Unreliable Measure Research Articles

Glide-snow avalanches: a mechanical, threshold-based release area model

Abstract. Glide-snow avalanches release at the ground–snow interface due to a loss in basal friction. They pose a threat to infrastructure because of the combination of unreliable mitigation measures, limited forecasting capabilities, and a lack of understanding of the release process. The aim of this study was to investigate the influence of spatial variability in basal friction and snowpack properties on the avalanche release area distribution and the release location. We developed a pseudo-3D, mechanical, threshold-based model that consists of many interacting snow columns on a uniform slope. Parameterizations in the model are based on our current understanding of glide-snow avalanche release. The model can reproduce the power law glide-snow avalanche release area distribution as observed on Dorfberg (Davos, Switzerland). A sensitivity analysis of the input parameters showed that the avalanche release area distribution was mostly influenced by the homogeneity (correlation length and variance) of the basal friction and whether the basal friction was reduced suddenly or in small increments. Larger release areas were modeled for a sudden decrease and a more homogeneous basal friction. The spatial variability of the snowpack parameters had little influence on the release area distribution. Extending the model to a real-world slope showed that the modeled location of avalanche releases qualitatively matched the observed locations. The model can help narrow down the length scales and timescales for field investigations. Simultaneously, it can grow in complexity with our increasing understanding of glide-snow avalanche release processes. Input parameters such as the basal friction or snowpack parameters could potentially all be connected to the liquid water content. This would allow for the use of meteorological measurements to drive the model. The model has the potential to help identify potentially dangerous conditions for large or numerous avalanches which would help improve glide-snow avalanche forecasting.

Impact of ground motion uncertainty evolution from post-earthquake data on building damage assessment

Accurate damage assessment after an earthquake is crucial for effective emergency response. Using ground motion information enables rapid building damage assessment when detailed damage data are unavailable. While uncertainty in earthquake parameters plays a significant role in the accuracy of rapid estimations, it is usually treated as a constant parameter rather than as a dynamic parameter that considers the amount of ground motion data collected that evolve over time. This work investigates the impact of incorporating evolving ground motion uncertainty in ground motion estimations from US Geological Survey’s (USGS) ShakeMap on post-disaster damage assessments from two methodologies: the revised Thiel–Zsutty (TZR) model and Federal Emergency Management Agency’s (FEMA) Hazus. Using data from the 2020 Indios earthquake in Puerto Rico and the 2014 Napa earthquake, we find that changes in uncertainty in estimates of peak ground acceleration reach 65% between early and late versions of the ShakeMap. We propose a process to integrate this evolution with the two damage assessment methodologies through a Monte Carlo simulation-based approach, demonstrating that it is critical to introduce dynamic ground motion uncertainty in the damage assessment process to avoid propagating unreliable measures. Both methodologies show that resulting damage estimates can be characterized by narrower distributions, indicative of reduced uncertainty and increased precision in damage estimates. For the TZR model, an improved estimate of post-disaster loss is achieved with narrower bounds in distributions of expected high scenario loss. For Hazus, the results show potential changes in the most probable damage state with an average change of 13% in the most probable damage state. The described methodology also demonstrates how uncertainty in the resulting damage state distributions can be reduced compared with the use of the current Hazus methodology.

Adjusting for covariates representing potential confounders, mediators, or competing predictors in the presence of measurement error: Dispelling a potential misapprehension and insights for optimal study design with nutritional epidemiology examples

Background Variables such as dietary intake are measured with error yet frequently used in observational epidemiology. Although this limitation is sometimes noted, these variables are still often modeled as covariates without formal correction or sincere dialogue about measurement unreliability potentially weakening the validity of statistical conclusions. Further, larger sample sizes increase power (bias) to detect spurious correlations. Counterintuitively, recent work suggested a non-monotonic relationship between confounder unreliability and how much controlling for the confounder reduces (or induces) bias when testing for an exposure-outcome association. If true, such non-monotonicity would be especially concerning for applications such as nutrition, where measurement reliability varies substantially, and large sample sizes are common. Methods We offer a detailed derivations of the square partial correlation between the outcome and exposure, controlling for the confounder. In our derivation, the measurement reliabilities of exposures and confounders are not arbitrarily constrained to be equal. Further, our theoretical results are investigated using simulations. Results Reassuringly, these derivations and simulations show that the counterintuitive non-monotonicity relationship between confounder unreliability and how much controlling for the confounder reduces (or induces) bias when testing for an exposure-outcome association is an artifact of the arbitrary constraint which forces the measurement reliabilities of exposures and confounders to be equal, which that does not always hold. Conclusions The profound and manifold effects of measurement error on estimation and statistical conclusion validity in realistic scenarios indicate that merely mentioning measurement error as a limitation and then dispensing with it is not an adequate response. We also explore questions for optimal study design subject to resource constraints when considering reliability of exposures, covariates, and outcomes.

