Measurement Error Variance Research Articles

Psychometric Testing of the Cross-Culturally Adapted Thai Version of the Caregiver Self-Efficacy in Contributing to Patient Self-Care Scale in Caregivers of Patients With Chronic Illnesses.

To evaluate the psychometric properties of the Caregiver Self-Efficacy in Contributing to Patient Self-Care Scale (CSE-CSC). The CSE-CSC scale was developed congruently with the Caregiver Contribution to Self-Care of Patient with Chronic Illness Inventory (CC-SC-CII) to assess caregiver self-efficacy (CSE), the motivation factor of caregiving. Its applicability in Asian populations, including Thailand, was limited. Psychometric tests were guided by COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) principles. This included translating the English version into Thai, following the principles of good practice for the translation and cultural adaptation process for patient-reported outcome (PRO) measures. Nine nursing experts evaluated the content validity. The analysis included caregivers of patients with chronic conditions from 16 primary care centres in Thailand. We tested the structural validity using exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and concurrent validity for CC-SC-CII-v2. We tested the scale's reliability with methods for multidimensional and unidimensional constructs, the intraclass correlation coefficients (ICCs) and measurement error variance. Kappa coefficient (k = 1.00) indicated the scale's excellent content validity. The final analysis included a total of 407 samples. The EFA with the first split-half subsample (n = 194) extracted a two-dimensional structure. One reflected CSE in Maintenance and Monitoring behaviour and another captured CSE in Management behaviour. CFA with the second split-half subsample (n = 213) and the overall sample (n = 407) supported the scale's bidimensional model with high factor loadings. Each dimension and the overall CSE-CSC scale positively correlated with each scale and the overall CC-SC-CII-v2. McDonald's ω and Cronbach's α (both ranged from 0.91-0.94) indicated excellent internal reliability. Test-retest reliability showed ICCs between 0.95 and 0.96. Measurement error yielded satisfactory results. Testing of the Thai CSE-CSC scale supported validity and reliability, indicating that the scale can apply to Thai caregivers. This scale can be used in clinical practice and research to evaluate CSE in contribution to patient self-care.

Modified torso vs distal limb electrode placement for performing ECGs in children: A method comparison study

BackgroundThe electrocardiogram (ECG) is routinely performed in children with the limb electrodes positioned on the torso, but few studies have investigated the effects of this modification on the pediatric ECG.Our objective was to assess the agreement between the standard limb lead configuration and a modified torso electrode configuration in normal, healthy children, and to assess the effect of height on that agreement. Methods185 children aged 5–18 years underwent two consecutive 12‑lead ECGs, one with standard distal limb lead placement and one with the limb leads placed on the torso. Agreement was assessed for 17 ECG parameters (intervals, axes, and amplitudes) using Bland-Altman plots, height-dependent mean error, and false positive rates. ResultsThe torso configuration systematically biased the QRS and P wave axes rightwards (towards aVF). Adequate agreement was observed for PR interval and QRS duration, but QTc limits of agreement (±40 ms) were wide. The torso configuration overestimated left-precordial Q, R, and S wave amplitudes and underestimated right-precordial R and S wave amplitudes compared to the distal limb placement. Mean measurement errors increased with the magnitude of the ECG parameter. Mean and variance of measurement errors were more pronounced in shorter children. False positive rates did not differ between the torso and distal limb configurations. ConclusionModified placement of the limb electrodes onto the torso resulted in multiple differences in the pediatric ECG signals. This may lead to misclassification of electrocardiographic abnormalities, particularly in children with measurement values at the upper limits of normal.

A primer on method effects on observed correlations with examples.

I use a path/confirmatory factor analytic framework to present an under-the-hood introduction to (a) the effects of measurement error and method variance on observed measures, (b) the effects of common latent Trait and Method factors on relationships between observed measures in the presence of measurement error and (c) examine implications of developments presented here for the possible reinterpretation of Campbell and Fiske's original criteria for convergent and discrimainat validity and presence of common method bias. I use results from one previously published and often-cited primary study and four meta-analyses of multitrait-multimethod studies to provide empirical examples to illustrate points made here. I show analytically that (a) some common interpretations of method effects are correct, (b) others are incorrect and (c) still others are more nuanced than is usually recognised. Finally, I offer some suggested directions for future research.

