Chapter Outline

1
Chapter Outline

• Populations and Sampling Frames
• Types of Sampling Designs
• Multistage Cluster Sampling
• Probability Sampling in Review

2
Political Polls and Survey Sampling

• In the 2004 Presidential election, pollsters
  generally agreed that the election was too close
  to call.
• To gather this information, they interviewed
  fewer than 2,000 people.

3
Election Eve Polls - U.S. Presidential
Candidates, 2004
Date Begun Agency Bush Kerry
10/28 Fox/OpinDynamics 50 50
10/28 TIPP 53 47
10/28 CBS/NYT 52 48
10/28 ARG 50 50
10/28 ABC 51 49
10/29 Fox/OpinDynamics 49 51
4
Election Eve Polls - U.S. Presidential
Candidates, 2004
Date Begun Agency Bush Kerry
10/29 Gallup/CNN/USA 51
10/29 NBC/WSJ 51 49
10/29 TIPP 51 49
10/29 Harris 52 48
10/29 Democracy Crops 49 51
10/29 CBS 51 49
5
Election Eve Polls - U.S. Presidential
Candidates, 2004
Date Begun Agency Bush Kerry
10/30 Fox/OpinDynamics 49 52
10/30 TIPP 51 49
10/31 Marist 50 50
10/31 GWU Battleground 2004 52 48
11/2 Actual Vote 52 48
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Bush Approval Raw Poll Data
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Observation and Sampling

• Polls and other forms of social research rest on
  observations.
• The task of researchers is to select the key
  aspects to observe (sample).
• Generalizing from a sample to a larger population
  is called probability sampling and involves
  random selection.

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Nonprobability Sampling

• Technique in which samples are selected in a way
  that is not suggested by probability theory.
• Examples include reliance on available subjects
  as well as purposive (judgmental), quota, and
  snowball sampling.

9
Types of Nonprobability Sampling

• Reliance on available subjects
• Only justified if less risky sampling methods are
  not possible.
• Researchers must exercise caution in generalizing
  from their data when this method is used.

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Types of Nonprobability Sampling

• Purposive or judgmental sampling
• Selecting a sample based on knowledge of a
  population, its elements, and the purpose of the
  study.
• Used when field researchers are interested in
  studying cases that dont fit into regular
  patterns of attitudes and behaviors

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Types of Nonprobability Sampling

• Snowball sampling
• Appropriate when members of a population are
  difficult to locate.
• Researcher collects data on members of the target
  population she can locate, then asks them to help
  locate other members of that population.

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Types of Nonprobability Sampling

• Quota sampling
• Begin with a matrix of the population.
• Data is collected from people with the
  characteristics of a given cell.
• Each group is assigned a weight appropriate to
  their portion of the population.
• Data should represent the total population.

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Informant

• Someone who is well versed in the social
  phenomenon that you wish to study and who is
  willing to tell you what he or she knows about it.

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Probability Sampling

• Used when researchers want precise, statistical
  descriptions of large populations.
• A sample of individuals from a population must
  contain the same variations that exist in the
  population.

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Populations and Sampling Frames

• Findings based on a sample represent the
  aggregation of elements that compose the sampling
  frame.
• Sampling frames do not always include all the
  elements their names imply.
• All elements must have equal representation in
  the frame.

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A Population of 100 Folks

• Sampling aims to reflect the characteristics and
  dynamics of large populations.
• Lets assume our total population only has 100
  members.

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Sample of Convenience Easy but Not Representative
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Types of Sampling Designs

• Simple random sampling (SRS)
• Systematic sampling
• Stratified sampling

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Representativeness

• Representativeness - Quality of a sample having
  the same distribution of characteristics as the
  population from which it was selected.
• EPSEM - Equal probability of selection method. A
  sample design in which each member of a
  population has the same chance of being selected
  into the sample.

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Population

• The theoretically specified aggregation of study
  elements.
• Study population - Aggregation of elements from
  which the sample is actually selected.
• Element - Unit about which information is
  collected and that provides the basis of analysis.

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Random selection

• Each element has an equal chance of selection
  independent of any other event in the selection
  process.

22
Sampling unit

• Element or set of elements considered for
  selection in some stage of sampling.

23
Parameter

• Summary description of a given variable in a
  population.

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A Population of 10 People with 09
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The Sampling Distribution of Samples of 1

• In this example, the mean amount of money these
  people have is 4.50 (45/10).
• If we picked 10 different samples of 1 person
  each, our estimates of the mean would range all
  across the board.

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Sampling Distributions
27
Sampling Distributions
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Sampling Distributions
29
Sampling Distributions
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Range of Possible Sample Study Results

• Shifting to a more realistic example, lets
  assume that we want to sample student attitudes
  concerning a proposed conduct code.
• Lets assume 50 of the student body approves
  and 50 disapproves - though the researcher
  doesnt know that.

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Results Produced by Three Hypothetical Studies

• Assuming a large student body, lets suppose we
  selected three different samples, each of
  substantial size.
• We would not expect those samples to perfectly
  reflect attitudes in the whole student body, but
  they should come close.

32
Statistic

• Summary description of a variable in a sample.

33
Sampling Error

• The degree of error to be expected of a given
  sample design.

34
Confidence Level

• The estimated probability that a population
  parameter lies within a given confidence
  interval.
• Thus, we might be 95 confident that between 35
  and 45 of all voters favor Candidate A.
• Confidence interval - The range of values within
  which a population parameter is estimated to lie.

35
Sampling Frame

• That list or quasi list of units composing a
  population from which a sample is selected.
• If the sample is to be representative of the
  population, it is essential that the sampling
  frame include all (or nearly all) members of the
  population.

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The Sampling Distribution

• If we were to select a large number of good
  samples, we would expect them to cluster around
  the true value (50), but given enough such
  samples, a few would fall far from the mark.

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Review of Populations and Sampling Frames
Guidelines

1. Findings based on a sample represent only the
   aggregation of elements that compose the sampling
   frame.
2. Sampling frames do not include all the elements
   their names might imply. Omissions are
   inevitable.
3. To be generalized, all elements must have equal
   representation in the frame.

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Simple Random Sampling

• Feasible only with the simplest sampling frame.
• Not the most accurate method available.

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A Simple Random Sample
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Systematic Sampling

• Slightly more accurate than simple random
  sampling.
• Arrangement of elements in the list can result in
  a biased sample.

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Sampling ratio

• Proportion of elements in the population that are
  selected.

42
Stratification

• Grouping of units composing a population into
  homogenous groups before sampling.
• This procedure, which may be used in conjunction
  with simple random, systematic, or cluster
  sampling, improves the representativeness of a
  sample, at least in terms of the stratification
  variables.

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Stratified Sampling

• Rather than selecting sample for population at
  large, researcher draws from homogenous subsets
  of the population.
• Results in a greater degree of representativeness
  by decreasing the probable sampling error.

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A Stratified, Systematic Sample with a Random
Start.
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Cluster Sampling

• A multistage sampling in which natural groups are
  sampled initially with the members of each
  selected group being subsampled afterward.

46
Multistage Cluster Sampling

• Used when it's not possible or practical to
  create a list of all the elements that compose
  the target population.
• Involves repetition of two basic steps listing
  and sampling.
• Highly efficient but less accurate.

47
Probability Proportionate to Size (PPS) Sampling

• Sophisticated form of cluster sampling.
• Used in many large scale survey sampling projects.

48
Weighting

• Giving some cases more weight than others.

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Probability Sampling

• Most effective method for selection of study
  elements.
• Avoids researchers biases in element selection.
• Permits estimates of sampling error.