The fact that survey data are obtained from units selected with complex sample designs
needs to be taken into account in the survey analysis: weights need to be used in analyzing
survey data and variances of survey estimates need to be computed in a manner that reflects the
complex sample design. This chapter outlines the development of weights and their use in
computing survey estimates and provides a general discussion of variance estimation for survey
data. It deals first with what are termed “descriptive” estimates, such as the totals, means, and
proportions that are widely used in survey reports. It then discusses three forms of “analytic”
uses of survey data that can be used to examine relationships between survey variables, namely
multiple linear regression models, logistic regression models and multi-level models. These
models form a set of valuable tools for analyzing the relationships between a key response
variable and a number of other factors. In this chapter we give examples to illustrate the use of
these modeling techniques and also provide guidance on the interpretation of the results.
experimental design. Analysts need to be clear that regression coefficients based on survey data
simply reflect relationships that exist between the dependent variable and the explanatory
variables in the population and do not necessarily imply causation. We have discussed how the
parameters of regression and logistic regression models relate to simple descriptive statistics and
how they may be interpreted for some relatively simple models.