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quant W1-3 pt3 - Coggle Diagram
quant W1-3 pt3
sampling
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representative sample- choosing a sample that contains sub-groups of people in proportion to their prevalence in the population we wish to generalise to avoid sampling bias
population- all possible members of a category from which a sample is drawn, the wider group you wish to learn about
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sampling bias- systematic tendency towards over-or-under representation of some categories in a sample
ppt variables- person variables differing in proportion across different experimental groups and possible confounding results
representative samples- type of sample aimed at if results of research are to be generalised, hoped that the subgroups will be proportion to the general population
sampling strategies
probability-based sample- everyone in the target population has an equal probability of being selected, random
probability sampling
random sampling- every member of the population of interest has equal chance of being selected (computer generation, random number table, manual
systematic random sampling- selected every nth case from the population, where n is any number, equal chance= randomly select a starting point
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non-probability sampling
opportunity/convenience sampling- most common but lowest credibility, whoever is available takes part, specific topics will attract specific people
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random allocation/assignment- ensures ppts have equal chance of being assigned to any level of the IV (eg drug trial, being randomly allocation to active drug or placebo)
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experimental design
allows us to investigate causal relationships (one thing causes another, explored by using experimental designs)
mixed deign/measures
research design can include combination of between-subejcts factors and within-subject factors: one or more IV uses the same ppts (within), one of the IV uses different ppts (between)