Stratified and Cluster Sampling
Lecture 8
Sections 2.6, 2.8
Robb T. Koether
Hampden-Sydney College
Tue, Jan 26, 2010
Robb T.Koether (Hampden-Sydney College) Stratified and Cluster Sampling Tue,Jan 26, 2010 1 / 23
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Guidelines and tips
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Community
Ask the community
Ask the community for help and clear up your study doubts
University Rankings
Discover the best universities in your country according to Docsity users
Free resources
Our save-the-student-ebooks!
Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors
Stratified and Cluster Sampling: A Comparative Analysis, Lecture notes of Medical statistics
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.
Typology: Lecture notes
2021/2022
1 / 35
This page cannot be seen from the preview
Don't miss anything!
Related documents
Partial preview of the text
Download Stratified and Cluster Sampling: A Comparative Analysis and more Lecture notes Medical statistics in PDF only on Docsity!
Stratified and Cluster Sampling
Lecture 8 Sections 2.6, 2.
Robb T. Koether
Hampden-Sydney College
Tue, Jan 26, 2010
Outline
(^1) Introduction
(^2) Stratified Random Samples
(^3) Estimating Parameters
(^4) Cluster Samples
(^5) Stratified vs. Cluster
(^6) Assignment
Introduction
Suppose we want to measure support for the recent Senate health-care bill in Massachusetts.
Introduction
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.
Introduction
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.
Introduction
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?
Introduction
We could choose 36 Democrats, 12 Republicans, and 52 Independents.
Introduction
We could choose 36 Democrats, 12 Republicans, and 52 Independents. What if we chose to survey 25 Democrats, 25 Republicans, and 50 Independents?
Outline
(^1) Introduction
(^2) Stratified Random Samples
(^3) Estimating Parameters
(^4) Cluster Samples
(^5) Stratified vs. Cluster
(^6) Assignment
Stratified Random Sample
Definition (Homogeneous)
A group is homogeneous if its member all have similar characteristics with regard to a variable of interest.
Definition (Stratum)
A stratum is a homogeneous subset of the population.
Definition (Stratified random sampling)
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.
Stratified Random Sampling
R I D
The strata
Stratified Random Sampling
R Y I D
N N
Select 3 Republicans
Stratified Random Sampling
R Y I D
N N Y
N
Y
Y
N
Select 2 Independents
Outline
(^1) Introduction
(^2) Stratified Random Samples
(^3) Estimating Parameters
(^4) Cluster Samples
(^5) Stratified vs. Cluster
(^6) Assignment