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1.3 Data Collection and Sampling Techniques
SWBAT identify the four basic sampling techniques
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What would you do to determine the favorite pizza
topping of New Jersey residents?
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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
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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)
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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)
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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.
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Summary: You want to know how freshmen feel about TKCS. Describe
how to use each of the sampling techniques to do this (A: Random B: Systematic
C: Stratified D: Cluster)
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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)
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