sampling techniques

• Distinguish between a population and a sample

  A population refers to the people the researcher is interested in investigating.

  A sample refers to a group of people drawn from the population who take part in the research. They should be representative of

  the population so that the findings can be generalised.
• Stratified sampling

  A sampling technique that ensures participants in the sample are equal to their representation in the population.
• systematic sampling

  A sampling technique that uses an objective number system to select

  participants, whereby every nth person is selected to take part.
• Random sampling

  A sampling technique whereby everyone in the population has an equal chance of being selected.
• opportunity sampling

  A sampling technique whereby participants who are readily available to the researcher (e.g. nearby) are used.
• volunteer sampling

  A sampling technique whereby participants self-select themselves to

  take part by responding to they study’s advertisement.
• how do you conduct systematic sampling

  1.Acquire every participants name in the population

  2. Form the names into a randomly ordered list.

  3. Calculate and select the nth term. (E.g. a sample of 20 from a population

  of 200 requires selecting every 10th participant because 200/20 = 10).
• how do you conduct random sampling

  1. Write the names of the people in the population on separate slips of

  paper and place them into a hat.

  2. Without looking, pull out the number of participants required for the

  study. These will form the sample.
• how do you conduct opportunity sampling

  1.The researcher goes to a place where they are likely to find people in their

  population (e.g. if their population is elderly people, they may go to care

  homes).

  2. The researcher then approaches the people in their population who are

  nearby and asks them to participate.

  3. Those who agree to take part are used in the sample.
• volunteer sampling

  1.Researchers advertise their study with their contact details in a place where members of their population are likely to see it (e.g. if their

  population is university students, they may advertise their study on a

  university campus).

  2. Participants respond to advertisement by emailing researcher expressing

  their desire to take part.

  3. Those who express a desire to take part are used in the sample.
• stratified sampling evaluations

  1. Removes Researcher Bias: Stratified sampling eliminates researcher bias in participant selection by randomly choosing participants from each strata (e.g., using the hat method).
  2. Produces a Representative and Generalisable Sample: It ensures the sample reflects the population's strata proportions, making it the most representative sampling technique. This enhances the generalisability of findings to the population.
• systematic sampling evaluations

  1.Removes Researcher Bias: Systematic sampling eliminates researcher bias by using an objective number system to select participants.

  2. Risk of Bias and Limited Generalisability: It may lead to a biased sample if certain characteristics repeat at regular intervals (e.g., every nth term), potentially making findings less generalizable to those who do not share those characteristics.
• evaluate random sampling

  1. Removes Researcher Bias: Random sampling eliminates researcher bias by using a random selection method (e.g., the hat method), ensuring all individuals in the population have an equal chance of being selected.
  2. Risk of Bias and Limited Generalisability: It may still result in a biased sample if the randomly selected participants share similar characteristics (e.g., all female), potentially making findings less generalizable to those who do not share those characteristics (e.g., males).
• opportunity sampling

  1. Risk of Researcher Bias: Opportunity sampling may involve researcher bias, as the researcher selects which individuals to approach, potentially favoring certain characteristics (e.g., approaching only extraverted individuals).
  2. Limited Generalisability: The sample may be biased if the participants share similar characteristics or are limited to those available in a specific location, making findings less generalizable to the wider population or those with different characteristics.
• evaluate volunteer sampling

  1. Removes Researcher Bias: Volunteer sampling eliminates researcher bias because participants self-select to take part, meaning the researcher does not choose who participates.
  2. Risk of Bias and Limited Generalisability: It may lead to a biased sample, as certain individuals (e.g., extroverts) are more likely to volunteer than others (e.g., introverts). This limits the generalisability of findings to those who do not share the characteristics of the volunteers.