Cross-sectional studies: Definition, benefits, and challenges

In terms of time frame, there are two types of approach researchers can use to conduct research studies: a cross-sectional approach or a longitudinal approach. Cross-sectional studies capture data at a single point in time, while longitudinal designs gather data in waves, typically at least two points in time.

In this article, we’ll be focusing on cross-sectional designs, exploring the types of research questions and topics they’re useful for addressing, the characteristics, benefits and weaknesses of the approach, and giving you some handy tips on how to use them.

A cross-sectional study involves gathering and analyzing data from a population of interest at one specific point in time. Typically, the researcher is conducting the study to describe the characteristics of the population, or to explain the relationship between a particular outcome and some other variable(s) of interest.

Cross-sectional research design can be used to assess the attitudes, interests, or behaviors of a study sample. Because of this, cross-sectional studies are particularly useful in  informing resource planning and allocation. For example, the owner of a smoothie shop might use a quick survey at the point of sale to learn which types of fruits and vegetables customers prefer. The shop owner can then use this data to decide which ingredients to buy, and which types of smoothie to offer.

Let’s take a look in a little more detail at some of the key characteristics of cross-sectional studies.

Cross-sectional studies are what researchers call observational studies (as distinct from experimental type studies). This means that researchers do not manipulate variables, and they don’t assign different cohorts in a sample to different study groups. Instead, the researcher simply records the information they observe within a population.

Although, as we will discuss shortly, cross-sectional market research designs may be explanatory in nature, this methodology is known as descriptive research because it cannot be used to determine the cause of something. 

Cross-sectional research studies gather data at one point in time, so insights from this type of research are a snapshot of a particular moment. For example, if the smoothie shop owner took a snap poll of customers on a Saturday and then again on a Monday, they might find that fruit and vegetable preferences differ across the two days. Each poll, in this case, would be considered an individual cross-sectional study with the data recorded describing preferences at each particular point in time.

In cross-sectional studies, researchers are responsible for designing and creating the tools involved in collecting cross-sectional data, but they do not manipulate the study environment.

For example, if you are interested in learning whether privacy concerns differ among people who primarily shop online versus those who primarily shop at brick and mortar stores, you would simply gather that information, as well as any other variables of interest. You would not attempt to influence either group of individuals to modify their behavior or concerns. In other words, with cross-sectional research, the researcher tries to gather data without interfering in the results.

Since cross-sectional research data is gathered all at once, multiple variables can be assessed simultaneously. This is especially useful if you’re interested in exploring associations between sets of variables.

One of the major uses of cross-sectional research is to assess the traits or qualities of a specified population. For that reason, these studies often gather multiple pieces of demographic data or other data that will enable you to draw up a picture of your target population.

Regular cross-sectional market research studies can be used to create a series of snapshots about what is happening in a population over time. For example, the smoothie shop owner might survey customers once every season to learn how preferences for certain types of fruit and vegetables change over time.

Cross-sectional studies are popular because they are quick and easy to conduct, and researchers can gain valuable insights from them. The data gathered from cross-sectional studies can be used in multiple ways: 

In cross-sectional research, researchers typically use a sampling frame to identify a population of interest, such as individuals who enjoy vaping. They then select a sample from this population. By collecting relevant data from the sample, researchers can generalize their findings to describe the characteristics of the broader vaping population.

Another purpose of cross-sectional studies is to infer the nature and strength of relationships between variables. For example, if you’re interested in whether younger people who vape are more or less concerned about their health than older populations, you should gather variables capturing health concerns and age from a target population. You might then use a correlational analysis to determine whether health concerns rise or fall as the age of your respondents increases.

Cross-sectional studies do not manipulate variables and, therefore, cannot determine cause-and-effect relationships. However, cross-sectional studies can offer valuable insights into a population and lay the foundations for future research using experimental or longitudinal studies.

There are several benefits of using cross-sectional studies.

One of the primary benefits of cross-sectional studies is their speed and cost-effectiveness, especially when compared to longitudinal studies. Longitudinal studies involve monitoring the same individuals over time, which can be expensive and time-consuming due to the need for large sample sizes to account for churn rates over the study period. It can also take a while to get results because of the extended data collection phase.

