Sampling

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Sampling
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Basic Concepts

• Population (or universe) a group of people or
  objects with similar characteristics
• Census examining the entire population or
  universe
• Sample examining a selected group of
  people/objects within the population sample
  information is considered to apply to the whole
  population
• Sampling frame list of people or objects that
  constitute the population/universe
• Stratified sampling formed when a sample is
  designed so that certain known characteristics in
  the population are represented in certain
  proportions cf. Sample cells

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Types of Sample

• Two broad types
• Random or probability sampling each
  object/person has a known chance of being
  selected
• Quota or non-probability sampling, also called
  purposive, judgemental or controlled sampling

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Simple Random Sampling

• Definition
• Ensures that every member of the population has
  an equal chance of selection
• Advantages
• Simple to design and interpret
• can calculate estimate of the population and the
  sampling error
• Limitations
• Need a complete and accurate population listing

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Stratified Random Sampling

• Definition
• Separation of population into different groups ,
  called strata
• Selection of sample from each stratum
• Advantage
• Helps have good representation of people/objects
  across the strata
• Other types
• Proportionate stratified sampling sample size of
  each stratum is dependent on the size of the
  stratum relative to the defined target population
• Disproportionate stratified sampling (Eg sampling
  large firms and SMEs)

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Cluster Sampling

• Definition
• Units in the population can often be found in
  certain geographic groups or "clusters" (e.g.
  primary school children in Derbyshire
• A random sample of clusters is taken, then all
  units within the cluster are examined
• Advantages
• Quick easy does not require complete
  population information
• good for face-to-face surveys
• Limitations
• Expensive if the clusters are large
• greater risk of sampling error

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Convenience Sampling

• Definition
• Uses those who are willing to volunteer
• Advantages
• Readily available large amount of information
  can be gathered quickly
• Limitations
• Cannot extrapolate from sample to infer about the
  population prone to volunteer bias

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Judgement Sampling

• Definition
• A deliberate choice of a sample - the opposite of
  random
• Advantages
• Good for providing illustrative examples or case
  studies
• Very prone to bias
• Limitations
• samples often small cannot extrapolate from
  sample

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Quota Sampling

• Definition
• Aim is to obtain a sample that is
  "representative" of the overall population
• the population is divided ("stratified") by the
  most important variables (e.g. income,. age,
  location)
• a required quota sample is drawn from each
  stratum
• Advantages
• Quick easy way of obtaining a sample
• Limitations
• Not random, so still some risk of bias need to
  understand the population to be able to identify
  the basis of stratification