Sources Of Measurement Error Research Articles

Integrating Bifactor Models into a Generalizability Theory Based Structural Equation Modeling Framework

Although generalizability theory (GT) designs have traditionally been analyzed within an ANOVA framework, identical results can be obtained with structural equation models (SEMs) but extended to represent multiple sources of both systematic and measurement error variance, include estimation methods less likely to produce negative variance components, and correct for score coarseness. In research reported here, we integrated principles of bifactor modeling into a GT-SEM framework to partition systematic variance for subscale and composite scores into general and group factor effects and measurement error into multiple components. GT-bifactor modeling allowed for partitioning of variance at different levels of aggregation, clearer definitions of constructs within sampled domains, extended indices of score consistency, insights into composite and subscale score viability, and markers for best enhancing score consistency. Results for domain and facet scores from the recently updated form of the Big Five Inventory highlighted the importance of taking all sources of measurement error into account and the diagnostic benefits of GT-bifactor designs over conventional univariate and multivariate GT designs. Findings further revealed that corrections for scale coarseness noticeably enhanced overall score consistency by reducing specific-factor and random-response measurement error. We provide code in R for applying all illustrated designs in a detailed online supplement.

Generalizability of Written Expression Curriculum-Based-Measurement in the German Language: What Are the Major Sources of Variability?

This study aimed to identify the sources of measurement error that contribute to the intraindividual variability of written expression curriculum-based measurement (CBM-W) and assess how many German writing samples of 3 or 5 min duration are necessary to make sufficiently reliable relative and absolute decisions. Students in grade 3 (N = 128) and grade 6 (N = 118) wrote five CBM-W probes of 5 min each within 1 week, which were scored for commonly used metrics (i.e., words written, correct writing sequences). Analyses within the generalizability theory framework showed that between-student differences accounted for 36–60% of the variance. The student × writing prompt interaction was the largest source of variability, particularly among younger students (44%), while writing prompt per se and writing time explained no variance. Two to four writing samples of 3 min are sufficient for most scoring methods to achieve relative reliability >0.80. CBM-W in German proved inadequate for the grade levels studied for absolute decisions. These findings imply that CBM-W in this form in German-speaking primary grades is suitable as a universal screening tool but not as a tool for progress monitoring of individual students.

TILT CORRECTION OF ONBOARD DRONE IRRADIANCE MEASUREMENTS – EVALUATION OF HYPERSPECTRAL METHODS

Abstract. An accurate onboard irradiance measurement on drones is a requirement direct reflectance transformation of aerial images and remote sensing data without use of on-ground reference targets. One of the major error sources in onboard irradiance measurement is the effect of sensor tilt to the observed irradiances. In this paper/presentation, we will present an intercomparison of two methods: the FGI AIRS method and the Köppl method. Both methods require an irradiance sensor with good cosine response and IMU system providing accurate tilt angle for each irradiance measurement. Additionally to this, the AIRS method requires special hardware with multiple tilted irradiance sensors, which effectively allow interpolation of a virtual horizontal sensor. The Köppl method requires the irradiance sensor to be a spectral sensor, which allows the direct and diffuse fractions of irradiance spectrum to be deducted from shape of the measured spectrum using the spectral unmixing between direct and diffuse spectrum shapes. We present a field experiment where the AIRS sensor was flown in a typical mapping pattern flight and evaluate the tilt correction methods during it. During a fully sunny period of the flight, the standard deviations of the irradiances was ±6.4% for the uncorrected tilted irradiances, ±1.8% for the AIRS method, and ±2.0% for the Köppl method. The data shows that an accurate irradiance measurement onboard a drone is feasible using a well-calibrated irradiance sensor system and both methods are capable to produce quite similar results.

Estimating multivariate density and its derivatives for mixed measurement error data

In this paper, we propose a nonparametric mixed kernel estimator for a multivariate density function and its derivatives when the data are contaminated with different sources of measurement errors. The proposed estimator is a mixture of the classical and the deconvolution kernels, accounting for the error-free and error-prone variables, respectively. Large sample properties of the proposed nonparametric estimator, including the order of the mean squares error, the consistency, and the asymptotic normality, are thoroughly investigated. The optimal convergence rates among all nonparametric estimators for different measurement error structures are derived, and it is shown that the proposed mixed kernel estimators achieve the optimal convergence rate. A simulation study is conducted to evaluate the finite sample performance of the proposed estimators.

