Sample Design 2-1 Sample Subset of a larger population Population Any complete group People Sales people Stores Students Teachers Census Investigation of all individual elements that make up a population Sampling Frame A list of elements from which the sample may be drawn Such as Mailing lists Commercial directories Telephone directory Databases Categories of Sampling Probability sampling Known chances Every member gets a chance Nonprobability sampling Unknown Probability of selecting any particular member Nonprobability Sampling Convenience Judgment Quota Snowball Convenience Sampling Obtaining the people who are most conveniently (readily) available Judgment (purposive) Sampling An experienced individual selects the sample based on his/her judgment about some characteristics required of the sample member Quota Sampling Various subgroups in a population are represented on relevant sample characteristics according to researchers desire Snowball Sampling Initial members are selected Additional members are selected based on information provided by the initial participants 2 1 1 3 Probability Sampling Simple random sample Systematic sample Stratified sample Cluster sample Multistage area sample Simple Random Sampling Every member of the population has an equal chance of being selected in the sample. Simple random sampling Systematic Sampling A simple process Every nth name from the list will be drawn Systematic sampling Stratified Sampling Subsamples are drawn within different strata Each stratum is more or less equal on some characteristic What is the right stratified sample? Cluster Sampling Dividing the population into groups, or clusters, Selecting some of these clusters at random. Obtaining the sample by choosing all the members within each of the selected clusters. Cluster sampling Section 1 Section 2 Section 3 Section 5 Section 4 Examples of Clusters Population Element Possible Clusters Airline travelers Airports travelers Planes travelers Sports fans Football fans Basketball fans Car race fans Karate fans