CRIM 430

1. CRIM 430 Sampling

2. Sampling • Sampling is the process of selecting part of a population • Target population represents everyone or everything that you are interested in studying • Research Goals for Sampling • Select a sample that represents the larger population • Generalize from a sample to an unobserved population the sample is intended to represent • A sample is representative if the aggregate characteristics of the sample closely approximate those same aggregate characteristics in the population

3. Probability Sampling • To meet the goals of sampling, it is best to use probability sampling • Probability sampling is a method of sampling in which each member of a population has a known chance or probability of being selected • Samples that are representative of the larger population share, in equal (or near equal) amounts, the variations found in the population • Probability sampling helps researchers achieve a representative sample • It protects sampling from sampling bias and allows researchers the ability to estimate the sample’s representativeness

4. Sampling Bias • Sampling bias refers to selecting subjects in a way that will not result in a sample that is not representative of the population • Examples: • Selecting the first 100 males encountered in a mall to represent all males • Interviewing judges that have viewpoints consistent with a research question and not interviewing judges with inconsistent viewpoints • Unless a researcher uses probability sampling from the population, it is impossible to declare that your sample is representative of that population

5. Prob. Sampling Terminology • Population: Grouping of study elements • Population Parameter: Summary description of a given variable in a population • Sampling Frame: List of potential study subjects that comprise the population • Sample Element: The unit about which information is collected and that provides the basis of of analysis • Sample Statistic: Summary description of a given variable in the sample • If a sample is representative of the population, the sample statistic should equal the population parameter for any given variable/characteristic

6. Application of Terminology

7. Probability Sampling Designs • Simple Random Sampling=Selection is completed by applying a random number procedure (similar to flipping a coin) until the desired sample size is achieved • Systematic Sampling=Every nth element in the list is selected. The “n” is calculated by dividing the total number in the population by the number desired in the sample (e.g., 100/1000) • Stratified Sampling=Selection begins by organizing sampling frame by specific characteristics (e.g., gender) and then applying simple random or systematic sampling to select subjects

8. Probability Designs, Cont’d. • Disproportionate Stratified Sampling=Selection of a number disproportionate to their representation in the population in order to yield sufficient cases of “rare” cases • Multi-Cluster Sampling=Selection begins by creating groups of elements followed by the selection of sampling elements from within each group or cluster

9. Non-Probability Sampling • Probability sampling designs are not possible in many situations • Non-probability sampling is an alternative; however, the samples are not representative of the population from which they are drawn • Non-probability sampling designs are prone to selection bias • Non-Probability sampling designs are, therefore, weaker than probability sampling designs

10. Non-Probability Sampling Designs • Purposive or Judgmental Sampling: Identifying a sample based on the presence of a particular characteristic • Quota Sampling: Identifying a sample using a matrix to represent the characteristics of the population • Convenience Sampling: Sample is selected because access is easy and convenient • Snowball Sampling: Using one respondent to provide contact to 2-3 additional respondents—continuous process to identify a larger sample