What Random sampling is
Random sampling is a method of selecting a subset of individuals from a larger population in order to conduct research. By randomly selecting participants, researchers are able to reduce the potential bias that could be caused by self-selection or convenience sampling.
Random sampling involves several steps:
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Define the population – The population is the group of individuals from which the sample will be drawn.
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Determine the sample size – The sample size is the number of individuals that will be included in the sample.
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Create a sampling frame – A sampling frame is a list of all possible individuals that could be included in the sample.
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Assign each individual a number – Each individual in the sampling frame should be assigned a unique number.
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Generate random numbers – A random number generator can be used to generate a list of numbers.
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Select the sample – Individuals with numbers matching those generated by the random number generator should be included in the sample.
Examples
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Stratified random sampling: A researcher wants to survey the opinions of a population and they want to ensure that all subgroups of the population are represented in the sample. To do this, they use stratified random sampling to create a sample that is proportional to the population.
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Systematic random sampling: A researcher wants to survey a population of 1,000 people and wishes to collect data from every 20th person in the population. To do this, they use systematic random sampling.
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Cluster random sampling: A researcher wants to survey the opinions of high school students in a state. To do this, they use cluster random sampling to randomly select a set of schools and then survey the students in those schools.