# Sampling design

Simple random sampling A visual representation of selecting a simple random sample In a simple random sample SRS of a given Sampling design, all such subsets of the frame are given an equal probability.

Implementation usually follows Sampling design simple random sample. There are, however, some potential drawbacks to using stratified sampling. People living on their own are certain to be selected, so we simply add their income to our estimate of the total.

As described above, systematic sampling is an EPS method, because all elements have the same probability of selection in the example given, one in ten. A probability sample is a sample in which every unit in the population has a chance greater than zero of Sampling design selected in the sample, and this probability can be accurately determined.

Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample or subset of that population.

Disadvantages Requires selection of relevant stratification variables which can be difficult. We visit each household in that street, identify all adults living there, and randomly select one adult from each household.

Although the method is susceptible to the pitfalls of post hoc approaches, it can provide several benefits in the right situation. This method is sometimes called PPS-sequential or monetary unit sampling in the case of audits or forensic sampling.

Time spent in making the sampled population and population of concern precise is often well spent, because it raises many issues, ambiguities and questions that would otherwise have been overlooked at this stage. For example, suppose we wish to sample people from a long street that starts in a poor area house No.

Although the population of interest often consists of physical objects, sometimes we need to sample over time, space, or some combination of these dimensions.

Or, if the budget is limited, a researcher might choose the design that provides the greatest precision without going over budget.

However, Sampling design the more general case this is not usually possible or practical. Sampling method refers to the rules and procedures by which some elements of the population are included in the sample. In a simple PPS design, these selection probabilities can then be used as the basis for Poisson sampling.

For example, consider a street where the odd-numbered houses are all on the north expensive side of the road, and the even-numbered houses are all on the south cheap side. Can be expensive to implement. Second, utilizing a stratified sampling method can lead to more efficient statistical estimates provided that strata are selected based upon relevance to the criterion in question, instead of availability of the samples.

A simple random selection of addresses from this street could easily end up with too many from the high end and too few from the low end or vice versaleading to an unrepresentative sample. Where voting is not compulsory, there is no way to identify which people will actually vote at a forthcoming election in advance of the election.

In the two examples of systematic sampling that are given above, much of the potential sampling error is due to variation between neighbouring houses — but because this method never selects two neighbouring houses, the sample will not give us any information on that variation.

In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy of results. Note also that the population from which the sample is drawn may not be the same as the population about which we actually want information.

Similarly, the formula for the standard error may vary from one sampling method to the next. These various ways of probability sampling have two things in common: Third, it is sometimes the case that data are more readily available for individual, pre-existing strata within a population than for the overall population; in such cases, using a stratified sampling approach may be more convenient than aggregating data across groups though this may potentially be at odds with the previously noted importance of utilizing criterion-relevant strata.

The estimation process for calculating sample statistics is called the estimator. We want to estimate the total income of adults living in a given street.Statistics - Lecture 13 1 Statistics - Lecture 13 Prof.

Kate Calder 1 Sampling Design The idea of sampling: We want to say something about a population - the entire group of individuals that we want information about. Sampling frame /Source list -complete list of all the members/ units of the population from which each sampling unitSample design / sample plan-is a definite plan for obtaining a sample from a given killarney10mile.comng unit-is a geographical one (state,district)Sample size-number of items selected for the studySampling Error-is the.

Sample Design. A sample design is made up of two elements. Sampling method. Sampling method refers to the rules and procedures by which some elements of the population are included in the sample.

Some common sampling methods are simple random sampling, stratified sampling, and cluster sampling.

Estimator. The. Non-probability sampling is a sampling technique where the samples are gathered in a process that does not give all individuals in the population equal chances of being. The sampling frame is the list from which the sample is selected, so the quality of the sampling frame affects the quality of the sample.

Cluster sampling is designed to address problems of a widespread geographical population. Random sampling from a large population is likely to lead to high costs of access.

This can be overcome by dividing the population into clusters, selecting only two or three clusters, and sampling from within those.

Sampling design
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