Reliability Estimates Research Articles

An Efficient Stress–Strength Reliability Estimate of the Unit Gompertz Distribution Using Ranked Set Sampling

In this paper, the estimation of the stress–strength reliability is taken into account when the stress and strength variables have unit Gompertz distributions with a similar scale parameter. The consideration of the unit Gompertz distribution in this context is because of its intriguing symmetric and asymmetric properties that can accommodate various histogram proportional-type data shapes. As the main contribution, the reliability estimate is determined via seven frequentist techniques using the ranked set sampling (RSS) and simple random sampling (SRS). The proposed methods are the maximum likelihood, least squares, weighted least squares, maximum product spacing, Cramér–von Mises, Anderson–Darling, and right tail Anderson–Darling methods. We perform a simulation work to evaluate the effectiveness of the recommended RSS-based estimates by using accuracy metrics. We draw the conclusion that the reliability estimates in the maximum product spacing approach have the lowest value compared to other approaches. In addition, we note that the RSS-based estimates are superior to those obtained by a comparable SRS approach. Additional results are obtained using two genuine data sets that reflect the survival periods of head and neck cancer patients.

Reliability estimation of randomly excited nonlinear structure with VNES

Reliability estimation of randomly excited nonlinear structure with VNES

Postpartum recovery of nulliparous women following scheduled cesarean delivery and spontaneous vaginal delivery: a prospective observational study

Inpatient postpartum recovery trajectories following cesarean delivery and spontaneous vaginal delivery are underexplored. This study primarily aimed to compare recovery following cesarean delivery and spontaneous vaginal delivery in the first postpartum week, and secondarily to evaluate psychometrically the Japanese version of the Obstetric Quality of Recovery-10 scoring tool. Following institutional review board approval, the EQ-5D-3L (EuroQoL 5-Dimension 3-Level) questionnaire and a Japanese version of the Obstetric Quality of Recovery-10 measure were used to evaluate inpatient postpartum recovery in uncomplicated nulliparous parturients delivering via scheduled cesarean delivery or spontaneous vaginal delivery. A total of 48 and 50 women who delivered via cesarean delivery and spontaneous vaginal delivery, respectively, were recruited. Women delivering via scheduled cesarean delivery experienced significantly worse quality of recovery on days 1 and 2 compared with those who had spontaneous vaginal delivery. Quality of recovery significantly improved daily, plateauing at days 4 and 3 for cesarean delivery and spontaneous vaginal delivery groups, respectively. Compared with cesarean delivery, spontaneous vaginal delivery was associated with prolonged time to analgesia requirement, decreased opioid consumption, reduced antiemetic requirement, and reduced times to liquid/solid intake, ambulation, and discharge. Obstetric Quality of Recovery-10-Japanese is a valid (correlates with the EQ-5D-3L including a global health visual analog scale, gestational age, blood loss, opioid consumption, time until first analgesic request, liquid/solid intake, ambulation, catheter removal, and discharge), reliable (Cronbach alpha=0.88; Spearman-Brown reliability estimate=0.94; and intraclass correlation coefficient=0.89), and clinically feasible (98% 24-hour response rate) measure. Inpatient postpartum recovery is significantly better in the first 2 postpartum days following spontaneous vaginal delivery compared with scheduled cesarean delivery. Inpatient recovery is largely achieved within 4 and 3 days following scheduled cesarean delivery and spontaneous vaginal delivery, respectively. Obstetric Quality of Recovery-10-Japanese is a valid, reliable, and feasible measure of inpatient postpartum recovery.

Scoring festination and gait freezing in people with Parkinson's: The freezing of gait severity tool-revised.

