Richard J. Fox · Wiley International Encyclopedia of Marketing · 2010
Abstract Sampling involves the selection of a subset from a population, and making inferences pertaining to the population based on information from the sample. Probability sampling refers to sampling methods in which chance or randomness is involved in the sample selection process. Each element in the population from which the sample is to be selected has a positive probability of being included in the sample. Probability sampling is the basis for the statistical calculation of the potential error associated with an estimate of a population parameter developed from sample data. Simple random sampling is the most basic form of probability sampling. A simple random sample of size n is selected in such a way that all subsets of size n are equally likely to be the sample selected. The probability that any single item is included in the sample when simple random sampling is used is the ratio of the sample size to the population size. This is the type of sampling that is typically assumed when making basic statistical calculations regarding the precision of sample estimates. For example, the typical 95% confidence interval associated with an estimate is based on the assumption that simple random sampling was used to select the sample. There are more sophisticated forms of probability sampling than simple random sampling, and different statistical calculations are required to determine the precision of estimates when these alternative forms of probability sampling are used. It is obviously important in practice to make sure that the statistical precision calculations are appropriate for the type of probability sampling used.
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Duncan Cramer, Dennis Howitt · The SAGE Dictionary of Statistics · 2004
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In Practice Any · 2007
with 1 or 2 uppermost, S 2 if it lands with 3 or 4 uppermost, or S 3 if it lands with 5 or 6 uppermost. Note that in general the set of possible samples need not consist of all possible samples of a given size (and indeed it can be useful to consider cases where the sample size is random), and not all possible samples need have the same probability. Once one has a method for finding a sample, we need to have a method of computing an estimate of any quantity of interest. For example, one could take the mean of the specimens in the sample. Alternatives to Random Sampling There are various alternative ways in which a sample can be taken. For example, take the most easily obtainable specimens. The pitfalls in such a procedure, which can be referred to as accessibility or haphazard sampling, are obvious in that such a sample is unlikely to be any any real sense `representative'
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The A-Z of Social Research · 2003
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The SAGE Encyclopedia of Qualitative Research Methods · 2008
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Gideon J. Mellenbergh · 2019
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The Following Account · 2007
ernative ways in which a sample can be taken. For example, take the most easily obtainable specimens. The pitfalls in such a procedure, which can be referred to as accessibility or haphazard sampling, are obvious in that such a sample is unlikely to be any any real sense `representative'. A second method would be to number the specimens in some more or less systematic manner and then take every nth specimen for some suitable value of n, which can be referred to as a systematic sample. However, there are warnings about its use to be garnered from section 5.2 of Gray and Gee (1972). The 1966 sample census attempted to use a systematic sample for caravan sites and for hospitals, schools, etc. The sort of thing that went wrong was that on one caravan site where the random start was 8, the enumerator took the 8th, 16th, 24th, : : : caravans, instead of the 8th, 18th, 28th, : : : (i.e. took the random start as the sampling interval). In the hospital and school records, interviewers were to
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Susan Mwangi, D Mackinnon, S Coxe et al. · Asian Research Journal of Business Management · 2017
This paper looked at probability and non-probability sampling. Probability is the measure of the possibility that an activity will happen. 0 and 1 are quantified in Probability where, loosely speaking, 0 indicates impossibility and 1 indicates certainty. Certainty comes in when there is higher probability of occurrence .A simple example is the rain expectation. Since the no one can tell when the rain will fall, ("rain" and "sun") are both equally probable; the probability of "rain" equals the probability of "sun"; and since no other outcomes are possible, the probability of either "rain" or "sun" is .This is a common scenario when you visit a town along the equator line (which could also be written as 0.5 or 50%). Therefore probability has been defined as branch of mathematics that deals with calculations of the likelihood of a given event and most of the times the trials of either have no effect on the other.
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1995
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Huw G. Davies, G. A. Hicks · 2014
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Vasja Vehovar, Vera Toepoel, Stephanie Steinmetz · 2016
A sample is a subset of a population and we survey the units from the sample with the aim to learn about the entire population. However, the sampling theory was basically developed for probability sampling, where all units in the population have known and positive probabilities of inclusion. This definition implicitly involves randomization, which is a process resembling lottery drawing, where the units are selected according to their inclusion probabilities. In probability sampling the randomized selection is used instead of arbitrary or purposive sample selection of the researcher, or, instead of various self-selection processes run by respondents. Within this context, the notion of non-probability sampling denotes the absence of probability sampling mechanism.
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P. G. Moore · Cambridge University Press eBooks · 1969
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Adam Ka Lok Cheung · 2014
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Greco, Fedele Pasquale · AMS Campus - AlmaDL · 2016
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Amber Wutich, H. Russell Bernard · 2023
Non-probability sampling is common in qualitative research. This lesson discusses how to teach three very different approaches to non-probability sampling: respondent-driven sampling, case-control sampling, and theoretical sampling.
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M Hawthorn, Elaine Goettler · College & Research Libraries News · 1993
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K. R. W. Brewer · Encyclopedia of Statistical Sciences · 2005
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Alison Galloway · Encyclopedia of Social Measurement · 2005
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Series on multivariate analysis · 1998
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William R. Nugent · 2001
robability theory concerns the relative frequency with which certain events occur. Probability is important in sampling because it is the vehicle that allows the researcher to use the information in a sample to make inferences about the population from which the sample was obtained. The purpose of this chapter is to give an overview of probability and sampling. The goals of this chapter are (a) to give the reader a brief introduction to probability and the basis of sampling in probability, (b) to cover basic sampling methods with a focus on probability sampling, and (c) to illustrate these sampling methods by use of numerical examples that the reader can replicate.
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