Venous blood gases in the assessment of respiratory failure in patients undergoing sleep studies: a randomized study.

Venous blood gases (VBGs) are not consistently considered suitable surrogates for arterial blood gases (ABGs) in assessing acute respiratory failure due to variable measurement error. The physiological stability of patients with chronic ventilatory failure may lead to improved agreement in this setting. Adults requiring ABGs for sleep or ventilation titration studies had VBGs drawn before or after each ABG, in a randomized order. Veno-arterial correlation and agreement were examined for carbon dioxide tension (PCO2), pH, oxygen tension (PO2), and oxygen saturation (SO2). We analyzed 115 VBG-ABG pairs from 61 patients. Arterial and venous measures were correlated (P < .05) for PCO2 (r = .84) and pH (r = .72), but not for PO2 or SO2. Adjusted mean veno-arterial differences (95% limits of agreement) were +5.0 mmHg (-4.4 to +14.4) for PCO2; -0.02 (-0.09 to +0.04) for pH; -34.3 mmHg (-78.5 to +10.0) for PO2; and -23.9% (-61.3 to +13.5) for SO2. VBGs obtained from the dorsal hand demonstrated a lower mean PCO2 veno-arterial difference (P < .01). A venous PCO2 threshold of ≥ 45.8 mmHg was > 95% sensitive for arterial hypercapnia, so measurements below this can exclude the diagnosis without an ABG. A venous PCO2 threshold of ≥ 53.7 mmHg was > 95% specific for arterial hypercapnia, so such readings can be assumed diagnostic. The area under the receiver operating characteristic curve of 0.91 indicated high discriminatory capacity. A venous PCO2 < 45.8 mmHg or ≥ 53.7 mmHg would exclude or diagnose hypercapnia, respectively, in patients referred for sleep studies, but VBGs are poor surrogates for ABGs where precision is important. Registry: Australian New Zealand Clinical Trials Register; Name: A comparison of arterial and blood gas analyses in sleep studies; URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=372717; Identifier: ACTRN12617000562370. Lindstrom SJ, McDonald CF, Howard ME, etal. Venous blood gases in the assessment of respiratory failure in patients undergoing sleep studies: a randomized study. J Clin Sleep Med. 2024;20(8):1259-1266.

Measurement error prediction-based reliability assessment framework for electric metering devices under harsh natural environments

Reliability assessment for electric metering devices (EMDs), which includes environmental stress analysis, measurement error (ME) prediction, and reliability estimation, can be utilized for predictive instrument maintenance, especially under harsh environmental stresses. Nevertheless, the actual evaluation process is limited by data noise and the unavailability of future environmental inputs for the degradation model. To this end, we first extract the main environmental factors affecting ME and then fuse them using the weighted principal component analysis (WPCA) method to provide future environmental input values for the prediction model. Next, a bi-directional (BD) outlier detection method based on MCD robust analysis and the Thompson tau method is proposed to detect outliers from horizontal and longitudinal perspectives. Then, an ME prediction method called multi-stress fusion nonlinear degradation (MFND) is put forward, which considers the cyclic variation of ME over time and the effect of environmental stresses on ME. Real-world datasets from a high-dry-hot area manifest that our proposed BD-based MFND reliability assessment framework enjoys pleasurable prediction performance. Compared to some well- known methods, our framework excels in outlier detection and ME prediction.

Enhancing discharge estimation from SWOT satellite data in a tropical tidal river environment

The Surface Water and Ocean Topography (SWOT) mission aims to provide essential data on river width, height and slope in order to estimate worldwide river discharge accurately. This mission offers a powerful tool for monitoring river discharge in dynamic coastal areas, like the Saigon-Dongnai estuary in Southern Vietnam. However, estimating discharge of tidally-influenced rivers using SWOT measurements can be challenging when hydraulic variables have the same order of magnitude as SWOT measurement errors. In this paper we present a methodology to enhance discharge estimation accuracy from SWOT measurements based on simulated SWOT products at the 200 meter node resolution and varying river reach size. We assess measurement error variability and its impact on discharge estimation by employing a Monte Carlo analysis. Our approach significantly improved discharge estimation in the Saigon tidal river, reducing RMSE from 1400 m3/s to 180 m3/s and increasing R² from 0.31 to 0.95. Notably, the percentage of Monte Carlo particles meeting the 30% rRMSE threshold rose from 0% to 79%. This study underscores the feasibility of obtaining reliable discharge estimates from SWOT data in complex coastal areas where hydraulic variables are of the same order of magnitude as SWOT errors. Additionally, the proposed methodology to improve discharge estimation from SWOT measurements is widely adaptable as it can be applied to similar regions and can be combined with any discharge estimation method.