To improve existing clinical assessments for freezing of gait (FOG) severity, a new clinician-rated tool which integrates the varied types of freezing (FOG Severity Tool-Revised) was developed. This cross-sectional study investigated its validity and reliability. People with Parkinson's disease who were able to independently ambulate eight-metres and understand study instructions were consecutively recruited from outpatient clinics of a tertiary hospital. Those with co-morbidities severely affecting gait were excluded. Participants were assessed with the FOG Severity Tool-Revised, three functional performance tests, the FOG Questionnaire, and outcomes measuring anxiety, cognition, and disability. The FOG Severity Tool-Revised was repeated for test-retest reliability. Exploratory factor analysis and Cronbach's alpha were computed for structural validity and internal consistency. Reliability and measurement error were estimated with ICC (two-way, random), standard error of measurement, and smallest detectable change (SDC95 ). Criterion-related and construct validity were calculated with Spearman's correlations. Thirty-nine participants were enrolled [79.5% (n=31) male; Median (IQR): age-73.0 (9.0) years; disease duration-4.0 (5.8) years], with fifteen (38.5%) who reported no medication state change contributing a second assessment for reliability estimation. The FOG Severity Tool-Revised demonstrated sufficient structural validity and internal consistency (α=0.89-0.93), and adequate criterion-related validity compared to the FOG Questionnaire (ρ=0.73, 95% CI 0.54-0.85). Test-retest reliability (ICC=0.96, 95%CI 0.86-0.99) and random measurement error (%SDC95 =10.4%) was acceptable in this limited sample. The FOG Severity Tool-Revised appeared valid in this initial sample of people with Parkinson's. While its psychometric properties remain to be confirmed in a larger sample, it may be considered for use in the clinical setting.

A bootstrapping-based weighted average asymptotic sampling formulation for reliability estimation of highly safe structures

A bootstrapping-based weighted average asymptotic sampling formulation for reliability estimation of highly safe structures

Stress-Strength Reliability Estimation of a Series System with Cold Standby Redundancy Based on Kumaraswamy Half-Logistic Distribution

This paper deals with study and estimation of stress-strength reliability for a system where strength and stress components are connected in series with cold standby redundancy at system level. It is supposed that the random stress and strength both follow Kumaraswamy half-logistic distribution. In this redundant system, we consider that there N subsystems and in each subsystem, there are M statistically independent strength components under the impact of M statistically independent stress components The problem of estimation is solved in two cases. First under the assumption that random stress and strength have common first shape parameter and different second shape parameter and second with the assumption that common shape parameter is known. The stress-strength reliability is estimated using maximum likelihood and Bayesian estimation methods. Also asymptotic and Bayesian intervals for the stress-strength reliability under both the cases are constructed. Monte Carlo simulations will be performed to compare the performance of various methods. Finally a real life data set is analyzed to demonstrate the findings.

Impact evaluation of universal basic education commission capacity building program on mathematics teacher testing skills in akwa ibom state, nigeria

This study evaluated the impact of the in-service training program offered by the Universal Basic Education Commission (UBEC) in Nigeria on the testing skill of Mathematics teachers in Akwa Ibom State, Nigeria. The study adopted the expo facto research design research. A two-stage sampling involving simple random and stratified techniques were employed to select 134 from the 530 Mathematics teachers in the public secondary schools in Akwa Ibom State, Nigeria. The instrument used for the data collection was, Teacher Testing Skills Assessment Scale (TTSAS) developed by the researchers based on the objective of the study. The instrument was validated by three measurement experts and trial tested. The reliability coefficient of .77 was obtained from the inter-rater method of reliability estimate. The data collected was analyzed using mean, standard deviation and independent sample t-test. The results of the analysis revealed that the UBEC in-service training program has significant positive impact on Mathematics teachers’ testing skills. It was concluded that the UBEC teachers’ capacity development program is a profitable venture with promising national gains. It was recommended that government should continue with her support for the UBEC in sustaining regular in-service training program for teachers in secondary schools across the nation.

Does birth order or gender influence students’ attitude toward mathematics in junior secondary schools in eket akwa ibom state, nigeria?

This study examined influence of birth order and gender on junior secondary school students’ attitude toward Mathematics in Eket southern Nigeria. Ex-post facto research design was used. A sample of 1000 students was selected by stratified, random sampling procedures. Data was collected with a structured Questionnaire; with a Cronbach Alpha reliability estimate of .77. One-way Analysis of Variance (ANOVA) and Independent t- test were used for statistical test of the hypotheses at .05 level of significance. The result showed that first-borns had more positive attitude toward Mathematics than their other siblings (ANOVA: F= 16.096; p=0.000). Male students showed more positive attitude to Mathematics than their female counterpart (t=6.110; p=0.001). Study concluded that birth order and gender influenced students’ attitude to mathematics Eket Nigeria. It was recommended that Mathematics teaching and evaluation strategies should be gender-sensitive so as to minimize gender-related bias and inequity.

