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66-609: [REDACTED] Look up PSU in Wiktionary, the free dictionary. PSU may refer to: Organizations [ edit ] Education [ edit ] United States [ edit ] Pacific States University , a private university in Los Angeles, California Pembroke State University , a public university in Pembroke, North Carolina Pennsylvania State University ,

132-680: A UK charity assisting individuals in court proceedings Prudential Staff Union , former trade union in the UK Public sector undertakings in India , companies owned by government in India Other uses [ edit ] Passenger service unit , above each seat in a passenger airplane Police support unit (United Kingdom) Polysulfone , family of high performance thermoplastics Power supply unit , an electronic device Power supply unit (computer) Phantasy Star Universe ,

198-511: A UK charity assisting individuals in court proceedings Prudential Staff Union , former trade union in the UK Public sector undertakings in India , companies owned by government in India Other uses [ edit ] Passenger service unit , above each seat in a passenger airplane Police support unit (United Kingdom) Polysulfone , family of high performance thermoplastics Power supply unit , an electronic device Power supply unit (computer) Phantasy Star Universe ,

264-472: A batch of material from production is of high enough quality to be released to the customer or should be scrapped or reworked due to poor quality. In this case, the batch is the population. Although the population of interest often consists of physical objects, sometimes it is necessary to sample over time, space, or some combination of these dimensions. For instance, an investigation of supermarket staffing could examine checkout line length at various times, or

330-406: A better understanding of human health, or one might study records from people born in 2008 in order to make predictions about people born in 2009. Time spent in making the sampled population and population of concern precise is often well spent because it raises many issues, ambiguities, and questions that would otherwise have been overlooked at this stage. In the most straightforward case, such as

396-435: A fairly accurate indicative result with a 95% confidence interval at a margin of error within 4-5%; ELD reminded the public that sample counts are separate from official results, and only the returning officer will declare the official results once vote counting is complete. Successful statistical practice is based on focused problem definition. In sampling, this includes defining the " population " from which our sample

462-415: A forthcoming election (in advance of the election). These imprecise populations are not amenable to sampling in any of the ways below and to which we could apply statistical theory. As a remedy, we seek a sampling frame which has the property that we can identify every single element and include any in our sample. The most straightforward type of frame is a list of elements of the population (preferably

528-506: A given country will on average produce five men and five women, but any given trial is likely to over represent one sex and underrepresent the other. Systematic and stratified techniques attempt to overcome this problem by "using information about the population" to choose a more "representative" sample. Also, simple random sampling can be cumbersome and tedious when sampling from a large target population. In some cases, investigators are interested in research questions specific to subgroups of

594-413: A given size, all subsets of a sampling frame have an equal probability of being selected. Each element of the frame thus has an equal probability of selection: the frame is not subdivided or partitioned. Furthermore, any given pair of elements has the same chance of selection as any other such pair (and similarly for triples, and so on). This minimizes bias and simplifies analysis of results. In particular,

660-606: A given street, and interview the first person to answer the door. In any household with more than one occupant, this is a nonprobability sample, because some people are more likely to answer the door (e.g. an unemployed person who spends most of their time at home is more likely to answer than an employed housemate who might be at work when the interviewer calls) and it's not practical to calculate these probabilities. Nonprobability sampling methods include convenience sampling , quota sampling , and purposive sampling . In addition, nonresponse effects may turn any probability design into

726-410: A nonprobability design if the characteristics of nonresponse are not well understood, since nonresponse effectively modifies each element's probability of being sampled. Within any of the types of frames identified above, a variety of sampling methods can be employed individually or in combination. Factors commonly influencing the choice between these designs include: In a simple random sample (SRS) of

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792-562: A persistent ORPG game by SEGA for PlayStation 2, Xbox 360 and PC Practical salinity unit , a unit for quantifying a fluid's salinity Primary sampling unit, in sampling (statistics) Program Storage Unit, a chip as used e.g. in the Fairchild F8 microprocessor Projective special unitary group , a mathematical quotient Prueba de Selección Universitaria , public university admission test in Chile Topics referred to by

