sampling

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sampling

the selection of part of a total population of consumers or products whose behaviour or performance can be analysed, in order to make inferences about the behaviour or performance of the total population, without the difficulty and expense of undertaking a complete census of the whole population.

Samples may be chosen randomly, with every consumer or product in the population having an equal chance of being included. Random samples are most commonly used by firms in QUALITY CONTROL where they are used as a basis for selecting products, components or materials for quality testing.

Alternatively, samples may be chosen by dividing up the total population into a number of distinct sub-groups or strata, then selecting a proportionate number of consumers or products from each sub-group since this is quicker and cheaper than random sampling. In MARKETING RESEARCH and opinion polling, quota sampling is usually employed where interviewers select the particular consumers to be interviewed, choosing the numbers of these consumers in proportion to their occurrence in the total population.

Samples may be:

  1. cross-sectional, where sample observations are collected at a particular point in time, for example data on company sales and the incomes of consumers in the current year, embracing a wide range of different income groups, as a basis for investigating the relationship between sales and income;
  2. longitudinal, where sample observations are collected over a number of time periods, for example data on changes in company sales over a number of years and changes in consumer incomes over the same time periods, as a basis for investigating the relationship between sales and income. See STATISTICAL INFERENCES, QUESTIONNAIRE.
Collins Dictionary of Business, 3rd ed. © 2002, 2005 C Pass, B Lowes, A Pendleton, L Chadwick, D O’Reilly and M Afferson
References in periodicals archive ?
The recommended selection procedure of (10) allows for fractional proportions of the overall sample size. This additional fraction allows for increased growth, which in turn leads to improved coverage.
First, Kieser and Wassmer (1996) did not specifically address the issue of how to modify the sample variance in sample size calculations so that the expected power of a two-sample t-test will attain the planned power.
Under Type of power analysis, select A priori: Compute required sample size given [alpha], power and effect size.
Using the unitary index of the comparative fit index (CFI), (28), to specify the acceptable fit (29) baseline model (~0.90) and the good fitting model ([greater than or equal to]0.95) (30), other accepted indices of model fit were also calculated for a given sample size including, the root mean squared error of approximation (RMSEA), (13) and the squared root mean residual (SRMR), (30).
Example 2: If we increase the sample size of the capability study to 120, and if our calculations find a Pp = 1.67, then the actual Pp could be between Pp = 1.45 and Pp = 1.9.
As the above shows, the power, Power(n, a, [H.sub.0], [H.sub.a]), is a function of sample size n, type I error [alpha] and values of [mu] specified in the null [H.sub.0] and alternative [H.sub.a].
A formal method for determining the most efficient distribution of sample size across species would be useful for the allotment of limited time, personnel, and funding; however, there is little to no existing guidance in the literature.
Lastly, when quantifying DIF, the fit statistic should not be affected by sample size. More specifically, in the absence of DIF, the fit measure should be near a constant value independent of the sample size while, in the presence of DIF, the value should only quantify DIF without being affected by sample size.
Sample size used to validate a scale: a review of publications on newly-developed patient reported outcomes measures.
In ACS the expected final sample size varies from sample to sample.
A number of studies have been carried out in eucalyptus on aspects related to experimental planning, such as experimental alternatives for evaluation of progenies and clones (SOUZA et al., 2003), the ideal size of the experimental plot (ZANON & STORCK, 1997; SILVA et al., 2003) and determination of sample size (ZANON et al., 1997; SILVA et al., 2007).