Cross-Sectional Design

Characteristics of Cross-Sectional Design

The most prominent characteristic of cross-sectional designs is that all of the observed data are collected at a single point in time. This differs from longitudinal and experimental designs, which make multiple observations over time. Often, cross-sectional designs are used to examine and compare single variables across multiple subgroups that are similar in other characteristics. For example, a researcher might be interested to know if a health promotional message urging individuals to wear sunscreen is more effective for some subgroups than for others. In this sense, cross-sectional designs can be used to analyze numerous variables at once like age, gender, education level, or geographic location in order to see if they relate to increased reports of message compliance to wear sunscreen. Also, these types of designs are commonly used to identify patterns and prevalence of an outcome within a population and its subgroups at the given time point; in this case, the data can help determine if the health message resonates more readily among some groups within a population than others. These sorts of studies are then used to develop further empirical tests or to base communication intervention strategies.

Because cross-sectional designs occur at one point in time, providing a “snapshot” of the population of interest, they contrast longitudinal designs, which follow and reexamine samples and even individuals over time. This extended process of observation allows for greater assessment of time-order between variables that cross-sectional designs cannot account for in their observations. Because cross-sectional designs are carried out at one point in time, they are limited and give no explanation of the sequence of events between a cause and an outcome. Rather, they are best used to identify patterns, correlations, and incidence rates of a subject of study within a population. Such data can be used to describe the population of interest and to generate a new set of research questions and hypotheses that are better suited to establishing cause-and-effect relationships.

Sometimes, cross-sectional designs are used repeatedly to help establish a causal time-order. Known as repeated cross-sectional designs, these studies can be used to establish greater understanding of a population of interest by observing the same variables at different cross-sections of time, usually defined by some predetermined interval, be it days, weeks, months, or years. Each observation stands as its own cross-sectional study that observes a single population at a single time point, but together the set of observations can help to establish trends, patterns, and rates of change on a subject of interest to researchers.

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