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Nonprobability Sampling
Sampling is the use of a subset of the population to represent the whole population or to inform about (social) processes that are meaningful beyond the particular cases, individuals or sites studied. Probability sampling, or random sampling, is a sampling technique in which the probability of getting any particular sample may be calculated. In cases where external validity is not of critical importance to the study's goals or purpose, researchers might prefer to use nonprobability sampling. Nonprobability sampling does not meet this criterion. Nonprobability sampling techniques ''are not intended to'' be used to infer from the sample to the general population in statistical terms. Instead, for example, grounded theory can be produced through iterative nonprobability sampling until theoretical saturation is reached (Strauss and Corbin, 1990). Thus, one cannot say the same on the basis of a nonprobability sample than on the basis of a probability sample. The grounds for drawing g ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sampling (statistics)
In statistics, quality assurance, and survey methodology, sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population in question. Sampling has lower costs and faster data collection than measuring the entire population and can provide insights in cases where it is infeasible to measure an entire population. Each observation measures one or more properties (such as weight, location, colour or mass) of independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly in stratified sampling. Results from probability theory and statistical theory are employed to guide the practice. In business and medical research, sampling is widely used for gathering information about a population. Acceptance sampling is used to determine if ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Population (statistics)
In statistics, a population is a set of similar items or events which is of interest for some question or experiment. A statistical population can be a group of existing objects (e.g. the set of all stars within the Milky Way galaxy) or a hypothetical and potentially infinite group of objects conceived as a generalization from experience (e.g. the set of all possible hands in a game of poker). A common aim of statistical analysis is to produce information about some chosen population. In statistical inference, a subset of the population (a statistical '' sample'') is chosen to represent the population in a statistical analysis. Moreover, the statistical sample must be unbiased and accurately model the population (every unit of the population has an equal chance of selection). The ratio of the size of this statistical sample to the size of the population is called a ''sampling fraction''. It is then possible to estimate the '' population parameters'' using the appropriate ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Random Sampling
In statistics, quality assurance, and survey methodology, sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population in question. Sampling has lower costs and faster data collection than measuring the entire population and can provide insights in cases where it is infeasible to measure an entire population. Each observation measures one or more properties (such as weight, location, colour or mass) of independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly in stratified sampling. Results from probability theory and statistical theory are employed to guide the practice. In business and medical research, sampling is widely used for gathering information about a population. Acceptance sampling is used to determine if ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Probability
Probability is the branch of mathematics concerning numerical descriptions of how likely an Event (probability theory), event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility of the event and 1 indicates certainty."Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory", Alan Stuart and Keith Ord, 6th Ed, (2009), .William Feller, ''An Introduction to Probability Theory and Its Applications'', (Vol 1), 3rd Ed, (1968), Wiley, . The higher the probability of an event, the more likely it is that the event will occur. A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes ("heads" and "tails") are both equally probable; the probability of "heads" equals the probability of "tails"; and since no other outcomes are possible, the probability of either "heads" or "tails" is 1/2 (which could also be written ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Accidental Sampling
Convenience sampling (also known as grab sampling, accidental sampling, or opportunity sampling) is a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand. This type of sampling is most useful for pilot testing. Definition A convenience sample is a type of non-probability sampling method where the sample is taken from a group of people easy to contact or to reach; for example, standing at a mall or a grocery store and asking people to answer questions. This type of sampling is also known as grab sampling or availability sampling. There are no other criteria to the sampling method except that people be available and willing to participate. In addition, this type of sampling method does not require that a simple random sample is generated since the only criterion is whether the participants agree to participate. Applications Convenience sampling is not often recommended for research due to the possibility o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Consecutive Sampling
In the design of experiments, consecutive sampling, also known as total enumerative sampling, is a sampling technique in which every subject meeting the criteria of inclusion is selected until the required sample size is achieved. Along with convenience sampling and snowball sampling, consecutive sampling is one of the most commonly used kinds of nonprobability sampling. Consecutive sampling is typically better than convenience sampling in controlling sampling bias. Care needs to be taken with consecutive sampling, however, in the case that the quantity of interest has temporal or seasonal trends. Bias can also occur in consecutive sampling when consecutive samples have some common similarity, such as consecutive houses on a street. References Sampling techniques {{statistics-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Snowball Sampling
