Types Of Measurement Errors Research Articles

Model misspecification, measurement error, and apparent supralinearity in the concentration-response relationship between PM2.5 and mortality.

A growing number of studies have produced results that suggest the shape of the concentration-response (C-R) relationship between PM2.5 exposure and mortality is "supralinear" such that incremental risk is higher at the lowest exposure levels than at the highest exposure levels. If the C-R function is in fact supralinear, then there may be significant health benefits associated with reductions in PM2.5 below the current US National Ambient Air Quality Standards (NAAQS), as each incremental tightening of the PM2.5 NAAQS would be expected to produce ever-greater reductions in mortality risk. In this paper we undertake a series of tests with simulated cohort data to examine whether there are alternative explanations for apparent supralinearity in PM2.5 C-R functions. Our results show that a linear C-R function for PM2.5 can falsely appear to be supralinear in a statistical estimation process for a variety of reasons, such as spatial variation in the composition of total PM2.5 mass, the presence of confounders that are correlated with PM2.5 exposure, and some types of measurement error in estimates of PM2.5 exposure. To the best of our knowledge, this is the first simulation-based study to examine alternative explanations for apparent supralinearity in C-R functions.

Beyond a fixed number: Investigating uncertainty in popular evaluation metrics of ensemble flood modeling using bootstrapping analysis

AbstractEvaluation of the performance of flood models is a crucial step in the modeling process. Considering the limitations of single statistical metrics, such as uncertainty bounds, Nash Sutcliffe efficiency, Kling Gupta efficiency, and the coefficient of determination, which are widely used in the model evaluation, the inherent properties and sampling uncertainty in these metrics are demonstrated. A comprehensive evaluation is conducted using an ensemble of one‐dimensional Hydrologic Engineering Center's River Analysis System (HEC‐RAS) models, which account for the uncertainty associated with the channel roughness and upstream flow input, of six reaches located in Indiana and Texas of the United States. Specifically, the effects of different prior distributions of the uncertainty sources, multiple high‐flow scenarios, and various types of measurement errors in observations on the evaluation metrics are investigated using bootstrapping. Results show that the model performances based on the uniform and normal priors are comparable. The statistical distributions of all the evaluation metrics in this study are significantly different under different high‐flow scenarios, thus suggesting that the metrics should be treated as “random” variables due to both aleatory and epistemic uncertainties and conditioned on the specific flow periods of interest. Additionally, the white‐noise error in observations has the least impact on the metrics.

Inferential and Behavioral Implications of Measurement Error in Agricultural Data

An evolving literature evaluates the inferential and behavioral implications of measurement error (ME) in agricultural data. We synthesize findings on the nature and sources of ME and potential remedies. We provide practical guidance for choosing among alternative approaches for detecting, obviating, or correcting for alternative sources of ME, as these have different behavioral and inferential implications. Some ME biases statistical inference and thus may require econometric correction. Other types of ME may affect (and shed light on) farmers’ decision-making processes even if farmers’ responses are objectively incorrect. Where feasible, collecting both self-reported and objectively measured data for the same variable may enrich understanding of policy-relevant agricultural and behavioral phenomena.

Finite mixture models for linked survey and administrative data: Estimation and postestimation

Researchers use finite mixture models to analyze linked survey and administrative data on labor earnings, while also accounting for various types of measurement error in each data source. Different combinations of error-ridden and error-free observations characterize latent classes. Latent class probabilities depend on the probabilities of the different types of error. We introduce a suite of commands to fit finite mixture models to linked survey-administrative data: there is a general model and seven simpler variants. We also provide postestimation commands for assessment of reliability, marginal effects, data simulation, and prediction of hybrid variables that combine information from both data sources about the outcome of interest. Our commands can also be used to study measurement errors in other variables besides labor earnings.

Reconciling reports: modelling employment earnings and measurement errors using linked survey and administrative data

AbstractWe develop and apply new statistical models for linked survey and administrative data on employment earnings, incorporating 4 types of measurement error. In addition, we allow error distributions to differ with individual characteristics, which improves model fit and allows us to investigate substantive hypotheses about factors associated with error bias and variance. Contributing the first UK evidence to a field dominated by findings about the USA, we show that measurement errors are pervasive, but the 4 types are quite different in nature. We also document substantial heterogeneity in each of the error distributions.

