Multistage Sample(BPS4e p. 200-1) : Useful when individuals are at the bottom of a sequence of categories: E.g. to choose a sample of college women, first select 10 colleges, at random, then from those colleges select 2 dorms at random, then from each dorm select 10 students to interview. Total sample = 200. Advantage: you only have to visit 10 colleges, 2 dorms in each. An SRS from the whole country, even if you could do it, might mean 200 colleges. (You can also mix this with stratification, for instance selecting the 10 colleges in a stratified way from large coed, small coed, womens,..., or with the other types of sampling.)
Systematic Random Sample(BPS4e p. 210, #8.44)
Using a list, to pick a sample of 1/20 of the list: First
pick
a number at random from 1,2,....20. Suppose you picked 8.
The
8th individual in the list is the first one in the sample. Then
take
every 20th individual after that, numbers 28, 48, 68,....
Advantage:
Easy to implement, avoids "clumps" that might occur with SRS.
Cluster Sampling (not in this text) Often used to
sample from a geographically large area. Example, in the news
Oct. 06, all of Iraq is divided into a list of roughly equally
populated "neighborhoods", and a random sample of neighborhoods is
taken. Then interviewers go into the chosen neighborhoods and
interview all the households in the neighborhood to find out how many
family members have been killed. The results are used to estimate
the total number of Iraqis killed. The neighborhoods are the
clusters; clusters are chosen randomly, but then all (or sometimes a
sample) of the individuals in the cluster are surveyed.
Advantage: Getting an equal number of individuals chosen at
random through a whole country would be prohibitively expensive in time
and money, even if it were possible.
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