Cross-sectional study
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In medical research, social science, and biology, a cross-sectional study (also known as a cross-sectional analysis, transverse study, prevalence study) is a type of observational study that analyzes data from a population, or a representative subset, at a specific point in time—that is, cross-sectional data.[definition needed]
In
In medical research, cross-sectional studies differ from
Healthcare
Cross-sectional studies involve data collected at a defined time. They are often used to assess the prevalence of acute or chronic conditions, but cannot be used to answer questions about the causes of disease or the results of intervention. Cross-sectional data cannot be used to infer causality because temporality is not known. They may also be described as censuses. Cross-sectional studies may involve special data collection, including questions about the past, but they often rely on data originally collected for other purposes. They are moderately expensive, and are not suitable for the study of rare diseases. Difficulty in recalling past events may also contribute bias.[citation needed]
Advantages
The use of routinely collected data allows large cross-sectional studies to be made at little or no expense. This is a major advantage over other forms of epidemiological study. A natural progression has been suggested from cheap cross-sectional studies of routinely collected data which suggest hypotheses, to case-control studies testing them more specifically, then to
Disadvantages
Routine data may not be designed to answer the specific question.
Routinely collected data does not normally describe which variable is the cause and which is the effect. Cross-sectional studies using data originally collected for other purposes are often unable to include data on
Most case-control studies collect specifically designed data on all participants, including data fields designed to allow the hypothesis of interest to be tested. However, in issues where strong personal feelings may be involved, specific questions may be a source of bias. For example, past alcohol consumption may be incorrectly reported by an individual wishing to reduce their personal feelings of guilt. Such bias may be less in routinely collected statistics, or effectively eliminated if the observations are made by third parties, for example taxation records of alcohol by area.[citation needed]
In addition, there may be cohort effect, in which differences in social and environmental influences are treated as developmental changes due to ageing.[3] Since the occurrence of differences is consistent with the division of generations and ethnic groups, that is, a group of people experiencing a common historical event is affected by a common influence, it is difficult to obtain the causal relationship of the event.[citation needed]
Weaknesses of aggregated data
Cross-sectional studies can contain individual-level data (one record per individual, for example, in national health surveys). However, in modern epidemiology it may be impossible to survey the entire population of interest, so cross-sectional studies often involve secondary analysis of data collected for another purpose. In many such cases, no individual records are available to the researcher, and group-level information must be used. Major sources of such data are often large institutions like the
Economics
In economics, cross-sectional analysis has the advantage of avoiding various complicating aspects of the use of data drawn from various points in time, such as
An example of cross-sectional analysis in economics is the regression of
See also
References
Sources
- 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
External links
- Study Design Tutorial Cornell University College of Veterinary Medicine