Final answer:
Purposive sampling involves selecting subjects with specific characteristics, which is an intentional selection method. Examples of other sampling types include stratified sampling in scenario a, cluster sampling in scenario b, and quota sampling in scenario c. Random sampling methods aim to provide accurate representation, while non-random methods like convenience sampling may be prone to bias.
Step-by-step explanation:
The specific selection of subjects with certain characteristics by the investigator to be included in the study is known as E. Purposive sampling. This technique involves intentional choice of participants due to the qualities the participant possesses, which are of interest to the study.
Examining the given scenarios for types of sampling, we can match them to their appropriate sampling methodologies:
- a. A soccer coach selects players based on their age groups to form a team - This is an example of stratified sampling, as the population is divided into strata (age groups) and a sample is chosen from each stratum.
- b. A pollster interviews all human resource personnel in five different high tech companies- This corresponds to cluster sampling, with each company acting as a cluster.
- c. An educational researcher interviews an equal number of male and female teachers - This is indicative of quota sampling, where the researcher decides in advance the proportion of subjects in various categories.
It is essential to note that random sampling methods such as simple random sampling, stratified sampling, cluster sampling, and systematic sampling are designed to create samples that represent the population more accurately. Meanwhile, non-random methods like convenience sampling are based on ease and accessibility rather than the probability of selection, potentially leading to biased data.