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  • Sampling
    • A good sample should...
      • Represent the target population (the set of people that researchers intend to investigate and apply findings to)
      • Be unbiased - sample may not accurately represent the target population (eg. some groups in the population may accidently be excluded.)
        • Represent the target population (the set of people that researchers intend to investigate and apply findings to)
        • Population Validity: Being able to generalise results from the sample to the target population and be true.
        • Types of sample bias:
          • Gender Bias: The sample only represents one gender despite the target population containing multiple genders.
            • Androcentric: disproportionate amount of males Gynocentric: disproportionate amount of females
          • Cultural Bias: Too focussed on one culture/ ignores other cultures
          • Ethnocentric: Research results that are generalised to different cultures despite only being conducted on one culture, thus disregarding how different cultures may have different results.
    • Sampling Techniques
      • Opportunity Sampling:  People who are available + consenting at the time of the research
        • Quick and time efficient
        • Not representative- chance of researcher bias (eg. only choosing certain people based on looks)
      • Self-selected Sample: Volunteered to partcipiate
        • Volunteers are more likely to commit to the study and be fully involved.
          • Less chance of participant attrition- they chose to participate- so unlikely to change their minds.
        • Chance not many people will see advert or be interested enough to respond, leading to small sample, creating invalid results.
        • Can gain a large sample quite quickly, that reaches a broad and representative audience
        • Inherent bias in participants as similar types of people may sign up due to interest/need for money/ experience doing studies.
          • Could lead to bias + unrepresentativeness.
      • Snowball Sampling: Existing participants recruit other participants
        • Access people that you may not otherwise be able to recruit
        • Bias- people’s friends/family likely to share characteristics- so sample is not representative
        • Little effort by researcher
      • Random Sampling: Each member of target population has same chance of being selected as any other member
        • Time consuming to gather details of every member of target population if it's quite large.
        • Not everyone picked may consent to take part- so can take a long time for experiment to actually begin
  • Stratified Sampling: All types of members of the population are represented proportionally by selecting different numbers of participants from all strata
    • If proportion of target population included 60% people over 50, the sample would too. 
    • Sample will be representative + have no biases.
    • Method is difficult, costly + time inefficient.  Target population must also be fully accessible


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