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In medical research, epidemiology, 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.
Cross-sectional data differs from time series data, in which the same small-scale or aggregate entity is observed at various points in time. Another type of data, panel data (or longitudinal data ), combines both cross-sectional and time series data aspects and looks at how the subjects (firms, individuals, etc.) change over a time series.
In statistics and econometrics, a cross-sectional regression is a type of regression in which the explained and explanatory variables are all associated with the same single period or point in time. This type of cross-sectional analysis is in contrast to a time-series regression or longitudinal regression in which the variables are considered ...
A cross-sequential design is a research method that combines both a longitudinal design and a cross-sectional design. It aims to correct for some of the problems inherent in the cross-sectional and longitudinal designs.
Cross-sectional study: involves data collection from a population, or a representative subset, at one specific point in time. Longitudinal study: correlational research study that involves repeated observations of the same variables over long periods of time. Cohort study and Panel study are particular forms of longitudinal study.
In cross-sectional studies, a sample (or samples) is drawn from the relevant population and studied once. [3] A cross-sectional study describes characteristics of that population at one time, but cannot give any insight as to the causes of population characteristics because it is a predictive, correlational design.
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A literature search often involves time series, cross-sectional, or panel data. Cross-panel data (CPD) is an innovative yet underappreciated source of information in the mathematical and statistical sciences. CPD stands out from other research methods because it vividly illustrates how independent and dependent variables may shift between ...