Longitudinal studies are a type of correlational research in which researchers observe and collect data on a number of variables without trying to influence them. In a longitudinal study, researchers repeatedly examine the same individuals for any changes that may occur over a period of time. Although most commonly used in medicine, economics, and epidemiology, longitudinal studies can also be found in other social or medical sciences.
According to Van Weel (2005), the advantage of a cross-sectional study design is that it allows researchers to compare many different variables at the same time. We could, for example, analyse age, sex, income and educational level in relation to walking and cholesterol levels, at little or no additional cost.
However, cross-sectional studies may not provide definitive information about cause-effect relationships. This is because such studies offer a snapshot of a single moment in time; they do not take into account what happens before or after taking the snapshot. Therefore, we cannot know for sure if our daily walkers had low cholesterol levels before starting their exercise regimens, or if the behavior of walking daily helped reduce cholesterol levels that were previously high.
How long does a longitudinal study last?
No set amount of time is required for a longitudinal study, as long as participants are observed repeatedly. They can range from a few weeks to several decades. However, they usually last at least a year, often several.
One of the longest longitudinal studies, the Harvard Adult Development Study, has been collecting data on the physical and mental health of a group of Boston men for more than 80 years.
Longitudinal versus cross-sectional studies
The opposite of a longitudinal study is a cross-sectional study. While longitudinal studies repeatedly look at the same participants over a period of time, cross-sectional studies examine different samples (or a "cross-section") of the population at any given time. They can be used to obtain a snapshot of a group or company code at a given time.
Both types of studies may be useful in research. Because cross-sectional studies are shorter and therefore cheaper to perform, they can be used to discover correlations that can then be investigated in a longitudinal study.
Select the most convenient type of study
The design of the study depends largely on the nature of the research question. In other words, knowing what kind of information the study should collect is a first step in determining how the study will be conducted (also known as methodology).
Suppose we want to investigate the relationship between daily walks and cholesterol levels in the body. One of the first things we would have to determine is the kind of study that will tell us more about that relationship. Do we want to compare cholesterol levels between different walker and non-walker populations at the same time? Or do we want to measure cholesterol levels in a single population of daily walkers over an extended period of time?
The first approach is typical of a cross-sectional study. The second requires a longitudinal study. To make our choice, we need to know more about the benefits and purpose of each type of study.
Common characteristics of both types of study
Both cross-sectional and longitudinal studies are observational studies. This means that researchers record information about their subjects without manipulating the study environment. In our study, we would limit ourselves to measuring the cholesterol levels of people who walk and those who do not walk on a daily basis, along with any other characteristics that might interest us. We would not influence non-walkers to perform that activity, nor would we advise daily walkers to modify their behavior. In short, we would try not to interfere.
The defining characteristic of a cross-sectional study is that it can compare different population groups at a single time. Think of it as a snapshot. The results are extracted from everything that fits into the framework.
Going back to our example, we could choose to measure the cholesterol levels of people who walk daily in two age groups, over and under 40, and compare them with the cholesterol levels of people who do not walk in the same age groups. We could even create subgroups by gender. However, we would not take into account past or future cholesterol levels, as these would be outside the framework. Only cholesterol levels would be tested at any given time.
Example cross-sectional study versus longitudinal study
We want to study the relationship between smoking and stomach cancer. He first conducts a cross-sectional study to see if there is a link between smoking and stomach cancer, and finds that there is a relationship in men but not in women.
He then decides to design a longitudinal study to further examine this relationship in men. Without the cross-sectional study first, I wouldn't have known I had to focus on men in particular.
In a retrospective study, patients' previous medical records can be examined to see if those who developed that cancer had previously smoked. In a prospective study, a group of smokers and nonsmokers could be tracked over time to see if they develop cancer later on.
How to perform a longitudinal study
If you want to conduct a longitudinal study, according to Van Belle et al (2004), you have two options: collect your own data or use data already collected by someone else.
Use data from other sources
Many governments or research centres conduct longitudinal studies and make the data available to the public free of charge. For example, anyone can access data from the 1970 British Cohort Study, which has followed the lives of 17,000 Britons since birth in a single week in 1970, via the UK Data Service website.
These statistics are generally very reliable and allow changes to be investigated over an extended period of time. However, they are more restrictive than the data one collects. To preserve the anonymity of the participants, the data collected is usually aggregated, so that it can only be analyzed at the regional level. It will also be limited to the variables that the original researchers decided to investigate.
If you choose this route, you should carefully examine the source of the data set, as well as the data available to you.
Collect your own data
If you decide to collect your own data, how you do so will be determined by the type of longitudinal study you choose to perform. You can choose to conduct a retrospective or prospective study.
In a retrospective study, data are collected on events that have already occurred.
In a prospective study, a group of subjects is chosen and followed over time, collecting data in real time.
Retrospective studies are usually less expensive and time-consuming than prospective studies, but are more prone to measurement errors.
Advantages and disadvantages of longitudinal studies
Like any other research design, longitudinal studies have their advantages and disadvantages: they provide a unique set of benefits, but they also have some drawbacks.
According to Newman (2010), longitudinal studies allow researchers to follow their subjects in real time. This means that the actual sequence of events can be better established, allowing for an understanding of cause-effect relationships.
A cross-sectional study on the impact of police on crime could find that more police officers are associated with increased crime and erroneously conclude that police cause crime when it is the other way around. However, a longitudinal study could observe an increase or decrease in crime some time after the number of police officers in an area has increased.
Longitudinal studies also allow the observations of the same individual to be repeated over time. This means that any change in the outcome variable cannot be attributed to differences between individuals.
You decide to study how a certain weight training program affects athletic performance. If you choose a longitudinal study, the impact of natural talent on performance will be eliminated, as it will not change over the study period.
Prospective longitudinal studies eliminate the risk of recall bias, or the inability to correctly remember past events.
You are studying the effect of low-carb diets on weight loss. If subjects are asked to remember how many carbohydrates or how much they weighed at some point in the past, they might have difficulty doing so. In a longitudinal study, you can track these variables in real time.
Longitudinal studies are time-consuming and often more expensive than other types of studies, so they require significant commitment and resources to be effective.
Because longitudinal studies repeatedly observe subjects over a period of time, any potential study ideas may take time to discover.
In the study examining the links between smoking and stomach cancer, it takes several years to see any results, as the negative effects of smoking accumulate over decades.
Attrition, which occurs when participants leave a study, is common in longitudinal studies and can lead to invalid conclusions.
In their study on the impact of low-carb diets on weight loss, participants who are not very successful may feel more discouraged and therefore more likely to quit. Therefore, it might seem that the diet is more successful than it actually is.
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Van Belle G, Fisher L, Heagerty PJ, et al. Biostatistics: A Methodology for the Health Sciences. Longitudinal Data Analysis. New York, NY: John Wiley and Sons, 2004.
Van Weel C. Longitudinal research and data collection in primary care. Ann Fam Med 2005; 3 Suppl 1:S46-51.
Newman AB. An overview of the design, implementation, and analyses of longitudinal studies on aging. J Am Geriatr Soc 2010; 58 Suppl 2:S287-91.