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Sampling Techniques and Sample size (Sampling Technique (Random (Cluster…

- - - - Minimise the variability within each stratum and maximise the differences between strata
      - Particularly useful when the distribution of the characteristic of interest in the target population is skewed or when extremes are present
    - - Assume that all the clusters are basically identical with regard to the factors used to identify the clusters
      - In each chosen cluster, either choose all the elements or choose a random sample of elements
      - Particularly useful when the only sampling frame readily available is a list of clusters, not elements
      - Categorize the target population into clusters and then choose a random sample of clusters
  - - - Useful in exploratory research for generating ideas, insights or hypotheses
      - Choice of elements is based on convenience and is entirely up to the person who does the sampling
    - - Choice of elements is based on the judgement of the researcher
      - market potential/ product testing/ merchandising displaying system
    - - Stage 1: specify quota(s), so as to ensure that the composition of the sample is the same as the composition of the population with regard to the quota(s) of interest
        Quotas, or control characteristics, can be demographic characteristics, specific attitudes or specific behaviours
        
        Stage 2: elements, as long as they fit the quota(s), are chosen by using convenience or judgement sampling
        A quota sample by itself cannot represent the population with any level of confidence
    - - More often used in occasions where the defined target population is small and unique and compiling a sampling frame is very difficult
      - The underlying logic is that small and unique groups of people tend to form their own social circles