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Sampling - the statistical process of selecting a subset (sample) of a…
Sampling - the statistical process of selecting a subset (sample) of a population of interest for purposes of making observations and statistical inferences about that population.
The Sampling Process
Population - all people or items (unit of analysis) with the characteristics that one wishes to study. Unit of analysis - person, group, organization, country, object, or any other entity that you wish to draw scientific inferences about.
Sampling Frame - an accessible section of the target population (usually a list with contact information) from where a sample can be drawn. Sampling frames may not entirely be representative of the population at large, and if so, inferences derived by such a sample may not be generalizable to the population.
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Sampling Techniques
Probability (random) 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 this technique, 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 - used if you have a population dispersed over a wide geographic region and you divide the population into clusters (usually along geographic boundaries), and randomly sample a few clusters and measure all units within that cluster.
Matched-Pairs Sampling - when researchers may want to compare two subgroups within one population based on a specific criterion.
Multi-Stage Sampling - this is a two-stage combination of stratified and systematic sampling that is used when combining all single-stage techniques together.
Non-Probability Sampling - a 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 (accidental or opportunity) Sampling - 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 and then a non-random set of observations is chosen from each subgroup to meet a predefined quota.
Proportional Quota Sampling - the proportion of respondents in each subgroup should match that of the population.
Non-Proportional Quota Sampling - is less restrictive; you don't have to achieve a proportional representation, but have to meet a minimum size in each subgroup.
Expert Sampling - a technique where respondents are chosen in a non-random manner based on their expertise on the phenomenon being studied.
Snowball Sampling - using this technique, 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.
Statistics of Sampling
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Frequency Distribution - graphing responses from different respondents to the same item or observation.
Normal Distribution - when there is a large number of responses in a sample, the frequency distribution tends to resemble a bell-shaped curve.
Sample Statistics - values that are estimated from observed data; sample estimates (i.e., sample mean - average of all observations in a sample; standard deviation - variability or spread of observations in a sample).
Population Parameters - since the entire population can never be samples; they are unknown population characteristics.
Sampling Error - the difference between sample statistics and population parameters if the sample is not perfectly representative of the population.
Sampling Distribution - a frequency distribution of a sample statistic (like sample mean) from a set of samples.
Standard Error - the variability or spread of a sample statistic in a sampling distribution (i.e., the standard deviation of a sampling statistic).
Confidence Interval - the estimated probability that a population parameter lies within a specific interval of sample statistic values.