How does recall bias in farm labor impact separability tests?

In the agricultural household literature, empirical tests of separability between production and consumption decisions commonly exploit theoretical predictions of household labor allocation. Many of these studies rely on data that asks respondents to recall labor usage over the entire growing season. Two recent field experiments in Tanzania and Ghana show that such labor use data, collected at the end of the growing season, is a systematically unreliable measure of actual labor allocation. In this study, we examine how inaccurate measures of labor influence the reliability of market failure tests based on separability. In Ghana, we find no statistical evidence that recall bias influences the reliability of the separability test. In Tanzania, we find that recall bias increases the probability that such tests fail to reject separability. Thus, we find partial evidence that classic tests based on typical household survey labor data may erroneously conclude that markets are adequately functioning.

Nuanced HEXACO: A Meta-Analysis of HEXACO Cross-Rater Agreement, Heritability, and Rank-Order Stability.

Most Five-Factor Model (FFM) questionnaire items contain unique variance that is partly heritable, stable, and consensually observable, demonstrates consistent associations with age and sex, and predicts life outcomes beyond higher order factors. Extending these findings to the HEXACO model, we meta-analyzed single-item cross-rater agreement, heritability, and 2-year stability using samples from six countries. We analyzed raw item scores and their residual variance and adjusted the estimates for measurement unreliability. The median cross-rater agreement, heritability, and stability estimates were, respectively, .30, .30, and .57, for raw items and .10, .16, and .39, for item residuals. Adjusted for reliability, the respective medians were .46 and .25 for cross-rater agreement, .46 and .39 for heritability, and .87 and .94 for stability. These results are strikingly consistent with FFM-based findings, providing nondismissible evidence that single items index a partly unique level of the trait hierarchy-personality nuances-with trait properties comparable to those of higher-order traits.

Multifactor and multidimensional data quality analysis of judge scoring in diving competition.

In sports competitions, judge scoring data serve as an objective measure of an athlete's performance level. However, research has indicated the unreliability of objective measurements. Controversy often arises regarding the quality of judge scoring data, undermining fairness and justice in sports competitions. This paper proposes a method utilizing the Kendall covariance coefficient and the Kendall correlation coefficient for the thorough evaluation of judging data quality in diving events. The analysis is structured around four key elements: overall competition, individual divers, specific rounds, and distinct diving techniques. Each element is analyzed across three dimensions: the collective data quality from the judging panel, interjudge data quality comparisons, and the alignment of individual judges' scores with the final tallied scores. Two case studies serve to illustrate the application of this method. The Kendall covariance coefficient is employed to assess the data quality from the judges as a unified entity, whereas the Kendall correlation coefficient is utilized to evaluate the data quality from individual judges. Results show that the data quality of the judge group's scoring is high, while the data quality of the judge group's scoring for the 6th diver, the 5th round, Dive No. 5152B, Judge 5 and 6 in the Competition 1, and the 1st diver, the 3rd round, Dive No. 6245D, Judge 4 in the Competition 2 is inconsistent with the others. This approach uncovers disparities in data quality attributed to the judges' panel across each diver, each round, and the various diving maneuvers. However, the Kendall correlation coefficient may not be suitable for evaluating data quality when both the data differences and the sample size are small.

