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Sampling - Coggle Diagram

- - - - In proportional quota sampling,
        the proportion of respondents in each subgroup should match that of the population.
        
        Non-proportional quota sampling is less restrictive in that you dont have to achieve a proportional representation, but perhaps meet a minimum size in each
        subgroup.
        
        The non-proportional technique is even less representative of the population but may be useful in that it allows
        capturing the opinions of small and underrepresented groups through oversampling.
- - - - The first stage is defining the target population.
        
        A population can be defined as all people or items (unit of analysis) with the characteristics that one wishes to study.
      - The second step in the sampling process is to choose a sampling frame.
        
        If your target population is professional employees at work, because you cannot access all professional employees around the world, a more realistic sampling frame will be employee lists of one or two local companies that are willing to participate in your study.
      - The last step in sampling is choosing a sample from the sampling frame using a well-
        defined sampling technique.
        
        Sampling techniques can be grouped into two broad categories:
        probability (random) sampling and non-probability sampling.
        
        Probability sampling is ideal if generalizability of results is important for your study, but there may be unique circumstances
        where non-probability sampling can also be justified.
- - - - The different types of
        probability sampling techniques include:
        
        Simple random sampling.
        
        In this technique, all possible subsets of a population are given an an equal being selected selected.
        
        Systematic sampling.
        
        In this technique, the sampling frame is ordered according to some criteria and elements are selected at regular intervals through that ordered list.
        
        Stratified sampling.
        
        In stratified sampling, the sampling frame is divided into homogeneous and non-overlapping subgroups (called "strata") and a simple random sample is drawn within each subgroup
        
        Cluster sampling.
        
        In such case, it may be reasonable to divide the population into "clusters"randomly sample a few clusters, and measure all units within that cluster.
        
        Matched-pairs sampling.
        
        Sometimes, researchers may want to compare two
        subgroups within one population based on a specific criterion.
        
        Multi-stage sampling.
        
        The probability sampling techniques described previously are
        all examples of single-stage sampling techniques.
- - - - For a large number of responses in a sample, this frequency
        distribution tends to resemble a bell-shaped curve called a normal distribution,which can be
        used to estimate overall characteristics of the entire sample, such as sample mean (average of all observations in a sample) or standard deviation (variability or spread of observations in a sample).
        
        These sample estimates are called sample statistics (a "statistic" is a value that is estimated from observed data).