We establish a mathematical framework that formally validates the two-phase ``super-population viewpoint'' proposed by Hartley and Sielken [Biometrics 31 (1975) 411--422] by defining a product probability space which includes both the design space and the model space. The methodology we develop combines finite population sampling theory and the classical theory of infinite population sampling to account for the underlying processes that produce the data under a unified approach. Our key results are the following: first, if the sample estimators converge in the design law and the model statistics converge in the model, then, under certain conditions, they are asymptotically independent, and they converge jointly in the product space; second, the sample estimating equation estimator is asymptotically normal around a super-population parameter.Comment: Published at http://dx.doi.org/10.1214/009053605000000651 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

We introduce a special type of graphs, which we use as tools for designing and improving survey questionnaires. While the idea of representing questionnaires as graphs is not new, our graphs constitute novel theoretical and practical tools, which could turn a complex questionnaire into a questionnaire that is easier to visualize, test and analyze.