858-441: A persistent ORPG game by SEGA for PlayStation 2, Xbox 360 and PC Practical salinity unit , a unit for quantifying a fluid's salinity Primary sampling unit, in sampling (statistics) Program Storage Unit, a chip as used e.g. in the Fairchild F8 microprocessor Projective special unitary group , a mathematical quotient Prueba de Selección Universitaria , public university admission test in Chile Topics referred to by

924-451: A population than for the overall population; in such cases, using a stratified sampling approach may be more convenient than aggregating data across groups (though this may potentially be at odds with the previously noted importance of utilizing criterion-relevant strata). Finally, since each stratum is treated as an independent population, different sampling approaches can be applied to different strata, potentially enabling researchers to use

990-536: A probability proportionate to size sample. This is done by treating each count within the size variable as a single sampling unit. Samples are then identified by selecting at even intervals among these counts within the size variable. This method is sometimes called PPS-sequential or monetary unit sampling in the case of audits or forensic sampling. Example: Suppose we have six schools with populations of 150, 180, 200, 220, 260, and 490 students respectively (total 1500 students), and we want to use student population as

1056-616: A probability sample is the fact that each person's probability is known. When every element in the population does have the same probability of selection, this is known as an 'equal probability of selection' (EPS) design. Such designs are also referred to as 'self-weighting' because all sampled units are given the same weight. Probability sampling includes: simple random sampling , systematic sampling , stratified sampling , probability-proportional-to-size sampling, and cluster or multistage sampling . These various ways of probability sampling have two things in common: Nonprobability sampling

1122-652: A public university in Garowe, Puntland, Somalia Military [ edit ] Police Support Unit , a paramilitary wing of the Zimbabwe Republic Police Port Security Unit , a U.S. Coast Guard expeditionary force protection unit Political parties [ edit ] Parti Socialiste Unifié (disambiguation) , various parties United Socialist Party (Bolivia) , Bolivia ( Partido Socialista Unificado ) Other organizations [ edit ] Personal Support Unit ,

1188-496: A public university in Garowe, Puntland, Somalia Military [ edit ] Police Support Unit , a paramilitary wing of the Zimbabwe Republic Police Port Security Unit , a U.S. Coast Guard expeditionary force protection unit Political parties [ edit ] Parti Socialiste Unifié (disambiguation) , various parties United Socialist Party (Bolivia) , Bolivia ( Partido Socialista Unificado ) Other organizations [ edit ] Personal Support Unit ,

1254-815: A public university in Pembroke, North Carolina Pennsylvania State University , a public university in Pennsylvania Pittsburg State University , a public university in Pittsburg, Kansas Plattsburgh State University , a public university in Plattsburgh, New York Plymouth State University , a public university in Plymouth, New Hampshire Portland State University , a public university in Portland, Oregon Worldwide [ edit ] Palawan State University ,

1320-405: A public university in Pennsylvania Pittsburg State University , a public university in Pittsburg, Kansas Plattsburgh State University , a public university in Plattsburgh, New York Plymouth State University , a public university in Plymouth, New Hampshire Portland State University , a public university in Portland, Oregon Worldwide [ edit ] Palawan State University ,

1386-770: A public university in Puerto Princesa, Philippines Pangasinan State University , a public university in Pangasinan, Philippines Papua State University , a public university in West Papua, Indonesia Partido State University , a public university in Camarines Sur, Philippines Prince of Songkla University , a public university in southern Thailand Prince Sultan University , a private university in Riyadh, Saudi Arabia Puntland State University ,

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1452-471: A public university in Puerto Princesa, Philippines Pangasinan State University , a public university in Pangasinan, Philippines Papua State University , a public university in West Papua, Indonesia Partido State University , a public university in Camarines Sur, Philippines Prince of Songkla University , a public university in southern Thailand Prince Sultan University , a private university in Riyadh, Saudi Arabia Puntland State University ,

1518-404: A single trip to visit several households in one block, rather than having to drive to a different block for each household. It also means that one does not need a sampling frame listing all elements in the target population. Instead, clusters can be chosen from a cluster-level frame, with an element-level frame created only for the selected clusters. In the example above, the sample only requires

1584-467: A study on endangered penguins might aim to understand their usage of various hunting grounds over time. For the time dimension, the focus may be on periods or discrete occasions. In other cases, the examined 'population' may be even less tangible. For example, Joseph Jagger studied the behaviour of roulette wheels at a casino in Monte Carlo , and used this to identify a biased wheel. In this case,