Визуализация в задачах реконструкции данных

Many application tasks of multidimensional data analysis which describe the state of real physical or other systems face with difficulties. This is a consequence of the low-quality source data, including missing values, the probability of errors or unreliability of measurements. Incomplete data can become an obstacle for research using many modern informational methods. The current work examines the potential and capabilities of visual analytics tools for preliminary preparation, correction or complete analysis of primary data volumes. A promising area of application of the approach discussed in the study is the targeted use of visualization capabilities as a data analysis tool. The implementation of specialized visual metaphors is used to solve problems of processing and interpreting data, the sources of which are cyberphysical systems of different complexity levels. Such systems operate in an autonomous or partially controlled mode. A characteristic feature of these systems is the presence of a large number of sensors that collect various types of data. Such data differ in the capacity of the corresponding information channels, their speed and reliability. Examples of such cyberphysical systems are unmanned aerial vehicles (UAVs), robotic stations, and multimodal monitoring systems. These systems can function in conditions where it is difficult to obtain objective observation experience (deep-sea robots). The effective use of data collected by cyberphysical monitoring systems is a condition for solving a large number of application and research tasks.

Critical Care Alarm Fatigue and Monitor Customization: Alarm Frequencies and Context Factors.

Alarm fatigue among nurses working in the intensive care unit has garnered considerable attention as a national patient safety priority. A viable solution for reducing the frequency of alarms and unnecessary noise is intensive care unit alarm monitor customization. A 24-bed cardiovascular and thoracic surgery intensive care unit in a large academic medical center identified a high rate of alarms and associated noise as a problem contributing to nurse alarm fatigue. An alarm monitor quality improvement project used both alarm frequency and nurse surveys before and after implementation to determine the effectiveness of interventions. Multimodal interventions included nurse training sessions, informational flyers, organizational policies, and an alarm monitor training video. Unexpected results inspired an extensive investigation and secondary analysis, which included examining the data-capturing capabilities of the alarm monitors and the impact of context factors. Alarm frequencies unexpectedly increased after the intervention. The software data-capturing features of the alarm monitors for determining frequency did not accurately measure nurse interactions with monitors. Measured increases in patient census, nurse staffing, and data input from medical devices from before to after the intervention substantially affected project results. Alarm frequencies proved an unreliable measure of nurse skills and practices in alarm customization. Documented changes in context factors provided strong anecdotal evidence of changed circumstances that clarified project results and underscored the critical importance of contemporaneous collection of context data. Designs and methods used in quality improvement projects must include reliable outcome measures to achieve meaningful results.

Potential factors contributing to observed sex differences in virtual-reality-induced sickness

Virtual reality (VR) technology has been widely adopted for several professional and recreational applications. Despite rapid innovation in hardware and software, one of the long prevailing issues for end users of VR is the experience of VR sickness. Females experience stronger VR sickness compared to males, and previous research has linked susceptibility to VR sickness to the menstrual cycle (Munafo et al., Exp Brain Res 235(3):889–901). Here we investigated the female versus male experience in VR sickness while playing an immersive VR game, comparing days of the menstrual cycle when hormones peak: day 15 (ovulation—peak estrogen) and day 22 (mid-luteal phase—peak progesterone). We found that immersion duration was greater in the second session than the first, and discomfort was lessened, suggesting a powerful adaptation with repeated exposure. Due to the estrogen levels changing along with the exposure, there was no clear independent impact of that; note, though, that there was a significant difference between self-report and physiological measures implying that GSR is potentially an unreliable measure of motion sickness. Although prior work found a delay over 2 days between session would not allow adaptation and habituation to reduce VR sickness susceptibility, we found that a week delay has potential success.

Drought-induced population decline in an exudivorous mammal and its relevance to forest management

Droughts are predicted to increase in frequency and severity with climate change, so understanding their influence on species is fundamental to future conservation actions. Knowledge of whether species are at risk of decline due to drought may change the direction of how species and forests are managed. Exudate feeding non-flying mammals may be particularly sensitive to drought conditions, due to negative effects on exudates, but few studies of adequate length have been conducted. We investigated whether occupancy and abundance of a threatened exudivorous mammal, the yellow-bellied glider (Petaurus australis), were influenced by an extreme drought in eastern Australia. We conducted repeat surveys over eight years, which coincided with a drought that peaked in year 6, and was followed by two years of above average rainfall. Modelling suggested a slight decline in occupancy over the eight years (0.96–0.89). A marked decline in the probability of detection from pre-drought (0.72; 95 %CI: 0.65–0.79) to post-drought (0.30; 0.19–0.40), suggested a decline in abundance had occurred. Data on the maximum number of individuals detected per occupied site per year confirmed a decline in abundance. Counts of ≥ 3 individuals were recorded at 42% of occupied sites before the drought but at only 6% of occupied sites after the drought, equating to a 48% decline in abundance. These findings suggest that occupancy may be an unreliable measure of drought response in this species. Other exudivorous mammals may be sensitive to drought and require detailed study. Identifying drought refuges for these species should be added to existing forest management actions.

