Regular statistical software (that is not designed for survey data) analyzes data as if the data were collected using simple random sampling. For experimental and quasi-experimental designs, this is exactly what we want. However, when surveys are conducted, a simple random sample is rarely collected. Not only is it nearly impossible to do so, but it is not as efficient (both financially and statistically) as other sampling methods. When any sampling method other than simple random sampling is used, we need to use survey data analysis software to take into account the differences between the design that was used and simple random sampling.

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The sampling design affects the calculation of the standard errors of the estimates. If you ignore the sampling design, e.g., if you assume simple random sampling when another type of sampling design was used, the standard errors will likely be underestimated, possibly leading to results that seem to be statistically significant, when in fact, they are not. The difference in point estimates and standard errors obtained using non-survey software and survey software with the design properly specified will vary from data set to data set, and even between variables within the same data set. While it may be possible to get reasonably accurate results using non-survey software, there is no practical way to know beforehand how far off the results from non-survey software will be.

Sampling designs
Most people do not conduct their own surveys. Rather, they use survey data that some agency or company collected and made available to the public. The documentation must be read carefully to find out what kind of sampling design was used to collect the data. This is very important because many of the estimates and standard errors are calculated differently for the different sampling designs. Hence, if you mis-specify the sampling design, the point estimates and standard errors will likely be wrong.

Below are some common features of many sampling designs

Weights: There are many types of weights that can be associated with a survey. Perhaps the most common is the sampling weight, sometimes called a pweight, which is used to denote the inverse of the probability of being included in the sample due to the sampling design (except for a certainty PSU, see below). The pweight is calculated as N/n, where N = the number of elements in the population and n = the number of elements in the sample. For example, if a population has 10 elements and 3 are sampled at random with replacement, then the pweight would be 10/3 = 3.33. The sum of the pweights should equal the population total.

PSU: This is the primary sampling unit. This is the first unit that is sampled in the design. For example, school districts from California may be sampled and then schools within districts may be sampled. The school district would be the PSU. If states from the US were sampled, and then school districts from within each state, and then schools from within each district, then states would be the PSU. One does not need to use the same sampling method at all levels of sampling. For example, probability-proportional-to-size sampling may be used at level 1 (to select states), while cluster sampling is used at level 2 (to select school districts). In the case of a simple random sample, the PSUs and the elementary units are the same.

Strata: Stratification is a method of breaking up the population into different groups, often by demographic variables such as gender, race or SES. Once these groups have been defined, one samples from each group as if it were independent of all of the other groups. For example, if a sample is to be stratified on gender, men and women would be sampled independent of one another. This means that the pweights for men will likely be different from the pweights for the women. In most cases, you need to have two or more PSUs in each stratum. The purpose of stratification is to improve the precision of the estimates.

FPC: This is the finite population correction. This is used when the sampling fraction (the number of elements or respondents sampled relative to the population) becomes large. The FPC is used in the calculation of the standard error of the estimate. If the value of the FPC is close to 1, it will have little impact and can be safely ignored. In some survey data analysis programs, such as SUDAAN, this information will be needed if you specify that the data were collected without replacement.