Repeated Cross Sectional Design

Most importantly researchers have developed unique methods of data collection and statistical analyses to overcome concerns of causality in cross sectional research including retrospective or life history analyses experimental survey designs and repeated cross sections or trend data.

Repeated cross sectional design. The repeated measures design is an embellishment of the cross sectional approach in which the original cross sectional study sample is followed longitudinally thus becoming a prospective cohort study. In this kind of study the subset of the population or the whole population is chosen and from the selected participants data is gathered for the purpose of helping answer research questions of interest. For an annual survey this means that respondents in one year will be different people to those in a prior year.

This poses two serious challenges. The reason why it is known as the cross sectional study design is due to the information regarding x and y which is collected shows what happens at a single point of time. Repeated cross sectional data are created where a survey is administered to a new sample of interviewees at successive time points.

Many important cross sectional surveys are repeated at regular or irregular intervals so that estimates of changes can be made at the aggregate or population level. First as with pcsts autocorrelation threatens inferences. Repeated cross sectional studies conflate several sources of variability differences in the initial status of individua ls individual differences in the growth curves and individual by measurement occasion differences in ways that are not easily separated.

Cross sectional design is one of the most well known and commonly used study designs. Cross sectional survey data are data for a single point in time. These surveys are designed to give good estimates for the current population and the changes or movements that have occurred since the last survey or previous surveys.

This chapter summarizes the design implementation and standard data analysis methods for cross sectional and repeated measures studies. Examples include monthly labor force surveys retail trade surveys television and radio ratings surveys and political opinion polls. Ideally repeated cross sectional study is that you take cross section of given population at different time frame and then define the changes in given parameter for example the weight of 5years.