Introduction
To drive any data-driven research project in the right direction, framing the research question properly is the first fundamental step. A key decision that goes into the framing of a research question is to decide how the data should be tracked. This requires some keen observations on the part of the researcher in order to arrive at the most effective method of statistical tracking, and this is where the comparison between longitudinal study and cross-sectional study comes in.
What they are
Both the cross-sectional and the longitudinal studies are observational studies. Researchers in the study record information about the test subjects without manipulating the environment. A cross-sectional study captures the data of the test subjects at one point in time. It functions like a snapshot of how the test subjects are responding to the study environment. Therefore, the most prominent feature of a cross-sectional study is that it can compare how different groups of test subjects react to the study environment at a single point in time. The benefit of a cross-sectional study design is that it allows researchers to compare many different variables at the same time.
In a longitudinal study, researchers conduct several observations of the same test subjects over a period of time that could last many years. The benefit of a longitudinal study is that researchers are able to detect developments or changes in the characteristics of the target population at both the group and the individual level. The key here is that longitudinal studies extend beyond a single moment in time which allows them to establish sequences of events within the study environment.
Verdict
It would be difficult to give a verdict with regards to which type of tracking is better than the other, as the better choice will be dependent on how much data is available, and how the research question is constructed. A longitudinal study can certainly be more accurate and comprehensive if the researchers already have data from the entire set of test subject at their disposal. This is undoubtedly difficult as it isn’t easy for researchers to have access to all the test subjects to collect a truly comprehensive data set. Though it should be noted that even if there’s only a sample set of data available, a longitudinal study could potentially still be used as a steppingstone to determine the statistical trend of the entire dataset. A cross-sectional study could be more favorable as a mechanism of comparison between different sets of test subjects as they undergo the same processes or changes.
Neither method is inherently superior and a combination of both could serve as an effective tool for data researchers. Researchers can first conduct a cross-sectional study as an entry point to determine if there are some causal links between certain variables, and follow that up with a longitudinal study that then delves into the overall cause and effect.
