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Coverage Error
Coverage error is a type of non-sampling error that occurs when there is not a one-to-one correspondence between the target population and the sampling frame from which a sample is drawn. This can bias estimates calculated using survey data.Scheaffer, Richard L. 1996. Section 5 of Teaching Survey Sampling, by Ronald S. Fecso, William D. Kalsbeek, Sharon L. Lohr, Richard L. Scheaffer, Fritz J. Scheuren, Elizabeth A. Stasny. ''The American Statistician'' 50:4 (Nov. 1996), pp 335–337. (ojstor For example, a researcher may wish to study the opinions of registered voters (target population) by calling residences listed in a telephone directory (sampling frame). Undercoverage may occur if not all voters are listed in the phone directory. Overcoverage could occur if some voters have more than one listed phone number. Bias could also occur if some phone numbers listed in the directory do not belong to registered voters. In this example, undercoverage, overcoverage, and bias due to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Non-sampling Error
In statistics, non-sampling error is a catch-all term for the deviations of estimates from their true values that are not a function of the sample chosen, including various systematic errors and random errors that are not due to sampling.Dodge, Y. (2003) ''The Oxford Dictionary of Statistical Terms'', OUP. Non-sampling errors are much harder to quantify than sampling errors.Fritz Scheuren (2005). "What is a Margin of Error?", Chapter 10, inWhat is a Survey?", American Statistical Association, Washington, D.C. Accessed 2008-01-08. Non-sampling errors in survey estimates can arise from: * Coverage errors, such as failure to accurately represent all population units in the sample, or the inability to obtain information about all sample cases; * Response errors by respondents due for example to definitional differences, misunderstandings, or deliberate misreporting; * Mistakes in recording the data or coding it to standard classifications; * Pseudo-opinions given by respondents w ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sharon Lohr
Sharon Lynn Lohr is an American statistician. She is an Emeritus Dean’s Distinguished Professor of Statistics at Arizona State University, and an independent statistical consultant. Her research interests include survey sampling, design of experiments, and applications of statistics in education and criminology. Education and career Lohr graduated from Calvin College in 1982. She completed her Ph.D. in statistics in 1987 at the University of Wisconsin–Madison. Her dissertation, ''Accurate Multivariate Estimation Using Double and Triple Sampling'', was supervised by Mark Finster. After retiring from Arizona State, she served a five-year term as vice president and senior statistician at Westat. Books Lohr is the author of: *''Sampling: Design and Analysis'' (Duxbury Press, 1999; 2nd ed., Cengage/CRC Press, 2010) *''Measuring Crime: Behind the Statistics'' (CRC Press, 2019) Recognition Lohr is a Fellow of the American Statistical Association and an elected member of the Interna ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Total Survey Error
In survey sampling, total survey error includes all forms of survey error including sampling variability, interviewer effects, frame errors, response bias, and non-response bias. Total survey error is discussed in detail in many sources including Salant and Dillman. Definition Total survey error is the difference between a population parameter (such as the mean, total or proportion) and the estimate of that parameter based on the sample survey or census. It has two components: sampling error and nonsampling error. Sampling error, which occurs in sample surveys but not censuses results from the variability inherent in using a randomly selected fraction of the population for estimation. Nonsampling error, which occurs in surveys and censuses alike, is the sum of all other errors, including errors in frame construction, sample selection, data collection, data processing and estimation methods. Sources of nonsampling error The survey literature decomposes nonsampling errors into five ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Survey Sampling
In statistics, survey sampling describes the process of selecting a sample of elements from a target population to conduct a survey. The term "survey" may refer to many different types or techniques of observation. In survey sampling it most often involves a questionnaire used to measure the characteristics and/or attitudes of people. Different ways of contacting members of a sample once they have been selected is the subject of survey data collection. The purpose of sampling is to reduce the cost and/or the amount of work that it would take to survey the entire target population. A survey that measures the entire target population is called a census. A sample refers to a group or section of a population from which information is to be obtained Survey samples can be broadly divided into two types: probability samples and super samples. Probability-based samples implement a sampling plan with specified probabilities (perhaps adapted probabilities specified by an adaptive procedur ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sampling Frame