Optimization and validation of a suprasystolic brachial cuff-based method for noninvasively estimating central aortic blood pressure.

Clinical studies have extensively demonstrated that central aortic blood pressure (CABP) has greater clinical significance in comparison with peripheral blood pressure. Despite the existence of various techniques for noninvasively measuring CABP, the clinical applications of most techniques are hampered by the unsatisfactory accuracy or large variability in measurement errors. In this study, we proposed a new method for noninvasively estimating CABP with improved accuracy and reduced uncertain errors. The main idea was to optimize the estimation of the pulse wave transit time from the aorta to the occluded lumen of the brachial artery under a suprasystolic cuff by identifying and utilizing the characteristic information of the cuff oscillation wave, thereby improving the accuracy and stability of the CABP estimation algorithms under various physiological conditions. The method was firstly developed and verified based on large-scale virtual subject data (n = 800) generated by a computational model of the cardiovascular system coupled to a brachial cuff, and then validated with small-scale in vivo data (n = 34). The estimation errors for the aortic systolic pressure were -0.05 ± 0.63 mmHg in the test group of the virtual subjects and -1.09 ± 3.70 mmHg in the test group of the patients, both demonstrating a good performance. In particular, the estimation errors were found to be insensitive to variations in hemodynamic conditions and cardiovascular properties, manifesting the high robustness of the method. The method may have promising clinical applicability, although further validation studies with larger-scale clinical data remain necessary.

Separating Measurement Error and Signal in Environmental Data: Use of Replicates to Address Uncertainty.

Measurement uncertainty has long been a concern in the characterizing and interpreting environmental and toxicological measurements. We compared statistical analysis approaches when there are replicates: a Naı̈ve approach that omits replicates, a Hybrid approach that inappropriately treats replicates as independent samples, and a Measurement Error Model (MEM) approach in a random effects analysis of variance (ANOVA) model that appropriately incorporates replicates. A simulation study assessed the effects of sample size and levels of replication, signal variance, and measurement error on estimates from the three statistical approaches. MEM results were superior overall with confidence intervals for the observed mean narrower on average than those from the Naı̈ve approach, giving improved characterization. The MEM approach also featured an unparalleled advantage in estimating signal and measurement error variance separately, directly addressing measurement uncertainty. These MEM estimates were approximately unbiased on average with more replication and larger sample sizes. Case studies illustrated analyzing normally distributed arsenic and log-normally distributed chromium concentrations in tap water and calculating MEM confidence intervals for the true, latent signal mean and latent signal geometric mean (i.e., with measurement error removed). MEM estimates are valuable for study planning; we used simulation to compare various sample sizes and levels of replication.

Methodology to calculating the temperature around an evaporating droplet using a set of thermocouples

A new approach is proposed to calculating the gas/vapor temperature field in the immediate vicinity of a droplet using a set of thermocouples. The schemes used in the experiments are based on convective and combined droplet heating. The air temperature during droplet heating was varied in the range of 100–500 °C, and the droplet radii from 1 mm to 1.7 mm. The paper describes data processing algorithms and methods for calculating the gas/vapor temperature field around a droplet using three to five series of measurements and shows the advantages of this approach over non-contact optical techniques. We also determine the applicability range of the proposed method for temperature field calculation and the limits of systematic and random measurement error variation. The instantaneous droplet radius and the initial air temperature around it are shown to be the main factors influencing the gas/vapor temperature distributions around an evaporating droplet. The experimental data are compared to earlier research findings. The results indicate that reliable gas/vapor temperature fields around evaporating liquid droplets can be obtained even with inexpensive equipment.

