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.

Clay pot irrigation for tomato (Lycopersicon esculentum Mill) production in the north east semiarid region of Ethiopia

Water shortage is one of the major constraints for production of horticultural crops in arid and semiarid regions. A field experiment was conducted to determine irrigation water and fertilizer use efficiency, growth and yield of tomato under clay pot irrigation at the experimental site of Sekota Dryland Agricultural Research Center, Lalibela, Ethiopia in 2009/10. The experiment comprised of five treatments including furrow irrigated control and clay pot irrigation with different plant population and fertilization methods, which were arranged in Randomized Complete Block Design with three replications. The highest total and marketable fruit yields were obtained from clay pot irrigation combined with application of nitrogen fertilizer with irrigation water irrespective of difference in plant population. The clay pot irrigation had seasonal water use of up to 143.71 mm, which resulted in significantly higher water use efficiency (33.62 kg m^-3) as compared to the furrow irrigation, which had a seasonal water use of 485.50 mm, and a water use efficiency of 6.67 kg m^-3. Application of nitrogen fertilizer with irrigation water in clay pots improved fertilizer use efficiency of tomato by up to 52% than band application with furrow or clay pot irrigation. Thus, clay pot irrigation with 33,333 plants ha^-1 and nitrogen fertilizer application with irrigation water in clay pots was the best method for increasing the yield of tomato while economizing the use of water and nitrogen fertilizer in a semiarid environment.

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

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

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Multilevel multitrait-multimethod model: application to the measurement of egocentered social networks

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

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

Educación para la libertad en Stuart Mill.

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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.

Sectoral investment composition, capital services measurement and growth in Colombia

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.

The rasch approach to 'objective measurement' in the presence of subjective evaluation from judges. 'Aproximación al modelo de 'medición objetiva' de rasch en presentación de la evaluación subjetiva por jueces'.

Resumen tomado de la publicación. Monográfico titulado: economía de la educación

Factor structure and measurement invariance of the Difficulties Emotion Regulation Scale (DERS) in Spanish adolescents

Resumen tomado de la publicaci??n