Quality & Quantity 38: 1–16, 2004.
© 2004 Kluwer Academic Publishers. Printed in the Netherlands.
The Method of Purging Applied to Repeated
Practical applications using logistic and linear regression analysis
MANFRED TE GROTENHUIS
, ROB EISINGA and PEER SCHEEPERS
Department of Social Science Research Methods, University of Nijmegen, PO Box 9104, 65000 HE
Nijmegen, The Netherlands
Abstract. 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
To assess these consequences, we describe a type of simulation in which longitudinal 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 major statistical 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.
Key words: purging, simulation, counter factual analysis, repeated cross-sectional survey, logistic
and linear regression analysis
There is a long tradition in social sciences of collecting cross-sectional survey data.
As a result, a massive quantity of longitudinal data is available nowadays. These
data are in many events more or less suitable to test causal models. Besides, these
data are often tailor-made to test to what extent parameters in these models vary
over time. However, to explain longitudinal social change, one has to go beyond
the causal modeling of effects and their over-time variation. To answer the question
on the causes of social change, we have to asses the consequences of shifting
distribution(s) and of varying effect(s). One way to assess these consequences,
is to simulate a counter factual situation in which both the distribution of the
independent variable(s) and its effect(s) remain constant over time. This kind of
simulation is often labelled ‘purging’ (for an overview see: Clogg, 1978; Clogg &