AC impedance spectroscopy of cement - based materials: measurement and interpretation

In this article, factors reflecting the sources of measurement errors of AC impedance spectroscopy (ACIS) of cement - based materials and the related ACIS data interpretation were reviewed. Associated with reduction measures and correction methods for measurement error of ACIS, reasonable measurement parameters and a simple and convenient measurement procedure for ACIS of cement - based materials were recommended to minimize the measurement errors. To account for the different responses of multi - scale unconnected pores, Maxwell model was found to be most appropriate for describing AC electrical response of cement - based materials. Inappropriate models and measurement error tend to increase the dispersion and frequency dependence of impedance parameters. Differential impedance analysis can be a potential method to perform parameter identification of ACIS. Additionally, averaging and mixing phenomena in the microstructure interpretation of dielectric properties and ACIS related with unsaturated sample, the multidimensional characterization should be as key considerations for ACIS of cement - based materials.

Methodological considerations on estimating medication adherence from self-report, electronic monitoring and electronic healthcare databases using the TEOS framework.

Measuring adherence to medication is complex due to the diversity of contexts in which medications are prescribed, dispensed and used. The Timelines-Events-Objectives-Sources (TEOS) framework outlined a process to operationalize adherence. We aimed to develop practical recommendations for quantification of medication adherence using self-report (SR), electronic monitoring (EM) and electronic healthcare databases (EHD) consistent with the TEOS framework for adherence operationalization. An adherence methodology working group of the International Society for Medication Adherence (ESPACOMP) analysed implications of the process of medication adherence for all data sources and discussed considerations specific to SR, EM and EHD regarding the information available on the prescribing, dispensing, recommended and actual use timelines, the four events relevant for distinguishing the adherence phases, the study objectives commonly addressed with each type of data, and the potential sources of measurement error and quality criteria applicable. Four key implications for medication adherence measurement are common to all data sources: adherence is a comparison between two series of events (recommended and actual use); it refers to one or more specific medication(s); it applies to regular repeated events coinciding with known recommended dosing; and it requires separate measurement of the three adherence phases for a complete picture of patients' adherence. We propose recommendations deriving from these statements, and aspects to be considered in study design when measuring adherence with SR, EM and EHD using the TEOS framework. The quality of medication adherence estimates is the result of several design choices that may optimize the data available.

PERFORMANCE OF NUMERICAL WEATHER PRODUCTS FOR INSAR TROPOSPHERIC CORRECTION: A CASE STUDY OF A TROPICAL REGION

Abstract. Tropospheric delay variability remains a significant source of error in the InSAR-derived measurements. Numerical weather models have been proposed as an alternative technique to mitigate tropospheric delays in InSAR and have become a standard procedure for some multi-temporal InSAR processing workflows. This study evaluates the viability of three numerical weather models for mitigating tropospheric delay in InSAR for a tropical region. We assess their performance in correcting tropospheric delay in Sentinel-1 interferograms at different spatial wavelengths using variograms. Their performance is validated using GNSS tropospheric delay and our proposed SAR-derived tropospheric delay estimates. The results indicate that numerical weather model estimates do not mitigate short-wavelength turbulent delays, but can mitigate long-wavelength stratified delays to some extent, which may also introduce additional errors in interferograms. At a spatial wavelength of 40 km, 36% of the interferograms showed increased spatial autocorrelation after correction with GACOS, 55% with ERA-5, and 51% with MERRA-2. In contrast the InSAR-derived tropospheric delays resulted in a significant reduction in variance at all wavelengths indicating the ability to mitigate both turbulent and stratified delays. Our study demonstrates the limited potential of numerical weather model estimates to satisfactorily mitigate trophospheric noise in InSAR and the capability of InSAR-derived trophospheric delay to significantly correct tropospheric noise in InSAR.

Improving the Measurement of Hostile Sexism

AbstractIn recent years, sexism has played an increasingly pivotal role in American politics, and scholarship examining the importance of gender attitudes for political behavior has surged. Researchers have largely relied on the hostile sexism scale to measure prejudice against women, and this scale seems particularly relevant to political science research. However, this scale measures attitudes with an agree-disagree response format, which has long been recognized as a source of substantial measurement error. In this paper, we introduce a revised version of the hostile sexism scale that instead relies on an item-specific question format. Across three studies, we show that the item-specific scale is strongly related to the agree-disagree scale, but that the item-specific version reduces problems with truncation and tends to improve discriminant and predictive validity. Given these advantages, we conclude by recommending that researchers adopt the item-specific hostile sexism scale.

