Chapter 7: Sampling- Estimating The Frequency Of Behaviors And Beliefs

Population

A larger group from which a sample is drawn; the group to which a study's conclusions are intended to be applied. Also called population of interest. (page 180)

Sample

The group of people, animals, or cases used in a study; a subset of the population of interest. (page 180)

Census

A set of observations that contains all members of the population of interest. (page 180)

Biased sample

A sample in which some members of the population of interest are systematically left out, and therefore the results cannot generalize to the population of interest. Also called unrepresentative sample. See also unbiased sample. (page 181)

Unbiased sample

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 results can generalize to the population of interest. Also called representative sample. See also biased sample. (page 181)

Convenience sampling

Choosing a sample based on those who are easiest to access and readily available; a biased sampling technique. (page 183)

Self-selection

A form of sampling bias that occurs when a sample contains only people who volunteer to participate. (page 185)

Probability sampling

A category name for random sampling techniques, such as simple random sampling, stratified 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. Also called random sampling. See also nonprobability sampling, unbiased sample. (page 186)

Nonprobability sampling

A category name for nonrandom sampling techniques, such as convenience, purposive, and quota sampling, that result in a biased sample. See also biased sample, probability sampling. (page 187)

Simple random sampling

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). (page 187)

Cluster sampling

A probability sampling technique in which clusters of participants within the population of interest are selected at random, followed by data collection from all individuals in each cluster. (page 188)

Multistage sampling

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. (page 188)

Stratified random sampling

A form of probability sampling; a random sampling technique in which the researcher identifies particular demographic categories, or strata, and then randomly selects individuals within each category. (page 188)

Oversampling

A form of probability sampling; a variation of stratified random sampling in which the researcher intentionally overrepresents one or more groups. (page 189)

Systematic sampling

A probability sampling technique in which the researcher uses a randomly chosen number N, and counts off every Nth member of a population to achieve a sample. (page 189)

Random assignment

The use of a random method (e.g., flipping a coin) to assign participants into different experimental groups. (page 191)

Purposive sampling

A biased sampling technique in which only certain kinds of people are included in a sample. (page 191)

Snowball sampling

A variation on purposive sampling, a biased sampling technique in which participants are asked to recommend acquaintances for the study. (page 192)

Quota sampling

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. (page 192)