BUSS1020 Lecture Notes - Lecture 7: Standard Score, Stratified Sampling, Convenience Sampling
5 Jun 2018
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If the same size is very large (N>30, at least 30), the sample mean is approximately normal,
regardless what distribution the population follows
1.
If the population is normal, then the same mean is always normal (no matter what the sample
size is)
2.
Note:
The mean is the same as the population mean
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The standard deviation is the population standard deviation
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Divided by the square root of sample size
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What are the parameters of the normal distribution associated with sample mean?
Sampling Methods
1.
Cannot get whole population
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Less time consuming
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Less costly
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analysis of a sample is often less cumbersome and more practical than an analysis of the entire
population.
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Reason for sampling:
list of items that are in the population AND can be sampled
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Includes: population lists, directories, databases, maps, etc.
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Inaccurate or biased results are excluded
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Population = US voters
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Sampling frame = US citizens with landline phones
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e.g. US election 1948
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Sampling frame:
Types of sampling:
7. Sampling Distributions & Confidence Interval
Estimations
Wednesday, 2 May 2018
2:08 PM
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Items are chosen without regard to their probability of occurrence:
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convenience sample: selected based only on being easy, inexpensive, quick to sample.
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judgement sample: perceived experts or most appropriate items are selected, by
convenience.
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quota sample: pre-set quotas of groups chosen, by convenience
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Non - probability sampling:
items chosen randomly using known probabilities that (closely) match those in the population
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Probability sampling:
Every individual or item in the frame has equal chance of being selected.
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may be with replacement or without replacement
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often obtained with the help of a random number generator or via softwar
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Simple random sample (SRS):
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Systematic sample:
Divide frame into strata according to an important characteristic
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An SRS is selected from each strata frame, sample sizes proportional to size of each strata
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Samples from subgroups are combined into one sample
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a common technique when sampling populations of voters, stratifying across racial, socio-
economic variables or other variables important
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Stratified sample:
Population is divided into several “clusters,” each representative of the population
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An SRS of clusters is selected
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All items in the selected clusters can be used, or items can be chosen from a cluster using
another probability sampling technique
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A common application of cluster sampling involves election exit polls, where certain election
districts are selected and (fully) sampled.
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Cluster sample:
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