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

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Published on 13 Apr 2013
School
U of S
Department
Sociology
Course
SOC 232
Professor
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
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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.

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