1.3 Data Collection and Sampling Techniques

SWBAT identify the four basic sampling techniques

What would you do to determine the favorite pizza

topping of New Jersey residents?

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)

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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