Cross-sectional surveys are a lightweight research tool that is easy to implement and requires limited resources compared to other research methods, making them quite popular among research groups. However, popularity and affordability are not always the best measures of a research method’s viability. Some research questions would demand the use of a different approach, such as a longitudinal survey. The key for research teams is to recognize which situations call for what approach.
To help illuminate when and why a cross-sectional survey methodology might work best for a research study, here are the right and the wrong times to use them.
Right Time: Population Descriptions
Describing a population is the most critical function of a cross-sectional survey. The name even refers to how survey results manifest themselves: the research team takes a “slice” measurement at the given time, similar to how scientists will cut out a slice from a tree to observe qualities within that section. By finding a sufficiently representative sample, research teams could make assumptions about the rest of the general population by describing qualities from the sample slice.
In the realm of market research, this type of methodology is useful when trying to “get to know” a population. For instance, a soft drink beverage brand has strong sales with an 18- to 25-year-old demographic. If the brand has plans to start distributing to an area, they may want to measure how many 18- to 25-year-olds are in that area, along with their soda preferences and habits.
Some companies or groups may also wish to describe a population as a form of general description about the state of an issue. A statistic like “18 percent of households living below the poverty line do not have access to clean water” can be obtained through cross-sectional surveying.
Wrong Time: Describing Deeper Relationships, Trends or Behaviors
Longitudinal surveys are not just about showing the effects of some phenomenon over time. They can also more-accurately measure attitudes and relationships by gleaning a more representative sample of behaviors and opinions. Rather than taking one slice, longitudinal surveys take many slices and can average them together.
An example would be trying to determine ownership satisfaction of a certain appliance. Cross-sectional surveys could be used to assess how satisfied owners of the appliance are immediately after purchase, six months after, one year after, five years and so on. Taking a cross-sectional “snapshot” of the current five-year owners may yield a different result than tracking the same population over the course of five years because of unconsidered variables. Current five-year owners may be more satisfied relative to the other products they had to choose from at the time, for instance. Yet, following a current population of new owners for five years can eliminate variables like these or at least describe them more accurately.
To rephrase: cross-sectional surveys may not grant as much confidence when trying to describe deeper trends between human subjects and concepts like product satisfaction. However, they can describe relationships and trends within a population with some confidence, although outside factors may be involved.
Regardless of the method chosen, research teams will want to ensure that their survey design follows best practices. Download our Free Guide to Survey Design Best Practices to learn more.
