Sampling in Social Science Chapter 7 Summary Sampling in social science developed at the same time as polling for political purposes. There are two main types of sampling: probability sampling and nonprobability sampling. Nonprobability sampling consists of four different sub- groups: reliance on available subjects, purposive or judgmental sampling, snowball sampling, and quota sampling. Nonprobability sampling is helpful when conducting qualitative research. It is limited, however, because it does not provide precise and accurate information. When researchers want accurate information about a population they are studying, they use probability sampling. The premise behind probability sampling is that a sample must have the same variations...The end:
..... from subgroups within a sample. Cluster sampling is appropriate when the population is too large to create a list to use as a sampling frame. Cluster sampling is a multistage process of first using a group and then sampling a sub set of that group. When the clusters sample are of differing size it is useful to employ probability proportionate to size sampling. With this method each group is given a chance, which is proportionate to its size to be selected in the sample. Probability sampling allows researchers to find answers about a larger population without having to interact with thousands of subjects. It also allows researchers to arrive at accurate statistics, which clearly specify the range of error and confidence in those statistics.