In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population are less to be included than others. It results in a biased sample, a non-random sample of a population in which all individuals, or instances, were not likely to have been selected. If this is not accounted for, results can be erroneously attributed to the phenomenon under study rather than to the method of sampling. Medical sources sometimes refer to sampling bias as ascertainment bias. Ascertainment bias has the same definition, but is still sometimes classified as a separate type of bias. Sampling bias is classified as a subtype of selection bias, sometimes termed sample selection bias, but some classify it as a separate type of bias. A distinction, albeit not universally accepted, of sampling bias is that it undermines the external validity of a test, while selection bias addresses internal validity for differences or similarities found in the sample at hand. In this sense, errors occurring in the process of gathering the sample or cohort cause sampling bias, while errors in any process thereafter cause selection bias.
However, selection bias and sampling bias are used synonymously. Selection from a specific real area. For example, a survey of high school students to measure teenage use of illegal drugs will be a biased sample because it does not include home-schooled students or dropouts. A sample is biased if certain members are underrepresented or overrepresented relative to others in the population. For example, a "man on the street" interview which selects people who walk by a certain location is going to have an overrepresentation of healthy individuals who are more to be out of the home than individuals with a chronic illness; this may be an extreme form of biased sampling, because certain members of the population are excluded from the sample. Self-selection bias, possible whenever the group of people being studied has any form of control over whether to participate. Participants' decision to participate may be correlated with traits that affect the study, making the participants a non-representative sample.
For example, people who have strong opinions or substantial knowledge may be more willing to spend time answering a survey than those who do not. Another example is online and phone-in polls, which are biased samples because the respondents are self-selected; those individuals who are motivated to respond individuals who have strong opinions, are overrepresented, individuals that are indifferent or apathetic are less to respond. This leads to a polarization of responses with extreme perspectives being given a disproportionate weight in the summary; as a result, these types of polls are regarded as unscientific. Pre-screening of trial participants, or advertising for volunteers within particular groups. For example, a study to "prove" that smoking does not affect fitness might recruit at the local fitness center, but advertise for smokers during the advanced aerobics class, for non-smokers during the weight loss sessions. Exclusion bias results from exclusion of particular groups from the sample, e.g. exclusion of subjects who have migrated into the study area.
Excluding subjects who move out of the study area during follow-up is rather equivalent of dropout or nonresponse, a selection bias in that it rather affects the internal validity of the study. Healthy user bias, when the study population is healthier than the general population. For example, someone in poor health is unlikely to have a job as manual laborer. Berkson's fallacy, when the study population is selected from a hospital and so is less healthy than the general population; this can result in a spurious negative correlation between diseases: a hospital patient without diabetes is more to have another given disease such as cholecystitis, since they must have had some reason to enter the hospital in the first place. Overmatching, matching for an apparent confounder, a result of the exposure; the control group becomes more similar to the cases in regard to exposure than does the general population. Survivorship bias, in which only "surviving" subjects are selected, ignoring those that fell out of view.
For example, using the record of current companies as an indicator of business climate or economy ignores the businesses that failed and no longer exist. Malmquist bias, an effect in observational astronomy which leads to the preferential detection of intrinsically bright objects; the study of medical conditions begins with anecdotal reports. By their nature, such reports only include those referred for treatment. A child who can't function in school is more to be diagnosed with dyslexia than a child who struggles but passes. A child examined for one condition is more to be tested for and diagnosed with other conditions, skewing comorbidity statistics; as certain diagnoses become associated with behavior problems or intellectual disability, parents try to prevent their children from being stigmatized with those diagnoses, introducing further bias. Studies selected from whole populations are showing that many conditions are much more common and much milder than believed. Geneticists are limited in.
As an example, consider a h
International Standard Serial Number
An International Standard Serial Number is an eight-digit serial number used to uniquely identify a serial publication, such as a magazine. The ISSN is helpful in distinguishing between serials with the same title. ISSN are used in ordering, interlibrary loans, other practices in connection with serial literature; the ISSN system was first drafted as an International Organization for Standardization international standard in 1971 and published as ISO 3297 in 1975. ISO subcommittee TC 46/SC 9 is responsible for maintaining the standard; when a serial with the same content is published in more than one media type, a different ISSN is assigned to each media type. For example, many serials are published both in electronic media; the ISSN system refers to these types as electronic ISSN, respectively. Conversely, as defined in ISO 3297:2007, every serial in the ISSN system is assigned a linking ISSN the same as the ISSN assigned to the serial in its first published medium, which links together all ISSNs assigned to the serial in every medium.
