appropriateness of comparisons with previous censuses, and such differences need to be interpreted carefully.

An assessment of census data quality is important not only for data users but also for the Census Bureau to help plan future censuses. For this purpose, it is important to conduct analyses to determine one or more underlying explanations for quality successes and problems so that changes can address relevant causal factors.

The ideal method for causal analysis is experimentation in which subjects are randomly assigned to treatment and control groups. Such experimentation is of limited use for understanding census data quality. Experiments were conducted in the 2000 census, but they were few in number, small in scale, and narrow in scope.¹ Census planning tests conducted between census years are often experimental in nature, but they, too, are necessarily limited in scope. Moreover, census tests cannot be conducted under the same conditions as an actual census with regard to publicity, scale of operations, and other features.

So, while experimentation can help identify some reasons for the levels of census data quality achieved nationwide and for areas and population groups, the search for underlying explanations must rely primarily on nonexperimental methods, such as multivariate analysis of quality indicators. We used such methods for analyzing mail return rates as a function of neighborhood characteristics and imputation rates as a function of geographic location and type of census enumeration. While these types of analysis will probably not produce definitive conclusions regarding cause and effect, they are important to pursue for clues to alternative census procedures that merit testing for 2010.

To convey our overall assessment of the 2000 census, we provide 11 major findings and a summary conclusion. These findings cover the successes of the census in the face of considerable adversity,