Development and validation of a quality control procedure for automatic segmentation of hippocampal subfields.

Automatic segmentation methods for in vivo magnetic resonance imaging are increasing in popularity because of their high efficiency and reproducibility. However, automatic methods can be perfectly reliable and consistently wrong, and the validity of automatic segmentation methods cannot be taken for granted. Quality control (QC) by trained and reliable human raters is necessary to ensure the validity of automatic measurements. Yet QC practices for applied neuroimaging research are underdeveloped. We report a detailed QC and correction procedure to accompany our validated atlas for hippocampal subfield segmentation. We document a two-step QC procedure for identifying segmentation errors, along with a taxonomy of errors and an error severity rating scale. This detailed procedure has high between-rater reliability for error identification and manual correction. The latter introduces at maximum 3% error variance in volume measurement. All procedures were cross-validated on an independent sample collected at a second site with different imaging parameters. The analysis of error frequency revealed no evidence of bias. An independent rater with a third sample replicated procedures with high within-rater reliability for error identification and correction. We provide recommendations for implementing the described method along with hypothesis testing strategies. In sum, we present a detailed QC procedure that is optimized for efficiency while prioritizing measurement validity and suits any automatic atlas.

Using Structural Equation Modeling to Reproduce and Extend ANOVA-Based Generalizability Theory Analyses for Psychological Assessments

Generalizability theory provides a comprehensive framework for determining how multiple sources of measurement error affect scores from psychological assessments and using that information to improve those assessments. Although generalizability theory designs have traditionally been analyzed using analyses of variance (ANOVA) procedures, the same analyses can be replicated and extended using structural equation models. We collected multi-occasion data from inventories measuring numerous dimensions of personality, self-concept, and socially desirable responding to compare variance components, generalizability coefficients, dependability coefficients, and proportions of universe score and measurement error variance using structural equation modeling versus ANOVA techniques. We further applied structural equation modeling techniques to continuous latent response variable metrics and derived Monte Carlo-based confidence intervals for those indices on both observed score and continuous latent response variable metrics. Results for observed scores estimated using structural equation modeling and ANOVA procedures seldom varied. Differences in reliability between raw score and continuous latent response variable metrics were much greater for scales with dichotomous responses, thereby highlighting the value of doing analyses on both metrics to evaluate gains that might be achieved by increasing response options. We provide detailed guidelines for applying the demonstrated techniques using structural equation modeling and ANOVA-based statistical software.

Detecting and correcting for heteroscedasticity in the presence of measurement error

The problem of measurement error in predictors has received a vast amount of attention for both linear and non-linear models. The major focus has generally been on inference for regression coefficients, while residual analysis, variance estimation and heteroscedasticity assessment have received limited attention. In this paper we propose and evaluate new methods, based on reconstructed predictors, to address these problems in the context of linear regression. Methods using moment reconstructed predictors are found to perform well, including a new model-based bootstrapping technique for testing heteroscedasticity. Motivated by an example where the predictor is methylation rate and the measurement error model is based on the binomial distribution, particular attention is given to being able to handle the situation where both the measurement error variances and the variance of the error in the equation may be changing as a quadratic function of the unobserved predictor.

A Hybrid Model for Orthogonal Regression

AbstractLinear functional relationships are intended to be symmetric and therefore cannot generally be accurately estimated using ordinary least squares regression equations. Orthogonal regression (OR) models allow for errors in both Y and X and therefore can provide symmetric estimates of these relationships. The most well‐established OR model, the errors‐in‐variables (EIV) model, assumes that the observed scatter around the line is due entirely to errors of measurement in Y and X and that the ratio of the error variances is known. If most of the variance around the line is known to be due to the errors of measurement in Y and X, the EIV model can provide an unbiased maximum likelihood estimate for a functional relationship. However, if a substantial part of the variability around the line is due to natural variability, which is not attributable to errors of measurement in Y or X, the ratio of the measurement error variances is not well defined and the EIV model is not directly applicable. The main contribution of this report is the development of a hybrid model that provides plausible estimates for linear functional relationships in cases with substantial natural variability and substantial errors of measurement. An analysis of female and male differential test functioning between an essay test and an objective test used as parts of a licensure examination provides an illustration of the use of the hybrid model.