Sensitivity of COVID-19 analysis results focusing on data source, time, and region

ABSTRACT This article attempts to solve a puzzle regarding the sensitivity of analysis results of COVID-19 infection and fatality. Our findings suggest that measurement errors, statistical significance of explanatory variables, and regional differences play a crucial role in the sensitivity of results. The significance of political stability, governance indicators, medical resources, demographic features is inconsistent and depends on the source of data, the choice of the time period, and region. This article also provides evidence that careful data screening and use of the moving average technique can alleviate the sensitivity issue and produce fairly robust results. We conclude that social science research on COVID-19 should not underestimate the issue of data quality and should refine raw data to minimize random error. If the sources of measurement error are not carefully managed and intensive statistical tests of sensitivity are not verified, data quality will end being subjected to the skepticism of evidence-based policy making.

Reliability of quantitative multiparameter maps is high for magnetization transfer and proton density but attenuated for R1 and R2* in healthy young adults

We investigate the reliability of individual differences of four quantities measured by magnetic resonance imaging‐based multiparameter mapping (MPM): magnetization transfer saturation (MT), proton density (PD), longitudinal relaxation rate (R1), and effective transverse relaxation rate (R2*). Four MPM datasets, two on each of two consecutive days, were acquired in healthy young adults. On Day 1, no repositioning occurred and on Day 2, participants were repositioned between MPM datasets. Using intraclass correlation effect decomposition (ICED), we assessed the contributions of session‐specific, day‐specific, and residual sources of measurement error. For whole‐brain gray and white matter, all four MPM parameters showed high reproducibility and high reliability, as indexed by the coefficient of variation (CoV) and the intraclass correlation (ICC). However, MT, PD, R1, and R2* differed markedly in the extent to which reliability varied across brain regions. MT and PD showed high reliability in almost all regions. In contrast, R1 and R2* showed low reliability in some regions outside the basal ganglia, such that the sum of the measurement error estimates in our structural equation model was higher than estimates of between‐person differences. In addition, in this sample of healthy young adults, the four MPM parameters showed very little variability over four measurements but differed in how well they could assess between‐person differences. We conclude that R1 and R2* might carry only limited person‐specific information in some regions of the brain in healthy young adults, and, by implication, might be of restricted utility for studying associations to between‐person differences in behavior in those regions.

The error of transferring the size of a unit of length – a meter in the nanometer range using the nanomeasuring machine

The paper describes the principle of measuring linear dimensions using a nano-measuring machine (NMM), which implements the method of measuring three coordinate axes in the range of 25X25X5 mm3. Sources of non-excluded systematic measurement errors are identified, which are conventionally divided into factors associated with the method of measuring length using an interferometer and factors determined by the design and technological features of the nano-measuring machine. Statistical estimates of the measurement result have been determined and the error in transferring the size – meter length in the nanometer range of measurements has been calculated. The results obtained assert that the nano-measuring machine is a unique tool that allows one to carry out measurements of millimeter dimensions with nanometer accuracy. The results obtained can be used for metrological assessment of the step height and step width, when calibrating measuring instruments in the nanometer range, roughness measures in a large range when calibrating profilometers and contourographs, as well as templates and micrometer objects for measuring high-precision microscopes.

Simulation and Calculation of Sea State Bias Based on the Electromagnetic Backscattering from the Simulated Nonlinear Rough Sea Surface

Abstract Due to the improvement of satellite orbit determination technology, sea state bias(SSB) has become the main source of altimeter height measurement error. In this paper, the physical mechanism and causes of SSB are simulated and analyzed by electromagnetic scattering, and the SSB correction method and model are given. Firstly, this paper simulates and studies four kinds of nonlinear rough sea surface and their electromagnetic scattering characteristics for the first time, calculates SSB from the physical mechanism of SSB, and analyzes the physical mechanism and process causes of SSB from the electromagnetic scattering characteristics. Secondly, a fitting model to correct SSB is proposed in terms of the electromagnetic simulation. Finally, the paper compares the “true value of SSB” of HY-2B satellite radar altimeter in China’s offshore waters with SSB calculated by the simulating model. The comparison results show that they are consistent with a weak bias, indicating that it is an effective method to correct SSB by simulating nonlinear rough sea surface and its electromagnetic scattering. It is expected to improve the accuracy of SSB correction by the new way of simulating the physical mechanism and causes of SSB.

