When it comes to performing empirical research, there are manifold methodologies available for you to choose from. With each approach bringing its own unique set of benefits and drawbacks and no method offering a truly universal applicability, it is paramount that you opt for a route that offers the greatest likelihood of generating usable, insightful data to test your hypotheses. With this in mind, we’re going to be diving a little deeper into one such approach – the longitudinal study – to help you determine whether it’s a good fit for your work.
Longitudinal Study – In a Nutshell
In sum, a longitudinal study offers the potential for truly insightful research that can make real contributions to your field:
- Employing the method in its ‘purest’ form (i.e. prospective data over a suitable time span) may allow you to reach definitive conclusions on causal relationships
- A longitudinal study deals with changes over time, whereas a cross-sectional study focuses on providing a snapshot of the population at one given time.
- There are two approaches to a longitudinal study: the retrospective and prospective approach.
- The drawbacks to this approach are minor. Nevertheless, you should carefully consider whether a longitudinal study is right for you.
Definition: Longitudinal Study
In simple terms, a longitudinal study is one where data is collected at predetermined intervals from a consistent group of participants. Performing a longitudinal study allows for stronger assertions to be made regarding the correlation between two (or more) variables, as trends can be observed both at the individual level and within the wider study population over a longer period of time.
Typically, a longitudinal study is commonly relevant in the fields of medical and social research and has been employed by scientists around the world for decades.
Example
Between 1979 and 1990, researchers in Minnesota looked at the lives of twins that had been raised separately in order to better understand the role that genetic factors play in determining human behaviour.
The length of a longitudinal study
As there are no ‘rules’ for longitudinal research, there is thus no specific timeframe you must examine to meet this classification. Though a year is a solid baseline to use for a longitudinal study, you will be guided largely by the variables under investigation as some changes take longer than others to be borne out in real life.
When deciding, it’s important to remember that being able to point to a correlation over a longer period of time will allow you to make stronger, more definitive statements about what you have observed and whether or not your hypothesis was ultimately proven.
Longitudinal study vs. cross-sectional study
A longitudinal study can be contrasted with one of the most popular alternatives: the cross-sectional study. Whilst a longitudinal study, as we have discussed, is concerned with changes over time, a cross-sectional study is focused on providing a ‘slice of life’ view of the population at one given time. You will find that cross-sectional studies will have a much broader participant base: true to the name, you are trying to gain as accurate a cross-section of society as is possible within the constraints of your work.
Participants in a longitudinal study will be observed repeatedly at multiple points in time, with the core group membership staying consistent. A cross-sectional study’s participants, however, are observed only once to provide a snapshot of their life experiences in that moment.
How to perform a longitudinal study
Once you have established the variables to be investigated, there are two approaches you can take to obtain the data that you need for your longitudinal study: you can make use of data that already exists, or you can gather your own. We refer to these as the retrospective and prospective methods, respectively. Once data collection is complete, you will simply proceed to take measurements at suitable intervals and see what the figures tell you about your initial hypotheses. Below, we can take a look at the key differences between the two approaches.
Retrospective approach
You will find that governments and other civic agencies frequently release large datasets to the public, and this data is great for a longitudinal study. Right off the bat, you can be assured that the data has come from a reputable source (thus strengthening any conclusions you draw from it), as well as being able to link in datasets from previous years as part of the record linkage method mentioned earlier.
However, privacy concerns mean that this data is heavily aggregated and, as such, you may not be able to drill down deeply enough into it for the purposes of your investigation. Additionally, you are limited to the variables that the third party was interested in surveying.
Prospective approach
Most researchers will instead opt to gather their own longitudinal study data over an appropriate time span. Here, you will of course need to design appropriate survey instrument(s) to obtain relevant data from your target population. Having full control over the design and participant selection processes means you are far more likely to get the kind of data that you actually need, whilst encoding the data yourself should also ensure that it remains error-free. The benefits here are certainly more pronounced but, as we will cover in the following section, so too are the drawbacks.
Advantages and disadvantages of a longitudinal study
| ✓ Advantages | ✗ Disadvantages |
| A longitudinal study provides a full chronological view of events, allowing you to make stronger claims about causal relationships. | Longitudinal studies are vastly more expensive in terms of both time and money than cross-sectional ones. |
| Repeatedly observing the same participants eliminates individual personality differences as an explanation for changes in your variables. | They are also invariably affected by attrition (people withdrawing their participation as time goes on) which weakens any assertions you make, whilst the ‘practice effect’ inherent to repeated observations can result, over time, in measurements that are less organic. |
| They also allow for greater accuracy, as participants will be providing you with information in the happening, as opposed to long after the fact (we call these errors resulting from delays in reporting ‘recall bias’). |
FAQs
A longitudinal study is an example of correlational research, wherein the relationship between two (or more) variables is investigated with the aim of identifying a positive or negative correlation between them.
The data generated from a longitudinal study can help us to understand the ways that things such as changes in governmental policy manifest in the real lives of the population, allowing for the appraisal of their success/failure as well as guiding future decisions.
- Most commonly in medical and social science due to the interest in understanding changes in population over time.
- Other disciplines such as linguistics make frequent use of this approach.
There are many different approaches to a longitudinal study depending on the variables that you are interested in:
- Panel study: Group composed of either disparate individuals or groups from different households
- Cohort study: All participants are from the same age group.
- Record linkage study: Uses multiple sources of historical data to establish temporal patterns.
