Sampling

• Population= large group of individuals that a particular researcher is interested in studying.
• Random sampling:

  • all members of the sample have an equal chance of being picked
  • Obtain a list of all the names, assign each name a number use a random number generator to pick numbers.
• Systematic sampling:


  when every Nth member of the target population is selected. A sampling frame is produced, which is a list of people in the target population organised for example in alphabetic order. A sampling system is nominated (e.g. every 3rd, 6th person). May begin from a randomly determined start to reduce bias.
• Opportunity sampling:


  Researchers simply select anyone who is willing and available.
• Volunteer sampling:


  Participants select themselves to be part of the sample. May be an advert or participants may simply raise their hands.
• Stratified sampling:


  When the composition of the sample reflects the proportions of people in certain subgroups within the target population. Researcher first identifies the different strata that make up the population. Then the proportions needed are worked out and finally the participants that make up each stratum are selected randomly.
• Random sampling:


  Potentially unbiased as confounding/extraneous variables should be equally divided between the different groups, enhancing internal validity.
• Random sampling:


  Difficult and time-consuming to conduct (have to gather a complete list of target population). Selected participants may also refuse to take part.
• Random sampling:


  The laws of probability suggest random sampling is more likely to produce a more representative sample, however, it is still possible that the random method selects for example all females.
• Systematic sampling:


  Time consuming and participants may refuse to take part. However, the method is objective. Once the system for selection has been established the researcher has no influence over who is chosen.
• Stratified sampling:


  Produces a representative sample because it is designed to accurately reflect the composition of the population. This means that generalisation of findings becomes possible.
• Stratified sampling:


  Stratification is not perfect. The identified strata cannot reflect all the ways that people are different, so complete representation of the target population is not possible.
• Opportunity sampling:


  It is convenient and less costly.
• Opportunity sampling:


  Suffers from bias. Sample is unrepresentative as it is drawn from a very specific area. so findings cannot be generalised. The researcher has complete control over the selection of participants and may avoid certain people (researcher bias).
• Volunteer sampling:


  Easy, less time-consuming and requires minimal input from the researcher.
• Volunteer sampling:


  Volunteer bias is a problem. Asking for volunteers may attract a certain 'profile' of person, that is one who is more curious and more likely to try and please the researcher.