Kiel Probes for Stagnation Pressure Measurement in Rotating Detonation Combustors

Kiel probes have the potential to be a versatile tool for determining stagnation pressure gain in rotating detonation combustors (RDCs), accompanying the commonly used equivalent available pressure method. Although average pressure gain values determined with Kiel probes are comparable to those from thrust stand experiments, one can expect several sources of measurement error from the unsteady trans- and supersonic environment. This work investigates the response of a Kiel probe in highly unsteady flow, similar to what would be encountered in an RDC. The probe is subjected to an underexpanded starting jet behind an incident shock with Mach numbers of 1.6–2.7, emanating from a shock tube with a reservoir ratio of about 394. The incidence angle of the probe is varied between 0 and 90 deg, as is the probe’s axial location with respect to the tube’s exit plane. High-speed schlieren images reveal the Mach number of the moving shock wave and the structure of the detached bow shock at the Kiel head, which is similar to that of a bluff body. It is shown that the measured stagnation pressure signal is independent of inflow angle over a range of , and that signal attenuation is caused by gas processing through the bow shock and viscous losses in the probe’s capillary. The frequency response of the Kiel probe to sinusoidal, small-amplitude pressure fluctuations is determined in an acoustic calibration facility up to 5500 Hz, confirming linear behavior and that no unwanted resonance is present in the probe. A Berg–Tijdeman representation delivers amplitude ratio and phase lag of comparable magnitude. An assessment of representative boundary conditions reveals that the average stagnation pressure measurement error is of the order of 1%.

An Approach of Empirical Model Uncertainty Estimation During Physical Quantities Investigation in Turning

This paper presents an approach of empirical modeling of the machining process physical quantity with measurement uncertainty parameter included in the resulting power mathematical model coefficients. The proposed approach is presented through an example of modeling of the average temperature in turning like a methodology that is under the influence of various sources of measurement errors. The uncertainty budget accounts for the sources of errors of the experimental measuring system and the cutting process itself. This approach enables estimation of the reliability of the gained mathematical models and gives the possibility of identification and lowering of the main sources of errors.

Improved measurement method for the Faraday rotation distribution using beam splitting.

In this work, two Faraday channels and one shadow channel are constructed by two non-polarizing beam splitters and one reflector to measure the Faraday rotation distribution. The intensity of the Faraday and shadow images is related to the state of polarization (SOP) of the incident light, thus achieving two-dimensional accurate measurement. The measurement sensitivity is influenced by the settings of two polarization analyzers in the Faraday channels and the parameters of beam splitters, which are explored numerically and verified with experiments. The fluctuation of the probe light is eliminated by using three evaluation indexes. Also, the measurement range and error sources under different experimental settings are discussed.

Sources of measurement error in pediatric intelligence testing

Pediatric intelligence tests, such as the Wechsler Intelligence Scale for Children, are commonly used diagnostic tools used in the process of diagnosing learning and behavior disabilities. Decisions concerning treatment are made based on the results of these tests and they are frequently used in educational and vocational contexts for important decisions that impact persons’ academic or professional lives. Research has however shown that important errors may occur despite the application of validation processes and adherence to quality criteria for psychometric tests. At the same time this evidence seems not to be pervasively acknowledged in psychological practice and research. In this article, I will showcase research that places attention on sources of measurement error in pediatric intelligence testing, discuss a process-performance approach to measurement in intelligence testing, and propose the “pretest methods,” methods stemming from the field of survey methodology commonly used in questionnaire construction, as a method to help address the problem of sources of measurement error in pediatric intelligence testing and improve the development of these intelligence tests.

Using virtual reality for anatomical landmark annotation in geometric morphometrics.

To study the shape of objects using geometric morphometrics, landmarks are oftentimes collected digitally from a 3D scanned model. The expert may annotate landmarks using software that visualizes the 3D model on a flat screen, and interaction is achieved with a mouse and a keyboard. However, landmark annotation of a 3D model on a 2D display is a tedious process and potentially introduces error due to the perception and interaction limitations of the flat interface. In addition, digital landmark placement can be more time-consuming than direct annotation on the physical object using a tactile digitizer arm. Since virtual reality (VR) is designed to more closely resemble the real world, we present a VR prototype for annotating landmarks on 3D models. We study the impact of VR on annotation performance by comparing our VR prototype to Stratovan Checkpoint, a commonly used commercial desktop software. We use an experimental setup, where four operators placed six landmarks on six grey seal (Halichoerus grypus) skulls in six trials for both systems. This enables us to investigate multiple sources of measurement error. We analyse both for the configuration and for single landmarks. Our analysis shows that annotation in VR is a promising alternative to desktop annotation. We find that annotation precision is comparable between the two systems, with VR being significantly more precise for one of the landmarks. We do not find evidence that annotation in VR is faster than on the desktop, but it is accurate.

