In terms of time frame, there are two types of approach that can be used to conduct research studies: a cross-sectional approach, or a longitudinal approach. Cross-sectional research studies are those research designs in which data is captured 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. The participants in this study are selected based on particular variables of interest, but typically the researcher is interested in describing the characteristics of the population, or in explaining the relationship between a particular outcome and some other variable or variables of interest. This type of research design can be used to assess the attitudes, interests or behaviors of a study sample, for instance, and is therefore particularly useful in informing the planning and allocation of resources. For example, an owner of a smoothie shop might use a quick survey administered at the point of sale to determine the types of fruits and vegetables that customers prefer. The data captured can be used in determining 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 (as distinct from experimental type studies). This means that researchers do not manipulate variables, such as when a researcher assigns different cohorts in a sample to different study groups. Rather, the role of you as a researcher is simply to record the information that is present 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 in nature because it cannot be used to determine the cause of something.
Cross-sectional research studies gather data at one point in time, and therefore the insights that they provide should be acknowledged as a snapshot of a current time period. For instance, returning to our example above, 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.
While you will likely be responsible for the design and creation of tools involved in collecting cross-sectional data, as a researcher, you will not be involved in manipulating the study environment. For example, if you are interested in 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 this 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 designs often gather multiple pieces of demographic data or other data that will enable you to draw up a picture of your target population.
It is also useful to use regular cross-sectional market research studies to build up a series of snapshots about what is currently happening in a population of interest. For instance, the smoothie shop owner might survey customers once every season to determine how preferences for certain types of fruit and vegetables change over time.
Although they are relatively quick and simple to perform, much can be gleaned from cross-sectional research studies - one of the reasons why they’re so popular. The data gathered from cross-sectional studies can be used in multiple ways:
The typical way in which cross-sectional research is used is with a sampling frame. Using this approach, the researcher identifies a population of interest - such as those who enjoy vaping - and then extracts a sample from that population. By capturing data of interest from the sample and generalizing the findings, the characteristics of the broader vaping population might be described.
Another use of cross-sectional studies is to make inferences about the nature and strength of relationships between variables. For instance, if you’re interested in whether younger people who vape are more or less concerned about their health compared to 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 involve the manipulation of variables. They cannot establish cause and effect relationships, but they can be the first step in later research taking an experimental or a longitudinal approach. That’s because they give you insight into the population of interest and establish foundations of knowledge that can be used in the planning of further research study.
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, a survey methodology might be used, with questions directed to assess respondents’ beliefs and attitudes.
These studies aim to assess the relationship between different parameters. For example, if you’re interested in how respondent age or gender affects their opinions about vaping and health, you might gather both demographic and opinion-related data, allowing you to conduct correlational analysis across these variables.
There are several benefits to undertaking cross-sectional studies.
Perhaps the greatest advantage of this approach is that cross-sectional studies can be undertaken quickly and relatively inexpensively, especially when compared to longitudinal studies. Longitudinal studies require tracking the same individuals over a certain length of time, and it may be necessary to obtain very large sample sizes in order to accommodate sample churn that will naturally occur over the time period of the study. Both of these activities are costly, both in terms of finance and time. Furthermore, depending on the length of the data collection period, it may be some time before the research findings are available. In contrast, the cross-sectional approach only requires the researcher to capture data from the sample at a single period in time, and research findings are potentially available as soon as data collection ends. For this reason, cross-sectional studies are useful for research studies that are time-constrained, where insights are needed quickly, or where there are financial limitations.
Another benefit is that multiple variables can be captured at once, which not only reduces the cost and time investment associated with data collection, but also enables you to compare and contrast different types of data across a single cohort of respondents. For example, imagine you run a sports equipment company. If you want to know the characteristics of your customers, the types of sports that they like to play and watch, as well as their reactions to some of your current product offerings, you could potentially capture all of this information in a single, cross-sectional survey-based study.
Since they provide investigators with a snapshot into the phenomenon of interest, cross-sectional studies are often a preliminary stage of broader research studies. For example, cross-sectional studies are often conducted to establish a baseline before a cohort study or in order 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. This information will be useful for designing more detailed research studies.
In spite of the many benefits, there are some challenges associated with the design and implementation of cross-sectional studies. Let’s consider some of the more prevalent drawbacks.
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 instance, 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 that arise because of the different traits, qualities, and characteristics of different groups of individuals. For instance, 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 a survey methodology. 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 recall or describe their habits and behaviors, and there is usually no mechanism for verifying the information that they provide. This is simply a weakness that should be acknowledged when carrying out this type of research.
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:
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:
This is because the same respondents must be tracked over time, and there are multiple points of data collection.
In order to counteract the problem of respondent dropout that occurs naturally over time.
Can be used for studies that determine whether a behavior or trait influences an outcome that occurs later. For example, if you’re interested in whether a low carbohydrate diet influences weight loss, you might follow a group of people following this diet over a period of a year or so.
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