planning a governance assessment - United Nations Development ...

Section
8
Sampling – The Basics
Every survey research project needs a sampling strategy. This applies to both qualitative and quantitative research.
Social scientists rely upon sampling to make inferences about a population.
• The population is the entire group of elements about which we would like to know something.
• A sample is a subset of these elements. Sometimes the researcher includes the entire population in the study,
which is called a census.
• Samples provide us with estimates of characteristics found in the population. Some estimates are better than others,
and most estimates contain some error.
• A sampling frame contains all the eligible elements for the study. Examples of sampling frames include a voters’ list,
membership list, or telephone book.
Two major types of sampling
Probability sampling is also commonly referred to as random sampling. In this type of sampling, each element in the
sampling frame has a known chance of ending up in the sample. Some of the major types in this category include Simple
Random, Systematic Random, Stratified, Multistage and Cluster sampling.
Non-probability sampling implies that personal judgment has somehow been involved in the decision about which
elements to include in the sample. One cannot say before the fact what the chances are of any one element being included
in the sample. The major types of non-probability (non-random) sampling include Purposive or Judgmental, Quota and
Snowball sampling.
Sample size and sampling error are related in probability-based samples. A poor sample can introduce error into results
in many ways. One, the sampling error, is easy to understand and calculate. Sampling error is directly related to the size of
the sample. It is the amount of error associated with the sample not representing the population on the measure of interest.
It is important that one knows the sampling error, or as it is commonly referred to, the margin of error (MOE). In probability
(i.e., random) samples, as sample size increase, the MOE decreases. Upon deciding on the amount of sampling error that one
can accept, always remember that this type of error increases when examining sub-groups in the overall sample (i.e. by sex,
age, education, regions).
For populations of more than 1,000, there should be a sample size of at least 500. However, an overall sample size of 500
restricts the ability to disaggregate the data and draw meaningful conclusions about factors such as sex, region, religion,
ethnicity or vulnerable groups. Once these groups are broken out, the sample sizes will shrink, increasing the amount of
sampling error associated with the results. Therefore, if possible, one should have a sample size of 500 for each group of
interest in the population.
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Planning a Governance Assessment: A Guide to Approaches, Costs and Benefits