Towards a robust criterion of anomalous diffusion

Anomalous-diffusion, the departure of the spreading dynamics of diffusing particles from the traditional law of Brownian-motion, is a signature feature of a large number of complex soft-matter and biological systems. Anomalous-diffusion emerges due to a variety of physical mechanisms, e.g., trapping interactions or the viscoelasticity of the environment. However, sometimes systems dynamics are erroneously claimed to be anomalous, despite the fact that the true motion is Brownian—or vice versa. This ambiguity in establishing whether the dynamics as normal or anomalous can have far-reaching consequences, e.g., in predictions for reaction- or relaxation-laws. Demonstrating that a system exhibits normal- or anomalous-diffusion is highly desirable for a vast host of applications. Here, we present a criterion for anomalous-diffusion based on the method of power-spectral analysis of single trajectories. The robustness of this criterion is studied for trajectories of fractional-Brownian-motion, a ubiquitous stochastic process for the description of anomalous-diffusion, in the presence of two types of measurement errors. In particular, we find that our criterion is very robust for subdiffusion. Various tests on surrogate data in absence or presence of additional positional noise demonstrate the efficacy of this method in practical contexts. Finally, we provide a proof-of-concept based on diverse experiments exhibiting both normal and anomalous-diffusion.

Measurement error in echocardiographic assessment of aortic stenosis: an epidemiological consideration of research methodology and clinical practice

Abstract Background/Introduction Current international guidelines on treatment of valvular heart disease contain recommendations for aortic valve replacement based exclusively on abnormal echocardiographic parameters in asymptomatic patients with aortic stenosis (AS). To prevent misclassification and subsequent mistreatment of patients, these recommendations require accurate and unbiased measurements of hemodynamic parameters. Purpose To illustrate consideration and impact of measurement error in echocardiographic assessment of aortic stenosis in both the research and clinical setting. Methods First, a systematic review was performed to investigate the recognition of and correction for measurement error in clinical studies on the prognostic value of peak aortic jet velocity (Vmax), mean pressure gradient (MPG), and effective orifice area (EOA). Second, all potential erroneous sources in the calculation of those primary parameters were listed stratified to random or systematic measurement error, and subsequently their magnitude was quantified. Third, the impact of various types of measurement error on current thresholds for intervention was graphically illustrated in different clinical scenarios. Results The presence of measurement error was acknowledged in 44% of the 36 included studies, while none utilized methods to correct for it. Interobserver variability ranged between 0.9–8.3% for Vmax and MPG but was substantially higher for EOA (range 7.7–12.7%) implying lower reliability (Figure 1). Furthermore, the invalid assumption of a circular left ventricular outflow tract area resulted in a median underestimation in EOA of 22.5% compared to 3D-transesophageal echocardiography (3D-TEE), computed tomography (CT), and cardiovascular magnetic resonance (CMR) planimetry. Figure 2 illustrates the impact of this discrepancy on the classification of AS using one-sided t-tests to determine the areas under the curve; the proportion of patients with non-severe AS in a hypothetical cohort (based on values from the PARTNER 3 trial [1[) increased by 42%. Conclusion(s) Measurement error is underrecognized in studies of echocardiographic assessment of aortic stenosis. This review demonstrates that random and systematic measurement errors affect echocardiographic assessment, leading to potential misdiagnosis and subsequent mistreatment. Clinicians and scientists should be aware of the implications of measurement errors to enhance rightful clinical decision-making and assure research validity. Funding Acknowledgement Type of funding sources: None.

Propagation of measurement error in opinion dynamics models: The case of the Deffuant model

