Longitudinal Vs. Cross-Sectional Studies
In the world of data collection, there are two primary methods of gathering information from respondents: longitudinal studies and cross-sectional studies.
Both have their place, but cross-sectional studies are the most common use of survey software.
This is understandable, as longitudinal analysis takes careful planning, considerable effort in execution, and consistent follow-up to be successful, whereas cross-sectional studies can be conceived and completed much more rapidly.
The Benefits of Longitudinal Studies
Cross-sectional surveys compare a sample of the population at a single point in time. They can capture quite a lot of detail, but they don’t provide much in way of cause and effect data.
Longitudinal studies, on the other hand, involve polling the same group of respondents at regular intervals, allowing you to study changes over time and determine causal relationships much more effectively.
1. Understanding Respondent Changes
Firstly, they offer an opportunity to learn about change on an individual respondent level.
When you survey the same people repeatedly you can track fluctuations in their responses over time, something that is simply not possible in a one time cross-sectional survey that is never duplicated. Coupled with demographic data, these intensely specific changes can offer great insights into behavior development over time.
In a longitudinal study on cat sleep, for example, we might see a particular black cat whose average daily sleep dropped from 20 hours to just 12 hours between surveys.
Diving into the data we could determine that her age, weight, location and eating habits remained constant since the last survey, but that a new kitten was introduced into her home.
Had we only executed a single cross-sectional survey of the same cat we wouldn’t have had the opportunity to track how her sleep was impacted by the new arrival.
2. Insight into Cause and Effect Relationships
The example above also illustrates another benefit of longitudinal studies: cause and effect relationships become apparent.
By monitoring this cat’s sleep habits and home life over time we are able to eliminate factors that remained consistent and discover the true cause of her abysmal sleep life.
Over the course of the longitudinal survey we would almost certainly encounter other cats whose owners had the temerity to bring home another pet, and we could then further investigate the cause/effect relationship of new pets and cat sleep quality.
Obviously none of these options exist with a cross-sectional survey alone.
3. Cohort Effects
Third, longitudinal studies control for what’s known as “cohort effects,” a phenomenon that can often significantly skew the results of cross-sectional studies.
Cohort effects are differences among various groups that are a result of their life experiences, not the factors being tracked in a survey.
So a cross-sectional study conducted in a single year might poll both 30 year-old and 60 year-old respondents, but their answers would be colored not only by their ages but by the eras in which they were born, making it difficult to determine if their age was really the determining factor.
A longitudinal survey that polled the 30 year olds and then re-polled them again when they turned 60 would ensure that no cohort effects were impacting the results.
4. Reduced Sample Size
Finally, longitudinal analysis requires a much smaller sample size to achieve statistically significant results because you are able to obtain a much higher volume of data over the life of the survey1.
This can be a great help to researchers when selecting respondents from a relatively small population, like cats whose owners have acquired a new kitten in the past 6 months.
Challenges of Longitudinal Surveys
Despite their many benefits, conducting longitudinal surveys is not without pitfalls.
One of the most common challenges is participant attrition. Over time participants may drop out of the study or fail to respond to one or more rounds of surveys, both of which will skew results.
They may also fail to answer particular questions in one or more rounds of surveys, also impacting the data’s relevance. Careful respondent tracking can eliminate some of these issues, but there will never be 100% participation over the life of a longitudinal study.
We’ll discuss how best to deal with these statistical challenges in more detail in the “Making Longitudinal Analysis Work For You” section.
Additionally, the sheer volume of data, along with its fluctuations over time, requires immensely complex methodologies for accurate analysis and interpretation.
While one of the major benefits of longitudinal analysis is the ability to see cause and effect relationships, it is also a challenge. These dependencies in the data demand statistical sophistication that is not yet fully realized.
Just as making statistical sense of the data in a longitudinal survey can be challenging, obtaining it in the first place can be just as difficult.
Keeping track of the participants, administering surveys and inputting data over and over again becomes expensive; this barrier is often enough to deter researchers.
Finally, great care is required during the setup process of longitudinal surveys.
Although you will be administering the same survey over a long period of time, it needs to contain all the relevant questions before it goes out the first time. You can’t make adjustments as the study goes on, or the responses will no longer be statistically significant.
Similarly, you can’t adjust your participant pool over time either. Thoughtful survey design and participant selection are vital to a successful longitudinal analysis.
Making Longitudinal Analysis Work for You
We’ve reviewed some of the benefits and challenges longitudinal analysis poses. Now here’s how to make this powerful survey type work for you.
- Be sure you have the time and resources to invest in your survey’s long term success.
- Determine how you’ll deal with missing data
- Design your survey with the utmost care
The ability to stay in touch with your participant pool over time through moves and other changes in contact information will help ensure a minimal level of attrition, but this requires personnel.
Staff are also necessary to accurately administer the surveys over time, and ideally the same people will administer each round of surveys to avoid any errors.
Aside from staff, you need to be sure you have the computational power to analyze the data you’re going to gather, otherwise you’ll be sitting on lots of information without the ability to draw conclusions from it.
Participant attrition and missing data are big challenges in longitudinal surveys. Donald Hedeker and Robert D. Gibbons offer two solutions in their book Longitudinal Data Analysis:
Completer analysis: With this approach you limit your analysis to only those respondents who complete the full study. While fairly simple to implement, this solution may result in data that is not as statistically relevant.
Last observation carried forward (LOCF): This solution involves inputting the last available measurement for all subsequent measurement occasions. It’s more labor intensive, as it involves manual entry of measurements, but it results in more robust final analyses.
What exactly are you hoping to learn from this survey? What secondary conclusions do you hope to draw? Are there cause and effect relationships that you need to track? What demographic data is relevant?
Carefully choosing question types and question content will ensure that you don’t get three rounds into your survey only to find that you’re not getting the kinds of responses that you need.
Testing the survey on colleagues or a beta group can be an excellent way to tweak questions. This type of initial review can also help to avoid the introduction of bias within questions.
Remember, once your longitudinal survey has begun there’s no tweaking allowed.
Whether it’s the long term effects of obesity on sleep patterns, or some other cat-related survey, longitudinal analysis can offer uniquely compelling insights when you take steps to avoid its potential pitfalls.
Hedeker, Donald and Robert D. Gibbons; Longitudinal Data Analysis; 2006; John Wiley & Sons