Sampling

• • Population: The large group of people that a researcher is interested in studying, for example college students from the North West.
  • Sample: It's usually not possible to include a whole population, so a smaller 'sample' is selected.
  • Generalisation: The sample that is drawn should be representative of the population so generalisations can be made.
  • Bias: The majority of samples are biased in that certain groups may be over - or under - represented.
• Opportunity sample -

  Pick people who are the most available, like asking people in your family or school


  Pro - quick method

  Con - inevitably biased
• Volunteer sample -

  Done by self selecting, usually done by advertising.


  Pro - willing participants

  Con - likely to be a biased sample
• Random sample -

  All participants are given an equal chance. Chosen by a lottery method, like a name generator.


  Pro - potentially unbiased

  Con - representation not guaranteed
• Systematic sample -

  Participants are selected using a set pattern. Every nth person is selected from a list of the target population.


  Pro - unbiased

  Con - requires time and effort
• Stratified sample -

  Participants are selected according to their frequency in the target population. Subgroups (or strata) are identified, such as gender or age groups. The relative percentages of the subgroups in the population are reflected in the sample.


  Pro - representative method

  Cons - stratification is not perfect