Sampling

Content Concepts Steps in the sampling process Types of samples Random sampling - Simple random sampling - Systematic random sampling - Stratified random sampling - Cluster sampling Geographic sampling - Quadrat sampling - Traverse sampling - Point sampling

Concepts At the core of inferential statistics is the distinction b/w a population and sample Ideally, sample is a good representation of the population Sampling error: uncertainty that arises from working with a sample rather than the entire population Sampling bias: occurs when the procedures used to select the sample tend to favor the inclusion of individuals in the population with certain population characteristics.

Sampling frame: or population frame, an ordered list of the individuals in a population. Example: telephone survey for evaluation of voter preferences for political parties. Population frame : all eligible voters Target population: the set of all individuals relevant to a particular study Sampled population: all the individuals in the sampling frame. – voters with telephones – bias

Sampling design: a procedure used to select individuals from the sampling frame for the sample. Two classes of sampling design: probability samples and nonprobability samples Probability sample: the probability of any individual member of the population being picked for the sample can be determined.

Non-probability samples Convenience/accessibility sample: only convenient/accessible members of the population are selected. Quota sample: obtain a representative sample by instructing interviewers to acquire data from given subgroups of the population. Volunteer sample: consists of individuals who self-select from the population.

Random sampling design 1. Simple random sample: each possible sample of a given size n has equal probability of being selected. 2. Systematic random sampling: every nth element of the sampling frame is chosen, beginning with a randomly chosen point. - is likely as good as a simple random sample, provided that the arrangement of the individuals in the sampling frame is random.

3. Stratified sample: obtained by forming classes, or strata, in the population and then selecting a simple random sample from each.

Cluster sampling: The population is first divided into mutually exclusive classes (usually defined on the basis of convenience). Next, certain clusters are selected by some random procedure for detailed study, however, a random sample is not drawn from within the clusters. - Often give very poor results. - Stratified samples are best and cluster samples are worst. Random samples lie somewhere b/w these two.

Geographic Sampling Traverse samples Quadrat samples Point samples

Point samples