Sampling

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Sampling

• The group chosen to be studied in specific
  research projects
• In quantitative research, generalizations are
  made about the population from conclusions drawn
  about the sample
• Sampling procedures used are critical to the
  generalizability of study conclusions
• Certain procedures lend themselves to
  generalizability more than others

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Population - a defined aggregate or set of
persons, objects or events that met a specified
set of criteriaSample - a subgroup of the
population which serves as a reference group from
which to draw conclusions about the population
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Generalizing from study conclusions in
qualitative studies is not a requirement of the
work. Participants are chosen for appropriateness
to the research question.
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Composition of Populations

• People
• Events
• Institutions
• Blood samples
• Nail clippings
• Saliva
• or just about any unit of interest.

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TARGET POPULATION
ACCESSIBLE POPULATION
SAMPLE
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Subject Criteria

• Inclusion Criteria
• qualifying characteristics of subjects for given
  study
• Example
• Subjects will have primary epilepsy
• Must have had a seizure in the last year
• Will be 60 years of age or older

• Exclusion Criteria
• criteria which exclude subjects from a study
• Example
• Epilepsy will result from trauma, stroke,
  infection or tumor
• Subjects will not have had a temporal lobectomy

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Sampling Techniques

• Probability Sampling
• Subjects randomly chosen - every potential
  subject has equal chance of being chosen
• Nonprobability Sampling
• Subjects chosen by nonrandom methods most
  commonly used in clinical studies

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Strengths of Random Sampling (Probability
Sampling)

• Allows for estimation of sampling error.
• Sampling error is the difference between sample
  statistics and population parameters
• In other words, the variation between the average
  of values found in the sample and those found in
  the population
• Random sampling gives the greatest confidence in
  the validity of findings in a study

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Techniques of Random Sampling

• Simple random sampling
• Systematic sampling
• Stratified random sampling
• Proportional stratified sample
• Disproportional sampling
• Cluster sampling

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Simple Random andSystematic Sampling

• Simple Random sampling a list drawn randomly
  from the list of the accessible population via
  use of a computer table of random numbers
• Systematic Random Sampling using the same list
  as above, every 4th name, for example, is drawn.
  The names between every 4th name is the sampling
  interval

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Stratified random sampling

• Sample which includes different groups (gender by
  age groups)
• Groups are divided into categories which do not
  overlap

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Subcategories of Stratified Samples

• Proportional Stratified Sample sample which is
  divided into categories first, then equal numbers
  are randomly drawn from each category
• Disproportional Sampling situation where size
  of categories are asymmetrical.
• Calculate the probability of one group being
  chosen
• Weight scores so that groups of scores are then
  multiplied by this weighting in the final data
  analysis

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Cluster Sampling

• Used with large populations spread over a big
  area
• Draw initially random locations 5 from 50
  states
• Then draw randomly from specified units within
  those locations 5 regions in each state
• Then draw therapists randomly from the 5 regions.
• Greater potential for sampling error difference
  between sample and population means on any
  measured variable

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Nonprobability Sample sample that is NOT
randomly selected. Representation of various
groups may be unequal
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Nonprobability Sampling

• Convenience sampling
• Consecutive sampling
• Quota sampling
• Purposive sampling
• Snowball sampling

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Convenience and Consecutive Sampling

• Convenience
• Subjects chosen on the basis of availability
• Volunteers are a good example of a convenience
  sample all study participants are volunteers,
  however people who volunteer for a random sample
  know they may not be chosen to participate in the
  study.

• Consecutive
• Subjects are chosen as they become available in a
  given location and when they meet the inclusion
  and exclusion criteria the entire accessible
  population for a study

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Sample size needs to be large enough to generate
statistical power - that is, the sample needs to
be large enough to demonstrate significant
differences between groups when they exist
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Other Nonprobability Sampling

• Quota sampling subset of stratified sampling
  where available subjects are chosen until groups
  are equal
• Purposive sampling the researcher handpicks
  subjects always used in qualitative research
• Snowball sampling subjects volunteer, then
  suggest other possible subjects the process
  continues until the sample size is recruited.