Do gender, age and tenure matter when behaving unethically for organizations: Meta-analytic review on organizational identity and unethical pro-organizational behavior

PurposeThis research aims to clarify the relationship between organizational identification and employees’ unethical pro-organizational behavior, which has been a topic of mixed findings in previous empirical studies. To address this issue, a meta-analytic review was conducted, focusing on the influence of key individual differences such as gender, age and organizational tenure on the relationship between organizational identification and unethical pro-organizational behavior.Design/methodology/approachThe study utilizes large scholarly databases including Google Scholar, PsycINFO, Business Source Premier and ProQuest Dissertations to identify relevant studies. A total of 31 independent samples with a combined sample size of 8,861 participants were included in the analysis.FindingsThe results showed that the estimated average correlation between organizational identification and unethical pro-organizational behavior after corrected for measurement unreliability was 0.188 (p < 0.001, 95% [CI: 0.125, 0.251]). Gender demonstrated a significant moderating effect (estimate = 0.004, p < 0.05, 95% [CI: 0.000, 0.007]), suggesting that there is stronger association between organizational identification and unethical pro-organizational behavior among male participants. Neither age nor organizational tenure had significant effect on organizational identification-unethical pro-organizational behavior relations.Originality/valueThis study revealed that the organizational identification-unethical pro-organizational behavior link was positive, and the relationship was stronger among male participants than their female counterparts. Age and organizational tenure show no significant impact on unethical pro-organizational behavior. These contribute to The authors' understanding of organizational identification-unethical pro-organizational behavior relationship, as well as identifying its boundary conditions. The study suggests directions for future research and implications for managers and practitioners.

Reflections on What Financial Economics Can and Cannot Teach Us About the Social Discount Rate

The principles of financial economics provide equally important insights into the optimal choice of discount rates for both public and private sector decision-makers. However, most governments largely ignore those principles, taking their cost of capital for most purposes as their borrowing rate. This article reviews the arguments often made in support of status quo discounting practices, along with the counterarguments to them. Governments have a choice between several methodologies for risk adjustment, and the practical and conceptual reasons that favor a fair value approach are recapped. The limitations of a financial economics approach become apparent for decisions involving very long time horizons, such as for climate policies. For policies with long-term impacts, intergenerational concerns become paramount, projections of cash flows and discount rates become highly uncertain, and present value calculations are an intrinsically unreliable measure of value. No approach to discount rate selection can overcome those problems; alternative decision criteria need to be established. However, most government investments involve much shorter horizons, and the adoption of standard approaches to risk adjustment could significantly improve social welfare.

Screening for silent aspiration in hyperacute stroke: A feasibility study of clinical swallowing examination and cough reflex testing.

