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Longitudinal Study

What is a Longitudinal Study?

A longitudinal study is a form of experiment that involves repeated observations of the same variables over an extended period of time. Often defined as an observational study, longitudinal experiments can be designed as randomized trials as well. Longitudinal studies usually study people as the variables of the experiments, as the design of the study is best fitted to observe changes over time. This design structure is commonly found in psychology and sociology, looking at developmental trends across an individual's life span.

Longitudinal studies are defined by the fact that, unlike cross-sectional studies, they observe the same variables. This allows for more accurate observations of change over time, as well as  reducing confounding variables such as cultural or generational factors. Additionally, longitudinal can be either retrospective, looking back across past data, such as medical records, or prospective, performing a study and observing change over time.

How does a Longitudinal Study work?

Imagine wanting to observe changes in optimism and attitude over time. One could use sets of individuals from various age ranges (20s, 30s, 40s) and compare their attitudes, however there may be confounding variables. For instance, the 30-year olds may be an unusually cynical group of individuals, skewing the data. In order to minimize these confounding factors, one could turn to a longitudinal study. In that case, the experimenter would observe a single group of individuals throughout their 20s, 30s, and 40s. This way, the same individuals are being measured over time, which improves accuracy of measurement and avoids confounding variables.

While longitudinal studies have the advantage of not requiring large numbers of participants (variables), they do however take a lot of time, resulting in high cost. Despite being more accurate than cross-sectional studies, their cost often limits their use due to general inconvenience.

Longitudinal Studies and Machine Learning

While longitudinal studies themselves don't have a direct correlation to machine learning, their data does. Machine learning algorithms can use longitudinal data to understand and infer trends, changes over time, and possibilities of specific occurrences. For example, machine learning has been implemented in

prediction of cardiovascular disease

using longitudinal genetic data. The incorporation of deep learning technology has led to improved predictions of cardiovascular disease and an enriched understanding of the importance of genetic markers in understanding health risks.