Different Types Of Measurement Errors Research Articles

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.

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.

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).

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.

Rethinking about the dose limiting toxicities (DLTs): They can be equivocal!

3064 Background: The primary goal of oncology phase I trials is to determine the maximum tolerated dose (MTD) in a small number of patients. Within the standard design methodology currently employed there remains many challenges and one pertains to the variability around defining dose limiting toxicities (DLTs). The pre-specified DLT criteria may not well reflect the clinician’s practical experience with some adverse events, while others may actually be related to the disease status more than the investigational drug and could be misclassified. DLT misclassification seen in testing a small sample size from each dose group could generate a large bias on dose finding and the MTD estimation. Methods: To mitigate the risk of dichotomizing and misclassifying DLTs, we proposed a strategy that introduced the new concept of “equivocal” DLT or AE. A novel dose escalation approach is applied to increase the variability associated with less interpretable AEs so that the model recommendations are more weighted towards the unequivocal AEs/DLTs. To evaluate this novel approach, we established a framework incorporating two types of systematic measurement errors on DLT misclassification, one for the misclassified DLT that is not related to the drug treatment while the other for the non-DLT AE that should be considered severe and relevant to dose finding. In our simulation studies, the Bayesian logistic regression model (BLRM) was used to guide dose escalation in simulated trials to compare the novel weighting approach with the traditional approach. A few numerical examples were also included for method illustration. Results: For different types of measurement errors, simulation studies showed that the weighting approach could successfully improve the trial performance, with higher chance of finding the correct MTD and treating more patients at the MTD level. Conclusions: The DLT weighting strategy provides a flexible but powerful tool that may incorporate the clinician’s valuable experience on some specific DLTs/AEs and improve MTD estimation in oncology phase I dose-escalation trials.

Using normal probability plots to determine parameters for higher‐level factorial experiments with orthogonal and orthonormal bases

ABSTRACTIn chemical engineering applications such as optimizing plant operations and product quality, factorial experiments are often conducted to obtain empirical models for systems. While the mathematical underpinning for two‐level factorial designs is well understood, a methodology for analyzing higher‐level experiments is not readily available. Often an orthogonal or orthonormal basis is selected for a factorial design matrix. In factorial design, an orthonormal basis is defined as an orthogonal matrix where the Euclidean two‐norms of the column vectors are equal. This investigation examines, for full factorial design, the selection of parameters using normal probability plots and the effect that the design basis has on parameter determination. When using normal probability plots to determine parameter significance, the traditional orthogonal basis for higher‐level experiments may result in erroneous conclusions. A Monte‐Carlo experiment was developed to simulate 3‐level and mixed‐level factorial experiments with different types of measurement error. The basis chosen is shown to affect the shape of probability plots, and measurement errors from a given normal distribution are shown to result in a constant standard deviation for all parameter estimates only when using an orthonormal basis.

A cautionary comment on the generation of Berkson error in epidemiological studies.

Exposure measurement error can be seen as one of the most important sources of uncertainty in studies in epidemiology. When the aim is to assess the effects of measurement error on statistical inference or to compare the performance of several methods for measurement error correction, it is indispensable to be able to generate different types of measurement error. This paper compares two approaches for the generation of Berkson error, which have recently been applied in radiation epidemiology, in their ability to generate exposure data that satisfy the properties of the Berkson model. In particular, it is shown that the use of one of the methods produces results that are not in accordance with two important properties of Berkson error.

Actor non-response in valued social networks: The impact of different non-response treatments on the stability of blockmodels

Social network data usually contain different types of errors. One of them is missing data due to actor non-response. This can seriously jeopardize the results of analyses if not appropriately treated. The impact of missing data may be more severe in valued networks where not only the presence of a tie is recorded, but also its magnitude or strength. Blockmodeling is a technique for delineating network structure. We focus on an indirect approach suitable for valued networks. Little is known about the sensitivity of valued networks to different types of measurement errors. As it is reasonable to expect that blockmodeling, with its positional outcomes, could be vulnerable to the presence of non-respondents, such errors require treatment. We examine the impacts of seven actor non-response treatments on the positions obtained when indirect blockmodeling is used. The start point for our simulation are networks whose structure is known. Three structures were considered: cohesive subgroups, core-periphery, and hierarchy. The results show that the number of non-respondents, the type of underlying blockmodel structure, and the employed treatment all have an impact on the determined partitions of actors in complex ways. Recommendations for best practices are provided.

