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Chapter 8 (Statistics of Sampling: sample estimates are called sample…
Chapter 8
Sampling: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
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. 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 welldefined sampling technique. Sampling techniques can be grouped into two broad categories: probability (random) sampling and non-probability sampling.
Probability Sampling
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.
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.
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.
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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.
Matched-pairs sampling. Sometimes, researchers may want to compare two subgroups within one population based on a specific criterion.
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.
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Nonprobability Sampling: 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.
Quota sampling 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
:check:Proportional Quota Sampling: the proportion of respondents in each subgroup should match that of the population.
:check: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: a technique where respondents are chosen in a non-random manner based on their expertise on the phenomenon being studied
Snowball sampling 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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Convenience sampling: technique in which a sample is drawn from that part of the population that is close to hand, readily available, or convenient.
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Examples
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organization, country, objects
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Statistics of Sampling: sample estimates are called sample statistics (a “statistic” is a value that is estimated from observed data)
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
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.
