Changes: Sampling (experimental)

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Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for the purposes of statistical inference. In particular, results from probability theory and statistical theory are employed to guide practice.

The sampling process consists of five stages:(Makerere University Institute of Statistics & Applied Economics (ISAE)

Definition of population of concern
Specification of a sampling frame, the source from which a sample is drawn. It is a list of all those within a population who can be sampled, and may include individuals, households or institutions
Specification of a sampling method for selecting items or events from the frame
Sampling and data collecting
Review of sampling process

Population definition

Successful statistical practice is based on focused problem definition. Typically, we seek to take action on some population, for example trying to reduce the incidence of psychological problems in people involved in major accidents. Time spent in making the population of concern precise is often well spent, often because it raises many issues, ambiguities and questions that would otherwise have been overlooked at this stage.

Sampling frame

In the most straightforward case, such as the sentencing of a batch of material from production (acceptance sampling by lots), it is possible to identify and measure every single item in the population and to include any one of them in our sample. However, in the more general case this is not possible. There is no way to identify all rats in the set of all rats. There is no way to identify every voter at a forthcoming election (in advance of the election).

These imprecise populations are not amenable to sampling in any of the ways below and to which we could apply statistical theory.

As a remedy, we seek a sampling frame which has the property that we can identify every single element and include any in our sample. For example, in an electoral poll, possible sampling frames include:

Electoral register
Telephone directory
Shoppers in Anytown, High Street on the Monday afternoon before the election.

The sampling frame must be representative of the population and this is a question outside the scope of statistical theory demanding the judgement of experts in the particular subject matter being studied. All the above frames omit some people who will vote at the next election and contain some people who will not. People not in the frame have no prospect of being sampled. Statistical theory tells us about the uncertainties in extrapolating from a sample to the frame. In extrapolating from frame to population its role is motivational and suggestive.

In defining the frame, practical, economic, ethical and technical issues need to be addressed. The need to obtain timely results may prevent extending the frame far into the future.

The difficulties can be extreme when the population and frame are disjoint. This is a particular problem in forecasting where inferences about the future are made from historical data. In fact, in 1703, when Jacob Bernoulli proposed to Gottfried Leibniz the possibility of using historical mortality data to predict the probability of early death of a living man, Leibniz recognised the problem in replying:

Nature has established patterns originating in the return of events but only for the most part. New illnesses flood the human race, so that no matter how many experiments you have done on corpses, you have not thereby imposed a limit on the nature of events so that in the future they could not vary.

Having established the frame, there are a number of ways of organising it to improve efficiency and effectiveness.

Simple sampling

In this case, all elements of the frame are treated equally and it is not subdivided or partitioned. One of the sampling methods below is applied to the whole frame.

Stratified sampling

Where the population embraces a number of distinct categories, the frame can be organised by these categories into separate strata or demographics. One of the sampling methods below is then applied to each stratum separately. Major gains in efficiency (either lower sample sizes or higher precision) can be achieved by varying the sampling fraction from stratum to stratum. The sample size should be made proportional to the stratum standard deviation. From the efficiency point of view (i.e. maximum precision for a given sample size) strata should be chosen to have

means which differ substantially from one another
variances which are different from one another, and lower than the overall variance

Cluster sampling

Random sampling of a population spread across a large area, eg all of Europe involves a lot of travelling, cost and delay. Cluster or area sampling addresses this problem. There are three stages: 1) the target population is divided into many regional clusters (groups) eg London, Berlin, Rome etc 2) a few clusters are randomly selected for study 3) A few subjects are randomly chosen from within a cluster

Quota sampling

In quota sampling, the population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgement is used to select the subjects or units from each segment based on a specified proportion. For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60.

It is this second step which makes the technique one of non-probability sampling. In quota sampling the selection of the sample is non-random. For example interviewers might be tempted to interview those people in the street who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection. This non-random element is its greatest weakness and quota versus probability has been a matter of controversy for many years.

Sampling method

Within any of the types of frame identified above, a variety of sampling methods can be employed, individually or in combination.

