Survey Weights in Demographic Data Analysis

Survey data, such as the widely used Demographic Health Surveys (DHS), are collected using complex survey methods involving various stages and selection processes. Analysing such data requires proper consideration of survey design aspects, including survey weights, stratification, and clustering. In this post, I will focus specifically on survey weights. In probability surveys, these weights represent the inverse probabilities of being chosen for the survey. The likelihood of being selected to participate in these surveys is rarely the same for everyone, as some groups may be oversampled. Applying weights is necessary to obtain estimates that are representative of the population from which the sample was drawn. However, things become more complex when we move beyond basic statistics to analyse relationships. Whether or not we should use weights in analysing relationships is a topic of ongoing debate.

There are some challenges in applying survey weights. One challenge arises when we attempt to combine data from multiple countries to obtain regional estimates or different time periods for the same country to look at trends. Survey weights are typically calculated separately for each survey, which can make it difficult to derive weights that can be applied in these situations. Similarly, questions arise when analysing subgroups, such as young people, for whom survey weights might not have been specifically calculated. Moreover, incorporating survey weights in procedures such as Hierarchical Linear Modelling (HLM) requires calculating level weights, which are not provided in many datasets. Finally, some procedures, like interval-censored regression as implemented in major statistical software, lack the option to specify survey weights.

Dealing with the complexities of survey weights can be challenging, and it's no surprise that survey weights frequently emerge as a topic of discussion within the DHS and other survey forums.