Sampling

• A population is a group of people who are the focus of the researcher's interest, from which a smaller sample is drawn.
• A sample is a group of people who take part in a research investigation.

  • the sample is drawn from a target population
  • the sample is presumed to be representative of that population
• Bias is 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 is the extent to which findings and conclusions from a particular investigation can be broadly applied to the population.

  • this is possible if the sample of participants is representative of the population
• The types of sampling include:

  • random sampling
  • systematic sampling
  • stratified sampling
  • opportunity sampling
  • volunteer sampling
• In random sampling, all members of the target population have an equal chance of being selected.

  When selecting a random sample:

  • a complete a list of all members of target population is obtained
  • all names on list are assigned a number
  • sample is generated via a lottery method (a computer-based randomiser or picking numbers from a hat)
• Strengths of random sampling:

  • No Researcher Bias → The selection process is completely random.
  • Representative (in large samples) → In theory, it should provide an unbiased sample of the population.
• Weaknesses of random sampling:

  • Time-Consuming & Difficult → A complete list of the population is often not available or difficult to obtain
  • Not Always Representative → Random selection does not guarantee equal representation of subgroups (e.g., gender, age)
• A systematic sample is when every nth person from a population list is selected.

  When creating a systematic sample:

  • a sample frame is produced, which is a list of people in target population organised e.g. into alphabetical order
  • a sampling system is nominated or this interval is randomly determined to reduce bias
  • researcher works through sampling frame until sample is completed
• Strengths of systematic sampling:

  • Reduces Researcher Bias → Selection is based on a fixed system, so researcher has no influence over who is chosen.
  • More Representative than Opportunity Sampling → Ensures even distribution across the list.
• Weaknesses of systematic sampling:

  • Still Not Fully Random → The pattern may accidentally create a biased sample.
  • List Needed → Requires an ordered list of the target population.
• A stratified sample is when the composition of the sample reflects the proportions of people in certain sub-groups (strata) within the target population or wider population.

  When creating a stratified sample:

  • researcher identifies the different strata that make up the population
  • the proportions needed for the sample to be representative are worked out
  • the participants needed for the sample to be representative are selected
• Strengths of stratified sampling:

  • Avoids researcher bias: participants that make up proportions are randomly assigned so researcher has no influence
  • Highly representative: ensures proportional representation of different groups
• Opportunity sample is when participants who are readily available at time of study are selected.

  • the researcher simply takes the chance to ask whoever is around at the time of their study
• Strengths of opportunity sampling:

  • Quick and Easy → The fastest and most convenient method.
  • Less Costly → Requires fewer resources and planning.
• Weaknesses of opportunity sampling:

  • Highly Biased → The sample is likely to be unrepresentative (e.g., only people who happen to be available at that location).
  • Researcher Bias → The researcher may select participants who fit their expectations and avoid those who don't.
• A volunteer sample is when participants select themselves to be part of the sample. It is also known as self-selection.

  • Researcher may place an advert in a newspaper, which participants respond to
• Strengths of volunteer sampling:

  • Easy and Ethical → Participants give informed consent by volunteering
  • Less time-consuming → Participants come to you
• Weaknesses of volunteer sampling:

  • Volunteer Bias → People who volunteer may share certain traits (e.g., being highly motivated, more extroverted), making the sample unrepresentative and hard to generalise
  • Demand Characteristics → Volunteers may try too hard to meet expectations, affecting validity.