Estimating the overall fraction of phenotypic variance attributed to high-dimensional predictors measured with error.

In prospective genomic studies (e.g., DNA methylation, metagenomics, and transcriptomics), it is crucial to estimate the overall fraction of phenotypic variance (OFPV) attributed to the high-dimensional genomic variables, a concept similar to heritability analyses in genome-wide association studies (GWAS). Unlike genetic variants in GWAS, these genomic variables are typically measured with error due to technical limitation and temporal instability. While the existing methods developed for GWAS can be used, ignoring measurement error may severely underestimate OFPV and mislead the design of future studies. Assuming that measurement error variances are distributed similarly between causal and noncausal variables, we show that the asymptotic attenuation factor equals to the average intraclass correlation coefficients of all genomic variables, which can be estimated based on a pilot study with repeated measurements. We illustrate the method by estimating the contribution of microbiome taxa to body mass index and multiple allergy traits in the American Gut Project. Finally, we show that measurement error does not cause meaningful bias when estimating the correlation of effect sizes for two traits.

Small area estimation with multiple covariates under structural measurement error models

Small area estimation is essential in making reliable inferences for areas where the sample is relatively small or no sample is available. In the case of developing a small area estimation model, if the covariates measured with errors were ignored, the estimation results may be worse than the direct estimate. Therefore, the problem of measurement error in covariates imposes challenges to data analytics in small area estimation. This paper studies a model with multiple covariates subject to structural measurement error and multiple error-free covariates. This study explores different scenarios in a simulation study by creating a finite super population spread across the area, investigating the sensitivity to sample size, and determining the variance of measurement error that was lower than the variance of the sampling error and the variance of the random effect. Jackknife method was employed to obtain a nearly unbiased estimator of the mean squared prediction error of empirical best predictors of the predictors of small area means. The research shows that the performance of the empirical best predictors is better in the case of a large sample size within the area and small variances of the measurement errors. The weighted version is recommended because it is more stable in terms of variability and bias.

Upper stem diameter and volume prediction strategies in the National Forest Inventory of Finland

In forest inventories, field data are needed for the prediction of tree volumes. However, gathering field data requires resources, such as labour, equipment, and data management operations. This means that time and budget, as well as quality, must be carefully considered when National Forest Inventory (NFI) field measurement activities are planned. Therefore, the development of cost efficient, simple, safe and reliable measurement methods and tools are of great interest. To date, upper stem diameter (d6), which provides a more reliable estimation of tree stem volume, has typically been measured with a parabolic calliper. In this study, the performance of the Criterion laser-based dendrometer was examined for d6 measurements. A total of 326 sample trees were measured multiple times with three different measurement instruments. These instruments were used to measure diameter at breast height (dbh) as well as d6 measurements. Bland-Altman plots and measurement error variances were used to determine measurement instrument reliability. For all trees, the standard deviation for the laser based dendrometer was 18.73 mm at dbh and 15.36 mm for the d6 measurements. When the performance of Criterion was analysed with reference to the mean value of repeated measurements, the standard deviation in the dbh measurements was 12.21 mm, and 8.88 mm in the d6 measurements.

Assessing the reliability of single-item momentary affective measurements in experience sampling.

Emotion researchers that use experience sampling methods (ESM) study how emotions fluctuate in everyday life. To reach valid conclusions, confirming the reliability of momentary emotion measurements is essential. However, to minimize participant burden, ESM researchers often use single-item measures, preventing a reliability assessment of people's emotion ratings. Furthermore, because emotions constantly change, checking reliability via conventional test-retest procedures is impractical, for it is impossible to separate measurement error from meaningful emotional variability. Here, drawing from classical test theory (CTT), we propose two time-varying test-retest adaptations to evaluate the reliability of single-item (emotion) measures in ESM. Following Method 1, we randomly repeat one emotion item within the same momentary survey and evaluate the discrepancy between test and retest ratings to determine reliability. Following Method 2, we introduce a subsequent, shortly delayed retest survey and extrapolate the size of test-retest discrepancies to the hypothetical instance where no time between assessments would exist. First, in an analytical study, we establish the mathematical relation between observed test-retest discrepancies and measurement error variance for both methods, based on common assumptions in the CTT literature. Second, in two empirical studies, we apply both methods to real-life emotion time series and find that the size of error in people's emotion ratings corresponds with almost a 10th of the scale, comprising around 27% of the total variability in participants' affective responses. Consequently, disregarding measurement error in ESM is problematic, and we encourage researchers to include a test-retest procedure in their future studies when relying on single-item measures. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Comparing the prediction performance, uncertainty quantification and extrapolation potential of regression kriging and random forest while accounting for soil measurement errors

