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Sampling Design

In: Business and Management

Submitted By ollo
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Sampling Designs
• 1. Simple random sampling (SRS) Steps:
– (1) Assign a single number to each element in the sampling frame. – (2) Use random numbers to select elements into the sample until the desired number of cases is obtained.

• The method is not very different from winning a lottery.

2. Systematic Sampling
• Steps:
– (1) Calculate the sampling interval as the ratio between population size and sample size, I = N/n. – (2) Arrange all elements in the population in an order. – (3) Select a case in the first interval randomly. – (4) Select every ith case from this point.

2. Systematic Sampling (continued) I
I
1st element, randomly chosen

I

I

I

I

– Systematic Sampling is easier and simpler than SRS – The text warns of a danger to this method. What is it?

3. Stratified Sampling
• Stratified sampling is more complicated than SRS. The advantage is the guaranteed representativeness in some important characteristics. • For example, say that we want to select a sample of 100 individuals. Sex ratio in the sample is up to chance if we do SRS. We can guarantee the 50-50 split if we do stratified sampling:

Stratified Sampling, Graphic Representation
Population

SRS

SRS

Sample

• Stratified sampling is often used to reduce the variability of a sample.

Oversampling, Graphic Representation
Population

SRS

SRS

Sample • Increasing the representation of a group in a sample. This is often done when groups are very different in size – e.g., race

4. Cluster Sampling
• Cluster sampling is desirable from an economic point of view: • It saves money, at the expense of lowering the quality of data. • e.g.: we are interested in studying students' experiences at the University of Wisconsin, the unit of analysis is the student. An economic way is to sample classes. Once a class is selected, everyone in the class is...

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