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Sampling - Coggle Diagram

- - - - 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 unit of analysis may be a person, group, organization, country, object, or any other entity that you wish to draw scientific inferences about. Sometimes the population is obvious.
        
        The second step in the sampling process is to choose a sampling frame.
        
        This is an accessible section of the target population (usually a list with contact information) from where a sample can be drawn.
        
        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.
- - - - 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. If you have a population dispersed over a wide geographic region, it may not be feasible to conduct a simple random sampling of the entire population.
        
        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.
        
        Nonprobability sampling is a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined.
        
        Convenience sampling. Also called accidental or opportunity sampling, this is a technique in which a sample is drawn from that part of the population that is close to hand, readily available, or convenient.
- - - - Expert sampling. This is a technique where respondents are chosen in a non-random
        manner based on their expertise on the phenomenon being studied.
        
        Snowball sampling. In snowball sampling, you start by identifying a few respondents that match the criteria for inclusion in your study, and then ask them to recommend others they know who also meet your selection criteria.
- - - - These sample estimates are called sample statistics (a "statistic" is a value that is estimated from observed data.)
        
        Sample statistics may differ from population parameters if the sample is not perfectly representative of the population; the difference between the two is called sampling error.
        
        Sampling distribution. If each random sample was truly representative of the population, then your three sample means from the three random samples will be identical (and equal to the population parameter), and the variability in sample means will be zero.
        
        The variability or spread of a sample statistic in a sampling distribution (i.e., the standard deviation of a sampling statistic) is called its standard error.
        
        Confidence interval is the estimated probability
        that a population parameter lies within a specific interval of sample statistic values.