Sampling:
The aim of sampling is to make generalisations beyond the individuals used in the study
Parameter = populations
Statistics = sample
Inferences are made from statistics to predict parameters
Inclusion and exclusions criteria:
Criteria or characteristics displayed by study participants as a prerequisite for being included or excluded from a study.
Sampling Methods
Census:
• whole of defined population is counted/measured
Probability sampling:
• by random selection, every unit in the population has equal chance of selection
• eliminates sampling bias
• difference between sample statistic and population parameter is the sampling error
• randomisation allows for estimation of degree of sampling error confidence in validity
Types:
1) Simple random sampling – each selection is independent and has equal chance of selection
2) Systematic sampling – every (eg 10th) element is sampled but start is random
3) Stratified sampling – sample is divided into strata and a simple random sample is taken from each stratum
Non-probability sampling:
In these samples the probability of selection is not known cannot estimate sampling error
Types:
1) Convenience
2) Quota
3) Snowball
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