This article gives an overview of cross-validation techniques in regression and covariance structure analysis. The method of cross-validation offers a means for checking the accuracy or reliability of results that were obtained by an exploratory analysis of the data. Cross-validation provides the possibility to select, from a set of alternative models, the model with the greatest predictive validity, that is, the model that cross-validates best. The disadvantage of cross-validation is that the data need to be split in two or more parts. This can be a serious problem when sample size is small. Various authors have therefore tried to find single sample criteria that provide the same kind of information as the cross-validation criteria but that do not require the use of a validation sample. Several of these criteria will be discussed, along with some results from studies comparing cross-validation and single sample criteria in covariance structure analysis.

In response to a changing environment, Statistics Netherlands has embarked on a large-scale redesign of the way statistics are produced. The aim is to increase the capability to respond to changing information demand, to lower the response burden for surveys, especially for businesses, and to improve efficiency, while preserving the overall quality level. The redesign is carried out within the framework of a so-called enterprise architecture, which gives overall guidance when structuring the processes of the organisation, including statistical methods and IT tools used. The article describes the redesign approach and explains the main features of the architecture. The emphasis is on experiences that may be relevant to other national statistical institutes operating in a similar environment.