The Impact of Measurement Error

2012 ◽  
pp. 353-362
Keyword(s):  
Measurement Error ◽  
The Impact

scholarly journals Design for a Highly Stable Laser Source Based on the Error Model of High-Speed High-Resolution Heterodyne Interferometers

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1083
Author(s):  
Hongxing Yang ◽  
Ziqi Yin ◽  
Ruitao Yang ◽  
Pengcheng Hu ◽  
Jing Li ◽  
...  
Keyword(s):  
Measurement Error ◽  
High Resolution ◽  
High Speed ◽  
Beat Frequency ◽  
Error Model ◽  
Laser Source ◽  
Measurement Error Model ◽  
High Resolution Measurement ◽  
Resolution Measurement ◽  
The Impact

Heterodyne interferometers with two opposite Doppler shift interference signals have been proposed for high-resolution measurement with high measurement speed, which can be used in the background of high-speed high-resolution measurement. However, a measurement error model for high-speed high-resolution heterodyne interferometers (HSHR-HIs) has not yet been proposed. We established a HSHR-HI measurement error model, analyzed the influence of beat frequency stability with a simplified optical structure, and then designed an offset-locked dual-frequency laser source with a digital control system to reduce the impact of beat frequency drift. Experiments were used to verify the correction of the measurement error model and the validity of the laser source. The results show that the new laser source has a maximum beat frequency range of 45 MHz, which shows the improvements in the measuring speed and resolution.

scholarly journals COSMIN Risk of Bias tool to assess the quality of studies on reliability or measurement error of outcome measurement instruments: a Delphi study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
L. B. Mokkink ◽  
M. Boers ◽  
C. P. M. van der Vleuten ◽  
L. M. Bouter ◽  
J. Alonso ◽  
...  
Keyword(s):  
Measurement Error ◽  
Delphi Study ◽  
Outcome Measurement ◽  
Research Question ◽  
Measurement Instrument ◽  
Measurement Properties ◽  
Measurement Instruments ◽  
Sources Of Variation ◽  
The Impact

Abstract Background Scores on an outcome measurement instrument depend on the type and settings of the instrument used, how instructions are given to patients, how professionals administer and score the instrument, etc. The impact of all these sources of variation on scores can be assessed in studies on reliability and measurement error, if properly designed and analyzed. The aim of this study was to develop standards to assess the quality of studies on reliability and measurement error of clinician-reported outcome measurement instruments, performance-based outcome measurement instrument, and laboratory values. Methods We conducted a 3-round Delphi study involving 52 panelists. Results Consensus was reached on how a comprehensive research question can be deduced from the design of a reliability study to determine how the results of a study inform us about the quality of the outcome measurement instrument at issue. Consensus was reached on components of outcome measurement instruments, i.e. the potential sources of variation. Next, we reached consensus on standards on design requirements (n = 5), standards on preferred statistical methods for reliability (n = 3) and measurement error (n = 2), and their ratings on a four-point scale. There was one term for a component and one rating of one standard on which no consensus was reached, and therefore required a decision by the steering committee. Conclusion We developed a tool that enables researchers with and without thorough knowledge on measurement properties to assess the quality of a study on reliability and measurement error of outcome measurement instruments.

Measurement Error: A Primer for Macroeconomists

2020 ◽  
Author(s):  
Simon van Norden
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Forecast Errors ◽  
National Accounts ◽  
Simple Arithmetic ◽  
Multiple Data ◽  
Realistic Assessment ◽  
Market Production ◽  
Considerable Controversy ◽  
The Impact

Most applied researchers in macroeconomics who work with official macroeconomic statistics (such as those found in the National Accounts, the Balance of Payments, national government budgets, labor force statistics, etc.) treat data as immutable rather than subject to measurement error and revision. Some of this error may be caused by disagreement or confusion about what should be measured. Some may be due to the practical challenges of producing timely, accurate, and precise estimates. The economic importance of measurement error may be accentuated by simple arithmetic transformations of the data, or by more complex but still common transformations to remove seasonal or other fluctuations. As a result, measurement error is seemingly omnipresent in macroeconomics. Even the most widely used measures such as Gross Domestic Products (GDP) are acknowledged to be poor measures of aggregate welfare as they omit leisure and non-market production activity and fail to consider intertemporal issues related to the sustainability of economic activity. But even modest attempts to improve GDP estimates can generate considerable controversy in practice. Common statistical approaches to allow for measurement errors, including most factor models, rely on assumptions that are at odds with common economic assumptions which imply that measurement errors in published aggregate series should behave much like forecast errors. Fortunately, recent research has shown how multiple data releases may be combined in a flexible way to give improved estimates of the underlying quantities. Increasingly, the challenge for macroeconomists is to recognize the impact that measurement error may have on their analysis and to condition their policy advice on a realistic assessment of the quality of their available information.

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