Reliability estimation of manufacturing system with four human operators

Reliability estimation of manufacturing system with four human operators

Spanish psychometric properties of the moral distress scale—revised: a study in healthcare professionals treating COVID-19 patients

BackgroundMoral distress appears when a healthcare professional is not able to carry out actions in accordance with their professional ethical standards. The Moral Distress Scale-Revised is the most widely used to assess levels of moral distress, but it is not validated in Spanish. The aim of the study is to validate the Spanish version of the Moral Distress Scale – utilised within a sample of Spanish healthcare professionals treating COVID–19 patients.MethodsThe original (english) and the portuguese and french versions of the scale were translated into spanish by native or bilingual researchers and reviewed by an academic expert in ethics and moral philosophy as well as by a clinical expert. Research design: Descriptive cross-sectional study carried out using a self-reporting online survey. The data was collected between June- November 2020. A total of 661 professionals responded to the survey (N = 2873). Participants: healthcare professionals with more than two weeks of experience treating COVID–19 patients at the end of their life and working in the public sector of the Balearic Islands Health Service (Spain). Analyses included descriptive statistics, competitive confirmatory factor analysis, evidence on criterion-related validity and estimates of reliability. The study was approved by the Research Ethics Committee at the University of Balearic Islands.ResultsAn unidimensional model in which a general factor of moral distress explained by 11 items of the Spanish version of the MDS–R scale was an adequate representation of the data: χ2(44) = 113.492 (p 0.001); Comparative Fit Index = 0.965; Root Mean Square Error of Approximation = 0.079[0.062,0.097]; and Standarized Root Mean-Square = 0.037. Evidence of reliability was excellent: Cronbach’s alpha = 0.886 and McDonald’s omega = 0.910. Moral distress was related to discipline, with nurses having statistically significant higher levels than physicians. Additionally, moral distress successfully predicted professional quality of life, with higher levels of moral distress being related to poorer quality of life.ConclusionsThe Spanish version of Moral Distress Scale–Revised can be used as a reliable and valid measurement tool for the evaluation of moral distress experienced by health professionals. This tool will be highly useful for managers and applicable to a variety of healthcare professionals and settings.

New Specific Kinesthetic Differentiation Tests for Female Volleyball Players: Reliability, Discriminative Ability, and Usefulness.

This study aimed to determine the test-retest reliability and discriminative ability of five sport-specific kinesthetic differentiation ability tests in female volleyball players. The sample of participants consisted of 98 female volleyball players aged 15.20 ± 1 years from six clubs in Bosnia and Herzegovina. Kinesthetic differentiation ability was determined by the overhead passing test, forearm passing test, float service with a net test, float service without a net test, and float service 6 m from the net test. To estimate test-retest reliability, a sub-sample of 13 players performed all tests on two testing occasions. Furthermore, the discriminative ability of the tests was determined by analyzing the performance between players of different playing positions and situational performances. Parameters of the intraclass correlation coefficient (ICC) were excellent (0.87-0.78) in all tests except for the float service with the net test, whose reliability was good (0.66). For the absolute reliability estimates, the SEM was higher than SWC (0.2) for all variables except the float service 6 m from the net test, and the SEM was lower than SWC (0.6, 1.2). One-way ANOVA detected no statistically significant inter-positional differences in all five tests (p > 0.05). A significant difference was found between less and more successful players (p < 0.01) for all applied tests. The results of this study show that a specific battery test is a reliable and valid measure and can be used to monitor kinesthetic differentiation ability in young female volleyball players.

Investigating the effect of virtual machine migration accounting on reliability using a cluster model

