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Sample Surveys

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Title: Sample Surveys


1
Chapter 12
  • Sample Surveys

2
Idea 1 Take a Sample
  • Examine a part of the whole.

Population
Sample
3
Idea 1 Take a Sample
  • Population
  • Group of people we want information from
  • Examples
  • Registered voters in US
  • ISU undergraduates
  • Generally large
  • Impractical or too expensive to talk to everyone

4
Idea 1 Take a Sample
  • Sample
  • Smaller group of people from population
  • Examples
  • 200 registered voters
  • 100 ISU undergrads
  • Group we get information from

5
Properties of a Sample
  • Would like the sample to be representative of the
    population.
  • This may not be possible, but at least we would
    like a sample that is not biased.

6
Idea 2 Select the Sample Randomly
  • Controls for factors that you know in the data
  • Examples Gender, Race, Religion, etc.
  • Controls for factors you dont know in data
  • Allows you to make inferences about Population
  • The point of Statistics
  • Without random selection, your sample does not
    tell you anything about population
  • Selecting items for the sample should be done at
    random so as to reduce the chance of getting a
    biased sample.

7
Idea 3 Sample Size Matters
  • Size of sample matters
  • Fraction of the population sampled is not
    important!
  • Want sample to be fairly large
  • Why not do a census?
  • Impractical
  • Expensive
  • Difficult to do
  • Populations are often dynamic
  • Can be more complex

8
Terminology
  • Information (what do we want to know?)
  • Examples
  • Percent of Registered Voters that would vote for
    a candidate.
  • Mean age of ISU undergraduates
  • Population
  • Parameter
  • Percent of all registered voters that will vote
    for a candidate
  • Mean age of all ISU undergrads
  • Sample
  • Statistic
  • Percent of the sample that will vote for a
    candidate
  • Mean age of sample

9
Terminology
  • Population All students at ISU.
  • Question Are the hours the Parks Library is
    open convenient?
  • Population parameter Proportion of all ISU
    students who would answer yes.
  • Sample 400 ISU students.
  • Sample statistic the proportion of the 400
    students in the sample who say yes.

10
Parameters and Statistics
  • Most common parameters and statistics

Name Statistic Parameter
Mean   m
Std. Dev. s s
Proportion   p
Correlation r r
11
How do we select the 400?
  • Put an ad in the ISU Daily with the question and
    ask students to drop off their answers.
  • Stand in front of the library and ask the first
    400 students who come by.

12
Simple Random Sample
  • Want a representative sample but will settle for
    one that is not biased.
  • SRS Each combination of 400 ISU students has
    the same chance of being the sample selected.

13
Simple Random Sample
  • Sampling Frame
  • A list of all students at ISU (the Registrar has
    such a list)
  • Use random numbers to select 400 students at
    random from this list.

14
Simple Random Sample
  • If one were to do this more than once
  • Different random numbers will give different
    samples of 400 students.
  • We have introduced variability by sampling!

15
Stratified Random Sample
  • Large population will be made up of smaller
    homogenous groups
  • Make sure each group is included in sample
  • Usually in proportion of population
  • Divide population into groups
  • Take SRS from each group
  • Combine SRSs Stratified Random Sample

16
Example Stratified Sample
  • Population 200 employees at a company 120 are
    men and 80 are women
  • Opinions on policy of arrival of children
  • Sample 20 people
  • Stratify into men and women
  • Sample 12 men and 8 women

17
Cluster Sampling
  • Difficult to get sampling frame for large
    population
  • Sample group or cluster first
  • Then take SRS from each cluster
  • Combined SRSs Cluster Sample

18
Example Cluster Sample
  • Opinion of Catholics church goers in Boston
  • Cluster Catholic churches
  • Take SRS of churches
  • Take SRS of members of selected churches

19
Systematic Sampling
  • Use a system to select the sample
  • Every 10th person on an alphabetical list of
    students
  • OK if the order of the list is not going to be
    associated with the responses
  • Must start a systematic sample randomly (randomly
    choose where to start on the list)

20
Sampling Variability
  • Take several samples from a population and
    compute a statistic (i.e. mean)
  • These means will not be the same
  • This is the natural tendency of randomly drawn
    samples to vary from trial to trial
  • Sometimes called sampling error, but it is not an
    error just a natural tendency

21
What Can Go Wrong?
  • Bias any systematic failure of a sample to
    represent its population
  • Biased Samples
  • Voluntary Response Sampling
  • A large group of people are invited to respond,
    and those who do respond are counted
  • Problem Not representative of pop - those with
    very strong opinions on subject are most likely
    to respond.
  • This is called voluntary response bias

22
What Can Go Wrong?
  • More Biased Samples
  • Convenience Sampling
  • This approach simply includes those at hand, or
    easily available
  • Problem Not representative of population

23
Cautions about Samples
  • Undercoverage Missing part of the population
  • Household Surveys
  • Phone Surveys
  • Avoid undercoverage by having an accurate and
    complete sampling frame
  • Non-response bias People elect not to
    participate in survey.

24
Cautions about Samples
  • Response bias People will lie
  • Illegal or unpopular behavior
  • Leading questions from interviewer
  • Faulty memory
  • Wording of questions
  • Confusing wording, i.e., use of double negatives
  • Leading questions

25
Inference about Population
  • Biased samples tell us nothing about the
    population
  • Good samples have sampling variability
  • Statistics will be different for each sample
  • Statistics will be different for population
    paramters
  • These differences obey certain laws of
    probability, but only for random samples
  • Larger samples give more accurate results
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