Certain types of housing units are more likely to be missed than others, yet the Bureau’s current design for the coverage measurement postenumeration survey does not adequately take this into account.

Thorough analysis of data from the coverage measurement survey offers a unique opportunity to learn how census errors occur and how census processes might be changed to reduce them in the future. Working with outside researchers to the extent possible, the Census Bureau should study and give consideration to a richer menu of analytic methods using data collected from the coverage measurement postenumeration survey.

To date, the Census Bureau has not given sufficient attention to developing statistical models that link the frequency of the four components of coverage error to census processes, person and housing characteristics, and other predictors. These models, which can be thought of as forms of discriminant analysis, could use a wide variety of approaches, including logistic regression and various data mining methods, such as classification trees, support vector machines, and neural nets. It may be that modeling the frequency of erroneous enumerations may benefit from an entirely different approach than modeling the frequency of census duplicate enumerations, or census omissions. Consideration should also be given to the potential for using predictor variables that are specific to each type of error. Also, the use of separate models for distinct population subgroups should be considered.