970 resultados para Indirect measurement
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Measurement is the act or the result of a quantitative comparison between a given quantity and a quantity of the same kind chosen as a unit. It is generally agreed that all measurements contain errors. In a measuring system where both a measuring instrument and a human being taking the measurement using a preset process, the measurement error could be due to the instrument, the process or the human being involved. The first part of the study is devoted to understanding the human errors in measurement. For that, selected person related and selected work related factors that could affect measurement errors have been identified. Though these are well known, the exact extent of the error and the extent of effect of different factors on human errors in measurement are less reported. Characterization of human errors in measurement is done by conducting an experimental study using different subjects, where the factors were changed one at a time and the measurements made by them recorded. From the pre‐experiment survey research studies, it is observed that the respondents could not give the correct answers to questions related to the correct values [extent] of human related measurement errors. This confirmed the fears expressed regarding lack of knowledge about the extent of human related measurement errors among professionals associated with quality. But in postexperiment phase of survey study, it is observed that the answers regarding the extent of human related measurement errors has improved significantly since the answer choices were provided based on the experimental study. It is hoped that this work will help users of measurement in practice to better understand and manage the phenomena of human related errors in measurement.
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The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable. As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.
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This work focuses on the analysis of the influence of environment on the relative biological effectiveness (RBE) of carbon ions on molecular level. Due to the high relevance of RBE for medical applications, such as tumor therapy, and radiation protection in space, DNA damages have been investigated in order to understand the biological efficiency of heavy ion radiation. The contribution of this study to the radiobiology research consists in the analysis of plasmid DNA damages induced by carbon ion radiation in biochemical buffer environments, as well as in the calculation of the RBE of carbon ions on DNA level by mean of scanning force microscopy (SFM). In order to study the DNA damages, besides the common electrophoresis method, a new approach has been developed by using SFM. The latter method allows direct visualisation and measurement of individual DNA fragments with an accuracy of several nanometres. In addition, comparison of the results obtained by SFM and agarose gel electrophoresis methods has been performed in the present study. Sparsely ionising radiation, such as X-rays, and densely ionising radiation, such as carbon ions, have been used to irradiate plasmid DNA in trishydroxymethylaminomethane (Tris buffer) and 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES buffer) environments. These buffer environments exhibit different scavenging capacities for hydroxyl radical (HO0), which is produced by ionisation of water and plays the major role in the indirect DNA damage processes. Fragment distributions have been measured by SFM over a large length range, and as expected, a significantly higher degree of DNA damages was observed for increasing dose. Also a higher amount of double-strand breaks (DSBs) was observed after irradiation with carbon ions compared to X-ray irradiation. The results obtained from SFM measurements show that both types of radiation induce multiple fragmentation of the plasmid DNA in the dose range from D = 250 Gy to D = 1500 Gy. Using Tris environments at two different concentrations, a decrease of the relative biological effectiveness with the rise of Tris concentration was observed. This demonstrates the radioprotective behavior of the Tris buffer solution. In contrast, a lower scavenging capacity for all other free radicals and ions, produced by the ionisation of water, was registered in the case of HEPES buffer compared to Tris solution. This is reflected in the higher RBE values deduced from SFM and gel electrophoresis measurements after irradiation of the plasmid DNA in 20 mM HEPES environment compared to 92 mM Tris solution. These results show that HEPES and Tris environments play a major role on preventing the indirect DNA damages induced by ionising radiation and on the relative biological effectiveness of heavy ion radiation. In general, the RBE calculated from the SFM measurements presents higher values compared to gel electrophoresis data, for plasmids irradiated in all environments. Using a large set of data, obtained from the SFM measurements, it was possible to calculate the survive rate over a larger range, from 88% to 98%, while for gel electrophoresis measurements the survive rates have been calculated only for values between 96% and 99%. While the gel electrophoresis measurements provide information only about the percentage of plasmids DNA that suffered a single DSB, SFM can count the small plasmid fragments produced by multiple DSBs induced in a single plasmid. Consequently, SFM generates more detailed information regarding the amount of the induced DSBs compared to gel electrophoresis, and therefore, RBE can be calculated with more accuracy. Thus, SFM has been proven to be a more precise method to characterize on molecular level the DNA damage induced by ionizing radiations.
