Examples:

1. In a stratified random sample, the population is first classified into groups (called strata) with similar characteristics. Then a simple random sample is chosen from each strata separately. These simple random samples are combined to form the overall sample.

Examples of characteristics
on which strata might be based include: gender, state, school district,
county, age.

Reasons to use a stratified rather than simple random sample include:

- The researchers may be interested in studying results by strata as well as overall. Stratified sampling can help ensure that there are enough observations within each strata to be able to make meaningful inferences by strata.
- Statistical techniques can be chosen taking the strata into account to allow stronger conclusions to be drawn.
- Practical considerations may make it impossible to take a simple random sample.

The results from cluster
samples are not as reliable as the results of simple random samples or
stratified samples, so it should only be used if practical
considerations do not allow a better sample scheme. For example, in the
convenience store example, it may be practically speaking impossible to
draw up a list of all convenience store employees in the city, but it
would be much less difficult to draw up a list of all the convenience
stores in the city.

3. There are also many adaptive sampling designs

1. See, for example, M. Davern et al (2007) Drawing Statistical Inferences from Historical Census Data, Minnesota Population Center; download from http://www.pop.umn.edu/research/mpc-working-papers-series/2007-working-papers-1/

2. See http://web.eecs.umich.edu/~qstout/AdaptSample.html for a brief discussion of adaptive sampling designs, focusing on clinical trials and computer applications.

See Seber, George A. F. and Mohammad M. Salehi (2013), Adaptive Sampling Designs: Inference for Sparse and Clustered Populations for discussion of several types of adaptive sampling designs, together with appropriate methods of analysis. (This book focuses mainly on biological applications. It requires some background in mathematical statistics. )

Last updated June 4, 2013