Let’s start with a little background before diving in: The applied methodology or research design of any study or research effort depends entirely on the desired outcome – what’s the stated goal? Understanding the kind of data the study should collect is entirely indicative of the practices and procedures set forth. This concept is pretty basic.
It sounds jargony, but a methodology is really just the set of rules or processes that sets the framework for the research itself. When it comes to research studies, there are several different types and each defines their own methodology, such as descriptive research, correlational, experimental, and so forth.
Descriptive research covers a vast array of studies, where the primary characteristic is that a given phenomena is being observed and recorded, both quantitative and qualitative in nature. Apropos to this blog, surveys on the whole are a kind of descriptive research.
Definitions
A longitudinal study is observational, meaning that there is no interference with the subjects, or respondents (if you happen to be surveying). What makes a longitudinal study unique is the timeline. Instead of a researcher collecting data from varying subjects in order to study the same variables, the same subjects are observed multiple times, and often over the course of many years.
Psychologists love using longitudinal studies to measure the impact of various therapy practices over time, usually using a control group as a baseline.
Another prime example might be a medical study that follows the same 100 individuals over the course of four years, measuring the impact of an experimental pharmaceutical. Using the same subjects in a longitudinal study allows for measureable change over a period of time to be collected.
There are three distinct kinds of longitudinal studies: panel, cohort, and retrospective. A panel usually involves a somewhat random sample of subjects, whereas a cohort study observes subjects in a similar group based on region, age, or common experiences. A retroactive study involves historical data, often times in comparison to updated data.
A cross-sectional study, the not-so-distant cousin to longitudinal, and also a kind of observational research, is intended to compare multiple population groups at a single point in time. Instead of collecting historical data on a single variable, a cross-section is framed, allowing a researcher to see differences among population subsets in several categories.
An applicable example might be a study on the benefits of jogging, where multiple measurements are taken; resting heart rate, body mass index, blood pressure, all across groups of varying levels of exercise. You aren’t collecting data from a single subject over several years to learn about the effects of jogging, but from many subjects just once. This is often referred to as a ‘snapshot’.
Advantages and Disadvantages
The key advantage to longitudinal studies is the ability to show the patterns of a variable over time. This is one powerful way in which we come to learn about cause-and-effect relationships. Depending on the scope of the study, longitudinal observation can also help to discover “sleeper effects” or connections between different events over a long period of time; events that might otherwise not be linked.
There are, of course, drawbacks to longitudinal studies, panel attrition being one of them. If you are dependent on the same group of 2,000 subjects for a study that takes place once every year, for twenty years, obviously some of those subjects will no longer be able to participate, either due to death, refusal, or even changes in contact information and address. That cuts down on useable data you can draw conclusions from.
Another weakness is that while longitudinal data is being collected at multiple points, those observation periods are pre-determined and cannot take into account whatever has happened in between those touch points. A third disadvantage is the idea of panel conditioning, where over time, respondents can often unknowingly change their qualitative responses to better fit what they consider to be the observer’s intended goal. The process of the study itself has changed how the subject or respondent views the questions.
Cross-sectional studies aren’t perfect either. Because of their single survey nature, they aren’t fit to make conclusive observations about the direction of any given association between variables. However, the benefits often outweigh the narrow scope disadvantages.
For one, cross-sectional studies are affordable when compared to a similar longitudinal study. With fewer touch points (no follow up), they are also much quicker in reaching an observational conclusion. Also, provided the sample size is carefully chosen, cross-sectional studies can be helpful in representing entire populations, rather than subsets. This can be very beneficial when considering policy change.
Which is better?
Neither, really. The idea behind both longitudinal and cross-sectional studies is, again, to create the best process in order to collect the most useful and actionable data. One is certainly not better than the other. They both serve a very important purpose, in different ways.
The deciding factor on which you use may be the number of variables you’re trying to study, the amount of time you have before published results are expected, your budget, or, perhaps most importantly, the nature of the event you’re studying.
Real World Examples
Longitudinal Studies:
The Harvard Grant Study
Health and Retirement Study (HRS)
Cross-Sectional Studies:
Tobacco Awareness Study
Breast Feeding and Obesity