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DHS sample designs are usually two-stage probability samples drawn from an existing sample frame, generally the most recent census frame. A probability sample is defined as one in which the units are selected randomly with known and nonzero probabilities. A sampling frame is a complete list of all sampling units that entirely covers the target population.
Stratification is the process by which the sampling frame is divided into subgroups or strata that are as homogeneous as possible using certain criteria. Within each stratum, the sample is designed and selected independently. The principal objective of stratification is to reduce sampling errors. In a stratified sample, the sampling errors depend on the population variance existing within the strata but not between the strata. Typically, DHS samples are stratified by geographic region and by urban/rural areas within each region.
Within each stratum, the sample design specifies an allocation of households to be selected. Most DHS surveys use a fixed take of households per cluster of about 25-30 households, determining the number of clusters to be selected. In the first stage of selection, the primary sampling units (PSUs) are selected with probability proportional to size (PPS) within each stratum. The PSUs are typically census enumeration areas (EAS). The PSU forms the survey cluster. In the second stage, a complete household listing is conducted in each of the selected clusters. Following the listing of the households a fixed number of households is selected by equal probability systematic sampling in the selected cluster.
Sampling weights are adjustment factors applied to each case in tabulations to adjust for differences in probability of selection and interview between cases in a sample, due to either design or happenstance. In DHS surveys, in most surveys the sample is selected with unequal probability to expand the number of cases available (and hence reduce sample variability) for certain areas or subgroups for which statistics are needed. In this case, weights need to be applied when tabulations are made of statistics to produce the proper representation. When weights are calculated because of sample design, corrections for differential response rates are also made.
Response rate groups are groups of cases for which response rates are calculated. In DHS surveys, households and individuals are grouped into sample strata and response rates are calculated for each stratum.
Sample design weights are produced by the DHS sampler using the sample selection probabilities of each household and the response rates for households and for individuals. The initial design weights are then normalized by dividing each weight by the average of the initial weights (equal to the sum of the initial weight divided by the sum of the number of cases) so that the sum of the normalized weights equals the sum of the cases over the entire sample. The normalization is done separately for each weight.
The stratification variable is typically v023 (or hv023 or mv023), however the stratification variables have not been consistently defined in many surveys and may need to be created. It is best to check the sample design in Appendix A of the DHS survey reports to verify the stratification used in the design of the sample. In many surveys, the stratification is based on urban and rural areas in each region (v024 x v025).
To apply the complex sample design parameters in estimating indicators each of the statistical software use a different set of commands applying the sample design and producing the indicator estimates:
The below examples for Stata, SPSS, and R, continuing on from example 1, demonstrate the use of the complex sample designs for estimates of current use of modern methods, together with standard errors and confidence intervals.
Sample weights are inversely proportional to the probability of selection and are used to correct for the under- or over-sampling of different strata during sample selection. If weights are not used, all calculations will be biased toward the levels and relationships in the over-sampled strata. Comparisons of regression coefficients, as well as rates, percentage, means, etc. coming from different surveys are only valid if weights have been used to correct for the sample designs of the different surveys.
For more information on DHS sample design, stratification and sample weights, see the DHS Sampling and Household Listing Manual ( -DHSM4-DHS-Questionnaires-and-Manuals.cfm). See also the DHS YouTube videos:
The all women factors are used because of the selection process used in the design of the sample. Therefore, it is only appropriate to use internally generated factors and not to use information external to the survey. For each subgroup to be estimated, the same factor is applied to each woman irrespective of the time period to be estimated since it is based on sample selection.
Region of residence (hv024, v024, v101, mv024) is defined for every cluster or enumeration area as part of the sample design for the survey. Region of residence is typically the first administrative level within the country, or a grouping of the first administrative level.
Type of place of residence (hv025, v025, v102, mv025) is the designation of the cluster or enumeration area as an urban area or a rural area. As for region of residence, type of place of residence is established for the cluster as part of the sample design for the survey, and cannot vary within cluster.
The definition of a cluster as urban or rural is made according to the definition used in each country. The traditional distinction between urban and rural areas within a country has been based on the assumption that urban areas, no matter how they are defined, provide a different way of life and usually a higher standard of living than rural areas. In many developed countries this distinction has become blurred, and the principal difference between urban and rural areas in terms of living standards tends to be the degree of population concentration or density (UNSD, 2017). UNSD recommends that the classification of a cluster as urban or rural is made first and foremost on a measure of population density. However, other criteria may also be considered in the designation of the cluster, including the percentage of the population involved in agriculture, the availability of electricity or piped water, and the ease of access to healthcare, schools, or transportation, among others. There is no one standard definition of urban and rural, and the definition used is necessarily country specific and may change over time.
This vignette assumes the reader is familiar with the topics covered in the getting started vignette. It expands on that content, demonstrating how to use the distance sampling survey design package, dssd (Marshall, 2019) when your study region is made up of multiple strata. This vignette will detail how you can select different designs (within the same design category, either lines or points) for each stratum and provide stratum specific design parameters. Please note that the examples provided in this vignette are designed to make the reader aware of what is possible inside the dssd package and the designs are not necessarily something that we would recommended for these example survey regions.
There are a number of reasons that we may wish to create a stratified design. Firstly, it may be for efficiency reasons. For example, we may wish to divide our region into a number of more convex shapes when using an equal spaced zigzag design to reduce off-effort transit time at the survey region boundary. Figure 1 presents an example of a Minke whale survey, in the left panel when the zigzag design is generated in the study region as a whole we can see that it is fairly inefficient with large distances between the ends of transects (note that only the lengths of transect inside the shaded study area will be surveyed). In the right panel we have reduced the off effort transit time by dividing the study area into a number of strata. As the purpose of this stratification is simply to improve efficiency we would still want to try to achieve equal coverage across all strata.
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