1650-477: A using a skip which ensures jumping between the two sides (any odd-numbered skip). Another drawback of systematic sampling is that even in scenarios where it is more accurate than SRS, its theoretical properties make it difficult to quantify that accuracy. (In the two examples of systematic sampling that are given above, much of the potential sampling error is due to variation between neighbouring houses – but because this method never selects two neighbouring houses,

1716-467: Is "everybody in the country, given access to this treatment" – a group that does not yet exist since the program is not yet available to all. The population from which the sample is drawn may not be the same as the population from which information is desired. Often there is a large but not complete overlap between these two groups due to frame issues etc. (see below). Sometimes they may be entirely separate – for instance, one might study rats in order to get

1782-410: Is any sampling method where some elements of the population have no chance of selection (these are sometimes referred to as 'out of coverage'/'undercovered'), or where the probability of selection cannot be accurately determined. It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection. Hence, because the selection of elements

1848-464: Is different from Wikidata All article disambiguation pages All disambiguation pages PSU [REDACTED] Look up PSU in Wiktionary, the free dictionary. PSU may refer to: Organizations [ edit ] Education [ edit ] United States [ edit ] Pacific States University , a private university in Los Angeles, California Pembroke State University ,

1914-474: Is different from Wikidata All article disambiguation pages All disambiguation pages Sampling (statistics) 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

1980-421: Is drawn. A population can be defined as including all people or items with the characteristics one wishes to understand. Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample (or subset) of that population. Sometimes what defines a population is obvious. For example, a manufacturer needs to decide whether

2046-407: Is eliminated.) However, systematic sampling is especially vulnerable to periodicities in the list. If periodicity is present and the period is a multiple or factor of the interval used, the sample is especially likely to be un representative of the overall population, making the scheme less accurate than simple random sampling. For example, consider a street where the odd-numbered houses are all on

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2112-404: Is nonrandom, nonprobability sampling does not allow the estimation of sampling errors. These conditions give rise to exclusion bias , placing limits on how much information a sample can provide about the population. Information about the relationship between sample and population is limited, making it difficult to extrapolate from the sample to the population. Example: We visit every household in

2178-424: Is often available – for instance, a survey attempting to measure the number of guest-nights spent in hotels might use each hotel's number of rooms as an auxiliary variable. In some cases, an older measurement of the variable of interest can be used as an auxiliary variable when attempting to produce more current estimates. Sometimes it is more cost-effective to select respondents in groups ('clusters'). Sampling

2244-434: Is often clustered by geography, or by time periods. (Nearly all samples are in some sense 'clustered' in time – although this is rarely taken into account in the analysis.) For instance, if surveying households within a city, we might choose to select 100 city blocks and then interview every household within the selected blocks. Clustering can reduce travel and administrative costs. In the example above, an interviewer can make

2310-429: Is sometimes introduced after the sampling phase in a process called "poststratification". This approach is typically implemented due to a lack of prior knowledge of an appropriate stratifying variable or when the experimenter lacks the necessary information to create a stratifying variable during the sampling phase. Although the method is susceptible to the pitfalls of post hoc approaches, it can provide several benefits in

2376-428: Is taken from each stratum so that the rare target class will be more represented in the sample. The model is then built on this biased sample . The effects of the input variables on the target are often estimated with more precision with the choice-based sample even when a smaller overall sample size is taken, compared to a random sample. The results usually must be adjusted to correct for the oversampling. In some cases

2442-408: The cause system of which the observed population is an outcome. In such cases, sampling theory may treat the observed population as a sample from a larger 'superpopulation'. For example, a researcher might study the success rate of a new 'quit smoking' program on a test group of 100 patients, in order to predict the effects of the program if it were made available nationwide. Here the superpopulation

2508-435: The 'population' Jagger wanted to investigate was the overall behaviour of the wheel (i.e. the probability distribution of its results over infinitely many trials), while his 'sample' was formed from observed results from that wheel. Similar considerations arise when taking repeated measurements of properties of materials such as the electrical conductivity of copper . This situation often arises when seeking knowledge about

