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Related to Cluster sampling: Systematic sampling, Convenience 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 ?
Proposed Ratio Estimators for Population Variance in Adaptive Cluster Sampling: Following (Isaki, 1983) the proposed ratio type variance estimatorin ACS:
'Design effect' is the factor taken for adjusting for heterogeneity of the population in cluster sampling and to adjust the difference in precision between a simple random sample and a cluster sample was taken as 2.
Using the CASPER cluster sampling methodology, a representative sample of 210 households in Burleigh County was selected to be interviewed.
A total of 480 Malaysian secondary school Form Four students sample was selected using Multistage Stratified Cluster Sampling. Data was analyzed using Exploratory Factor Analysis (EFA) techniques with the software of SPSS version 15.0.
One example is adaptive cluster sampling (ACS; Thompson, 1990; Thompson and Seber, 1996), which has been explored both in the field (e.g., Lo et al., 1997; Woodby, 1998; Conners and Schwager, 2002; Hanselman et al., 2003) and in simulation studies (Christman, 1997; Brown, 1999; Christman and Pontius, 2000; Christman and Lan, 2001; Brown, 2003; Su and Quinn, 2003).
Among them are simple random sampling, stratified random sampling, systematic sampling cluster sampling, two-stage cluster sampling, and estimating the population size.
(34) Since the study entailed a cluster sampling design, data were set up with the survey design function utilising the svy commands for handling the cluster sampling effect.
Besides, there are survey techniques available like respondent driven sampling (RDS), time location cluster sampling, snowball sampling, capture recapture sampling and inverse sampling for estimation of hidden population or diseases which are rare in nature.
In the current analysis--part of WHO's global study examining maternal and perinatal care--the researchers used stratified multistage cluster sampling and random selection to choose 128 health facilities in Cambodia, China, India, Japan, Nepal, the Philippines, Sri Lanka, Thailand and Vietnam for assessment.

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