sampling

• A target population is the wider group that the researchers draws the sample from and who they want to generalise the findings to.
• The different types of sampling methods are random sampling, stratified sampling, systematic sampling, opportunity sampling and volunteer (self-selected) sampling.
• A target population refers to the entire group of individuals that a researcher wants to study, while a sample refers to the specific group of individuals that are selected to participate in the study.
• A target population is usually too large to study in its entirety, so sampling methods are used to select smaller samples in which to study.
• representative and un

  • A representative sample is a smaller group selected from the target population who have similar characteristics, which would allow us to generalise. Having a representative sample increases the generalisability of the results.
  • An unrepresentative sample is one that does not reflect the distribution of characteristics of the target group, so cannot be generalised to the target population, and is therefore biased.
  • Having a unrepresentative sample adds bias to the findings and limits the ability to generalise.
• random sampling - strengths

  • potentially is unbiased meaning confounding and extraneous variables should be equally divided between the different groups, enhancing internal validity

  weaknesses

  • difficult + time consuming as a complete list of the target population may be difficult to obtain
  • may end up with unrepresentative sample
  • p's may refuse to take part
• opportunity sample - where researchers select whoever is willing+ available to take part in their study.

  strengths:

  • convenient as is less costly in terms of time and money
  • no need to divide population into different strata

  weaknesses:

  • 2 forms of bias - sample is unrepresentative of the target population as it is drawn from a very specific area such as a street in a town so findings cannot be generalised to target populations
  • researcher has complete control and may avoid people if they don't like the look of them (researcher bias)
• stratified sample - where the composition of sample reflects the proportions of people in certain subgroups within the target or wider population

  strengths:

  • produces a representative sample bcs it is designed to accurately reflect the composition of a population
  • generalisation of findings becomes possible

  weaknesses:

  • the identified strata cannot reflect all the ways that people are different, so complete representation of the target pop. is not possible
• volunteer sample/self selected - involves p's selecting themselves to be part of the sample e.g with an advert from researcher etc.

  strengths:

  • easy -> requires minimal input from researcher is less time- consuming .
  • researcher also ends up with p's who are more engaged rather than someone stopped in the street

  weaknesses:

  • volunteer bias -> asking for volunteers may attract a certain 'profile' of a person i.e one who is anxious and more likely to try please the researcher therefore will then affect how far studies can be generalised
• systematic sample - where every nth number of the target pop. is selected e.g target pop. is put in alphabetical order and every 3rd person is chosen

  strengths:

  • objective, once the system for selection has been established, the researcher no influence who is chosen

  weaknesses:

  • time consuming
  • p's may refuse
• systematic sampling


  1. define the population - list of the population you want to study
  2. chose the sample size - how many participants you want in your sample
  3. determine the interval - divide the total population size by the desired sample size to get "n"
  4. select a starting point - randomly choose a starting point within the first interval
  5. select samples - from the starting point, select every nth individual based on the interval
• • random sampling


  1. define the population - list of the population you want to study
  2. chose a sample size - how many participants you want in your sample
  3. random selection - using a random method to select participants from the population that fits your sample size (e.g participant names out of a hat, random participant generator)