Final answer:
The researcher using a sample that matches the demographic proportion of the club is employing stratified sampling. Other examples provided also illustrate stratified and cluster sampling methods, which depend on dividing the population into groups before sampling.
Step-by-step explanation:
The researcher is using stratified sampling when ensuring that 40% of the sample is Caucasian to match the 40% Caucasian representation in the club. In stratified sampling, a population is divided into groups known as strata, and then samples are taken from each stratum to ensure that the sample represents the population proportionally.
Looking at the examples provided:
- a) A soccer coach creating a team from different age groups represents stratified sampling because the population (players) is divided into strata (age groups) and then samples are taken from each.
- b) Interviewing all human resource personnel in selected companies would be an example of cluster sampling. The companies act as clusters, and all members within those selected clusters are included.
- c) Choosing an equal number of male and female teachers represents stratified sampling because the population (teachers) is divided by gender and then samples are taken from each gender category.
Therefore, the type of sampling where the instructor takes five randomly selected students from each math class at Lake Tahoe Community College is also an example of stratified sampling, as the classes are the strata.
Similarly, when a survey includes every tenth customer, it represents systematic sampling. A pattern (every tenth person) is followed to select the sample rather than selecting from pre-defined groups or clusters.