Effect Of Measurement Errors Research Articles

On classes of robust estimators in presence of correlated measurement errors

It is an established fact that the existence of measurement errors (ME) vitiates the conventional properties of the estimators. The impact of correlated measurement errors (CME) was first assessed by Shalabh and Tsai (2017) using classical ratio and product estimators. This article makes a modest attempt to suggest some classes of robust estimators of population mean in the presence of CME. The bias and mean square error of the suggested estimators are reported with the first-order approximation and the efficiency conditions are derived. The theoretical results are extended with the help of a simulation study executed over a hypothetically rendered population. Apart from this, the effect of ME on the suggested estimators is also studied both theoretically and empirically.

Evaluating the performance of variable sampling interval Hotelling T2 charting scheme for compositional data in the presence of measurement error

AbstractThe variable sampling interval (VSI) scheme is a well‐known technique for improving the detection ability of control charts (CCs). In the proposed study, measurement error (ME) has been applied to investigate the effectiveness of the Hotelling T2 charting scheme for compositional data (CoDa) using VSI. The current study considered the case of the monitoring phase, assuming that process parameters are known using the continuous times Markov chain model. The evaluation of the proposed scheme has been done using the average time to signal. The authors studied the impact of MEs on the performance of the Hotelling T2 CC for CoDa using VSI, and additionally, the authors also examined the impact of linearly covariate error model parameters on the performance of Hotelling T2 VSI CoDa CC. Six cases for the variance–covariance matrix were used to study the impact of different involved parameters. The effect of ME, sampling interval (SI), powering operator (b), error variance (, and subgroup size (m) has been studied on the performance of proposed CCs by increasing the value of one parameter while keeping the other parameters fixed. The ME and σ have a negative impact on the performance of the proposed CC, while SI, b and m have a positive impact on the performance of the proposed CC. Among all the six cases, the correlated cases (negatively and positively) with equal variance performed better than all the other four cases. In the end, an illustrative example of muesli production with part CoDa (where p is the number of variables in CoDa vector) is provided for the practical implementation of the Hotelling T2 VSI CoDa CC in the presence of ME.

A new robust parameter estimation approach for multinomial categorical response data with outliers and mismeasured covariates

Being confronted with multinomial categorical response data often occurs in the medical, health, social sciences and other fields. However, the data in practice are subject to measurement error (ME) or outliers due to some unavoidable reasons. There are few studies dealing with problems of ME and outliers in the data with unordered and multi-classified responses simultaneously. To address the effects of ME and outliers in multinomial logistic models, which are most suitable for analyzing categorical outcome variables, we propose novel approaches. Firstly, we develop the extensively weighed corrected score method to estimate parameters in multinomial logistic models with ME. Furthermore, to tackle the problem of that how to obtain parameter estimations when the ME and outliers occur simultaneously, we develop a new robust method, namely the robust extensive weighted corrected score method. Some simulation studies are conducted to evaluate the estimators’ finite sample performance. It has been verified that these estimators are consistent and asymptotically normally distributed under general conditions. They are easy to compute, perform well and stably and are robust against the normality assumption for the ME. The proposed methods are also applied to two real datasets to illustrate the practicability of methods.

Reliability of Radiographic Assessments of the Hip in Cerebral Palsy.

