2.3 Sampling methods
　　Data are often collected from a sample rather than from a population. If the whole population is examined, the survey is called a census.
　　There are two types of sampling methods:
　　Probability sampling method – is a sampling method in which there is a known chance of each member of the population appearing in the sample.
　　- Random
　　- Stratified random
　　- Systematic
　　- Multistage
　　- Cluster
　　If random sampling is used, it is necessary to construct a sampling frame.
　　A sampling frame should have following characteristics
　　- completeness
　　- accuracy
　　- adequacy
　　- up to dateness
　　- convenience
　　- non-duplication
　　Non probability sampling method – is a sampling method in which the chance of each member of the population appearing in the sample is not know, i.e. quota sampling.
　　Example 1:
　　The following statements relate to which different types of data
　　（i）。 Secondary data are data collected especially for a specific purpose
　?。╥i）。 Discrete data can take on any value.
　?。╥ii） Qualitative data are data that cannot be measured
　?。╥v）。 Population data are data arising as a result of investigating a group of people or
　　Objects.
　　Which of the statements are true?
　　A.（i）and（ii）only
　　B.（ii）and（iii）only
　　C.（ii）and（iv）only
　　D.（iii）and（iv）only
　　Solution is D
　　It is primary data that is collected for a specific purpose so （i） is false. Continuous data can take on any value so （ii） is false. Both （iii） and （iv） are true.
　　Example 2:
　　Which of the following statements are not true?
　　I If a sample is selected using random sampling, it will be free from bias.
　　II A sampling frame is a numbered list of all items in a sample.
　　III In cluster sampling there is very little potential for bias.
　　IV In quota sampling, investigators are told to interview all the people they meet
　　Up to a certain quota.
　　A I, II, III and IV
　　B I, II and III
　　C II and III
　　D II only
　　Solution is C
　　A sampling frame is a numbered list of all items in a population （not a sample）
　　Cluster sampling involves selecting one definable subsection of the population which therefore makes the potential for bias considerable.
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