2574-457: The US, the 1936 Literary Digest prediction of a Republican win in the presidential election went badly awry, due to severe bias [1] . More than two million people responded to the study with their names obtained through magazine subscription lists and telephone directories. It was not appreciated that these lists were heavily biased towards Republicans and the resulting sample, though very large,

2640-492: The approach best suited (or most cost-effective) for each identified subgroup within the population. There are, however, some potential drawbacks to using stratified sampling. First, identifying strata and implementing such an approach can increase the cost and complexity of sample selection, as well as leading to increased complexity of population estimates. Second, when examining multiple criteria, stratifying variables may be related to some, but not to others, further complicating

2706-414: The basis for a PPS sample of size three. To do this, we could allocate the first school numbers 1 to 150, the second school 151 to 330 (= 150 + 180), the third school 331 to 530, and so on to the last school (1011 to 1500). We then generate a random start between 1 and 500 (equal to 1500/3) and count through the school populations by multiples of 500. If our random start

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2772-437: The criterion in question, instead of availability of the samples). Even if a stratified sampling approach does not lead to increased statistical efficiency, such a tactic will not result in less efficiency than would simple random sampling, provided that each stratum is proportional to the group's size in the population. Third, it is sometimes the case that data are more readily available for individual, pre-existing strata within

2838-415: The design, and potentially reducing the utility of the strata. Finally, in some cases (such as designs with a large number of strata, or those with a specified minimum sample size per group), stratified sampling can potentially require a larger sample than would other methods (although in most cases, the required sample size would be no larger than would be required for simple random sampling). Stratification

2904-579: The entire population) with appropriate contact information. For example, in an opinion poll , possible sampling frames include an electoral register and a telephone directory . A probability sample is a sample in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined. The combination of these traits makes it possible to produce unbiased estimates of population totals, by weighting sampled units according to their probability of selection. Example: We want to estimate

2970-406: The error. These were not expressed as modern confidence intervals but as the sample size that would be needed to achieve a particular upper bound on the sampling error with probability 1000/1001. His estimates used Bayes' theorem with a uniform prior probability and assumed that his sample was random. Alexander Ivanovich Chuprov introduced sample surveys to Imperial Russia in the 1870s. In

3036-413: The first to the k th element in the list. A simple example would be to select every 10th name from the telephone directory (an 'every 10th' sample, also referred to as 'sampling with a skip of 10'). As long as the starting point is randomized , systematic sampling is a type of probability sampling . It is easy to implement and the stratification induced can make it efficient, if the variable by which

3102-428: The high end and too few from the low end (or vice versa), leading to an unrepresentative sample. Selecting (e.g.) every 10th street number along the street ensures that the sample is spread evenly along the length of the street, representing all of these districts. (If we always start at house #1 and end at #991, the sample is slightly biased towards the low end; by randomly selecting the start between #1 and #10, this bias

3168-403: The list is ordered is correlated with the variable of interest. 'Every 10th' sampling is especially useful for efficient sampling from databases . For example, suppose we wish to sample people from a long street that starts in a poor area (house No. 1) and ends in an expensive district (house No. 1000). A simple random selection of addresses from this street could easily end up with too many from

3234-403: The north (expensive) side of the road, and the even-numbered houses are all on the south (cheap) side. Under the sampling scheme given above, it is impossible to get a representative sample; either the houses sampled will all be from the odd-numbered, expensive side, or they will all be from the even-numbered, cheap side, unless the researcher has previous knowledge of this bias and avoids it by

3300-478: The population. For example, researchers might be interested in examining whether cognitive ability as a predictor of job performance is equally applicable across racial groups. Simple random sampling cannot accommodate the needs of researchers in this situation, because it does not provide subsamples of the population, and other sampling strategies, such as stratified sampling, can be used instead. Systematic sampling (also known as interval sampling) relies on arranging

3366-529: The practice. In business and medical research, sampling is widely used for gathering information about a population. Acceptance sampling is used to determine if a production lot of material meets the governing specifications . Random sampling by using lots is an old idea, mentioned several times in the Bible. In 1786, Pierre Simon Laplace estimated the population of France by using a sample, along with ratio estimator . He also computed probabilistic estimates of