Children with cerebral palsy are at risk for progressive hip displacement. Since surveillance for hip displacement uses specific radiographic measurements to guide decision making, it is important to establish the reliability of these measurements, which include Reimer's migration percentage (MP), acetabular index or acetabular angle (AI or AA), and pelvic obliquity (PO). The purpose of this study was to determine the intraobserver and interobserver reliability of these radiographic measures among an international group of pediatric orthopaedic surgeons participating in the prospective international multicenter Cerebral Palsy Hip Outcomes Project (CHOP) currently underway to evaluate the outcomes of hip interventions in cerebral palsy. Two compact discs (CDs) containing the same 25 anteroposterior pelvis radiographs in Digital Imaging and Communications in Medicine (DICOM) format were provided to participating surgeons at least 2 weeks apart. To reduce the likelihood of recall or any effects of learning or fatigue, the order of the radiographs varied on the 2 CD versions, and participating surgeons received the 2 CDs in random order. The intraclass correlation coefficients (ICCs) were calculated to assess interobserver and intraobserver reliability. Mean absolute differences of hip measurements obtained at 2 time points were also calculated. The MP had the highest reliability followed by PO, AI, and AA with a mean intrarater ICC (SD; range) of 0.95 (0.04; 0.84 to 0.98); 0.92 (0.03; 0.85 to 0.97); 0.84 (0.05; 0.75 to 0.92); and 0.82 (0.14; 0.51 to 0.98); respectively. The mean interrater ICC (SD; range) for MP, PO, AI, and AA were 0.94 (0.05; 0.78 to 0.99); 0.90 (0.04; 0.76 to 0.99); 0.79 (0.08; 0.52 to 0.93); and 0.69 (0.23; 0.42 to 0.98) for MP, PO, AI, and AA, respectively. The mean (SD; 95% confidence interval) for the absolute difference between the 2 measurements for the raters was 4.9% (2.9%; 3.4%-6.4%); 3. 8 degrees (1.2 degrees; 3.1-4.5 degrees); 2.6 degrees (1.5 degrees; 1.7-3.5 degrees); and 1.3 degrees (0.3 degrees; 1.29-1.31 degrees) for MP, AI, AA, and PO, respectively. MP is a reproducible measure with excellent intrarater and interrater reliability. However, differences in MP of <7% should be treated with caution as these might be a consequence of measurement error. Although we found a high level of intrarater and interrater reliability of the AI, AA, and PO, these measurements are more variable and not ideal for use as discrete outcome measures. Instead, these parameters might be useful for prognostication and decision making when consistent trends are observed longitudinally over time which might be better indications of true change.

Consequences of measurement error in qPCR telomere data: A simulation study.

The qPCR method provides an inexpensive, rapid method for estimating relative telomere length across a set of biological samples. Like all laboratory methods, it involves some degree of measurement error. The estimation of relative telomere length is done subjecting the actual measurements made (the Cq values for telomere and a control gene) to non-linear transformations and combining them into a ratio (the TS ratio). Here, we use computer simulations, supported by mathematical analysis, to explore how errors in measurement affect qPCR estimates of relative telomere length, both in cross-sectional and longitudinal data. We show that errors introduced at the level of Cq values are magnified when the TS ratio is calculated. If the errors at the Cq level are normally distributed and independent of true telomere length, those in the TS ratio are positively skewed and proportional to true telomere length. The repeatability of the TS ratio declines sharply with increasing error in measurement of the Cq values for telomere and/or control gene. In simulated longitudinal data, measurement error alone can produce a pattern of low correlation between successive measures of relative telomere length, coupled with a strong negative dependency of the rate of change on initial relative telomere length. Our results illustrate the importance of reducing measurement error: a small increase in error in Cq values can have large consequences for the power and interpretability of qPCR estimates of relative telomere length. The findings also illustrate the importance of characterising the measurement error in each dataset—coefficients of variation are generally unhelpful, and researchers should report standard deviations of Cq values and/or repeatabilities of TS ratios—and allowing for the known effects of measurement error when interpreting patterns of TS ratio change over time.

Adsorbed Layer Thickness Determination for Convective-Based Media from Pressure Drop Data.

In the present work, we derive a theoretical framework to determine the adsorbed layer thickness from pressure drop measurements for convective-based media without any assumptions about the geometry of the pore structure of the stationary phase matrix. Equations are presented to calculate accuracy of the estimated adsorbed layer thickness as a consequence of measurement error and approximations of the mathematical model. We discovered that there is a minimum in the error for certain pressure drops that results in optimal experimental conditions for determining the adsorbed layer thickness. We demonstrate that the adsorbed layer thickness can be determined with less than 10% error using a wide range of experimental conditions simply from pressure drop data. By careful selection of porous bed dimensions and flow rates, the adsorbed layer thicknesses from subnanometer dimensions up to several hundred nanometers can be determined by measurement of the pressure drop in a range of several bars. The method was experimentally tested on methacrylate monolithic columns using monodisperse latex nanoparticles as a reference standard and two different proteins as unknowns demonstrating close agreement with calculations.

