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SAMPLING - Coggle Diagram
SAMPLING
random sampling
unbiased: confounding and extraneous variables should be equally dived between the different groups, enhancing internal validity
time consuming, list of population may be difficult to obtain.
all members of the population have an equal chance of being selected. first you select a list of all the members of the target population (eg register). all the names are assigned a number. the sample is then selected by using a lottery method (eg picking from a hat)
ppts may refuse to take part, which means you end up with something more like a volunteer sample
stratified sampling
the researcher first identifies the different strata. then the proportions needed for each sample are worked out. finally the ppts that make up each stratum are picked through random sampling
produces a representative sample because its designed accurately to reflect the composition of the population. makes it easy to generalise.
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opportunity sample
convenient, less costly. no need to divide the population into strata's or obtain lists
issue: two forms of bias unrepresentative for the target population as it is drawn from a specific area
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Population = a group of people who are the focus of the researchers interest, from which a smaller sample is drawn
Sample = a group of people who take part in a research investigation. this is drawn from the population and is meant to representative of that population
Generalisation = representative of the target population so generalisation is possible. this is very difficult as populations are diverse (gender, age, interests, experience)