When designing a research project, the study team has many data collection methods available. A cross-sectional study is a powerful tool that captures data at a single point in time from a large pool of subjects. Researchers usually collect data about their hypothesized phenomenon, but they also gather demographic and other relevant characteristics so that they can compare their findings to other groups. A cross-sectional study can be used in virtually any discipline that conducts scientific research.
Cross-sectional describes the time frame in which the study is conducted. This is opposed to a longitudinal study, which captures data at several points in time either from the same study participants or from similar subject pools. A cross-sectional study is typically less expensive to conduct than a longitudinal study, because subjects do not have to be tracked over time. Also, this type of analysis does not suffer from participant attrition as longitudinal research does. Another advantage of cross-sectional studies is that data analysis can commence immediately after collection has concluded.
The cross-sectional study method does have some disadvantages. Since data is collected at a single point in time, researchers can not draw conclusions about causation from them. For example, if a researcher finds that heart disease is common among office workers, this research method precludes him from claiming that office work contributes to heart disease. In some cases, a cross-sectional study may not be feasible because of a lack of participants. For example, in the case of rare disease, the researcher may not have access to enough research subjects to make a generalizable conclusion about his or her hypothesis.
Researchers who chose a cross-sectional study design may be confounded by historical factors during or before data collection. For example, a researcher studying emergency preparedness may not get accurate results if he conducted a survey immediately after a major hurricane. In the same circumstance, a longitudinal study would show trends in emergency preparedness and demonstrate if the hurricane had an effect on the phenomenon.
If a researcher wants to describe the prevalence of a given time, he or she may select a cross-sectional study design. For example, a team of researchers might want to know more about autism and education. They could survey teachers about the number of autistic students in their classes, educational and behavioral characteristics of their students, and resources available to autistic children. The study might also capture demographic characteristics such as the genders of autistic students, the age and grade level of the students, and the region of the country in which the school is located for comparative analysis.
Cross-sectional and longitudinal studies describe the timing of data collection. Thus, a cross-sectional study can be combined with most quantitative research methods. A cross-sectional survey may ask participants to describe their experience with breast cancer. While studying the same disease, a cross-sectional content analysis may examine how medical journals approach breast cancer or how many articles are dedicated to breast cancer research.