Influence of measurement error on Maxwell's demon.

In any general cycle of measurement, feedback, and erasure, the measurement will reduce the entropy of the system when information about the state is obtained, while erasure, according to Landauer's principle, is accompanied by a corresponding increase in entropy due to the compression of logical and physical phase space. The total process can in principle be fully reversible. A measurement error reduces the information obtained and the entropy decrease in the system. The erasure still gives the same increase in entropy, and the total process is irreversible. Another consequence of measurement error is that a bad feedback is applied, which further increases the entropy production if the proper protocol adapted to the expected error rate is not applied. We consider the effect of measurement error on a realistic single-electron box Szilard engine, and we find the optimal protocol for the cycle as a function of the desired power P and error ɛ.

Correcting Measurement Error in Content Analysis

ABSTRACTConducting and reporting reliability tests has become a standard practice in content analytical research. However, the consequences of measurement error in coding data are rarely discussed or taken into consideration in subsequent analyses. In this article, we demonstrate how misclassification in content analysis leads to biased estimates and introduce matrix back-calculation as a simple remedy. Using Monte Carlo simulation, we investigate how different ways of collecting information about the misclassification process influence the effectiveness of error correction under varying conditions. The results show that error correction with an adequate set-up can often substantially reduce bias. We conclude with an illustrative example, extensions of the procedure, and some recommendations.

Understanding the time‐varying importance of different uncertainty sources in hydrological modelling using global sensitivity analysis

AbstractSimulations from hydrological models are affected by potentially large uncertainties stemming from various sources, including model parameters and observational uncertainty in the input/output data. Understanding the relative importance of such sources of uncertainty is essential to support model calibration, validation and diagnostic evaluation and to prioritize efforts for uncertainty reduction. It can also support the identification of ‘disinformative data’ whose values are the consequence of measurement errors or inadequate observations. Sensitivity analysis (SA) provides the theoretical framework and the numerical tools to quantify the relative contribution of different sources of uncertainty to the variability of the model outputs. In traditional applications of global SA (GSA), model outputs are aggregations of the full set of a simulated variable. For example, many GSA applications use a performance metric (e.g. the root mean squared error) as model output that aggregates the distances of a simulated time series to available observations. This aggregation of propagated uncertainties prior to GSA may lead to a significant loss of information and may cover up local behaviour that could be of great interest. Time‐varying sensitivity analysis (TVSA), where the aggregation and SA are repeated at different time steps, is a viable option to reduce this loss of information. In this work, we use TVSA to address two questions: (1) Can we distinguish between the relative importance of parameter uncertainty versus data uncertainty in time? (2) Do these influences change in catchments with different characteristics? To our knowledge, the results present one of the first quantitative investigations on the relative importance of parameter and data uncertainty across time. We find that the approach is capable of separating influential periods across data and parameter uncertainties, while also highlighting significant differences between the catchments analysed. Copyright © 2016 The Authors. Hydrological Processes. Published by John Wiley &amp; Sons Ltd.

Improving Our Understanding of Moderation and Mediation in Strategic Management Research

We clarify differences among moderation, partial mediation, and full mediation and identify methodological problems related to moderation and mediation from a review of articles in Strategic Management Journal and Organization Science published from 2005 to 2014. Regarding moderation, we discuss measurement error, range restriction, and unequal sample sizes across moderator-based subgroups; insufficient statistical power; the artificial categorization of continuous variables; assumed negative consequences of correlations between product terms and its components (i.e., multicollinearity); and interpretation of first-order effects based on models excluding product terms. Regarding mediation, we discuss problems with the causal-steps procedure, inferences about mediation based on cross-sectional designs, whether a relation between the antecedent and the outcome is necessary for testing mediation, the routine inclusion of a direct path from the antecedent to the outcome, and consequences of measurement error. We also explain how integrating moderation and mediation can lead to important and useful insights for strategic management theory and practice. Finally, we offer specific and actionable recommendations for improving the appropriateness and accuracy of tests of moderation and mediation in strategic management research. Our recommendations can also be used as a checklist for editors and reviewers who evaluate manuscripts reporting tests of moderation and mediation.

