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a) Simple Random Sampling
b) Stratified Random Sampling- dividing population to homogeneous sub-groups (age, gender, location etc), then taking a simple random sampling
? Specifies attributes or characteristics the researcher wishes to be equally distributed among the samples (eg. gender or work department)
? Provided the sampling frame can easily be identified by these characteristics, then the strata for each characteristic are identified within each group, random or systematic sampling can proceed
c) Systemic Random Sampling- eg. taking 10th , 20th , 30th , 40th etc until samples exhausted
? Sample chosen from a sample frame ( need not be in any order, except alphabetical)
? Once sample proportion known, start with the random number generated in the list , then choose the nth item till sample is complete
d) Multi-Stage Cluster Random-dispersed population over a large geographical area, population divided into clusters (eg. counties, regions etc) ? Sample frame first broken into clusters (eg. geographic areas) and a random or systematic sample taken
? Then the population of each cluster is sampled randomly to provide a logically feasible random sample. This introduces bias, but using both cluster and systematic sampling usually produce effective samples
Kavungya answered the question on March 7, 2019 at 07:43
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