# The Power of Survey Design: A User's Guide for Managing Surveys, Interpreting Results, and Influencing Respondents

By Giuseppe Iarossi | Go to book overview

Chapter 4
A Practical Approach to Sampling

The major strength of
probability sampling is that
the probability selection
mechanism permits the
development of statistical
theory to examine the
properties of sample
estimators. […] The
weakness of all
nonprobability methods is
that no such theoretical
development is possible; as a
consequence, nonprobability
samples can be assessed only
by subjective valuation
.

—Graham Kalton,
Introduction to Survey Sampling

If proper sampling procedures are followed, in a matter of days, an opinion poll conducted on approximately 1,000 individuals can be reasonably taken as a measure of public opinion for a population as large as China’s. This is the power of sampling, its ability to approximate from a small group the characteristics of the whole population within a know margin of error.

Different methods of respondents’ selection can be employed when conducting a survey, that is, interviewing experts, the typical respondent, or a group of respondents. Because only part of the population is sampled, the estimated parameters are subject to a sampling error. Sampling error is a measure of “how closely we can reproduce from a sample the results that would be obtained if we should take a complete count or census” (Hansen, Hurwitz, and Madow 1953,10).

The ability to estimate and reduce this error depends on how the sample is selected. If the researcher knows what chances each population member has to be included in the sample, he can use statistical theory to estimate the properties of the survey statistics. On the contrary, when the selection of respondents is based on personal judgment, it is not possible to have an objective measure of the reliability of the sample results (Kalton 1983). This is not to say that there is no room for subjective judgment in probability sampling. Rather, subjective judgment plays an important role in sample design as long as the final selection of the sample elements is left to a random process (Hansen, Hurwitz, and Madow 1953).

Volumes have been written on probability sampling. Hence, far from being a discussion on sampling techniques, what follows is a short review of how to determine the sample size using four of the most commonly used sampling procedures: simple random sampling (SRS), stratified random sampling, systematic sampling, and probability proportional to size (PPS) sampling. Particular attention is dedicated to how to deal with

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