Sampling methods

• Volunteer: definition 


  involves participants selecting themselves to be part of the sample, known as ‘self reclection’
• Volunteer: How it’s done


  poster or online ad
• Volunteer: strengths 


  easy and minimal input, not time consuming
• Volunteer: weaknesses

  volunteer bias, volunteering attracts certain characteristics so they will be very motivation and their views may be extreme
• Random: definition

  when all members of the population have an equal change of being selected
• Random: How it’s done

  flip a coin, lottery method, out of a hat, name generator
• Random: strengths


  free from researcher bias
• Random: weaknesses

  need to have a list of all members of target population, difficult to access, time consuming, sample may be unrepresentative
• Opportunity: definition 


  when the researcher decides to select anyone who is willing and available
• Opportunity: how it’s done


  they take anyone who’s available to them
• Opportunity: strength 


  convenient, money saving, time saving
• Opportunity: weaknesses


  unrepresentative sample, bias from researcher as they choose who they want to include
• Systematic: definition

  when every nth member of target population is selected
• Systematic: how it’s done 


  names in a list
• Systematic: strengths 


  free from researcher bias
• Systematic: weaknesses


  need to have need to have list of all members of target population, difficult to access, time consuming to contact all member, sample may be unrepresentative
• Snowball: definition

  when researcher realised on referral from initial participants to generate additional participants
• Snowball: how it’s done 


  researcher recruits one person who gets more and more and more
• Snowball: strengths 


  easy for researcher, more access, cheaper
• Snowball: weaknesses 


  small sample and select group
• Stratified: definition 


  the composition of the sample reflects the proportions of people in certain sub groups within the target population
• Stratified: how it’s done 


  divided the groups into subgroups then randomly select from those subgroups
• Stratified: strengths

  free from researcher bias, produces a more representative sample as there’s a proportional representation of sub groups
• Stratified: weaknesses


  a complete representation of the population is never exact, very time consuming to identify each subgroup then select and contact them