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Other types of samples…

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1 Other types of samples…
5.3: SAMPLING Other types of samples…

2 Review: Three sample designs covered so far: Simple Random Sample (SRS) Voluntary Response Sample Convenience Sample

3 Other ways to take samples
Example: At a university, there are 1500 professors. However, 1000 are male and 500 are female. To take an accurate sample of the university faculty’s opinions, we decide to take a sample of 100 male professors and 50 female. Their total sample is these 150 professors. Why is this not an SRS? What was their process? Why is this sampling method better than just an SRS?

4 This is not an SRS because they do not put all people into one group and randomly select from that. They break them up into two groups and select some from each group. An SRS does not take into account a variable like Gender. Also, an SRS gives each person an equal chance of being selected. This does not. In this sample, the males have a 1/1000 chance but each female has a 1/500 chance. This is better because its more representative of the population. The population has more males in it, and this sampling method allows the sample to have the same percent of males as the population.

5 Stratified Random Sample:
(NOT SRS) Divide population into groups with something in common (called STRATA) Example: gender, age, etc. 2) Take separate SRS in each strata and combine these to make the full sample - can sometimes be a % of each strata

6 Skip the book examples

7 Systematic Sample: Randomly picking a first individual, and then selecting every ____ person after that. Good for situations where you do not know your population ahead of time * Example: Interview every 5th person that walks thru the back door of CB South in the morning. * Example: Write down the type of car in every 3rd spot in the CB South parking lot.

8 Cluster Sample Pick a certain area/part of the population. Try to get every individual in that selection Example: select one lunch period, and talk to everyone at that lunch Census Gather info from everyone in the entire population.


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