1. CRIM 430 Sampling

2. 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 1. Select a sample that represents the larger population 2. 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 Population Women offenders aged 18-25 with children in CA criminal justice system (N=10,000) Population Parameters Average Age=22.7 Average # of Children 2.5 Sampling Frame A list of all female offenders that meet the criteria Sampling Element Female offenders chosen as part of the sample Sample Statistics Average Age of Sample Selected=23.4 Average Number of Children=3.1

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 n th 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