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Nonprobability Sampling
Nonprobability sampling is a form of Sampling (statistics), sampling that does not utilise random sampling techniques where the probability of getting any particular sample may be calculated. Nonprobability samples are not intended to be used to infer from the sample to the general population in statistical terms. In cases where external validity is not of critical importance to the study's goals or purpose, researchers might prefer to use nonprobability sampling. Researchers may seek to use iterative nonprobability sampling for theoretical purposes, where analytical generalization is considered over statistical generalization. Advantages and disadvantages While probabilistic methods are suitable for large-scale studies concerned with representativeness, nonprobability approaches may be more suitable for in-depth qualitative research in which the focus is often to understand complex social phenomena. The in-depth analysis of a small purposive sample or case study enables the dis ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sampling (statistics)
In this statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a population (statistics), statistical population to estimate characteristics of the whole population. The subset is meant to reflect the whole population, and statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to recording data from the entire population (in many cases, collecting the whole population is impossible, like getting sizes of all stars in the universe), and thus, it 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 samplin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Convenience 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. Convenience sampling is not often recommended by official statistical agencies for research due to the possibility of sampling error and lack of representation of the population. It can be useful in some situations, for example, where convenience sampling is the only possible option. A trade off exists between this method of quick sampling and accuracy. Collected samples may not represent the population of interest and can be a source of bias, with larger sample sizes reducing the chance of sampling error occurring. Advantages Convenience sampling can be used by almost anyone and has been around for generations. One of the reasons that it is most often used is due to the numerous advantages it provides. This method is extremely speedy, easy, readily ... [...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 Nonprobability sampling is a form of Sampling (statistics), sampling that does not utilise random sampling techniques where the probability of getting any particular sample may be calculated. Nonprobability samples are not intended to be used to .... 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. ... [...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] |
Judgment Sample
A judgment sample, also known as an expert or purposive sample, is a type of non-random sample, where a researcher or expert selects the sample based on who they believe would be most useful or appropriate for the study. Results obtained from a judgment sample are subject to some degree of bias Bias is a disproportionate weight ''in favor of'' or ''against'' an idea or thing, usually in a way that is inaccurate, closed-minded, prejudicial, or unfair. Biases can be innate or learned. People may develop biases for or against an individ ... and may be hard to generalize, due to the chosen sample not representing the larger population. A random sample would provide less bias, but potentially less raw information. The pitfalls of this system are significant because of bias, limited statistical methods, and limits to an expert's ability to choose a good sample. References {{DEFAULTSORT:Judgment Sample Sampling (statistics) ... [...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 exa ... [...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 elemental The population wit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multistage Sampling
Multistage may refer to: * Armitage–Doll multistage model of carcinogenesis * Multistage amplifiers * Centrifugal pump, Multistage centrifugal pump * Multi-stage flash distillation * Multistage interconnection networks * Multistage rocket * Multistage sampling * Multistage testing {{dab ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Simple Random Sample
In statistics, a simple random sample (or SRS) is a subset of individuals (a sample) chosen from a larger set (a population) in which a subset of individuals are chosen randomly, all with the same probability. It is a process of selecting a sample in a random way. In SRS, each subset of ''k'' individuals has the same probability of being chosen for the sample as any other subset of ''k'' individuals. Simple random sampling is a basic type of sampling and can be a component of other more complex sampling methods. Introduction The principle of simple random sampling is that every set with the same number of items has the same probability of being chosen. For example, suppose ''N'' college students want to get a ticket for a basketball game, but there are only ''X'' < ''N'' tickets for them, so they decide to have a fair way to see who gets to go. Then, everybody is given a number in the range from 0 to ''N''-1, and random numbers are generated, either electronically or from a t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Systematic Sampling
In survey methodology, one-dimensional systematic sampling is a statistical method involving the selection of elements from an ordered sampling frame. The most common form of systematic sampling is an equiprobability method. This applies in particular when the sampled units are individuals, households or corporations. When a geographic area is sampled for a spatial analysis, bi-dimensional systematic sampling on an area sampling frame can be applied. In one-dimensional systematic sampling, progression through the list is treated circularly, with a return to the top once the list ends. The sampling starts by selecting an element from the list at random and then every ''k''th element in the frame is selected, where ''k'', is the sampling interval (sometimes known as the ''skip''): this is calculated as: :k = \frac Nn where ''n'' is the sample size, and ''N'' is the population size. Using this procedure each element in the population has a known and equal probability of selectio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