Increased treatment durations lead to greater improvements in non-weight bearing dorsiflexion range of motion for asymptomatic individuals immediately following an anteroposterior grade IV mobilisation of the talus

Manual therapy aims to minimise pain and restore joint mobility and function. Joint mobilisations are integral to these techniques, with anteroposterior (AP) talocrural joint mobilisations purported to increase dorsiflexion range of motion (DF-ROM). This study aimed to determine whether different treatment durations of single grade IV anteroposterior talocrural joint mobilisations elicit statistically significant differences in DF-ROM. Sixteen asymptomatic male football players (age = 27.1 ± 5.3 years) participated in the study. Non-weight bearing (NWB) and weight bearing (WB) DF-ROM was measured before and after 4 randomised treatment conditions: control treatment, 30 s, 1 min, 2 min. NWB DF-ROM was measured using a universal goniometer, and WB DF-ROM using the weight-bearing lunge test. A within-subjects design was employed so that all participants received each of the treatment conditions. A 4 × 4 balanced Latin square design and 1 week interval between sessions reduced any residual effects. Two-way repeated measures ANOVA revealed a significant improvement in DF-ROM following all AP mobilisation treatments (p < 0.001). The within subjects contrasts showed that increases in treatment duration was associated with statistically significant improvements in DF-ROM (NWB DF-ROM control = 0.01%, 30 s = 14.2%, 1 min = 21.6%, 2 min = 32.8%; WB DF-ROM control = 0.01%, 30 s = 5.0%, 1 min = 7.6%, 2 min = 10.9%; p < 0.05). However, WB DF-ROM improvements were below the minimal detectable change scores needed to conclude that improvements were not a consequence of measurement error. This research shows that single session mobilisations can elicit NWB DF-ROM improvements in asymptomatic individuals in the absence of pain, whilst increases in treatment duration confer greater improvements in NWB DF-ROM within this population.

Specifying measurement errors for required lifetime estimation performance

Lifetime estimation based on the measured health monitoring data has long been investigated and applied in reliability and operational management communities and practices, such as planning maintenance schedules, logistic supports, and production planning. It is known that measurement error (ME) is a source of uncertainty in the measured data considerably affecting the performance of data driven lifetime estimation. While the effect of ME on the performance of data driven lifetime estimation models has been studied recently, a reversed problem—“the specification of the ME range to achieve a desirable lifetime estimation performance” has not been addressed. This problem is related to the usability of the measured health monitoring data for estimating the lifetime. In this paper, we deal with this problem and develop guidelines regarding the formulation of specification limits to the distribution-related ME characteristics. By referring to one widely applied Wiener process-based degradation model, permissible values for the ME bias and standard deviation can be given under a specified lifetime estimation requirement. If the performance of ME does not satisfy the permissible values, the desirable performance for lifetime estimation cannot be ensured by the measured health monitoring data. We further analyze the effect of ME on an age based replacement decision, which is one of the most common and popular maintenance policies in maintenance scheduling. Numerical examples and a case study are provided to illustrate the implementation procedure and usefulness of theoretical results.