Opinion dynamics models have an enormous potential for studying current phenomena such as vaccine hesitancy or diffusion of fake news. Unfortunately, to date, most of the models have little to no empirical validation. One major problem in testing these models against real-world data relates to the difficulties in measuring opinions in ways that map directly to representations in models. Indeed, measuring opinions is a complex process and presents more types of measurement error than just classical random noise. Thus, it is crucial to know how these different error types may affect the model’s predictions. In this work, we analyze this relationship in the Deffuant model as an example. Starting from the psychometrics literature, we first discuss how opinion measurements are affected by three types of errors: random noise, binning, and distortions (i.e. uneven intervals between scale points). While the first two are known to most of the scientific community, the third one is mostly unknown outside psychometrics. Because of that, we highlight the nature and peculiarities of each of these measurement errors. By simulating these types of error, we show that the Deffuant model is robust to binning but not to noise and distortions. Indeed, if a scale has 4 or more points (like most self-report scales), binning has almost no effect on the final predictions. However, prediction error increases almost linearly with random noise, up to a maximum error of 40%. After reaching this value, increasing the amount of noise does not worsen the prediction. Distortions are most problematic, reaching a maximum prediction error of 80%. Error propagation is already established in other fields, such as statistics and engineering. We believe its application in opinion dynamics will contribute to the expansion and development of this field. Indeed, as we show here, it allows researchers to test models’ reliability and prediction quality even before testing the model against real world data.

The Effects of Selected Measurement Errors on Surface Texture Parameters.

Surface texture measurement, characterized by areal parameters, is very susceptible to measurement errors. Various types of errors differently affect surface texture parameters. In this paper, two types of measurement errors were investigated. To analyze the impact of the presence of scratches, circular valleys of various diameters were added to surface textures measured by a white light interferometer. Measurement errors were larger for higher scratches. Skewness and kurtosis were mostly affected by the presence in valleys, and changes of spatial parameters were also comparatively high. The difference between the results of measurement of the same surface details two times after a break of three months was also studied. This difference was caused by errors of relocation, spikes and non-measured point presences, high-frequency noise, and surface ageing. Spatial parameters were found to be the most stable.

Understanding the Business Relevance of ESG Issues

ABSTRACTThere is a growing interest to understand the business relevance of ESG issues, especially those related to the firm performance. We propose a framework to better understand the relationship between true and observed materiality and outline the associated measurement errors, which reflect the complex, nuanced and time evolving nature of the underlying construct as well as the ESG data. Our framework calls for researchers to consider the different types of measurement errors with care, because the choices made could exacerbate or mitigate the relationship between true and observed materiality. Overall, our framework has implications to the emerging stream of literature that assesses the business relevance of ESG issues.

Quantification of Neurite Degeneration with Enhanced Accuracy and Efficiency in an In Vitro Model of Parkinson's Disease.

Neurite degeneration is associated with early stages of neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease (PD), and amyotrophic lateral sclerosis. One method that is commonly used to analyze neurite degeneration involves calculation of a Degeneration Index (DI) following utilization of the Analyze Particles tool of ImageJ to detect neurite fragments in micrographs of cultured cells. However, DI analyses are prone to several types of measurement error, can be time consuming to perform, and are limited in application. Here, we describe an improved method for performing DI analyses. Accuracy of measurements was enhanced through modification of selection criteria for detecting neurite fragments, removal of image artifacts and non-neurite materials from images, and optimization of image contrast. Such enhancements were implemented into an ImageJ macro that enables rapid and fully automated DI analysis of multiple images. The macro features operations for automated removal of cell bodies from micrographs, thus expanding the application of DI analyses to use in experiments involving dissociated cultures. We present experimental findings supporting that, compared with the conventional method, the enhanced analysis method yields measurements with increased accuracy and requires significantly less time to perform. Furthermore, we demonstrate the utility of the method to investigate neurite degeneration in a cell culture model of PD by conducting an experiment revealing the effects of c-Jun N-terminal kinase (JNK) on neurite degeneration induced by oxidative stress in human mesencephalic cells. This improved analysis method may be used to gain novel insight into factors underlying neurite degeneration and the progression of neurodegenerative disorders.

Man versus machine? Self-reports versus algorithmic measurement of publications.

This paper uses newly available data from Web of Science on publications matched to researchers in Survey of Doctorate Recipients to compare the quality of scientific publication data collected by surveys versus algorithmic approaches. We illustrate the different types of measurement errors in self-reported and machine-generated data by estimating how publication measures from the two approaches are related to career outcomes (e.g., salaries and faculty rankings). We find that the potential biases in the self-reports are smaller relative to the algorithmic data. Moreover, the errors in the two approaches are quite intuitive: the measurement errors in algorithmic data are mainly due to the accuracy of matching, which primarily depends on the frequency of names and the data that was available to make matches, while the noise in self reports increases over the career as researchers’ publication records become more complex, harder to recall, and less immediately relevant for career progress. At a methodological level, we show how the approaches can be evaluated using accepted statistical methods without gold standard data. We also provide guidance on how to use the new linked data.