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In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression
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Resumen tomado de la publicación
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Our goal in this paper is to assess reliability and validity of egocentered network data using multilevel analysis (Muthen, 1989, Hox, 1993) under the multitrait-multimethod approach. The confirmatory factor analysis model for multitrait-multimethod data (Werts & Linn, 1970; Andrews, 1984) is used for our analyses. In this study we reanalyse a part of data of another study (Kogovšek et al., 2002) done on a representative sample of the inhabitants of Ljubljana. The traits used in our article are the name interpreters. We consider egocentered network data as hierarchical; therefore a multilevel analysis is required. We use Muthen's partial maximum likelihood approach, called pseudobalanced solution (Muthen, 1989, 1990, 1994) which produces estimations close to maximum likelihood for large ego sample sizes (Hox & Mass, 2001). Several analyses will be done in order to compare this multilevel analysis to classic methods of analysis such as the ones made in Kogovšek et al. (2002), who analysed the data only at group (ego) level considering averages of all alters within the ego. We show that some of the results obtained by classic methods are biased and that multilevel analysis provides more detailed information that much enriches the interpretation of reliability and validity of hierarchical data. Within and between-ego reliabilities and validities and other related quality measures are defined, computed and interpreted
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Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression
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Resumen tomado de la publicaci??n
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This is a research paper. Research presented in this paper aimed to investigate how to measure collaborative design performance and, in turn, improve the final design output during a design process, with a clear objective to develop a Design Performance Measurement (DPM) matrix to measure design project team member's design collaboration performance.
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The work aims to demonstrate that an indirect expropriation of rights occurs when mining concession agreements are made for subsoil exploitation. The article looks at examples such as when administrative divesture for public utilities or for social interest is decreed to implement projects or environmental works; when any regulation or administrative action is taken determining protected areas or environment control of type; and when environmental control plans are implemented. The indirect expropriation occurs because a conflict exists between general interests and equality principles of the burdens, the contractual right of adherence to agreed upon provisions and the legality of prior legal regulations for the carrying out of such expropriation.
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Introducción: el lupus eritematoso sistémico (LES) es considerado una enfermedad de alto costo. La expresión clínica de la enfermedad depende de la ubicación geografía y la etnicidad. El objetivo de este estudio fue el calcular los costos ambulatorios relacionado al LES en una cohorte colombiana, identificar los predictores de costos y comparar nuestro resultados con otras poblaciones. Métodos: Se realizó una aproximación de tipo prevalencia en 100 pacientes LES en quienes se evaluaron los costos directos médicos, directos no médicos, indirectos e intangibles. Todos los costos médicos fueron evaluados usando una metodología abajo hacia arriba. Los costos directos fueron valorados desde una perspectiva social usando una metodología de micro-costeo. Los costos indirectos se evaluaron mediante una aproximación de capital humano, y los costos intangibles calculados a partir de los años de vida ajustados por calidad (AVAC). Se analizaron los datos por medio de un análisis multivariado. Para comparaciones con otras poblaciones todos los costos fueron expresados como la razón entre los costos y producto interno bruto nacional per cápita. Resultados: La media de costos totales fue 13.031±9.215 USD (ajustados por el factor de conversión de paridad del poder adquisitivo), lo cual representa el 1,66 del PIB per capita de Colombia. Los costos directos son el 64% de los costos totales. Los costos médicos representan el 80% de los costos directos,. Los costos indirectos fueron el 10% y los costos intangibles el 25% de los costos totales. Los medicamentos representaron el 45% de los costos directos. Mayores costos se relacionaron con el estrato socioeconómico, seguro médico privado, AVAC, alopecia, micofenolato mofetilo, y terapia anticoagulante. Los costos directos ajustados de los pacientes con LES en Colombia fueron mayores que en Norte América y en Europa. Conclusiones: el LES impone una carga económica importante para la sociedad. Los costos relacionados con la atención médica y AVAC fueron los principales contribuyentes al alto costo de la enfermedad. Estos resultados pueden ser referencia para determinar políticas en salud pública así como comparar el gasto en salud de forma internacional.
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This is mainly a discussant paper on measurement criteria upon sector’s investment and capitalservices and the way these composition and measurement issues come to have an impact ongrowth figures for some major sectors of the Colombian economy. The main focus is on distinctionmatters regarding the measurement of capital stock and capital services in the productionprocess. The availability of appropriate data, widely discussed throughout the document, impliesthat major affirmations are more hypothetic than indicative or descriptive in style. Moststatements are established as a motivation device for studies on sector’s activities with a focus onconsistency with aggregate figures.
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Resumen tomado de la publicación. Monográfico titulado: economía de la educación