LOS 7.a

Question 1

What is the difference between probability sampling and nonprobability sampling?

Answer 1

Probability sampling: Each item has a known probability of selection; simple random sampling assumes equal probability.

Nonprobability sampling: Based on ease of access or researcher judgment, leading to greater sampling error due to less randomness.

Question 2

What are the main probability sampling methods: simple random, systematic, stratified

Answer 2

Simple random sampling: Every item has an equal probability of being selected (e.g., random number generator).

Systematic sampling: Select every nth member from the population (approximate random sampling).

Stratified random sampling: Divide the population into subgroups (strata) and randomly sample from each subgroup proportionally.

Cluster sampling: Randomly select entire clusters, assuming each cluster represents the population; either:

One-stage: Use all items in selected clusters.

Two-stage: Randomly sample items within selected clusters.

Question 3

What is stratified random sampling and when is it used?

Answer 3

Stratified random sampling: Divides population into subgroups (strata) based on characteristics; random samples are drawn from each stratum proportionally.

Example: Bond indexing — bonds are categorized by duration, maturity, coupon rate, etc., and sampled from each category.

Question 4

What is cluster sampling and how does it differ from stratified sampling?

Answer 4

Cluster sampling is also based on subsets of a population, but in this case, we are assuming that each subset (cluster) is representative of the overall population with respect to the item we are sampling. For example, we may have data on personal incomes for a state’s residents by county. The data for each county is a cluster.

In one-stage cluster sampling, a random sample of clusters is selected, and all the data in those clusters comprise the sample.
In two-stage cluster sampling, random samples from each of the selected clusters comprise the sample. Contrast this with stratified random sampling, in which random samples are selected from every subgroup.