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SAMPLING - Coggle Diagram
SAMPLING
Random sampling
This involves identifying everyone in the target population and then selecting the number of participants you need in a way that gives everyone in the population an equal chance of being picked.
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This is a sampling technique which is defined as a sample in which every member of the population has an equal chance of being chosen.
Random number generator – random number assigned to each person, computer program selects numbers randomly
It is widely accepted that since each member has the same probability of being selected, there is a reasonable chance of achieving a representative sample
- Some may refuse to take part so can be extremely time consuming
Opportunity sampling
- It consists of taking the sample from people who are available at the time the study is carried out and fit the criteria you are looking for.
- E.g. those in your school or those walking past you on the street
- This method is easy and inexpensive to carry out
- The consequent sample may not be representative as it could be subject to bias (e.g. the conveniently located employer may undertake a selection process for job applicants, making it likely that employees possess certain similar characteristics that are unrepresentative of the wider target group)
- Participants who are both accessible and willing to take part are targeted, e.g. employees from a conveniently located employer near the laboratory could be selected for the sample group
Stratified sampling
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Participants are obtained from each subgroup in proportion to their occurrence in the target population
- It avoids the problem of misrepresentation sometimes caused by purely random sampling
- Care must be taken to ensure each key characteristic present in the population is selected across strata, otherwise this will design a biased sample + time consuming to plan
- Stratified sampling is where the researcher divides or 'stratifies' the target group into sections, each representing a key group (or characteristic) that should be present in the final sample.
- E.g, if a class has 20 students, 18 male and 2 female, and a researcher wanted a sample of 10, the sample would consist of 9 randomly chosen males and 1 randomly chosen female, to represent this population
Population and sample
Population: group of individuals a researcher is interested in e.g. ‘people in the UK’ or ‘young people in Bristol’ or ‘all babies in the western world’
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e.g. ‘all babies in the western world’ they might select babies born in two hospitals in London in Jan 2015
Sample: obviously we cannot study all people the population, instead we identify a smaller group called the ‘sample’
Volunteer sampling
- Produced by asking for volunteers. The volunteers select themselves
- Advert on internet, newspaper, noticeboard etc
- This often achieves a large sample size through reaching a wide audience, for example with online advertisements.
- Those who respond to the call for volunteers may all display similar characteristics (such as being more trusting or cooperative than those who did not apply) thus increasing the chances of yielding an unrepresentative sample
Systematic sampling
- Selecting every nth person e.g. every 5th or 10th person
- This numerical interval is applied consistently
- Assuming the list order has been randomised, this method offers an unbiased chance of gaining a representative sample
- If the list has been assembled in any other way, bias may be present. For example if every fourth person in the list was male, you would have only males in your sample
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