Sampling

Population

The entire set of entities (individuals, cities, states, neighborhoods, schools, etc.) in which we are interested.

Sample

A subset of entities from the population that is studied to make conclusions about the entire population.

Representativeness

How similar the sample is to the population in all respects relevant to the study.

Generalizability

The validity of the conclusions that can be drawn from studying the sample.

Target Population

The population to which the researcher wishes to generalize.

Sampling Frame

The list of cases/units that the researcher can sample from.

Sampling Error

Differences in a value between the sample and the population that occur even in random samples by chance.

Systematic Error/Bias

Difference between sample and population value due to a non-random sampling method.

Probability Sampling

Each entity in the population has a known and non-zero probability of being selected in the sample.

Non-Probability Sampling

Probability of selecting each entity is unknown; it may be related to something being studied.

Simple Random Sampling

Randomly selecting cases from a list of entities in the population.

Systematic Random Sampling

Picking every nth name from a list to produce a random sample unless the list is sorted.

Stratified Random Sampling

Sorting into groups and randomly selecting an exact number from each group.

Multi-stage Cluster Sampling

Elements selected in two or more stages, with the first stage being the random selection of naturally occurring clusters.

Convenience Sampling

Choosing a sample based on what is convenient or available.

Quota Sampling

A convenience sample with quotas to ensure representativeness of some elements.

Purposive Sampling

Selecting sample entities based on the researcher's judgment for a specific purpose.

Snowball Sampling

Identifying one member of the population and asking them to identify others, useful for hard-to-reach populations.