Cross-sectional study
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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.
[edit] 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 caused by 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.
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