In statistics, a sampling frame is the source material or device from which a sample is drawn. It is a list of all those within a population who can be sampled, and may include individuals, households or institutions. Importance of the sampling frame is stressed by Jessen and Salant and Dillman.Salant, Priscilla, and Don A. Dillman. "How to Conduct your own Survey: Leading professional give you proven techniques for getting reliable results" (1995) Obtaining and organizing a sampling frame In the most straightforward cases, such as when dealing with a batch of material from a production run, or using a census, it is possible to identify and measure every single item in the population and to include any one of them in our sample; this is known as ''direct element sampling''. However, in many other cases this is not possible; either because it is cost-prohibitive (reaching every citizen of a country) or impossible (reaching all humans alive). Having established the frame, there ar ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Longitudinal Study
A longitudinal study (or longitudinal survey, or panel study) is a research design that involves repeated observations of the same variables (e.g., people) over short or long periods of time (i.e., uses longitudinal data). It is often a type of observational study, although it can also be structured as longitudinal randomized experiment. Longitudinal studies are often used in social-personality and clinical psychology, to study rapid fluctuations in behaviors, thoughts, and emotions from moment to moment or day to day; in developmental psychology, to study developmental trends across the life span; and in sociology, to study life events throughout lifetimes or generations; and in consumer research and political polling to study consumer trends. The reason for this is that, unlike cross-sectional studies, in which different individuals with the same characteristics are compared, longitudinal studies track the same people, and so the differences observed in those people are less like ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Zero-inflated Model
In statistics, a zero-inflated model is a statistical model based on a zero-inflated probability distribution, i.e. a distribution that allows for frequent zero-valued observations. Zero-inflated Poisson One well-known zero-inflated model is Diane Lambert's zero-inflated Poisson model, which concerns a random event containing excess zero-count data in unit time. For example, the number of insurance claims within a population for a certain type of risk would be zero-inflated by those people who have not taken out insurance against the risk and thus are unable to claim. The zero-inflated Poisson (ZIP) model mixes two zero generating processes. The first process generates zeros. The second process is governed by a Poisson distribution that generates counts, some of which may be zero. The mixture distribution is described as follows: : \Pr (Y = 0) = \pi + (1 - \pi) e^ :\Pr (Y = y_i) = (1 - \pi) \frac ,\qquad y_i = 1,2,3,... where the outcome variable y_i has any non-negative in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Capture-recapture
Mark and recapture is a method commonly used in ecology to estimate an animal population's size where it is impractical to count every individual. A portion of the population is captured, marked, and released. Later, another portion will be captured and the number of marked individuals within the sample is counted. Since the number of marked individuals within the second sample should be proportional to the number of marked individuals in the whole population, an estimate of the total population size can be obtained by dividing the number of marked individuals by the proportion of marked individuals in the second sample. Other names for this method, or closely related methods, include capture-recapture, capture-mark-recapture, mark-recapture, sight-resight, mark-release-recapture, multiple systems estimation, band recovery, the Petersen method, and the Lincoln method. Another major application for these methods is in epidemiology, where they are used to estimate the completeness of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sampling Error
In statistics, sampling errors are incurred when the statistical characteristics of a population are estimated from a subset, or sample, of that population. Since the sample does not include all members of the population, statistics of the sample (often known as estimators), such as means and quartiles, generally differ from the statistics of the entire population (known as parameters). The difference between the sample statistic and population parameter is considered the sampling error.Sarndal, Swenson, and Wretman (1992), Model Assisted Survey Sampling, Springer-Verlag, For example, if one measures the height of a thousand individuals from a population of one million, the average height of the thousand is typically not the same as the average height of all one million people in the country. Since sampling is almost always done to estimate population parameters that are unknown, by definition exact measurement of the sampling errors will not be possible; however they can often be ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Survey Methodology
Survey methodology is "the study of survey methods". As a field of applied statistics concentrating on human-research surveys, survey methodology studies the sampling of individual units from a population and associated techniques of survey data collection, such as questionnaire construction and methods for improving the number and accuracy of responses to surveys. Survey methodology targets instruments or procedures that ask one or more questions that may or may not be answered. Researchers carry out statistical surveys with a view towards making statistical inferences about the population being studied; such inferences depend strongly on the survey questions used. Polls about public opinion, public-health surveys, market-research surveys, government surveys and censuses all exemplify quantitative research that uses survey methodology to answer questions about a population. Although censuses do not include a "sample", they do include other aspects of survey methodology, li ... [...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] |