The format of the ISSN is an eight digit code, divided by a hyphen into two four-digit numbers. As an integer number, it can be represented by the first seven digits; the last code digit, which may be 0-9 or an X, is a check digit. Formally, the general form of the ISSN code can be expressed as follows: NNNN-NNNC where N is in the set, a digit character, C is in; the ISSN of the journal Hearing Research, for example, is 0378-5955, where the final 5 is the check digit, C=5. To calculate the check digit, the following algorithm may be used: Calculate the sum of the first seven digits of the ISSN multiplied by its position in the number, counting from the right—that is, 8, 7, 6, 5, 4, 3, 2, respectively: 0 ⋅ 8 + 3 ⋅ 7 + 7 ⋅ 6 + 8 ⋅ 5 + 5 ⋅ 4 + 9 ⋅ 3 + 5 ⋅ 2 = 0 + 21 + 42 + 40 + 20 + 27 + 10 = 160 The modulus 11 of this sum is calculated. For calculations, an upper case X in the check digit position indicates a check digit of 10. To confirm the check digit, calculate the sum of all eight digits of the ISSN multiplied by its position in the number, counting from the right.
The modulus 11 of the sum must be 0. There is an online ISSN checker. ISSN codes are assigned by a network of ISSN National Centres located at national libraries and coordinated by the ISSN International Centre based in Paris; the International Centre is an intergovernmental organization created in 1974 through an agreement between UNESCO and the French government. The International Centre maintains a database of all ISSNs assigned worldwide, the ISDS Register otherwise known as the ISSN Register. At the end of 2016, the ISSN Register contained records for 1,943,572 items. ISSN and ISBN codes are similar in concept. An ISBN might be assigned for particular issues of a serial, in addition to the ISSN code for the serial as a whole. An ISSN, unlike the ISBN code, is an anonymous identifier associated with a serial title, containing no information as to the publisher or its location. For this reason a new ISSN is assigned to a serial each time it undergoes a major title change. Since the ISSN applies to an entire serial a new identifier, the Serial Item and Contribution Identifier, was built on top of it to allow references to specific volumes, articles, or other identifiable components.
Separate ISSNs are needed for serials in different media. Thus, the print and electronic media versions of a serial need separate ISSNs. A CD-ROM version and a web version of a serial require different ISSNs since two different media are involved. However, the same ISSN can be used for different file formats of the same online serial; this "media-oriented identification" of serials made sense in the 1970s. In the 1990s and onward, with personal computers, better screens, the Web, it makes sense to consider only content, independent of media; this "content-oriented identification" of serials was a repressed demand during a decade, but no ISSN update or initiative occurred. A natural extension for ISSN, the unique-identification of the articles in the serials, was the main demand application. An alternative serials' contents model arrived with the indecs Content Model and its application, the digital object identifier, as ISSN-independent initiative, consolidated in the 2000s. Only in 2007, ISSN-L was defined in the
National Agricultural Statistics Service
The National Agricultural Statistics Service is the statistical branch of the U. S. Department of Agriculture and a principal agency of the U. S. Federal Statistical System. NASS has 12 regional offices throughout the United States and Puerto Rico and a headquarters unit in Washington, D. C.. NASS conducts hundreds of surveys and issues nearly 500 national reports each year on issues including agricultural production, economics and the environment. NASS conducts the United States Census of Agriculture every five years. During the Civil War, USDA collected and distributed crop and livestock statistics to help farmers assess the value of the goods they produced. At that time, commodity buyers had more current and detailed market information than did farmers, a circumstance that prevented farmers from getting a fair price for their goods. Producers in today's marketplace would be handicapped were it not for the information provided by NASS; the creation of USDA's Crop Reporting Board in 1905 was another landmark in the development of a nationwide statistical service for agriculture.
A USDA reorganization in 1961 led to the creation of the Statistical Reporting Service, known today as National Agricultural Statistics Service. The 1997 Appropriations Act shifted the responsibility of conducting the Census of Agriculture from U. S. Census Bureau to USDA. Since the census has been conducted every five years by NASS. Results from the 2012 Census of Agriculture were released on May 2, 2014; the primary sources of information for NASS reports are farmers, livestock feeders, slaughterhouse managers, grain elevator operators and other agribusinesses. NASS relies on these survey respondents to voluntarily supply data for most reports. NASS surveys are conducted in a variety of ways, including mail surveys, telephone interviews, online response, face-to-face interviews and field observations. Once the information is gathered and interpreted, NASS issues estimates and forecasts for crops and livestock and publishes reports on a variety of topics including production and supplies of food and fiber, prices paid and received by farmers, farm labor and wages, farm income and finances, agricultural chemical use.
