The Method of Purging Applied to Repeated Cross-Sectional Data

In cross-sectional survey research, it is quite common to estimate the(standardized) effect of independent variable(s) on a dependent variable. However, if repeated cross-sectional data are available, much is to be gained if the consequences of these effects on longitudinal social change are considered.To assess these consequences, we describe a type of simulation in whichlongitudinal shifts in the independent variable's distribution, and longitudinal variation in effect on the dependent variable are `purged' from the data. Although the method of purging is known for many years, we add new practical features by relating the method to logistic and linear regression analysis. Because both logistic and linear regression analysis can be found in all majorstatistical packages, the method of purging is made available to a wider group of social scientists. With the use of repeated cross-sectional data, gathered in the Netherlands between 1970 and 1998, the new practical features of the purging method are shown, using the SPSS package.