sampling

• Population - a group of people who are the focus of the researcher’s interest, from which a smaller sample is drawn (the target population)
• Sample - a group of people who take part in a research investigation. The sample is drawn from a target population and is presumed to be representative of that population e.g it stands ‘fairly’ for the population being studied
• Bias - in the context of sampling, when certain groups may be over or under represented within the sample selected. This limits the extent to which generalisations can be made to the target population
• Generalisation - the extent to which findings and conclusions from a particular investigation can be broadly applied to the population. This is made possible if the sample of pps is representative of the population
• Sampling techniques - the method used to select people from the population
• Stratified sampling - the composition of the sample reflects the proportion of people in certain subgroups (strata) within the target population or the wider population
• To produce a stratified sample, identify different strata that make up the population. Work out proportions needed to be representative. Select pps using random sampling to make up each strata
• Strengths of stratified sampling:

  • Can avoid researcher bias
  • Produces a representative sample because it is designed to accurately reflect the composition of the population, meaning that generalisation of findings becomes possible
• Limitations of stratified sampling:

  • Not perfect or truly representative - cannot show all the possible ways people are different
• Systematic sampling - Taking every nth member of the target population
• to produce a systematic sample, compile a list of target population and organise it (e.g alphabetically). Nominate sampling system (e.g every 2nd or 5th). Work through the list until sample is complete
• strengths of systematic sampling:

  • Avoids researcher bias. Further reduces bias if you randomly choose the sampling system
  • Likely to be more representative
• limitations of systematic sampling:

  • Could still be possible to achieve an unrepresentative sample
  • time consuming - more effort than other techniques
• Random sampling - All members of the target population have an equal chance of being selected
• to produce a random sample, compile a list of all members of the target population. Assign each participant a number. Use a computer-based randomiser or pick out of a hat
• strengths of random sampling:

  • Free from researcher bias
• limitations of random sampling:

  • difficult and time-consuming to conduct, especially if it is a large target population - a complete list may not be possible
  • Still able to achieve an unrepresentative sample
  • selected pps may refuse to take part
• Opportunity sampling - Researcher asks whoever is around at the time of their study
• to produce an opportunity sample, a researcher may ask students in a class or ask people on the street - whoever is available
• strengths of opportunity sampling:

  • Convenient - saves time and effort and is less costly
• limitations of opportunity sampling:

  • Suffers from two forms of bias.
  • Unrepresentative of the population 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 people they do not like the look of (researcher bias)
• Volunteer sampling - Participants self-select themselves to be part of the sample
• to produce a volunteer sample, place an advert in a newspaper or online for pps to respond or ask people to raise their hand in a class if they want to take part
• strengths of volunteer sampling:

  • Easy - requires minimal input from the researcher and so is less time consuming than other sampling techniques
• limitations of volunteer sampling:

  • Volunteer bias - a certain group of people are likely to come forward (helpful, keen, curious) which might affect the generalisability of the results