NASS's field offices publish local data about many of the same topics. Producers, farm organizations, agribusinesses and government agencies all rely on the information produced by NASS. For instance: Statistical information on acreage, stocks and value is essential for the smooth operation of federal farm programs. Agricultural data are indispensable for planning and administering related federal and state programs in such areas as consumer protection and environmental quality, trade and recreation. NASS data helps to ensure an orderly flow of goods and services among agriculture's producing and marketing sectors. Reliable and detailed crop and livestock statistics help to maintain a stable economic climate and minimize the uncertainties and risks associated with the production and distribution of commodities. Farmers and ranchers rely on NASS reports in making various production and marketing decisions, such as how much grain to plant, how much livestock to raise and when to buy or sell agricultural commodities.
NASS estimates and forecasts are used by the transportation sector and storage companies and other lending institutions, commodity traders and food processors. The businesses that provide farmers with seeds, equipment and other goods and services use the data when planning their marketing strategies. Analysts transform the statistics into projections of coming trends, interpretations of the trends’ economic implications and evaluations of alternative courses of action for producers and policymakers. Crop reports relating to acreage and production. Agricultural Resource Management Survey United States Census of Agriculture World Agricultural Supply and Demand Estimates NASS Web site 7 U. S. C. § 2204g NASS in the Federal Register USDA Web site Census of Agriculture
Edith de Leeuw
Edith Desiree de Leeuw is a Dutch psychologist, research methodologist, Professor in survey methodology and survey quality, at the University of Utrecht. She is known for her work in the field of survey research. Born in Amsterdam, De Leeuw attended the Lely Lyceum in Amsterdam, she obtained her BA in psychology in 1975 at the University of Amsterdam, where in 1982 she obtained her MA in psychology. In 1992 she obtained her PhD in the social and cultural sciences at the VU University Amsterdam under Hans van der Zouwen and Don Mellenbergh with the thesis, entitled "Data quality in mail and face to face surveys." De Leeuw started her academic career as Assistant Coordinator at the SISWO institute, Research Institute for Social and Economic Sciences in 1981. In 1983–84 she was Assistant Professor of Psychology at the University of Utrecht. At the University of Amsterdam she started as Assistant Professor of Psychology in 1983, Associate Professor of Education in 1985. From 1988 to 1991 she was Research Fellow for the Netherlands Organisation for Scientific Research, from 1991 to 1995 Senior Research Fellow at the VU University Amsterdam.
In 1999 she moved back to the University of Utrecht as Senior lecturer Methods & Statistics, was appointed Full Professor in survey methodology and survey quality in 2009. In 1987 De Leeuw had received a Fulbright scholarship to study at the Social and Economic Sciences Research Center of Washington State University, she was visiting scholar at University of California, Los Angeles, research fellow at the Inter Universities Joint Institute for Psychometrics and Socio Metrics in the Netherlands, a visiting fellow at the International University of Surrey. She is Associate Editor of the Journal of Official Statistics since 2000, she came into prominence as assistant to Wim T. Schippers in the National Science Quiz, where she participated from 1994 to 2002. de Leeuw, Edith Desiree. Data Quality in Mail and Face to Face Surveys. TT Publikaties, 1992. de Leeuw, Edith Desirée, Joop J. Hox and Don A. Dillman, eds. International handbook of survey methodology. Taylor & Francis, 2008. Articles, a selectionDe Leeuw, Edith D. and Johannes Van der Zouwen.
"Data quality in telephone and face to face surveys: a comparative meta-analysis." Telephone survey methodology: 283–299. Hox, Joop J. and Edith D. De Leeuw. "A comparison of nonresponse in mail and face-to-face surveys." Quality and Quantity 28.4: 329–344. Deeg, Dorly JH, et al. "Attrition in the Longitudinal Aging Study Amsterdam: The effect of differential inclusion in side studies." Journal of clinical epidemiology 55.4: 319–328. De Leeuw, Edith D. and W. de Heer. "Trends in household survey nonresponse: A longitudinal and international comparison." In: Survey Nonresponse, Groves, R. M. et al.: 41–54. De Leeuw, Edith D. "To mix or not to mix data collection modes in surveys." Journal of Official Statistics. Stockholm – 21.2: 233. Prof. dr. Edith de Leeuw at University of Utrecht Edith de Leeuw homepage
Direct marketing is a form of advertising where organizations communicate directly to customers through a variety of media including cell phone text messaging, websites, online adverts, database marketing, catalog distribution, promotional letters, targeted television, magazine advertisements, outdoor advertising. Among practitioners, it is known as direct response marketing; the prevalence of direct marketing and the unwelcome nature of some communications has led to regulations and laws such as the CAN-SPAM Act, requiring that consumers in the United States be allowed to opt out. A 2010 study by the Direct Marketing Association reports that in 2010, marketers—commercial and nonprofit—spent $153.3 billion on direct marketing, which accounted for 54.2% of all ad expenditures in the United States. Measured against total US sales, these advertising expenditures generated $1.798 trillion in incremental sales. In 2010, direct marketing accounted for 8.3% of total US gross domestic product. In 2010, there were 1.4 million direct marketing employees in the US.
