, a cross-sectional study is required to identify a link between tobacco use and stomach cancer. The discovery shows a link in men but not in women.

Now researchers can conduct a longitudinal study to seek further links between tobacco use and stomach cancer in men. Without the cross-sectional study, researchers would not have been able to focus on the men in particular.

How is a longitudinal study performed?

In order to conduct a longitudinal study, there are two means of collecting data. Either collect the data yourself or rely on data that other sources have collected.

Using data from other sources
There are plenty of publicly available data from government and research centres that can be used to conduct longitudinal studies. These data are usually credible and reliable. For example, anyone on the internet can access the 1970 British Cohort Study data from the UK Data Service website.

Researchers can use these data to investigate changes over a span of time. But these are more restrictive than the ones you collect yourself. For example, such data is often aggregated to ensure the participants’ privacy, which means such data can only be analysed on a regional level. Researchers will also be restricted to the variables used by the original researchers of the data.

If you choose to collect data from such sources, make sure that you carefully analyse the source of the data and the type of data.

Collecting own data
When you choose to collect your data, it will determine the type of longitudinal study you wish to do. In addition, you have the option of choosing between a perspective and a retrospective study.

• Prospective study: Collecting real-time data from the sample set over a time period
• Retrospective study: Collecting data from past events

Example: Prospective vs retrospective
In the case of a retrospective study, researchers might pull up past medical data to see whether cancer patients had a history of tobacco use. In a prospective study, researchers will analyse if cancer develops in individuals in a group of both tobacco users and non-users from a similar age group over a time period.

The advantages and disadvantages
Similar to any other research design, longitudinal study has both its advantages and drawbacks.

Advantages: Researchers can analyse the participants in real-time. It helps them to establish the cause-effect relationship better.

Example: A cross-sectional study would be able to associate the increasing number of police, increases the criminal activities. However, it would be not correct since the police work to maintain law and order. If a longitudinal study is conducted in such a case, researchers would observe whether crime rose or fell over time by increasing the number of police personnel.

Longitudinal studies also help in conducting repeated observations on the same individual over a time period. It allows researchers to plot any changes over time.

Example: You are looking to study the impact of a specific health program on athletes and their performance. By using a longitudinal study, you would be able to eliminate variables that would be constant over the period of the study, like natural talent.

With the help of a prospective longitudinal study, researchers can eliminate any recall bias or inability to remember results from past events.

Example: If you are looking to find the impact of a low carb diet on body weight, asking the participants to recall their weight in the past would not yield credible and reliable data. However, these data can be easily tracked in real-time using longitudinal studies.

Disadvantages: One of the main disadvantages of a longitudinal study is its expensive and time-consuming nature when compared to other types of research. However, longitudinal studies require considerable commitment and effective resources as well.

Since the study is conducted over a time period, it might take a while to discover the study insights.

Example: When looking at the relation between cancer and tobacco use, you will have to conduct the study over many years since the ill effects of tobacco can take decades to manifest physically.

Attrition is a common problem when conducting a longitudinal study as many participants might drop out of the study, which might compromise the results.

Example: When conducting the study on the impact of a low carb diet on body weight, many participants might lose interest if they don’t see any success, causing them to drop out. This might have an improper impact on the outcome of the study.

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