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Introduction to Complex Samples
Procedures
An inherent assumption of analytical procedures in traditional software packages is that the
observations in a data file represent a simple random sample from the population of interest. This
assumption is untenable for an increasing number of companies and researchers who find it both
cost-effective and convenient to obtain samples in a more structured way.
The Complex Samples o ption allows you t o select a sample according to a complex design and
incorporate the design specifications into the data analysis, thus ensuring that your results are valid.
Properties of Complex Samples
A complex sample can differ from a simple random sample in many ways. In a simple random
sample, individual sampling units are selected at random with equal probability and without
replacement (WOR) directly from the entire population. By contrast, a given complex sample
can have some or all of the following features:
Stratification. Stratified sampling involves selecting samples independently within
non-overlapping subgroups of the population, or strata. For example, strata may be socioeconomic
groups, job categories, age groups, or ethnic groups. With stratification, you can ensure adequate
sample sizes for subgroups of interest, improve the precision of overall estimates, and use different
sampling methods from stratum to stratum.
Clustering. Cluster sampling involves the selection of groups of sampling units, or clusters. For
example, clusters may be schools, hospitals, or geographical areas, and sampling units may be
students, patients, or citizens. Clustering is common in multistage designs and area (geographic)
samples.
Multiple stages. In multistage sampling, you select a first-stage sample based on clusters. Then
you create a second-stage sample by drawing subsamples from the selected clusters. If the
second-stage sample is based on subclusters, you can then add a third stage to the sample. For
example, in the first stage of a survey, a sample of cities could be drawn. Then, from the selected
cities, households could be sampled. Finally, from the selected households, individuals could be
polled. The Sampling and Analysis Preparation wizards allow you to specify three stages in
adesign.
Nonrandom sampling. When selection at random is difficult to obtain, units can be sampled
systematically (at a fixed interval) or sequentially.
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