Chapter Summary: Population Sampling (Other (Not Listed) Sample)

Chapter Summary: Population Sampling Name University/Institution The need to reduce the cost of a research study calls for sampling. By definition, a sample refers to a portion of the population considered in the survey. A population on the other hand means all the units or elements of interest for a certain study put together. When sampling, a researcher may choose either probability sampling or non-probability sampling. Probability sampling refers to where each member of the population has an equal chance of being selected to be part of the sample while the non-probability sampling refers to where members of a population do not have an equal chance of being selected. When carrying out sampling, a researcher should avoid bias, for the sample to be considered as representing the whole population. The process of sampling begins with the selection of a sample frame (list) followed by selection of a sampling design (sample type). A sample frame is by definition that list of population units of members from which the sample is drawn. For probability sampling, the sampling designs or sample types include simple random samples, systematic samples, stratified samples and cluster samples while for non-probability sampling the sample types include purposive samples and snowball samples. In simple random samples, the researcher simply selects a part of the population on the basis of random chance, that is, each member of the population has an equal chance of being selected. Although almost similar to simple random sampling, systematic sampling differs in that a researcher chooses every kth unit or member of the population from the sample frame list. For example, in a list, the research may select every fifth unit. Stratified sampling is most suited to a population consisting of many different types of units or elements as it takes care of differences between the various groups. Stratified samples are selected through considering various groups within the population that have similar characteristics and this is what is referred to as subgrouping. Cluster sampling on the other hand is where the population is divided into groups with each group subjected to simple random sampling. The groups in cluster sampling differ from those in stratified sampling in that they do not share any similarities. In purposive sampling, the researcher selects the sample on the basis of his or her previous knowledge of the population characteristics. ...