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3432-405: The right situation. Implementation usually follows a simple random sample. In addition to allowing for stratification on an ancillary variable, poststratification can be used to implement weighting, which can improve the precision of a sample's estimates. Choice-based sampling is one of the stratified sampling strategies. In choice-based sampling, the data are stratified on the target and a sample

3498-403: The same term [REDACTED] This disambiguation page lists articles associated with the title PSU . If an internal link led you here, you may wish to change the link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=PSU&oldid=1256229237 " Category : Disambiguation pages Hidden categories: Short description

3564-403: The same term [REDACTED] This disambiguation page lists articles associated with the title PSU . If an internal link led you here, you may wish to change the link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=PSU&oldid=1256229237 " Category : Disambiguation pages Hidden categories: Short description

3630-403: The sample designer has access to an "auxiliary variable" or "size measure", believed to be correlated to the variable of interest, for each element in the population. These data can be used to improve accuracy in sample design. One option is to use the auxiliary variable as a basis for stratification, as discussed above. Another option is probability proportional to size ('PPS') sampling, in which

3696-418: The sample will not give us any information on that variation.) As described above, systematic sampling is an EPS method, because all elements have the same probability of selection (in the example given, one in ten). It is not 'simple random sampling' because different subsets of the same size have different selection probabilities – e.g. the set {4,14,24,...,994} has a one-in-ten probability of selection, but

3762-439: The sampling of a batch of material from production (acceptance sampling by lots), it would be most desirable to identify and measure every single item in the population and to include any one of them in our sample. However, in the more general case this is not usually possible or practical. There is no way to identify all rats in the set of all rats. Where voting is not compulsory, there is no way to identify which people will vote at

3828-446: The selection probability for each element is set to be proportional to its size measure, up to a maximum of 1. In a simple PPS design, these selection probabilities can then be used as the basis for Poisson sampling . However, this has the drawback of variable sample size, and different portions of the population may still be over- or under-represented due to chance variation in selections. Systematic sampling theory can be used to create

3894-448: The set {4,13,24,34,...} has zero probability of selection. Systematic sampling can also be adapted to a non-EPS approach; for an example, see discussion of PPS samples below. When the population embraces a number of distinct categories, the frame can be organized by these categories into separate "strata." Each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected. The ratio of

3960-517: The size of this random selection (or sample) to the size of the population is called a sampling fraction . There are several potential benefits to stratified sampling. First, dividing the population into distinct, independent strata can enable researchers to draw inferences about specific subgroups that may be lost in a more generalized random sample. Second, utilizing a stratified sampling method can lead to more efficient statistical estimates (provided that strata are selected based upon relevance to

4026-429: The study population according to some ordering scheme and then selecting elements at regular intervals through that ordered list. Systematic sampling involves a random start and then proceeds with the selection of every k th element from then onwards. In this case, k =(population size/sample size). It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within

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4092-528: The total income of adults living in a given street. We visit each household in that street, identify all adults living there, and randomly select one adult from each household. (For example, we can allocate each person a random number, generated from a uniform distribution between 0 and 1, and select the person with the highest number in each household). We then interview the selected person and find their income. People living on their own are certain to be selected, so we simply add their income to our estimate of

4158-441: The total. But a person living in a household of two adults has only a one-in-two chance of selection. To reflect this, when we come to such a household, we would count the selected person's income twice towards the total. (The person who is selected from that household can be loosely viewed as also representing the person who isn't selected.) In the above example, not everybody has the same probability of selection; what makes it

4224-416: The variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy of results. Simple random sampling can be vulnerable to sampling error because the randomness of the selection may result in a sample that does not reflect the makeup of the population. For instance, a simple random sample of ten people from

4290-417: Was 137, we would select the schools which have been allocated numbers 137, 637, and 1137, i.e. the first, fourth, and sixth schools. The PPS approach can improve accuracy for a given sample size by concentrating sample on large elements that have the greatest impact on population estimates. PPS sampling is commonly used for surveys of businesses, where element size varies greatly and auxiliary information

4356-544: Was deeply flawed. Elections in Singapore have adopted this practice since the 2015 election , also known as the sample counts, whereas according to the Elections Department (ELD), their country's election commission, sample counts help reduce speculation and misinformation, while helping election officials to check against the election result for that electoral division. The reported sample counts yield

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