The consequences of measurement error when estimating the impact of obesity on income

AbstractThis paper examines the consequences of using self-reported measures of BMI when estimating the effect of BMI on income for women using both Irish and US data. We find that self-reported BMI is subject to substantial measurement error and that this error deviates from classical measurement error. These errors cause the traditional least squares estimator to overestimate the relationship between BMI and income. We show that neither the conditional expectation estimator nor the instrumental variables approach adequately address the bias and briefly discuss alternative approaches that could be considered when faced with non-classical measurement error.JEL codesC13, C26, I14

Hedonic price models with omitted variables and measurement errors: a constrained autoregression–structural equation modeling approach with application to urban Indonesia

Omitted variables and measurement errors in explanatory variables frequently occur in hedonic price models. Ignoring these problems leads to biased estimators. In this paper, we develop a constrained autoregression–structural equation model (ASEM) to handle both types of problems. Standard panel data models to handle omitted variables bias are based on the assumption that the omitted variables are time-invariant. ASEM allows handling of both time-varying and time-invariant omitted variables by constrained autoregression. In the case of measurement error, standard approaches require additional external information which is usually difficult to obtain. ASEM exploits the fact that panel data are repeatedly measured which allows decomposing the variance of a variable into the true variance and the variance due to measurement error. We apply ASEM to estimate a hedonic housing model for urban Indonesia. To get insight into the consequences of measurement error and omitted variables, we compare the ASEM estimates with the outcomes of (1) a standard SEM, which does not account for omitted variables, (2) a constrained autoregression model, which does not account for measurement error, and (3) a fixed effects hedonic model, which ignores measurement error and time-varying omitted variables. The differences between the ASEM estimates and the outcomes of the three alternative approaches are substantial.

Impact of measurement error in radon exposure on the estimated excess relative risk of lung cancer death in a simulated study based on the French Uranium Miners’ Cohort

Measurement error (ME) can lead to bias in the analysis of epidemiologic studies. Here a simulation study is described that is based on data from the French Uranium Miners' Cohort and that was conducted to assess the effect of ME on the estimated excess relative risk (ERR) of lung cancer death associated with radon exposure. Starting from a scenario without any ME, data were generated containing successively Berkson or classical ME depending on time periods, to reflect changes in the measurement of exposure to radon ((222)Rn) and its decay products over time in this cohort. Results indicate that ME attenuated the level of association with radon exposure, with a negative bias percentage on the order of 60% on the ERR estimate. Sensitivity analyses showed the consequences of specific ME characteristics (type, size, structure, and distribution) on the ERR estimates. In the future, it appears important to correct for ME upon analyzing cohorts such as this one to decrease bias in estimates of the ERR of adverse events associated with exposure to ionizing radiation.

The Consequences of Measurement Error When Estimating the Impact of BMI on Labour Market Outcomes

This paper uses data on both self-reported and true measures of individual Body Mass Index (BMI) to examine the nature of measurement error in self-reported BMI and to look at the consequences of using self-reported measures when estimating the effect of BMI on economic outcomes. In keeping with previous studies we find that self-reported BMI is subject to significant measurement error and this error is negatively correlated with the true measure of BMI. In our analysis this non-classical measurement error causes the traditional approach to overestimate the relationship between BMI and both income and education. Furthermore we show that popular alternatives estimators that have been adopted to address problems of measurement error in BMI, such as the conditional expectation approach and the instrumental variables approach, also exhibit significant biases.

Accounting for Misclassification Bias in Binary Outcome Measures of Illness: The Case of Post-Traumatic Stress Disorder in Male Veterans

The theoretical consequences of measurement error in outcome variables that are continuous are widely known by practitioners, at least for the classical model: purely random errors will lead to a loss of efficiency but not to bias in regression coefficients. When the outcome variable is binary, however, regression coefficients, both linear and nonlinear, will contain bias, even if the measurement error (in this setting more commonly referred to as classification error) is purely random. This paper illustrates a method of correcting for misclassification bias that relies solely on the primary survey data. It is particularly suited to analyses of surveys where external validation of survey responses is unavailable but where there is strong reason to suspect contaminated data. This situation is common in observational studies of the health of populations. The technique is applied to a model of the antecedents of post-traumatic stress disorder (PTSD) using data from a large-scale cross-sectional survey of Vietnam-era veterans. Results show that when adjusted for errors in diagnoses, the sample PTSD prevalence estimate falls significantly; that failure to correct for misclassification in PTSD dramatically understates the effects of risk factors; and that this downward bias remains even when the model incorporates differential classification errors—that is, errors that are correlated with some of the explanatory variables in the model.