Towards a solution to the H0 tension

The tension between current expansion rate H$_0$ using Planck data and direct model-independent measurements in the local universe has reached a tension above 5$\sigma$ in the context of the $\Lambda$CDM model. The growing tension among early time and local measurements of H$_0$ has not ameliorated and remains a crucial and open question in cosmology. Solutions to understand this tension are possible hidden sources of systematic error in the observable measurements or modifications to the concordance $\Lambda$CDM model. In this work, we investigate a solution to the H$_0$ tension by modifying $\Lambda$CDM and we add at early times extra relativistic energy density $\rho_{ex}$. In some context this $\rho_{ex}$ corresponds to EDE or Bound Dark Energy. We implement our model in CAMB and perform a full COSMO-MC (MCMC) allowing to simultaneously fit the latest data from CMB anisotropies and the value of H$_0 = 74.03\pm 1.42,\mathrm{km\, s^{-1}Mpc^{-1}}$ from A. Riess [R-19] \ci{Riess:2019cxk}. The inclusion of $\rho_{ex}$ ameliorates the tension between early and late time measurements only slightly and we obtain a value H$_0 = (68.70\pm 0.45$\ksm) still in conflict with local measurements [R-19]. We follow up our analysis by proposing two forecasting standard deviation $\sigma_H=1$ and $\sigma_H=0.5$ (in units of \ksm) for local distance measurements H$_0 = (74.03\pm \sigma_H)$ \ksm. From MCMC we obtain a value of H$_0 =(72.83\pm 0.47)$ \ksm\, at 68\% confidence level for $\sigma_H =0.5$, fully consistent with [R-19], while the price to pay is a percentage increase of $0.12\%$ in CMB $\chi_{cmb}^2$. Finally, the extra energy density $\rho_{ex}$ leaves distinctive imprints in the matter power spectrum at scales $k \sim k_c$ with $k_c=a_cH(a_c)$ and in the CMB power spectrum, allowing for independent verification of our analysis.

A Low-Cost and Efficient Single Probe Based MIMO OTA Measurement Method

As the multiple-input–multiple-output (MIMO) technologies are increasingly emerging in the arena of modern wireless systems, their cost-effective and efficient over-the-air (OTA) performance verification under arbitrary fading channels, especially, for high-volume beam-reconfigurable product verifications, play a crucial role toward their technology adoption. The 2-D multi-probe anechoic chamber (MPAC) MIMO OTA measurement method has been well-developed, but requires both precise infrastructure and heavy calibration. Moreover, the extension to 3-D channel model appears impractical for the MPAC method with its significantly increased probe numbers in 3-D scenario. To address these challenges, we propose a novel single-probe anechoic chamber (SPAC) measurement method that transforms spatial synthesization of MPAC into time synthesization whereby the only source of measurement error can be restrained in time synchronization. The wireless community can be widely benefited with this proposed low-cost and efficient methodology, especially in the research and product prototyping stage. In this article, a proof-of-concept measurement system has been designed and implemented, which validates the efficiency of the proposed solution.

ПРОГНОЗИРОВАНИЕ ПОЛНОГО ЭЛЕКТРОННОГО СОДЕРЖАНИЯ ИОНОСФЕРЫ НА ОСНОВЕ АЛГОРИТМОВ МАШИННОГО ОБУЧЕНИЯ

Introduction. The paper presents and describes the technology of applying the machine learning algorithm in predicting the vertical total electron content of the ionosphere. The ionospheric error is one of the most significant sources of pseudorange measurement errors from GNSS signals. Increasing every year requirements for the accuracy of positioning and navigation by GNSS signals leads to the need to develop new methods to reduce the impact of various measurement errors, including the ionospheric error. At present, ionospheric models of various types are used for ionospheric correction of measurements. The currently widely used ionospheric models do not allow a significant increase in the accuracy of positioning based on GNSS signals. At the moment, the creation of a new effective method for modeling and forecasting the ionosphere that meets modern requirements for positioning accuracy is an important and urgent task. The purpose of this work is to create a methodology for modeling and predicting the total electron content of the ionosphere using machine learning algorithms. Machine learning is currently a fairly common and popular method for solving problems of classification, recognition and prediction. The method has been used for many years in medicine, robotics, industry, finance and many other branches of modern science and economics. To achieve this goal, it is necessary to solve a number of tasks. First of all, you need to select and collect data for training the model, then you need to select a machine learning method and hyperparameters for the selected method. Next, it is necessary to perform TEC prediction based on the trained model and evaluate the accuracy of the results obtained. comparison of the obtained results with the accuracy of other existing models It is shown that machine learning does a good job of predicting full electronic content. The resulting trained model makes it possible to obtain a forecast with an accuracy comparable to the accuracy of the Klobuchar, NeQuick models, and in some cases much more accurate.