Subjects, Participants, and Sampling

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Subjects, Participants,and Sampling
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The proof of the pudding is in the eating. By
a small sample we may judge of the whole
piece.Miguel de Cervantes Saavedra Spanish
Writer, 1547-1616
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Definitions

• Subject or participant A person from whom data
  are collected.
• Subject is the term often used in a
  quantitative context participant is used in a
  qualitative context.
• Sample The collective group of subjects or
  participants from whom data are collected
• Population A large group of individuals to
  whom the results of a study are to be generalized.

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Two Types of Sampling Procedures

• Probability Statistically-driven sampling
  techniques where the probability of being
  selected is known. The purpose is to select a
  group of subjects representative of the
  population. (Think quantitative)
• Non-Probability Pragmatically-driven sampling
  techniques where the probability of being
  selected is unknown. The purpose is to select
  particularly knowledgeable participants. (Think
  qualitative)

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Sampling for Quantitative Research Studies
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Goals for Quantitative Sampling

• To select a sample that is representative of
  the population you will generalize your results
  to.
• To reduce sampling error and bias
• Sampling error The difference between the
  true result and the observed result that can
  be attributed to using samples rather than
  populations.
• Sampling bias The difference between the
  observed and true results that can be
  attributed to errors made by the researcher.

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Strategies for Quantitative Sampling

• Simple Random
• Stratified Sampling
• Cluster Sampling
• Convenience Sampling

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Simple Random

• A number is assigned to each subject in the
  population and a table of random numbers or a
  computer is used to select subjects randomly from
  the population.

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1970 Viet Nam War Draft Lottery
Last Called
Call Number
Birth Date
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Systematic Sampling

• Proportional Stratified Sampling The
  proportion of subjects in each strata in the
  population are reflected in the proportions of
  subjects in each strata of the sample.
• Disproportional Stratified Sampling The
  proportions of subjects in each strata in the
  sample are the same regardless of the proportions
  of subjects in the strata of the population.

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Example Stratified Sample
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Cluster Sampling

• Similar to random sampling except that
  naturally occurring groups are randomly selected
  first, then subjects are randomly selected from
  the sampled groups.
• Typical educational clusters are districts,
  schools, or classrooms.

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Convenience Sampling

• Typical of much educational (and other)
  research given the constraints under which it is
  conducted.
• The major concern is the limited ability to
  generalize the results from the sample to a
  population the audience cares about.

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Steps in Quantitative Sampling
Key first step Define the target population. Who
do you want to generalize your results to?
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Sampling for QualitativeResearch Studies
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Goals for Qualitative Sampling

• To select participants that are particularly
  knowledgeable about the topic/phenomenon you are
  researching.
• Who does an investigator or reporter interview?
  People who are most knowledgeable or have the
  closest experience with the issue.

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Strategies for Qualitative Sampling

• Typical Case Selecting a representative
  participant.
• Extreme Case Selecting a unique or atypical
  participant.
• Maximum Variation Selecting at least two
  participants who represent extreme cases.
• Snowball (aka Network) Selecting participants
  from recommendations of other participants.
• Critical Case Selecting the most important
  participants related to the phenomenon.

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Evaluating Sampling in Research Studies
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Criteria for Evaluating Sampling

• The subjects or participants were clearly
  described.
• The population was clearly defined.
• The sampling procedure was clearly described.
• The sampling procedure was appropriate for the
  problem being investigated.
• The selection of subjects was free of bias.
• Adequate sample sizes were used.
• The return rate was reported and analyzed.
• The qualitative study had knowledgeable
  participants.

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Risks Associated with Volunteers

• Different characteristics between volunteers
  and non-volunteers can lead to non-representative
  responses.
• Educational level
• Socio-economic status
• Need for social approval
• Ability to socialize
• Conformity
• Commonly used due to availability and
  convenience.

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Sample Sizes for Experiments

• For experimental designs, sample size is a
  function of level of significance, effect size,
  and power. Change one of these, and the minimum
  sample size will change.
• For a t-test at the 0.05 level of significance,
  a power of 0.80, and a small effect size, the
  minimum sample to produce a statistically
  significant result is 393 in each group for a
  total of 786 participants. With a large effect
  size, the minimum sample would be 26 in each
  group for a total of 52 participants.

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Sample Sizes for Experiments
ES 0.6
1.0
ES 0.5
minimum power
0.8
ES 0.4
0.6
Power
0.4
0.2
0.0
50
100
150
200
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Sample Size Per Group
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Sample Sizes for Correlations

• For correlational designs, sample size is also
  a function of level of significance, effect size,
  and power.
• For a statistically significant Pearson
  product-moment correlation at a 0.05 level of
  significance, a power of 0.80, and a medium
  effect size, you need 85 people.

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Sample Sizes for Surveys

• The minimum sample size for surveys is a
  function of
• The margin of error you are willing to accept.
  This is usually set at 5.
• The confidence interval you set. Typical
  choices are 90, 95, or 99. Go with 95.
• The population size you are targeting. Usually
  it is quite large, and any number over 20,000 has
  little effect on sample size.
• For the assumptions above you would need a
  sample of 384.
• A sample size of 1,000 will give a margin of
  error of 3.
• A sample size of 100 will give a margin of
  error of 10.