Another approach to estimating the reliability of the glycaemic index: a different interpretation – response by Williams et al.

We agree with Professor Wolever that if glycaemic index (GI), which is intended to be a characteristic of food, is to be clinically useful it needs to be the same for nearly everyone. The results of our study show that this is not the case. The GI of white bread, for instance, in our study of twenty individuals was between 46 and 138 (Fig. 1(B) of our study). The GI for chickpeas, generally thought of as a low-GI food, was between 36 and 88 (Fig. 3 of our study). We showed that the reliability of the GI of white bread, potato and chickpeas was poor. We also showed that the measurement procedures on which GI was based, the incremental area under the curve (iAUC) for the glucose beverage and white bread, did not provide the same values when repeated in the same people under the same conditions (Fig. 1(A) of our study). Poor reliability, a consequence of measurement error, may not affect the estimate of the mean of a set of measurements, but does affect its precision. The 95 % CI for white bread in our study indicated that plausible values of the ‘true’ GI of white bread were between 74 and 90. Smaller studies have shown possible values of the GI of rice between 55 and 85, and spaghetti between 39 and 70. Estimates such as these make ranking foods according to their GI difficult. As GI is one of the mainstays of dietary advice for the management of diabetes, reliable estimates, in the sense that similar glucose responses to a food are obtained in similar circumstances, are important. It is difficult to see how these can be achieved, when the within-person responses for glucose and white bread are so variable. The concepts underlying GI may be useful in guiding people’s choice of food. However, some caution is required because it seems that some people’s responses are remarkably variable.

Early insulin response and insulin sensitivity are equally important as predictors of glucose tolerance after correction for measurement errors

Aims : We estimated measurement error (ME) corrected effects of insulin sensitivity (M/I), from euglycaemic insulin clamp, and insulin secretion, measured as early insulin response (EIR) from oral glucose tolerance test (OGTT), on fasting plasma glucose, HbA1c and type 2 diabetes longitudinally and cross-sectional. Methods : In a population-based study ( n = 1128 men) 17 men made replicate measurements to estimate ME at age 71 years. Effect of 1 SD decrease of predictors M/I and EIR on longitudinal response variables fasting plasma glucose (FPG) and HbA1c at follow-ups up to 11 years, were estimated using uncorrected and ME-corrected (with the regression calibration method) regression models. Results : Uncorrected effect on FPG at age 77 years was larger for M/I than for EIR (effect difference 0.10 mmol/l, 95% CI 0.00;0.21), while ME-corrected effects were similar (0.02 mmol/l, 95% CI −0.13;0.15 mmol/l). EIR had greater ME-corrected impact than M/I on HbA1c at age 82 years (−0.11%, −0.28; −0.01%). Conclusions : Due to higher ME effect of EIR on glycaemia is underestimated as compared with M/I. By correcting for ME valid estimates of relative contributions of insulin secretion and insulin sensitivity on glycaemia are obtained.

Methods for Collecting Panel Data: What Can Cultural Anthropology Learn from Other Disciplines?

In this article, we argue for the increased use of panel data in cultural anthropology. Panel data, or repeated measures from the same units of observation at different times, have proliferated across the social sciences, with the exception of anthropology. The lack of panel data in anthropology is puzzling since panel data are well-suited for analyzing continuity and change—central concerns of anthropological theory. Panel data also establish temporal order in causal analysis and potentially improve the reliability and accuracy of measurement. We review how researchers in anthropology and neighboring disciplines have dealt with the unique challenges of collecting panel data and draw lessons for minimizing the adverse consequences of measurement error, for reducing attrition, and for ensuring continuity in management, archiving, documentation, financing, and leadership. We argue that increased use of panel data has the potential to advance empirical knowledge and contribute to anthropological theory.