Chapter 7: Sampling - Estimating the Frequency of Behaviours and Beliefs

Question 1

Population / Population of Interest

Answer 1

A larger group from which a sample is drawn; is the group to which a study’s conclusions are intended to be applied.

Question 2

Sample

Answer 2

The group of people, animals, or cases used in a study; a subset of the population of interest.

Question 3

Census

Answer 3

A set of observations that contains all members of the population of interest

Question 4

Biased Sample / Unrepresentative Sample

Answer 4

A sample in which some members of the population of interest are systematically left out, and therefore the results cannot be generalize to the population of interest.

Question 5

Unbiased Sample / Representative Sample

Answer 5

A sample in which all members of the population of interest are equally likely to be included (usually through some random method), and therefore the result can be generalized to the population of interest.

Question 6

Convenience Sampling

Answer 6

Choosing a sample based on those who are easier to access and readily available; a biased sample technique.

Question 7

Self-Selection

Answer 7

A form of sampling bias occurs when a sample contains only people who volunteer to participate.

Question 8

Probability Sampling / Random Sampling

Answer 8

A category name for random sampling techniques, such as simple random sampling, and cluster sampling, in which a sample is drawn from a population of interest so each member has an equal and known chance of being included in the sample.
- obtaining a representative sample

Question 9

Nonprobability Sample / Biased Sample

Answer 9

A category name for nonrandom sampling techniques, such as convenience, purposive, and quota sampling, that result in a biased sample.

Question 10

Simple Random Sampling

Answer 10

The most basic form of probability sampling, in which the sample is chosen completely at random from the population of interest (e.g., drawing names out of a hat).

Question 11

Systematic Sampling

Answer 11

A probability sampling technique in which the researcher uses a randomly chosen number N, and counts off every Nth member of the population to achieve a sample.

Question 12

Cluster Sampling

Answer 12

A probability sampling technique in which clusters of participants within the population of interest are selected randomly, followed by data collection from all individuals in each cluster.
- like selecting 100 random highschools from a list of 952 highschools, then including every student from each of those 100 schools in the sample.

Question 13

Multistage Sampling

Answer 13

A probability sampling technique involving at least two stages: a random sample of clusters followed by a random sample of people within the selected clusters.

Question 14

Stratified Random Sampling

Answer 14

A form of probability sampling is a technique in which the researcher identifies particular demographic categories or strata and then randomly selects individuals within each category.
- To obtain a stratified sample of hospital employees, we first divided this population into three meaningful strata: 10 social workers, 20 doctors, and 30 nurses. We randomly select 2 social workers, 6 doctors, and nine nurses. Therefore, our sample’s proportions of social workers, doctors, and nurses are proportional to those in the full population.

Question 15

Oversampling

Answer 15

A form of probability sampling, a variation of stratified random sampling in which the researcher intentionally overrepresents one or more groups.
- A survey that includes an oversample adjusts the final results so members in the oversampled group are weighted to their actual proportion in the population.

Question 16

Weighting

Answer 16

If they determine that the final sample contains fewer members of a subgroup than it should (such as fewer adults or fewer low-income people), they adjust the data so responses from members of underrepresented categories count more and overrepresented members count less.

Question 17

Random Assignment

Answer 17

The use of a random method (e.g., flipping a coin) to assign participants into different experimental groups.

Question 18

Purposive Sampling

Answer 18

A biased sampling technique in which only certain kinds of people are included in a sample.

Question 19

Snowball Sampling

Answer 19

A variation on purposive sampling, a biased sampling technique in which participants are asked to recommend acquaintances for the study.

Question 20

Quota Sampling

Answer 20

A biased sampling technique in which a researcher identifies subsets of the population of interest, sets a target number for each category in the sample, and nonrandomly selects individuals within each category until the quotas are filled.
- In quota sampling, the participants are selected nonrandomly (e.g., convenience or purposive sampling), and in stratified random sampling, they are selected using a random selection technique.

Question 21

For what type of claim is it most important for a researcher to use a representative sample?

Answer 21

A frequency claim

Question 22

When might researchers decide to use a nonprobability sample, even though a probability same would ensure external validity?

Answer 22

Researchers might not have funds to obtain random samples for their studies, and their priorities lie elsewhere - internal validity and establishing causality is more important.