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An in-depth analysis of stratified and cluster sampling techniques, with real-life examples and calculations. The lecture covers the definitions, advantages, and disadvantages of both methods, as well as their applications in estimating population parameters.
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Lecture 8 Sections 2.6, 2.
Robb T. Koether
Hampden-Sydney College
Tue, Jan 26, 2010
(^1) Introduction
(^2) Stratified Random Samples
(^3) Estimating Parameters
(^4) Cluster Samples
(^5) Stratified vs. Cluster
(^6) Assignment
Suppose we want to measure support for the recent Senate health-care bill in Massachusetts.
Suppose we want to measure support for the recent Senate health-care bill in Massachusetts. Suppose further that we know that the Massachusetts population is 36% Democrat, 12% Republican, and 52% Independent.
Suppose we want to measure support for the recent Senate health-care bill in Massachusetts. Suppose further that we know that the Massachusetts population is 36% Democrat, 12% Republican, and 52% Independent. Suppose even further that we suspect that party affiliation is a relevant variable. We plan to take a sample of 100 individuals.
Suppose we want to measure support for the recent Senate health-care bill in Massachusetts. Suppose further that we know that the Massachusetts population is 36% Democrat, 12% Republican, and 52% Independent. Suppose even further that we suspect that party affiliation is a relevant variable. We plan to take a sample of 100 individuals. What might go wrong if we take a simple random sample?
We could choose 36 Democrats, 12 Republicans, and 52 Independents.
We could choose 36 Democrats, 12 Republicans, and 52 Independents. What if we chose to survey 25 Democrats, 25 Republicans, and 50 Independents?
(^1) Introduction
(^2) Stratified Random Samples
(^3) Estimating Parameters
(^4) Cluster Samples
(^5) Stratified vs. Cluster
(^6) Assignment
A group is homogeneous if its member all have similar characteristics with regard to a variable of interest.
A stratum is a homogeneous subset of the population.
Stratified random sampling is a sampling method in which the population is first divided into strata. Then a simple random sample is taken from each stratum. The combined results constitute the sample.
R I D
The strata
R Y I D
N N
Select 3 Republicans
R Y I D
N N Y
N
Y
Y
N
Select 2 Independents
(^1) Introduction
(^2) Stratified Random Samples
(^3) Estimating Parameters
(^4) Cluster Samples
(^5) Stratified vs. Cluster
(^6) Assignment