Geostatistics and machine learning have been extensively applied for modelling and predicting the spatial distribution of continuous soil variables. In addition to providing predictions, both techniques quantify the uncertainty associated with the predictions, although geostatistics is more developed in this respect. Despite the increased use of these techniques, most algorithms ignore that the soil measurements are not error-free. Recently, concern has also arisen about the extrapolation risk of these techniques, be it in geographic space, feature space, or both. In this paper, regression kriging (RK) and random forest (RF) were compared with respect to their ability to deliver accurate predictions and quantify prediction uncertainties, while accounting for measurement errors in the soil data. The sensitivity of results of both models to soil measurement errors was also evaluated, as well as their spatial extrapolation potential. This was done for a case study in Cameroon where soil pH, clay and organic carbon were mapped from measurements obtained using both conventional and proximal soil sensing methods. The results showed that both models produced comparable ranges and maps of predicted values for the soil properties of interest. Compared to RF, RK outperformed RF by presenting generally a higher Model Efficiency Coefficient (MEC), lower Root Mean Squared Error (RMSE) values and better extrapolation performance. The improvement in RMSE was about 10, 12 and 2 % while the improvement in MEC was on average 5, 22 and 1 % for pH, clay and SOC, respectively Overestimation of the local uncertainty observed for RK was larger than that of RF as shown by accuracy plots, indicating that prediction uncertainties were better quantified by the RF model. Better extrapolation performance was obtained with RK that derived better predictions than RF at unsampled locations as shown by cross-validation metrics and scatter plots, particularly when RK and RF were used for spatial extrapolation. The effects of incorporating measurement errors were not significant both for the predictions and for the prediction uncertainties due to the fact that most calibration data had the same measurement error variance. Model comparison should go beyond common validation metrics that only evaluate prediction accuracy but must also account for the ability to quantify prediction uncertainty at unsampled locations.

Control charts for measurement error models

We consider a linear measurement error model (MEM) with AR(1) process in the state equation which is widely used in applied research. This MEM could be equivalently re-written as ARMA(1,1) process, where the MA(1) parameter is related to the variance of measurement errors. As the MA(1) parameter is of essential importance for these linear MEMs, it is of much relevance to provide instruments for online monitoring in order to detect its possible changes. In this paper we develop control charts for online detection of such changes, i.e., from AR(1) to ARMA(1,1) and vice versa, as soon as they occur. For this purpose, we elaborate on both cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts and investigate their performance in a Monte Carlo simulation study. The empirical illustration of our approach is conducted based on time series of daily realized volatilities.

Does Religiosity Matter? Experimental Research on Abusive Supervision in Budgetary Slack Creation

Abusive supervision behavior has long been a problem in the budgeting process. Its nature has been proven to damage individuals and significantly impact the corporate environment. Several studies related to Abusive supervision show evidence that individuals who experience abusive supervision tend to do Budgetary Slack. Meanwhile, religiosity, which is predicted as a personal value that can control individuals from committing acts outside the norm or harming the company, sometimes has little effect. Several studies have shown that high religiosity does not prevent individuals from taking despicable actions such as earnings management, Budgetary Slack, commitment escalation, and others. Is there anything wrong with religiosity as an individual's value? Does this occur due to variable measurement error (instrument) or method bias in sampling? Researchers tried to compare various instruments related to religiosity to obtain the most appropriate measurement, especially by photographing this phenomenon. We surveyed 83 participants using the experimental method with a 2x2 factorial design to understand how religious belief might buffer against the destructive effects of abusive supervision on budgetary slack. The results of this study are expected to be a reference (empirical evidence) related to the role of religiosity as a personal value in preventing destructive behavior of individuals, especially in the budgeting process.