1.3 data collection and sampling techniques da… ·  · 2016-08-071.3 data collection and...

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

3

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)

4

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)

5

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)

6

7

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.

8

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)

10