Cross-sectional study

(psychology) The study of groups of individuals differing on the basis of specified criteria (for example, age) at the same point in time.

The scientific method for the analysis of data gathered from two or more samples at one point in time.

A cross-sectional study is the simplest variety of descriptive or observational epidemiology that can be conducted on representative samples of a population. Simply put, it is a study that aims to describe the relationship between diseases (or other health-related states) and other factors of interest as they exist in a specified population at a particular time, without regard for what may have preceded or precipitated the health status found at the time of the study. For instance, a single cross-sectional study may include questions about smoking behavior, occupational exposure to dusts and fumes, respiratory symptoms (cough, breathlessness), and physical examinations of physical fitness—including simple tests of lung function. Such a study would throw some light on the relationship of both occupational exposures and smoking behavior to respiratory symptoms and respiratory function. However, it is impossible either to establish causal relationships or to get reliable perspectives on the natural history of respiratory disease from such a study.

Cross-sectional studies must be done on representative samples of the population if generalizations from the findings are to have any validity. These studies gather information about the prevalence of health-related states and conditions, but they cannot distinguish between newly occurring and long-established conditions. All they can do is measure the frequency (prevalence) of conditions and demonstrate associations. They cannot identify cause-and-effect relationships, though they do identify the existence of health problems.

Cross-sectional studies, also known as surveys, are a useful way to gather information on important health-related aspects of people's knowledge, attitudes, and practices (such studies are known as "KAP" surveys). In the area of reproductive health, such a survey might include questions such as: How much do girls and women in their reproductive years know about pregnancy and control over their own fertility? What are their beliefs, values, and attitudes towards making decisions about getting pregnant and about desired family size? How do they control their own fertility? KAP surveys are a good example of a tried and tested form of cross-sectional study. Many have been conducted serially to measure the efficacy of family-planning programs, anti-smoking measures, and other public health and health-promotion interventions. The distinction between a cohort study and a repeated cross-sectional study is that a cohort study is conducted with the same individuals who participate over a long period; repeated or serial cross-sectional studies, on the other hand, do not necessarily (or even usually) study the same individuals repeatedly.

The U.S. National Health Surveys are a form of cross-sectional study. Like many other cross-sectional studies, they can identify health problems and provide data from which many useful inferences can be made and hypotheses generated.

Cross-sectional studies are often used as a basis for health-policy decisions, and it is important to ensure that only current, rather than obsolete, information is used for this purpose. When health department resources are limited, information gathered in a cross-sectional study in the past can usually be refreshed with up-to-date facts from a small sub-sample; it would be necessary to repeat a large cross-sectional study only if the findings from a current sub-sample are seriously discrepant from earlier results.

(SEE ALSO: Case-Control Study; Cohort Study; Epidemiology; Survey Research Methods; Surveys)

Bibliography

Abramson, J. H., and Abramson, Z. H. (2000). Survey Methods in Community Medicine, 5th edition. Edinburgh & London: Livingstone.

Kelsey, J. E.; Whittemore, A. S.; Evans, A. S.; and Thompson, D. (1996). Methods in Observational Epidemiology, 2nd edition. New York: Oxford University Press.

— JOHN M. LAST

A study in which a statistically significant sample of a population is used to estimate the relationship between an outcome of interest and population variables as they exist at one particular time. Since both the outcome and the variables are measured at the one time these studies are not strong at showing cause–effect relationships.

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Cross-sectional studies (also known as Cross-sectional analysis) form a class of research methods that involve observation of some subset of a population of items all at the same time, in which, groups can be compared at different ages with respect of independent variables, such as IQ and memory. The fundamental difference between cross-sectional and longitudinal studies is that cross-sectional studies take place at a single point in time and that a longitudinal study involves a series of measurements taken over a period of time. Both are a type of observational study. Cross-sectional studies are used in most branches of science, in the social sciences and in other fields as well. Cross-sectional research takes a 'slice' of its target group and bases its overall finding on the views or behaviours of those targeted, assuming them to be typical of the whole group.

Cross-sectional studies in medicine

Cross-sectional studies can be thought of as providing a "snapshot" of the frequency and characteristics of a disease in a population at a particular point in time. This type of data can be used to assess the prevalence of acute or chronic conditions in a population. However, since exposure and disease status are measured at the same point in time, it may not always be possible to distinguish whether the exposure preceded or followed the disease.

The cross-sectional survey--which, like a snapshot, "freezes" a specific moment in time--aims at finding the same kind of relationships that might be shown by the "moving picture" of the cohort study, but at far less cost. In a cross-sectional survey, a specific group is looked at to see if a substance or activity, say smoking, is related to the health effect being investigated--for example, lung cancer. If a significantly greater number of smokers already have lung cancer than those who don't smoke, this would support the hypothesis that lung cancer is correlated with smoking.

Cross-sectional analysis studies the relationship between different variables at a point in time. For instance, the relationship between income, locality, and personal expenditure. Unlike time series, cross-sectional analysis relates to how variables affect each other at the same time and period.

See also

• Cross-sectional data
• Cross-sectional regression

References

• Epidemiology for the Uninitiated by Coggon, Rose, and Barker, Chapter 8, "Case-control and cross-sectional studies", BMJ(British Medical Journal) Publishing, 1997
• Research Methods Knowledge Base by William M. K. Trochim, Web Center for Social Research Methods, copyright 2006
• Cross-Sectional Design by Michelle A. Saint-Germain

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