Ch. 4, Sampling: How to Select a Few to Represent the Many (Pt. 1)

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Ch. 4, Sampling How to Select a Few to Represent
the Many (Pt. 1)

• Neumann, pp. 86-105.

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HOW AND WHY DO SAMPLES WORK?

• A proper, representative sample lets you study
  features of the sample and produce highly
  accurate generalizations about the entire
  population

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The most representative samples use random
selection

• The random process allows us to build on
  mathematical theories about probability
• Due to their use of random selection, probability
  samples are also called random samples

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Sample, population, random sample

• sample a small collection of units taken from a
  larger collection
• population a larger collection of units from
  which a sample is drawn
• random sample a sample drawn in which a random
  process is used to select units from a population

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Sampling in qualitative research

• Qual quant researchers both use sampling, but
  qualitative researchers have different goals than
  to get a representative sample of a large
  population
• Qualitative researchers believe a small
  collection of cases, units, or activities can
  illuminate key features of an area of social life
• Use sampling less to represent a population than
  to highlight informative cases, events, or
  actions
• Goal is to clarify and deepen understanding based
  on highlighted cases

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FOCUSING ON A SPECIFIC GROUP 4 TYPES OF
NONRANDOM SAMPLES

• Random samples are difficult to conduct
• Researchers who cannot draw random samples use
  nonprobability sampling techniques
• Convenience sampling
• Quota sampling
• Purposive or judgmental sampling
• Snowball sampling

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Convenience sampling

• convenience sampling a nonrandom sample in which
  you use a nonsystematic selection method that
  often produces samples very unlike the population
• its cheap and fast, but of limited use
• with caution, can be used for preliminary phase
  of an exploratory study
• also called accidental or haphazard sampling

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Quota sampling

• quota sampling nonrandom sample in which you use
  any means to fill preset categories that are
  characteristics of the population
• Not as accurate as a random sample, but much
  easier and faster

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Quota sampling in steps

• Identify several categories of people or units
  that reflect aspects of diversity in population
  you believe to be important
• -e.g., gender or age
• Decide how many units to get for each category,
  i.e., what the quota will be
• Select units by any method

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Purposive or judgmental sampling

• purposive sampling a nonrandom sample in which
  you use many diverse means to select units that
  fit very specific characteristics
• Its like convenience sampling for a highly
  targeted, narrowly defined population
• Used in 2 types of situations
• to select especially informative cases
• to select cases from a specific but hard-to-reach
  population

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Snowball sampling

• snowball sampling a nonrandom sample in which
  selection is based on connections in a
  preexisiting network
• It is a multistage technique
• Each person or case has a connection with the
  others
• also called network, chain-referral or
  reputational sampling

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Examples of networks studied using snowball
sampling

• Scientists around world investigating same issue
• Elites of a medium-sized city who consult with
  one another
• Drug dealers suppliers in a distribution
  network
• People on a college campus who have had sexual
  relations with one another

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COMING TO CONCLUSIONS ABOUT LARGE POPULATIONS

• sampling element a case or unit of analysis of
  the population that can be selected for a sample
• e.g., a person, a group, an organization, a
  written document or symbolic message, or a social
  action or event (e.g., an arrest, a protest
  event, divorce, a kiss)

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Universe, population, and target population
increasing degrees of specificity

• universe the broad group to whom you wish to
  generalize your theoretical results
• e.g., all people in FL
• population a collection of elements from which
  you draw a sample
• e.g., all adults in the Miami metro area
• target population the specific population that
  you used
• e.g., all adults who had a permanent address in
  Dade country, FL in Sept 2007, and who spoke
  English, Spanish, or Haitian Creole

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Use target population to create a list of its
sampling elements, a sampling frame

• sampling frame a specific list of sampling
  elements in the target population
• population parameter any characteristic of the
  entire population that you estimate from a sample
• sampling ratio the ratio of the sample size to
  the size of the target population

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A model of the logic of sampling
What youd like to talk about
Population
Sampling frame
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Why use random samples?

• Theyre most likely to produce a sample that
  truly represents the population
• True random processes
• are purely mechanical or mathematical without
  human involvement
• allow us to calculate the probability of outcomes
  with great precision

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All samples contain a margin of error

• A random process makes it possible to estimate
  mathematically the degree of match between sample
  and population, or sampling error
• sampling error the degree to which a sample
  deviates from a population

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Key features of random samples

• Theyre based on an accurate sampling frame
• They use a random selection process without
  subjective human decisions
• They rarely use substitutions for sampling
  elements

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Types of random samples

• Simple random samples
• Systematic sampling
• Stratified sampling
• Cluster sampling

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Simple random samples

• In simple random sampling
• First develop an accurate sampling frame
• Select elements from the frame based on a
  mathematically random selection procedure
• Locate the exact selected elements to be in your
  sample

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Over many separate samples, the true population
parameter is the most frequent result

• sampling distribution a plot of many random
  samples, with a sample characteristic across the
  bottom and the number of samples indicated along
  the side
• The sampling distribution shows the same
  bell-shaped pattern whether your sample size is
  1000 or 100
• but the more samples drawn, the clearer the
  pattern

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Example of sampling distribution

• Number of blue white marbles that were randomly
  drawn from a jar of 5,000 marbles with 100 drawn
  each time, repeated 130 times for 130 independent
  random samples

Blue marbles White marbles of samples
42 58 1
43 57 1
45 55 2
46 54 4
47 53 8
48 52 12
49 51 21
50 50 31
51 49 20
52 48 13
53 47 9
54 46 5
55 45 2
57 43 1
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Systematic sampling

• If you lack tools to select a pure random sample,
  systematic sampling is a quasi-random method
• systematic sampling an approximation to random
  sampling in which you select one in a certain
  number of sample elements the number is from the
  sampling interval
• sampling interval the size of the sample frame
  over the sample size, used in systematic sampling
  to select units

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Stratified sampling

• Sometimes researchers want to include specific
  kinds of diversity in their sample, e.g., racial
  diversity
• stratified sampling a type of random sampling in
  which a random sample is drawn from multiple
  sampling frames, each for a part of the
  population
• Because you control the relative size of each
  stratum rather then letting random processes
  control it, you can be sure your sample will be
  representative of strata
• Stratified sampling generally results in a
  slightly more representative sample than simple
  random sampling

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Selecting a stratified sample

• Divide population into subpopulations (strata)
• -To use this method, you must have info about
  strata in population (i.e., the population
  parameter).
• Create multiple sampling frames, one for each
  subpopulation
• Draw random samples, one from each sampling frame

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Cluster sampling

• In some situations where there is no good
  sampling frame, you can use multiple-stage
  sampling with clusters
• A cluster is grouping of the elements in the
  final sample that you are interested in
• cluster sampling a multistage sampling method in
  which clusters are randomly sampled, and then a
  random sample of elements is taken from sampled
  clusters

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THREE SPECIALIZED SAMPLING SITUATIONS

• Random-Digit Dialing (RDD)
• Within-Household Sampling
• Sampling Hidden Populations

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Random-digit dialing

• random-digit dialing computer based random
  sampling of telephone numbers

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Within-household sampling

• A household can be thought of as a cluster in
  which there can be multiple sampling elements or
  individuals
• To ensure random selection, create selection
  rules, and follow them consistently

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Sampling hidden populations

• hidden population a group that is very difficult
  to locate and may not want to be found and is
  therefore difficult to sample