Silent aspiration (SA) is common post-stroke and associated with increased risk of pneumonia, length of stay and healthcare costs. Clinical swallow examinations (CSEs) are unreliable measures of SA. There is no consensus on the clinical components that best detect SA. Cough reflex testing (CRT) is an alternative/adjunct whose SA detection accuracy also lacks consensus. To investigate the feasibility of CSE versus CRT against gold standard flexible endoscopic evaluation of swallowing (FEES) for SA identification and to estimate its prevalence in a hyperacute stroke setting. A single-arm preliminary, prospective, feasibility study of patients less than 72 h post-stroke, over a 31-day period on a hyperacute stroke unit: the Royal Victoria Infirmary, Newcastle-upon-Tyne, UK. Ethical approval for the study was obtained. The study tested the feasibility and acceptability of introducing CRT and developing a standardized CSE. Consent/assent was obtained for all participants. Patients unfit for study were excluded. A total of 62% of patients less than 72 h post-stroke (n = 61) were eligible. A total of 75% of those approached (n = 30) consented. A total of 23 patients completed all tests. The principal barrier was anxiety regarding FEES. Mean test time for CRT = 6 min; CSE = 8 min; FEES = 17 min. Patients rated CRT and FEES on average as moderately uncomfortable. A total of 30% (n = 7) of participants who received FEES presented with SA. CRT, CSE and FEES are feasible in 58% of hyperacute stroke patients in this setting. FEES anxiety is the main recruitment barrier and is not always well tolerated. Results support further work to establish optimum methods and differential sensitivity/specificity of CRT and CSE in hyperacute stroke for SA identification. What is already known on this subject SA significantly increases the risk of pneumonia in the early days post-stroke. CSEs are unreliable for identification of SA risk in this population. CRT is gaining popularity as a potential tool to identify stroke patients at risk of SA, though there are questions regarding the efficacy of the clinical protocol currently being used in the UK. What this study adds to existing knowledge This study demonstrates that it is practical and feasible to carry out a larger scale study in this setting to compare CSE and CRT including a consideration of an approach combining both methods for clinical identification of SA versus FEES. Preliminary findings suggest that CSE may have higher levels of sensitivity than CRT for SA identification. What are the potential or actual clinical implications of this work? The results of this study suggest that further work is needed to establish the optimum methods and differential sensitivity/specificity of clinical tools for SA detection in hyperacute stroke.

Impending Civil Strife or Further Evidence of Non-Attitudes? A Review Article

Abstract In this review article, Shanto Iyengar summarizes the book's key findings on the psychological antecedents of support for violence and the political contexts that either facilitate or discourage outbreaks of violence. Given recent events, the book is certainly timely and theoretically insightful. However, the evidence on which the book is based is limited; it is likely that that the authors have provided vastly inflated estimates of support for violence caused by “non-attitudes” and measurement unreliability.

Epigenetic-based age acceleration in a representative sample of older Americans: Associations with aging-related morbidity and mortality

Biomarkers developed from DNA methylation (DNAm) data are of growing interest as predictors of health outcomes and mortality in older populations. However, it is unknown how epigenetic aging fits within the context of known socioeconomic and behavioral associations with aging-related health outcomes in a large, population-based, and diverse sample. This study uses data from a representative, panel study of US older adults to examine the relationship between DNAm-based age acceleration measures in the prediction of cross-sectional and longitudinal health outcomes and mortality. We examine whether recent improvements to these scores, using principal component (PC)-based measures designed to remove some of the technical noise and unreliability in measurement, improve the predictive capability of these measures. We also examine how well DNAm-based measures perform against well-known predictors of health outcomes such as demographics, SES, and health behaviors. In our sample, age acceleration calculated using "second and third generation clocks," PhenoAge, GrimAge, and DunedinPACE, is consistently a significant predictor of health outcomes including cross-sectional cognitive dysfunction, functional limitations and chronic conditions assessed 2 y after DNAm measurement, and 4-y mortality. PC-based epigenetic age acceleration measures do not significantly change the relationship of DNAm-based age acceleration measures to health outcomes or mortality compared to earlier versions of these measures. While the usefulness of DNAm-based age acceleration as a predictor of later life health outcomes is quite clear, other factors such as demographics, SES, mental health, and health behaviors remain equally, if not more robust, predictors of later life outcomes.

Childhood Adversity, Cognition, and Response to Cognitive Remediation in Major Depressive Disorder.

Cognitive impairment is experienced by many individuals with major depressive disorder (MDD) and is significantly related to sustained disability. Recent work has begun to explore the relationship between childhood adversity (CA) and cognitive impairment in MDD, but this work is limited by unreliable measures of CA. Furthermore, no previous research has examined whether CA relates to cognitive remediation response. The current study clarifies how CA and clinical characteristics of illness explain cognitive variance. In addition, we investigate whether CA is associated with response to cognitive remediation. Thirty-nine individuals who completed cognitive remediation were rerecruited to complete a retrospective interview on CA. Results showed that CA, repeated depressive episodes, and earlier age at diagnosis were associated with poorer cognition. We did not observe a difference in treatment response based on CA. Findings suggest that CA is an important variable to consider when examining the expression of depressive illness and areas for intervention.

