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Sampling: is the statistical process of selecting a subset (called a…
Sampling: is the statistical process of selecting a subset (called a “sample”) of a population of interest for purposes of making observations and statistical inferences about that population.
Sampling Process:
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. 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.
Probability Sampling: 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.
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.
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.
Cluster Sampling: to divide the population into “clusters” (usually along geographic boundaries), randomly sample a few clusters, and measure all units within that cluster
Matched-pairs sampling: to compare two subgroups within one population based on a specific criterion.
Multi Stage Sampling: Sampling technique that involves multiple stages including cluster, simple random etc.
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 mutuallyexclusive 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 don’t 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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