In sociology and statistics research, snowball sampling (or chain sampling, chain-referral sampling, referral sampling) is a nonprobability sampling technique where existing study subjects recruit future subjects from among their acquaintances. Thus the sample group is said to grow like a rolling snowball. As the sample builds up, enough data are gathered to be useful for research. This sampling technique is often used in hidden populations, such as drug users or sex workers, which are difficult for researchers to access. As sample members are not selected from a sampling frame, snowball samples are subject to numerous biases. For example, people who have many friends are more likely to be recruited into the sample. When virtual social networks are used, then this technique is called virtual snowball sampling. It was widely believed that it was impossible to make unbiased estimates from snowball samples, but a variation of snowball sampling called respondent-driven sampling has b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Self-selection Bias
In statistics, self-selection bias arises in any situation in which individuals select themselves into a group, causing a biased sample with nonprobability sampling. It is commonly used to describe situations where the characteristics of the people which cause them to select themselves in the group create abnormal or undesirable conditions in the group. It is closely related to the non-response bias, describing when the group of people responding has different responses than the group of people not responding. Self-selection bias is a major problem in research in sociology, psychology, economics and many other social sciences. In such fields, a poll suffering from such bias is termed a self-selected listener opinion poll or "SLOP". The term is also used in criminology to describe the process by which specific predispositions may lead an offender to choose a criminal career and lifestyle. While the effects of self-selection bias are closely related to those of selection bias, the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cluster Sampling
In statistics, cluster sampling is a sampling plan used when mutually homogeneous yet internally heterogeneous groupings are evident in a statistical population. It is often used in marketing research. In this sampling plan, the total population is divided into these groups (known as clusters) and a simple random sample of the groups is selected. The elements in each cluster are then sampled. If all elements in each sampled cluster are sampled, then this is referred to as a "one-stage" cluster sampling plan. If a simple random subsample of elements is selected within each of these groups, this is referred to as a "two-stage" cluster sampling plan. A common motivation for cluster sampling is to reduce the total number of interviews and costs given the desired accuracy. For a fixed sample size, the expected random error is smaller when most of the variation in the population is present internally within the groups, and not between the groups. Cluster elements The population with ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Judgment Sample
Judgment sample, or Expert sample, is a type of non-random sample that is selected based on the opinion of an expert An expert is somebody who has a broad and deep understanding and competence in terms of knowledge, skill and experience through practice and education in a particular field. Informally, an expert is someone widely recognized as a reliable s .... Results obtained from a judgment sample are subject to some degree of bias, due to the frame and population not being identical. The frame is a list of all the units, items, people, etc., that define the population to be studied. Judgement sampling is the noble to provide detailed information about the difficulties in obtaining the distinction. A random sample would provide less bias, but potentially less raw information. The downfalls of this system are significant as any non-random sample brings bias into question, which limits the types of statistical analyses that you may reasonably perform, and there are consid ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multistage Sampling
In statistics, multistage sampling is the taking of samples in stages using smaller and smaller sampling units at each stage. Multistage sampling can be a complex form of cluster sampling because it is a type of sampling which involves dividing the population into groups (or clusters). Then, one or more clusters are chosen at random and everyone within the chosen cluster is sampled. Using all the sample elements in all the selected clusters may be prohibitively expensive or unnecessary. Under these circumstances, multistage cluster sampling becomes useful. Instead of using all the elements contained in the selected clusters, the researcher randomly selects elements from each cluster. Constructing the clusters is the first stage. Deciding what elements within the cluster to use is the second stage. The technique is used frequently when a complete list of all members of the population does not exist and is inappropriate. In some cases, several levels of cluster selection may be ap ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Quota Sampling
Quota sampling is a method for selecting survey participants that is a non-probabilistic version of stratified sampling. Process In quota sampling, a population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgment 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. This means that individuals can put a demand on who they want to sample (targeting). This second step makes the technique non-probability sampling. In quota sampling, there is non-random sample selection and this can be unreliable. For example, interviewers might be tempted to interview those people in the street who look most helpful, or may choose to use accidental sampling to question those closest to them, to save time. The problem is these samples may be biased in a way that is difficult to quantify or adjust for. For exampl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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