Random sampling

Main article: Random sampling

In random sampling, also known as probability sampling, every combination of items from the frame, or stratum, has a known probability of occurring, but these probabilities are not necessarily equal. With any form of sampling there is a risk that the sample may not adequately represent the population but with random sampling there is a large body of statistical theory which quantifies the risk and thus enables an appropriate sample size to be chosen. Furthermore, once the sample has been taken the sampling error associated with the measured results can be computed. With non-random sampling there is no measure of the associated sampling error. While such methods may be cheaper this is largely meaningless since there is no measure of quality. There are several forms of random sampling. For example, in simple random sampling, each element has an equal probability of occurring. It may be infeasible in many practical situations. Other examples of probability sampling include stratified sampling and multistage sampling.

Systematic sampling

Selecting (say) every tenth name from the telephone directory is simple to implement and is an example of systematic sampling. Though simple to implement, asymmetries and biases in the structure of the data can lead to bias in results. It is a type of nonprobability sampling unless the directory itself is randomized.

Mechanical sampling

Mechanical samplingdoes not occurs typically in sampling solids, liquids and gases, using devices such as grabs, scoops, thief probes, the coliwasa and riffle splitter.

Mechanical sampling is not random and is a type of nonprobability sampling. Care is needed in ensuring that the sample is representative of the frame. Much work in this area was developed by Pierre Gy.

Convenience sampling

Sometimes called, grab or opportunity sampling, this is the method of choosing items arbitrarily and in an unstructured manner from the frame. Though almost impossible to treat rigorously, it is the method most commonly employed in many practical situations. In social science research, snowball sampling is a similar technique, where existing study subjects are used to recruit more subjects into the sample.

Sample size

Where the frame and population are identical, statistical theory yields exact recommendations on sample size. However, where it is not straightforward to define a frame representative of the population, it is more important to understand the cause system of which the population are outcomes and to ensure that all sources of variation are embraced in the frame. Large number of observations are of no value if major sources of variation are neglected in the study. In other words, it is taking a sample group that matches the survey category and is easy to survey.

Sampling and data collection

Good data collection involves:

Following the defined sampling process
Keeping the data in time order
Noting comments and other contextual events
Recording non-responses

Review of sampling process

After sampling, a review should be held of the exact process followed in sampling, rather than that intended, in order to study any effects that any divergences might have on subsequent analysis. A particular problem is that of non-responses.

Non-responses

In survey sampling, many of the individuals identified as part of the sample may be unwilling to participate or impossible to contact. In this case, there is a risk of differences, between (say) the willing and unwilling, leading to selection bias in conclusions. This is often addressed by follow-up studies which make a repeated attempt to contact the unresponsive and to characterise their similarities and differences with the rest of the frame.

Weighting of samples

In many situations the sample fraction may be varied by stratum and data will have to be weighted to correctly represent the population. Thus for example, a simple random sample of individuals in the United Kingdom might include some in remote Scottish islands who would be inordinately expensive to sample. A cheaper method would be to use a stratified sample with urban and rural strata. The rural sample could be under-represented in the sample, but weighted up appropriately in the analysis to compensate.

History of sampling

The idea of random sampling by the use of lots is an old one, mentioned several times in the Bible. In 1786 Pierre Simon Laplace estimated the population of France by using a sample, along with ratio estimator. He also computed probabilistic estimates of the error. These were not expressed as modern confidence intervals but as the sample size that would be needed to achieve a particular upper bound on the sampling error with probability 1000/1001. His estimates used Bayes' theorem with a uniform prior probability and it assumed his sample was random.The theory of small-sample statistics developed by William Sealy Gossett put the subject on a more rigorous basis in the 20th century. However, the importance of random sampling was not universally appreciated and in the USA the 1936 Literary Digest prediction of a Republican win in the presidential election went badly awry, due to severe bias. A sample size of one million was obtained through magazine subscription lists and telephone directories. It was not appreciated that these lists were heavily biased towards Republicans and the resulting sample, though very large, was deeply flawed.

Graduate Degree Programs specializing in Sampling/Survey Methods

Psychology

Doctoral and Masters Degrees

Masters Degrees Only

Bibliography

Cochran, W G (1977) Sampling Techniques
Deming, W E (1975) On probability as a basis for action, The American Statistician, 29(4), pp146-152
Gy, P (1992) Sampling of Heterogeneous and Dynamic Material Systems: Theories of Heterogeneity, Sampling and Homogenizing
Sarndal, Swenson, and Wretman (1992), Model Assisted Survey Sampling, Springer-Verlag.
Stuart, Alan (1962) Basic Ideas of Scientific Sampling, Hafner Publishing Company, New York

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