A Brief Ode to the Empirical Likelihood Concept

robust likelihood-type techniques. Towards this end, the modern biostatistical literature has shifted focus towards robust and efficient nonparametric likelihood methods [2]. The empirical likelihood (EL) methodology employs the likelihood concept in a distribution-free manner, approximating optimal parametric likelihood-based procedures. Since EL techniques and parametric likelihood methods are closely related concepts, one may apply corresponding EL functions to replace their parametric likelihood counterparts in known and well developed parametric procedures, e.g. constructing novel nonparametric Bayesian inference [3] and the confidence interval estimation [4]. This provides the impetus for an impressive expansion in the number of EL developments based on combinations of likelihoods of different types, e.g., when incomplete data should be analyzed, nonparametric likelihood ratio techniques can be combined with parametric likelihoods [5]. The classical EL methodology, which is a distribution functionbased approach, has been shown to have attractive properties for testing hypotheses regarding parameters (e.g. moments) of distributions [6]. In practice, statisticians commonly face a variety of distribution-free comparisons and/or evaluations over all distribution functions of complete and incomplete data subject to different types of measurement errors. In these frameworks, the density-based EL methodology is shown to be very efficient [710]. According to the Neyman-Pearson lemma, the most powerful test statistics have structures that are related to density-based likelihood ratios. The density-based EL method can be easily and satisfactorily applied to construct highly efficient test procedures, approximating non-parametrically most powerful NeymanPearson test-rules, given aims of clinical studies. Similarly to the parametric likelihood concept, the EL methodology provides relatively simple strategies to construct powerful statistical tests that can be applied in various complex biostatistical studies. The extreme generality of EL methods and their wide ranges of usefulness partly result from the simple derivation of the EL statistics as components of composite parametric and nonparametric likelihood based systems, efficiently attending to any observed data and relevant information. The EL based methods are employed in much of modern biostatistical practice.

How Reliable Are Lower-Limb Kinematics and Kinetics during a Drop Vertical Jump?

As drop vertical jumps (DVJ) are widely used as a screening task, the assessment of the reliability of lower-limb biomechanical parameters during DVJ is important. The aim of this study was to assess the reliability of the kinematic and kinetic peak values as well as of the waveforms for lower-limb parameters obtained with the Liverpool John Moores University biomechanical model (LJMU model) during performance of DVJ. The reliability was analyzed by calculating the intertrial (o(trial)), intersession (o(sess)), and intertherapist (o(ther)) errors of hip and knee joint parameters in a repeated-measures design including two therapists and a total of six sessions. The results showed o(trial) that ranged from 1.1° to 3.5° for all peak kinematic parameters and from 3.6 to 12.9 N · m for all peak kinetic parameters. The o of the peak values ranged from 1.9° to 5.7° for all angles and from 5.4 to 19.8 N · m for the hip and knee joint moments in all planes. The o(sess) of the peak values ranged from 2.7° to 6.4° for all angles and from 5.8 to 22.4 N · m for all moments. Most of the kinematic and kinetic peak parameters had o(ther-trial) ≤ 2.0° and 4.3 N · m, respectively, suggesting a small extrinsic variability. Furthermore, the entire waveforms also showed a rather high o(trial) relative to other types of variability. The present findings indicated that DVJ kinetics and kinematics show small extrinsic variability. The reported errors are useful for clinical interpretation processes of DVJ performance as screening task for injury risk and rehabilitation outcome taking into consideration the different types of measurement error over time.

A practical data recovery technique for long-term strain monitoring of mega columns during construction.

A practical data recovery method is proposed for the strain data lost during the safety monitoring of mega columns. The analytical relations among the measured strains are derived to recover the data lost due to unexpected errors in long-term measurement during construction. The proposed technique is applied to recovery of axial strain data of a mega column in an irregular building structure during construction. The axial strain monitoring using the wireless strain sensing system was carried out for one year and five months between 23 July 2010 and 22 February 2012. During the long-term strain sensing, three different types of measurement errors occurred. Using the recovery technique, the strain data that could not be measured at different intervals in the measurement were successfully recovered. It is confirmed that the problems that may occur during long-term wireless strain sensing of mega columns during construction could be resolved through the proposed recovery method.

Commentary on “Common Method Bias in Marketing: Causes, Mechanisms, and Procedural Remedies”

Common method bias is a potentially serious methodological problem in research in marketing. Several statistical remedies have been proposed in the literature, and used by academic researchers. MacKenzie and Podsakoff (2012) identify the causes of common method bias, and then provide a set of procedural remedies that might prevent the occurrence of the problem. In this commentary, we expand on their contribution by articulating the different types of measurement error that could occur in survey research, how a procedural remedy might simultaneously affect more than one type of error, and how common method bias might manifest itself in the domain of stimulus-centered measures.

