Characterization of human and instrument related measurement errors

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.

Model Diagnostics in the presence of measurement error

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.

‘Ethical’, but Gender-Biased?

The migration of healthcare professionals from developing to developed countries, often aided by recruitment agencies, is a phenomenon of great international concern, as reflected in the construction of numerous ethical recruitment codes, which aim to govern the process. In an attempt to provide an overview of the situation, dealing specifically with the migration of nurses, as well as a critical and gender sensitive analysis of the codes, this paper follows three broad steps: first, it reviews the literature dedicated to the migration of nurses from developing to developed countries, adding a gendered account to more conventional push-pull explanations; second, it delineates the positive and negative effects that nurse migration has at the stakeholders levels of the individual, institutional, national and international level, paying particular attention to the role of gender; and third, it reviews and compares numerous codes for the ethical recruitment of nurses, highlighting the gendered rationale and consequences they may have. In showing that nurse migration is a gendered phenomenon, the paper questions whether the codes, written in gender neutral language, will come to bear unintended consequences that will effectively work to uphold gender stereotypes and inequalities.

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

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

Time-delayed fronts from biased random walks

We generalize a previous model of time-delayed reaction–diffusion fronts (Fort and Méndez 1999 Phys. Rev. Lett. 82 867) to allow for a bias in the microscopic random walk of particles or individuals. We also present a second model which takes the time order of events (diffusion and reproduction) into account. As an example, we apply them to the human invasion front across the USA in the 19th century. The corrections relative to the previous model are substantial. Our results are relevant to physical and biological systems with anisotropic fronts, including particle diffusion in disordered lattices, population invasions, the spread of epidemics, etc

Evaluación de la ejecución mediante el modelo Many-Facet Rasch Measurement.

Resumen tomado de la publicación

Correcting Measurement Error Bias in Interaction Models with Small Samples

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

Biased Technological Change, Impatience and Welfare

En este artículo utilizamos un modelo de generaciones traslapadas con heterogeneidad en la tasa de impaciencia para mostrar que los efectos de un cambio tecnológico aumentador de capital no son simétricos en los agentes y pueden conllevar una reducci on en el consumo. La asimetría en la tasa de impaciencia de los agentes en un período, tiene consecuencias sobre los beneficios del cambio tecnológico para las generaciones futuras. Menores tasas de impaciencia llevan a mayores niveles de capital y de consumo, si se entiende que la economía tiene el suficiente nivel de capital per capita.

Development of a design performance measurement matrix for improving collaborative design during a design process

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.

Evaluación de la efectividad de una organización en el campo de percepción de marca usando un sistema de simulación computarizado basado en agentes

El presente proyecto de investigación tiene como objetivo general evaluar la efectividad de los esfuerzos de una unidad de negocio particular de la compañía Novartis de Colombia S.A. en el área de la percepción de marca mediante un sistema de simulación que implementa una metodología para la medición de esta última. Se tiene en cuenta que contar con datos exactos acerca de cómo los clientes finales perciben una marca es un tarea dispendiosa y que aún no tiene una fórmula matemática, por lo tanto, es muy subjetivo el proceso de entender a los consumidores por parte de los directivos de la empresa. El proceso planea que por medio del procedimiento planteado que se basa en una simulación por computador y más concretamente con una modelación basada en agentes se permita acercar a las partes involucradas en el proceso de compra, es decir, la empresa involucrada, vendedores, clientes y finalmente clientes potenciales.

Labor supply, biased technological change and economic growth

Se utiliza un modelo de innovaciones sesgadas para estudiar los efectos de cambios exógenos en la oferta laboral. En un contexto de innovaciones sesgadas, a medida que las economías acumulan capital, el trabajo se hace relativamente más escaso y más caro, por este motivo, hay incentivos para adoptar tecnologías ahorradoras de trabajo. Del mismo modo un cambio en la oferta laboral afecta la abundancia de factores y sus precios relativos. En general, una reducción de la oferta laboral, hace que el trabajo sea más caro y genera incentivos para cambio tecnológico ahorrador de trabajo. Así, el efecto inicial que tiene el cambio en la oferta laboral sobre los precios de los factores es mitigado por el cambio tecnológico. Finalmente, los movimientos en la remuneración a los factores afectan las decisiones de ahorro y, por lo tanto, la dinámica del crecimiento. En este trabajo se exploran las consecuencias de una reducción de la oferta laboral en dos contextos teóricos diferentes: un modelo de agentes homogéneos y horizonte infinito y un modelo de generaciones traslapadas.

Biased technological change, human capital and factor shares

We propose a one-good model where technological change is factor saving and costly. We consider a production function with two reproducible factors: physical capital and human capital, and one not reproducible factor. The main predictions of the model are the following: (a) The elasticity of output with respect to the reproducible factors depends on the factor abundance of the economies. (b) The income share of reproducible factors increases with the stage of development. (c) Depending on the initial conditions, in some economies the production function converges to AK, while in other economies long-run growth is zero. (d) The share of human factors (raw labor and human capital) converges to a positive number lower than one. Along the transition it may decrease, increase or remain constant.

Biased selection within the social health insurance market in Colombia

Las regulaciones como primaje comunitario, paquetes estandarizados y afiliación abierta, orientadas a reducir el impacto de las fallas en los mercados de seguros, tienen un efecto limitado puesto que abren espacio a la selección sesgada. A partir de 1993, el sistema de seguridad social en salud en Colombia fue reformado hacia un enfoque de mercado con la expectativa de mejorar el desempeño de los monopolios preexistentes exponiéndolos a la competencia de nuevos entrantes. La hipótesis que se maneja en el trabajo es que las fallas de mercado pueden llevar a selección sesgada favoreciendo a los nuevos entrantes. Se analizaron dos encuestas de hogares utilizando el estado de salud auto reportado y la presencia de enfermedad crónica como indicadores prospectivos del riesgo de los afiliados. Se encuentra que hay selección sesgada, llevando a selección adversa entre los aseguradores preexistentes, y a selección favorable entre los nuevos entrantes. Este patrón se observa en 1997 y se incrementa en el 2003. Aunque las entidades preexistentes son entidades públicas, y su tamaño disminuyó sustancialmente entre estos años, se analizan sus implicaciones fiscales en términos de financiación adicional por parte del gobierno.