Their collective sales efforts directly supported 8.4 million other jobs, accounting for a total of 9.8 million US jobs. Direct marketing, using catalogues was practiced in 15th-century Europe; the publisher Aldus Manutius of Venice printed a catalogue of the books. In 1667, the English gardener, William Lucas, published a seed catalogue, which he mailed to his customers to inform them of his prices. Catalogues spread to colonial America, where Benjamin Franklin is believed to have been the first cataloguer in British America. In 1744, he produced a catalogue of academic books. Meeting the demands of the consumer revolution and growth in wealth of the middle classes that helped drive the Industrial Revolution in Britain, the Following the industrial revolution of the late 18th-century, a growing middle class created new demand for goods and services. Entrepreneurs, including Matthew Boulton and pottery manufacturer, Josiah Wedgwood, pioneered many of the marketing strategies used today, including direct marketing.
The Welsh entrepreneur Pryce Pryce-Jones set up the first modern mail order in 1861. Starting off as an apprentice to a local draper in Newtown, Wales, he took over the business in 1856 and renamed it the Royal Welsh Warehouse, selling local Welsh flannel. Improvements in transportation systems combined with the advent of the Uniform Penny Post in the mid-19th century provided the necessary conditions for rapid growth in mail order services. In 1861, Pryce-Jones hit upon a unique method of selling his wares, he distributed catalogues of his wares across the country, allowing people to choose the items they wished and order them via post. It was an ideal way of meeting the needs of customers in isolated rural locations who were either too busy or unable to get into Newtown to shop directly; this was the world's first mail order business, an idea which would change the nature of retail in the coming century. One of Price-Jones most popular products was the Euklisia Rug, the forerunner of the modern sleeping bag, which Pryce-Jones exported around the world, at one point landing a contract with the Russian Army for 60,000 rugs.
By 1880, he had more than 100,000 customers and his success was rewarded in 1887 with a knighthood. In the 19th century, the American retailer, Aaron Montgomery Ward, believed that using the technique of selling products directly to the customer at appealing prices could, if executed and efficiently, revolutionize the market industry and therefore be used as a model for marketing products and creating customer loyalty; the term "direct marketing" was coined long after Montgomery Ward's time. In 1872, Ward produced the first mail-order catalog for his Montgomery Ward mail order business. By buying goods and reselling them directly to customers, Ward was removing the middlemen at the general store and, to the benefit of the customer, drastically lowering the prices; the Direct Mail Advertising Association, predecessor of the present-day Direct Marketing Association, was first established in 1917. Third class bulk mail postage rates were established in 1928. In 1967, Lester Wunderman identified and defined the term "direct marketing".
Wunderman—considered to be the father of contemporary direct marketing—is behind the creation of the toll-free 1-800 number and numerous loyalty marketing programs including the Columbia Record Club, the magazine subscription card, the American Express Customer Rewards program. Direct marketing is attractive to many marketers because its results, positive or otherwise, can be measured directly. For example, if a marketer sends out 1,000 solicitations by mail and 100 respond to the promotion, the marketer can say with confidence that campaign led directly to a 10% conversion; this metric is known as the'response rate', it is one of many quantifiable success metrics employed by direct marketers. In contrast, general advertising uses indirect measurements, such as awareness or engagement, since there is no direct response from a consumer. Measurement of results is a fundamental element in successful direct marketing. One of the other significant benefits of direct marketing is that it enables promoting products or services that might not be known to consumers.
Products or service with a sound value proposition, matched with an attractive offer, supported with effective communication, delivered through a suitable direct marketing channel and targeting the relevant customer segment can result in a effective cost of acquisition. Relative to other channels of distribution direct marketing as a practice principally relies on the proposition, comm