In contrast, cross-sectional studies collect data at just one point in time, allowing for quicker access to research findings once data collection is complete. This makes cross-sectional studies particularly valuable for research projects that are under tight time constraints, have limited budgets, or where insights are needed quickly.

Another advantage of cross-sectional studies is their ability to collect data on multiple variables simultaneously. This not only saves time and money in data collection, but it also allows you to compare and contrast different data types within the same group of respondents. 

For example, imagine you run a sports equipment company. With a single cross-sectional survey, you can gather comprehensive information about your customers' characteristics, the types of sports that they like to play and watch, and their reactions to some of your current product offerings. 

Cross-sectional studies offer researchers a snapshot of the phenomenon of interest, serving as a crucial preliminary phase in broader research projects. For example, researchers often use cross-sectional studies to establish a baseline before a cohort study, or to understand a population before planning a longitudinal study. In this case, cross-sectional research designs can provide  information about the prevalence of certain attitudes, habits, and behaviors of interests that will be useful for designing more detailed research studies.

Despite their numerous advantages, cross-sectional studies come with several design and implementation challenges. Some of the most significant drawbacks include:

Cross-sectional research studies capture multiple types of data at a single point in time.  Therefore, even where the research design is explanatory in nature, it is not possible to determine the direction of causality between pairs of variables. 

Typically, the type of analysis that is performed when using a cross-sectional research design is a correlational analysis, which seeks to determine whether there is a relationship between two variables, and what the nature of that relationship is. For example, let’s say you’re interested in whether there is a relationship between concern with health and the amount of time spent vaping among users of vape products. 

You might find that as respondents’ concern for their health increases, the amount of time spent vaping decreases. This is useful information, but it tells you nothing about the direction of causality. In other words, you will not be able to tell from a cross-sectional research study whether being more concerned about your health reduces the amount of time spent vaping, or whether in fact, people who vape less become healthier. Only longitudinal research designs are able to disentangle cause from effect.

Another issue that you should be aware of when planning or conducting a cross-sectional research study is that your results might be affected by cohort differences: for example, the different traits, qualities, and characteristics present within groups of individuals. For example, customers who visit a smoothie shop on a Saturday might differ from those who go during the week in systematic ways. That’s why it's important to add control variables in any analysis  conducted on data gathered using a cross-sectional research design.

The most common way to gather cross-sectional research data is using surveys. However, it is important to remember that surveys designed to capture information about certain aspects of people's lives may not always result in accurate reporting and could cause biases. For example, respondents may not be able to accurately remember or describe their habits and behaviors. Furthermore, there is usually no mechanism to verify the information that survey respondents provide. This is simply a weakness that should be acknowledged when carrying out this type of research.

One thing to remember is that cross-sectional studies can be used for both analytical and descriptive purposes:

Generally aim to provide estimates of the characteristics of a sample, their attitudes, behaviors, or traits. For example, if you’re interested in people’s opinions about vaping and health, a descriptive cross-sectional study would probably be the best approach. In this case, you might use a survey with questions that assess respondents’ beliefs and attitudes towards vaping. 

These studies aim to assess the relationship between different parameters. For example, suppose you’re interested in how respondent age or gender affects their opinions about vaping and health. In that case, you might gather both demographic and opinion-related data, allowing you to conduct correlational analysis across these variables.

As we have discussed, a cross-sectional study is merely a snapshot of a certain group of people at a specified point in time. Longitudinal studies, on the other hand, are designed to explore how traits of a group of people change over a certain period of time, or to establish cause-and-effect relationships between input variables and outcomes. 

There are also three key differences between the two approaches:

Cross-sectional research designs are extremely versatile and can be used to answer a range of different research questions and in many different scenarios. Examples of the types of research questions that can be explored using this approach include:

Get accurate, data-driven insights with Usage & Attitudes: a purpose-built solution for your cross-sectional research into buyer attitudes and behaviors. Or, learn the latest research techniques by taking a look at our ultimate guide to market research.