Simulation and measurement of systematic errors of stitching interferometry for high precision X-ray mirrors with large radius of curvature.

To accurately measure the surface figure of curved mirrors with large radius of curvature (RoC) using stitching interferometry methods, three types of measurement errors are systematically studied, including retrace error, defocusing error within a single subaperture, and stitching angle error among different subapertures. It was found that part of the retrace error caused by the mismatch between the reference wavefront and reflected wavefront has little effect, while the overall retrace error, including the influence of the imperfect optical elements, will cause an error of 1-2 nm RMS within a single subaperture. Defocusing error will enlarge the error due to amplification of optical path error caused by the deviation of the position of the CCD. Because the error is mainly in the edge area, a slope threshold, which controls the maximum surface slope of each subaperture, can be optimized to reduce the effect of the defocus on stitching measurement error. Constant angle error among neighboring subapertures has the biggest accumulation effect on the final stitched figure. For the spherical mirror with RoC of 100 m of 80mm×40mm, the error of the one-dimensional residual profile is 4.67 nm PV, assuming a constant angle error of 2×10-7rad. For the elliptical mirror with RoC of 60-140 m, it is more than 15 nm peak-to-valley (PV). It is because the profile difference caused by constant angle error is closer to a circle, which can be mostly removed after subtraction of a best-fit sphere. Based on the above error analysis, the developed algorithm-based stitching method was used to measure an elliptical cylindrical mirror of 74mm×40mm with RoC of 60-140 m, and the result was compared with a slope measurement instrument from the Beijing Synchrotron Radiation Facility. After removing the best fitting ellipse profile, the one-dimensional difference between the two results is only 0.77 nm RMS, which demonstrated good measurement accuracy.

Predicting in multivariate incomplete time series. Application of the expectation-maximisation algorithm supplemented by the Newton-Raphson method

Statistical practice requires various imperfections resulting from the nature of data to be addressed. Data containing different types of measurement errors and irregularities, such as missing observations, have to be modelled. The study presented in the paper concerns the application of the expectation-maximisation (EM) algorithm to calculate maximum likelihood estimates, using an autoregressive model as an example. The model allows describing a process observed only through measurements with certain level of precision and through more than one data series. The studied series are affected by a measurement error and interrupted in some time periods, which causes the information for parameters estimation and later for prediction to be less precise. The presented technique aims to compensate for missing data in time series. The missing data appear in the form of breaks in the source of the signal. The adjustment has been performed by the EM algorithm to a hybrid version, supplemented by the Newton-Raphson method. This technique allows the estimation of more complex models. The formulation of the substantive model of an autoregressive process affected by noise is outlined, as well as the adjustment introduced to overcome the issue of missing data. The extended version of the algorithm has been verified using sampled data from a model serving as an example for the examined process. The verification demonstrated that the joint EM and Newton-Raphson algorithms converged with a relatively small number of iterations and resulted in the restoration of the information lost due to missing data, providing more accurate predictions than the original algorithm. The study also features an example of the application of the supplemented algorithm to some empirical data (in the calculation of a forecasted demand for newspapers).

Challenges and opportunities for comparative studies of survival rates: An example with male pinnipeds.

Survival rates are a central component of life‐history strategies of large vertebrate species. However, comparative studies seldom investigate interspecific variation in survival rates with respect to other life‐history traits, especially for males. The lack of such studies could be due to the challenges associated with obtaining reliable datasets, incorporating information on the 0–1 probability scale, or dealing with several types of measurement error in life‐history traits, which can be a computationally intensive process that is often absent in comparative studies. We present a quantitative approach using a Bayesian phylogenetically controlled regression with the flexibility to incorporate uncertainty in estimated survival rates and quantitative life‐history traits while considering genetic similarity among species and uncertainty in relatedness. As with any comparative analysis, our approach makes several assumptions regarding the generalizability and comparability of empirical data from separate studies. Our model is versatile in that it can be applied to any species group of interest and include any life‐history traits as covariates. We used an unbiased simulation framework to provide “proof of concept” for our model and applied a slightly richer model to a real data example for pinnipeds. Pinnipeds are an excellent taxonomic group for comparative analysis, but survival rate data are scarce. Our work elucidates the challenges associated with addressing important questions related to broader ecological life‐history patterns and how survival–reproduction trade‐offs might shape evolutionary histories of extant taxa. Specifically, we underscore the importance of having high‐quality estimates of age‐specific survival rates and information on other life‐history traits that reasonably characterize a species for accurately comparing across species.

