# SOC 232 Lecture Notes - Stratified Sampling, Nonprobability Sampling, Cluster Sampling

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Soc 232

March 18th 2013

1

Terms

Element or unit: a single case in the population.

Population: all cases in which a researcher is interested.

Sampling frame: the list of elements from which the sample will be selected.

Sample: the elements (subset of a population) selected for investigation.

Representative sample: a sample that contains the same essential

characteristics as the population.

Probability sample: a sample selected using a random process so that each

element in the population has a known likelihood of being selected.

Non-probability sample: a sample selected using a non-random method.

Sampling error: the error that occurs because of differences between the

characteristics of the sample and those of the population.

Non-response: when an element selected for the sample does not supply the

required data.

Census: data that comes from an attempt to collect information from all elements

in the population.

Sampling error difference between sample and population.

A biased sample does not represent population. Some groups are over-

represented; others are under-represented. sources of bias non-probability

sampling, inadequate sample frame, non-response. Probability sampling reduces

sampling error and allows for inferential statistics.

Case study

Kinsey et al (1948) Sexual Behavior in the Human

Male.

Alfred Kinsey was a sexologist who suspected that there was a greater diversity of

sexual behaviour in the USA than had so far been acknowledged. He set out to

investigate this by collecting the personal narratives of 18,000 men (and later a

sample of women), inviting them to write about their sexual life histories. There

were two main stages of recruitment in this study. At first, Kinsey was content to

use a

## Document Summary

Element or unit: a single case in the population. Population: all cases in which a researcher is interested. Sampling frame: the list of elements from which the sample will be selected. Sample: the elements (subset of a population) selected for investigation. Representative sample: a sample that contains the same essential characteristics as the population. Probability sample: a sample selected using a random process so that each element in the population has a known likelihood of being selected. Non-probability sample: a sample selected using a non-random method. Sampling error: the error that occurs because of differences between the characteristics of the sample and those of the population. Non-response: when an element selected for the sample does not supply the required data. Census: data that comes from an attempt to collect information from all elements in the population. Some groups are over- represented; others are under-represented. sources of bias non-probability sampling, inadequate sample frame, non-response.