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Sampling - Coggle Diagram
Sampling
Sampling is the statistical process of selecting a subject (called a "sample") of a population of interest for purposes of making observation and statistical inferences about that population.
It is extremely important to choose a sample that is truly representative of the population so that the inferences derived from the sample can be generalized back to the population of interest.
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Probability sampling is a technique in which every unit in the population has a chance (non-zero probability) of being selected in the sample, and this chance can be accurately determined.
Simple random sampling. In this technique, all possible subsets of a population (more accurately, of a sampling frame) are given an equal probability of being selected.
Systematic sampling. In t his 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. 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.
Quota sampling. In this technique, the population is segmented into mutually-exclusive subgroups (just as in stratified sampling), and then a non-random set of observations is chosen from each subgroup to meet a predefined quota.
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.
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.
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