Survey Sampling - Probability Sampling

Probability Sampling

In a probability sample (also called "scientific" or "random" sample) each member of the target population has a known and non-zero probability of inclusion in the sample. A survey based on a probability sample can in theory produce statistical measurements of the target population that are:

• unbiased, the expected value of the sample mean is equal to the population mean E(ȳ)=μ, and
• have a measurable sampling error, which can be expressed as a confidence interval, or margin of error.

A probability-based survey sample is created by constructing a list of the target population, called the sample frame, a randomized process for selecting units from the sample frame, called a selection procedure, and a method of contacting selected units to and enabling them complete the survey, called a data collection method or mode. For some target populations this process may be easy, for example, sampling the employees of a company by using payroll list. However, in large, disorganized populations simply constructing a suitable sample frame is often a complex and expensive task.

Common methods of conducting a probability sample of the household population in the United States are Area Probability Sampling, Random Digit Dial telephone sampling, and more recently Address-Based Sampling.

Within probability sampling there are specialized techniques such as stratified sampling and cluster sampling that improve the precision or efficiency of the sampling process without altering the fundamental principals of probability sampling. Dynamic sampling in surveys was first introduced by Govindarajulu, Z. and MN Katehakis in 1991.