Snapshot or Marathon: Why to Use Cross-Sectional or Longitudinal Research

If you are not already trailblazing through your proposal, you probably at least have an idea of what your dissertation is going to be about. In that case, one of the next questions you might run into has to do with your study’s design, and whether you should take a cross-sectional or longitudinal approach. This is one of the first divisions in research types you will face after choosing to use a quantitative methodology, and the answer relies mostly on what your goals are. Still, sometimes you will find yourself stuck with one over the other based on the data that is most conveniently available or feasible to collect. Below we talk about what each of these are.

Cross-sectional research is a kind of research that is focused on relationships between variables, but does not need to take into account the fact that any variables might fluctuate over time. This kind of research is usually used in correlational studies, but can also take the form of a comparative study. The importance of cross-sectional research is in taking time-wise fluctuations out of the data, taking a cross-section of the data, and looking at relationships or differences that exist at that moment.

 An example of research that takes this form would be the examination of student test scores in relation to teachers’ perceptions of the subject matter. In a study like this, the goal of taking a cross-section would be to measure student scores, which could fluctuate over time, and also to measure teachers’ perceptions of the subject matter, which could also change from one day to the next. By taking a freeze-frame of the data, the researcher is able to check whether high student scores also correspond with positive perceptions of the subject matter (or vice versa) without worrying about the fact that both may be slightly different next time they are analyzed. If the two are assumed to correlate, even if they were examined at a freeze frame of a second time, the same direct or inverse relationship should exist – if a teachers’ perceptions have become more positive, student scores should still correspond with those perceptions at any specific time. Though changes over time might be interesting, the cross-sectional approach captures both variables at the same point in time for a more accurate comparison.

Longitudinal research is used when a researcher is interested in long-lasting effects, the influence of a treatment or intervention, or trends over time. The importance of this kind of research lies in an ability to examine how things change; it is this ability that lets the researcher make causal interpretations. Though it is not always classified as longitudinal, a good true experimental design often has a longitudinal component, meaning it tracks at least one pre-score and one post-score, though more subsequent measurements can give the analysis more detail into long-term effects. A second kind of study, time series studies, are the very definition of longitudinal research, and make the main goal an assessment of trends over time, sometimes going so far as assessing whether two trends correspond to one another.

An example of longitudinal research might take the form of a time series study where researchers assess global temperatures over time. By taking the same measurement over and over, researchers might have the goal of checking for a repeating trend (think seasons), and whether this trend has held steady, or begun to deviate from what is measured as typical. A different form of longitudinal research is an experimental study. A strong experimental study makes use of the multiple time points to check both a treatment and a control group for changes after some kind of intervention. This is the gold standard among many researchers, mainly because of the causal inferences that experimental studies can make.

With any type of longitudinal research, the most important thing to keep in mind is how you will track your measurements. For this kind of research to really hold up to its potential, you need to know how each pre and post score matches up. This is easy when you are measuring things like global temperatures – it all matches up to the Earth. However, for intervention studies, it can be a little more tedious. The repeated measurements need to be matched to the participants that produced the measurements. An easy way to make sure everyone is accurately tracked is to assign them an ID, but do not make the mistake of giving any duplicate IDs!