Aids

Key terms

Area Sample Census Chain sampling Chance selection Custer sample Convenience sample Enumeration Haphazard sample Interval sample Multistage sample Network sample Nonprobability Parameter Population Probability sampling Purposive sample Quota sample Random selection

Response rate Sample Sample frame Sample design Sampling element Sampling interval Sampling unit Self weighted sample Simple random sample Snowball sample Stratified random sample Statistic Systematic random sample Target population Weighted sample

Main points

1. Samples are used because it is frequently impractical to collect data from the large groups of persons or other entities, called the population, that we want to learn about.
2. Probability sampling is based on selection of a sample entirely by chance, without any human judgment or preference. Probability sampling is also called random sampling.
3. Only probability sampling allows an investigator to generalize findings from a sample to the population from which it was drawn.
4. Methods of probability sampling are: the simple random sample; systematic or interval random sampling; stratified random sampling; and cluster or multistage sampling.
5. Necessary steps in sampling include: (1) defining the target population, which is the specific, concrete definition of the population based on elements that will be sampled; (2) identification of a suitable sampling frame; and (3) random selection of the desired number of sample elements to compose the sample.
6. The most frequent problems in sampling are: (1) the target population is not clearly defined and delimited; (2) the sample frame does not match the target population; and (3) mistakes or errors are made in the process of selecting the sample.
7. Nonprobability samples sometimes have to be used. Kinds of nonprobability samples are: (1) the convenience or haphazard sample; (2) quota sample; purposive sample; and (4) the network sample, also called a chain or snowball sample.
8. Factors to be considered in deciding on sample size are: (1) how heterogeneous the population is — larger samples are needed for populations with greater variation; (2) how accurately one wishes to estimate parameters in the population — larger samples provide more accurate estimates; and (3) the number of variables that will be analyzed simultaneously — for seeking relationships among two or more variables, larger sample sizes are desirable. This said, samples of at least 100 cases are recommended, but generally larger sample sizes are preferable. Practical limitations, however, often limit the number of interviews that can be completed and, hence, also limit the size of the sample.