Quantification of Bias in Direct Effects Estimates Due to Different Types of Measurement Error in the Mediator

Assessing whether the effect of exposure on an outcome is completely mediated by a third variable is often done by conditioning on the intermediate variable. However, when an association remains, it is not always clear how this should be interpreted. It may be explained by a causal direct effect of the exposure on the disease, or the adjustment may have been distorted due to various reasons, such as error in the measured mediator or unknown confounding of association between the mediator and the outcome.In this paper, we study various situations where the conditional relationship between the exposure and the outcome is biased due to different types of measurement error in the mediator. For each of these situations, we quantify the effect on the association parameter. Such formulas can be used as tools for sensitivity analysis or to correct the association parameter for the bias due to measurement error. The performance of the bias formulas is studied by simulation and by applying them to data from a case-control study (Leiden Thrombophilia Study) on risk factors for venous thrombosis. In this study, the question was the extent to which the relationship between blood group and venous thrombosis might be mediated through coagulation factor VIII. We found that measurement error could have strongly biased the estimated direct effect of blood group on thrombosis. The formulas we propose can be a guide for researchers who find a residual association after adjusting for an intermediate variable and who wish to explore other possible explanations before concluding that there is a direct causal effect.

O3-1.3 Sensitivity analysis for an apparent direct effect after conditioning on an intermediate variable

IntroductionDetermining factors in the causal pathway between an exposure and a disease is often the aim of etiologic research. For example, if a gene is associated with a certain disease,...

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies

BackgroundTwo distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta.MethodsDaily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits.ResultsMeasurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed.ConclusionsFor multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.

Variable Selection Methods as a Tool To Find Sensor Locations for Distributed Parameter Systems

The problem of finding suitable sensor locations for distributed parameter systems (DPS) is tackled as a variable selection problem. Two existing variable selection methods are used: one is based on principal component analysis (PCA) and the other on the principal variable (PV) method. A new PCA-based variable selection method, called “orthogonal variables in loading space” (OVL) is introduced. The best sensor location for DPS is dependent on sensor characteristics and also on the time interval of interest. This is illustrated in a case study where the best point in time to replace a packed bed filter is studied. Sensor positions are determined for different time intervals and different types of measurement errors. The resulting sensor positions characterize the overall time behavior of the DPS in the selected time interval. As a test, the specific problem of predicting the exit concentration of the packed bed filter is considered. Lagged PLS models are built and a full search is done to determine the best possible sensor locations. These “benchmark” sensor positions are compared to the sensor locations found by the variable selection methods. The OVL method and the PV method both perform well, but the OVL method is additionally computationally less demanding.

Measurement Error Bias in Estimates of Income and Income Growth among the Poor: Analytical Results and a Correction Formula

In any population, income growth among the poor may be higher or lower than overall income growth. Estimates of income growth among the poor are almost always based on household surveys, but income and expenditure data in those surveys are almost always measured with error. This article uses the assumption that income follows a lognormal distribution to demonstrate that such measurement error can lead to biased estimates of the mean income of the poor and the growth of that mean income over time. In particular, when both income and the measurement error are lognormally distributed, (i) measurement error leads to underestimation of the mean income among the poor at any point in time, (ii) increases (decreases) in measurement error over time, for a given level of inequality, lead to underestimation (overestimation) of income growth among the poor, and (iii) increases (decreases) in inequality over time, for a given level of measurement error, lead to overestimation (underestimation) of income growth among the poor. This article derives a correction formula that calculates the mean income of the poor as a function of the mean of the observed income of the poor, the variance of observed (log) income, and the variance of the (log of) measurement error. This formula can then be used to calculate consistent estimates of income growth among the poor. This article also presents several simulations that relax the assumptions that measurement errors are lognormally distributed, have a mean of zero, and are uncorrelated with income. Relaxing these assumptions has little effect on the results, which implies that the derivations are robust to many different types of measurement error.

Deconvolving kernel density estimators

This paper considers estimation of a continuous bounded probability density when observations from the density are contaminated by additive measurement errors having a known distribution. Properties of the estimator obtained by deconvolving a kernel estimator of the observed data are investigated. When the kernel used is sufficiently smooth the deconvolved estimator is shown to be pointwise consistent and bounds on its integrated mean squared error are derived. Very weak assumptions are made on the measurement-error density thereby permitting a comparison of the effects of different types of measurement error on the deconvolved estimator