Correcting for the multiplicative and additive effects of measurement unreliability in meta-analysis of correlations.

As a powerful tool for synthesizing information from multiple studies, meta-analysis has gained high popularity in many disciplines. Conclusions stemming from meta-analyses are often used to direct theory development, calibrate sample size planning, and guide critical decision-making and policymaking. However, meta-analyses can be conflicted, misleading, and irreproducible. One of the reasons for meta-analyses to be misleading is the improper handling of measurement unreliability. We show that even when there is no publication bias, the current meta-analysis procedures would frequently detect nonexistent effects, and provide severely biased estimates and intervals with coverage rates far below the intended level. In this study, an effective approach to correcting for unreliability is proposed and evaluated via simulation studies. Its sensitivity to the violation of the homogeneous reliability and residual correlation assumption is also tested. The proposed method is illustrated using a real meta-analysis on the relationship between extroversion and subjective well-being. Substantial differences in meta-analytic results are observed between the proposed method and existing methods. Further, although not specifically designed for aggregating effect sizes with various measures, the proposed method can be used to fulfill the purpose. The study ends with discussions on the limitations and guidelines for implementing the proposed approach. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

From Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health Research

Advances in computational statistics and corresponding shifts in funding initiatives over the past few decades have led to a proliferation of neuroscientific measures being developed in the context of mental health research. Although such measures have undoubtedly deepened our understanding of neural mechanisms underlying cognitive, affective, and behavioral processes associated with various mental health conditions, the clinical utility of such measures remains underwhelming. Recent commentaries point toward the poor reliability of neuroscientific measures to partially explain this lack of clinical translation. Here, we provide a concise theoretical overview of how unreliability impedes clinical translation of neuroscientific measures; discuss how various modeling principles, including those from hierarchical and structural equation modeling frameworks, can help to improve reliability; and demonstrate how to combine principles of hierarchical and structural modeling within the generative modeling framework to achieve more reliable, generalizable measures of brain-behavior relationships for use in mental health research.

Accounting for ecosystem services and asset value: pilot accounts for KwaZulu-Natal, South Africa

Pilot monetary ecosystem accounts were compiled for KwaZulu-Natal Province, South Africa, in order to highlight any data, methodological or process issues in their compilation and to contribute towards charting a strategy for ecosystem accounting. The Province is highly diverse, with eight biomes, large proportions under communal, private and state tenure, globally important biodiversity, variable landscape condition and encompassing catchment areas of nine river systems. We accounted for the supply and use of wild biomass, reared animal production, cultivation (including silviculture), nature-based tourism, property value, carbon storage and sequestration, pollination, flow regulation (maintenance of base flows), sediment retention, water quality amelioration and flood attenuation. For each ecosystem service, we devised conceptually valid methods that were suitable for the existing data to produce values consistent with the System of National Accounts. These were then summed to estimate total annual flows from each 100 x 100 m spatial unit and its asset value. Challenges encountered included lack of data on small-scale and subsistence production, mismatches in the classification of landcover and government production statistics, unreliable measures of ecosystem condition, the large scale of hydrological modelling and lack of centralised data organisation relating to hydrological services. There was heavy reliance on past empirical research and on global datasets. The combined value of the annual flow of the ecosystem services valued was R52.5 billion in 2011, equivalent to 12% of the provincial GDP. However, the values of many of the services have decreased over the accounting period, due to a combination of changes in demand and ecosystem condition. Asset value was undermined to some extent by unsustainable use of provisioning services. Some areas will require careful messaging, particularly in regard to the contentious issue of valuing carbon retention and the use of exchange values rather than welfare values that are used in economic analysis.

Relative risk reduction: Misinformative measure in clinical trials and COVID-19 vaccine efficacy

•Unreliability of relative measures in clinical trials is graphically illustrated, demonstrating constant relative measures as absolute measures change.•Misuse of relative measures in clinical research is historically linked to misinterpretation of Jerome Cornfield's advice on measuring causative and associative effects.•Consequences of disinformation and misinformation related to COVID-19 vaccine efficacy and modern clinical medicine are described.•The proper use of absolute measures in meta-analyses is explained.