Doubly Latent Multilevel Procedures for Organizational Assessment and Prediction

Organizational research has a rich tradition of multilevel investigations anchored in a long-standing recognition that conclusions obtained at the individual level of analysis cannot be expected to generalize at other levels. Doubly latent multilevel methods, which provide the way to combine multilevel analyses and structural equation models into a single analytic framework, enrich this tradition. Yet, some critical technical considerations have yet to be systematically integrated in applied research. This article aims to introduce organizational researchers to the estimation of doubly latent multilevel models while taking into account: (a) The need to systematically assess the multilevel measurement structure of the constructs included in the study; (b) the various types of measurement errors that can be controlled for (rather than simply estimated) as part of these models; (c) the importance of relying on a clear understanding of contextual versus climate constructs given their role in centering decisions. These issues are illustrated by an investigation of the multilevel relations between psychological empowerment, psychological health, and turnover intentions among a large sample (N = 5875 employees nested within 49 work units) of Canadian Defence employees.

AVERAGE DERIVATIVE ESTIMATION UNDER MEASUREMENT ERROR

In this paper, we derive the asymptotic properties of the density-weighted average derivative estimator when a regressor is contaminated with classical measurement error and the density of this error must be estimated. Average derivatives of conditional mean functions are used extensively in economics and statistics, most notably in semiparametric index models. As well as ordinary smooth measurement error, we provide results for supersmooth error distributions. This is a particularly important class of error distribution as it includes the Gaussian density. We show that under either type of measurement error, despite using nonparametric deconvolution techniques and an estimated error characteristic function, we are able to achieve a $\sqrt {n}$ -rate of convergence for the average derivative estimator. Interestingly, if the measurement error density is symmetric, the asymptotic variance of the average derivative estimator is the same irrespective of whether the error density is estimated or not. The promising finite sample performance of the estimator is shown through a Monte Carlo simulation.

Calibrated zero-norm regularized LS estimator for high-dimensional error-in-variables regression

This paper is concerned with high-dimensional error-in-variables regression that aims at identifying a small number of important interpretable factors for corrupted data from many applications where measurement errors or missing data can not be ignored. Motivated by CoCoLasso due to Datta and Zou \cite{Datta16} and the advantage of the zero-norm regularized LS estimator over Lasso for clean data, we propose a calibrated zero-norm regularized LS (CaZnRLS) estimator by constructing a calibrated least squares loss with a positive definite projection of an unbiased surrogate for the covariance matrix of covariates, and use the multi-stage convex relaxation approach to compute the CaZnRLS estimator. Under a restricted eigenvalue condition on the true matrix of covariates, we derive the $\ell_2$-error bound of every iterate and establish the decreasing of the error bound sequence, and the sign consistency of the iterates after finite steps. The statistical guarantees are also provided for the CaZnRLS estimator under two types of measurement errors. Numerical comparisons with CoCoLasso and NCL (the nonconvex Lasso proposed by Poh and Wainwright \cite{Loh11}) demonstrate that CaZnRLS not only has the comparable or even better relative RSME but also has the least number of incorrect predictors identified.

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment.

Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear, and Berkson models, and on the concepts of nondifferential and differential error. We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the article deals with more advanced topics.

What’s my wage again? Comparing survey and administrative data to validate earning measures

ABSTRACTOne question frequently included in surveys asks about respondents’ earnings. As this information serves, for example, as a basis for evaluating policy interventions, it must be of high quality. This study aims to advance knowledge about possible measurement errors in earnings data and the potential of data linkage to improve substantive conclusions. We use the German sample of the Programme for the International Assessment of Adult Competencies (PIAAC), a subsample of which could be linked to administrative data from the German Integrated Employment Biographies (IEB). We define measurement error as the difference between administrative and survey data. Our results show differences in the ordinary least squares estimates when the administrative and survey measures of earnings were used as the respective dependent variable, which suggests that measurement error causes biased results. Learning more about the size and type of measurement error can help to correct existing biases and improve the quality of survey data.