1. 1.3 Data Collection and Sampling Techniques
By the end of class you will be able to summarize the 5 different sampling techniques

2. What would you do to determine the favorite pizza topping of New Jersey residents?
Think-Pair-Share

3. Do you recognize these people??

4. Sample: A portion of the population
Sampling saves time and money
Sampling represents realistic data gathering
This sample size must always be LARGE and RANDOM
Random Sample: Each member of the population has an equal chance of being selected

5. Common Ways to Conduct Surveys (Pros/Cons)
Telephone Surveys (wide range of people, anonymity/ minimal responses, validity, poor presentation)
Mailed Questionnaires (better data, reach everyone/ low responses, takes time)
Personal Interviews (in depth responses/ costly, potential for bias & lies)

6. Sampling Types
Random Samples: participants are selected by using chance methods or random numbers (pull from hat)
Every member of population has a chance of being selected.
Systematic Sampling: a starting point is selected and every kth subject is selected
Stratified Sampling: subdivide the population into at least two different groups that share a characteristic, then draw a sample from each (frosh/soph)

7. Sampling Types (cont’d)
Cluster Sampling: divide the population into sections, then randomly select sections, then sample the people in those sections.(city neighborhoods)
Convenience Sampling: using samples that are readily available (mall interview)

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Practice
You ask people at every 3rd table at a restaurant what their favorite dessert is
You sort students into groups based on hair color, then ask random students in each group how often they cut their hair
You select random neighborhoods in Mount Laurel and ask residents if they approve of the mayor.
(c) 2007 brainybetty.com ALL RIGHTS RESERVED.

10. C: Stratified D: Cluster)
Summary:
You want to know how freshmen feel about Lenape. Describe how to use each of the sampling techniques to do this (A: Random B: Systematic
C: Stratified D: Cluster)

11. Sampling Vocabulary
Sampling Error: the difference between the sample result and the true population result caused by the actual sample. (can’t be prevented)
Non-Sampling Error: the difference between the sample result and the true population caused by incorrect data, calculations etc. (can be prevented)