The purpose of the article is to develop and verify with the help of mathematical modeling a software method of deploying a fault-tolerant computing cluster with a virtual machine, which consists of two physical servers (main and backup), on which a distributed data storage system with synchronous data replication from the source server to the backup server is deployed. For this purpose, the task is to conduct a computational experiment on a model of a fault-tolerant cluster, which neglects costs during recovery for the migration of virtual machines by means of the mathematical application Mathcad. Combining computing resources into clusters is a way to ensure high reliability, fault tolerance, and continuity of the computing process of computer systems. This is achieved through virtualization, which enables the movement of virtual resources, services, or applications between physical servers while maintaining the continuity of computing processes. The focus of this study is on a failover cluster, which is composed of two physical servers (primary and backup) connected through a switch, and each server has a local hard disk. A distributed storage system with synchronous data replication from the source server to the backup server is deployed on the local disks of the servers, and a virtual machine is running on the cluster. Markovian processes, flows of podias, and Kolmogorov's systems of differential equations are built into the mathematical tools of the model of a water cluster. To ensure the continuity of the computing process in case of a failure of the main server, a shadow copy of the virtual machine is launched on the backup server. The reliability of the failover cluster is measured by the coefficient of non-stationary readiness. A Markov model is proposed to assess the reliability of the failover cluster, taking into account the costs of migrating virtual machines and mechanisms that ensure the continuity of the computing process in the cluster in case of a failure of one physical server. The memory migration process maintains two copies of the virtual machine on different physical servers, enabling them to continue working on the other in the event of failure. A simplified model of the failover cluster neglects the cost of migrating virtual machines and provides an upper estimate of reliability. The study shows that the reliability of a failover cluster, as measured by the non-stationary availability factor, is significantly impacted by the virtual machine migration process. The findings of this study can be used to inform decisions about the technology chosen to ensure the failure stability and continuity of the computing process of computer systems with cluster architecture. The calculations allow us to draw a conclusion about the significant impact of virtual machine migration accounting on reliability. The calculations allow us to draw a conclusion about the significant impact of virtual machine migration accounting on reliability. The calculation was performed under the following failure rates of the server, disk, and switch: λ0 = 1,115×10-5 1/h, λ1 = 3,425×10-6 1/h, λ2 = 2,3×10-6 1/h recovery respectively: μ0 = 0,33 1/h, μ1 = 0,171/h, μ2 = 0,33 1/h. The intensity of synchronization of the distributed storage system: μ3 = 1 1/h, μ4 = 2 1/h. The difference of non-stationary cluster availability coefficients is d = К2(t) – К1(t) = 2.7×10-10

Methods for Imputing Scores When All Responses Are Missing for One or More Polytomous Items: Accuracy and Impact on Psychometric Property

AbstractThough a substantial amount of research exists on imputing missing scores in educational assessments, there is little research on cases where responses or scores to an item are missing for all test takers. In this paper, we tackled the problem of imputing missing scores for tests for which the responses to an item are missing for all test takers. We considered three missing‐data imputation methods—the median method, the item response theory (IRT) method, and the two‐way method—for imputing scores. We compared the performance of these three imputation methods with respect to their accuracy in estimating scaled scores and test reliability for the aforementioned problem. Real data were used in the comparison. All three methods performed well in imputing scaled scores with negligible imputation error: The IRT method and the median method provided slightly more accurate scaled scores. The two‐way method provided the most accurate reliability estimates. Recommendations for practice are provided.

Reliability Estimation of Parallel Repairable System under Uncertainty in Lifetime Data

Abstract Reliability is a popular concept that has been used in the manufacturing industry. In this paper, we consider a parallel system containing n non-identical and independent components in which each component is repairable except when all components are failed. As a special case, estimating the reliability of the system with identical components is considered. In real life, the data obtained for repair rate and failure rate could be subject to uncertainty. Here, to address this situation, failure and repair rates are considered as fuzzy numbers to estimate the reliability of the system. Fuzzy system reliability is estimated using fuzzy failure and repair rates, which are obtained by using confidence intervals and point estimators of failure rate and repair rate.

Reliable affordances: A generative modeling approach for test-retest reliability of the affordances task.

The affordances task serves as an important tool for the assessment of cognition and visuomotor functioning, and yet its test-retest reliability has not been established. In the affordances task, participants attend to a goal-directed task (e.g., classifying manipulable objects such as cups and pots) while suppressing their stimulus-driven, irrelevant reactions afforded by these objects (e.g., grasping their handles). This results in cognitive conflicts manifesting at the task level and the response level. In the current study, we assessed the reliability of the affordances task for the first time. While doing so, we referred to the "reliability paradox," according to which behavioral tasks that produce highly replicable group-level effects often yield low test-retest reliability due to the inadequacy of traditional correlation methods in capturing individual differences between participants. Alongside the simple test-retest correlations, we employed a Bayesian generative model that was recently demonstrated to result in a more precise estimation of test-retest reliability. Two hundred and ninety-five participants completed an online version of the affordances task twice, with a one-week gap. Performance on the online version replicated results obtained under in-lab administrations of the task. While the simple correlation method resulted in weak test-retest measures of the different effects, the generative model yielded a good reliability assessment. The current results support the utility of the affordances task as a reliable behavioral tool for the assessment of group-level and individual differences in cognitive and visuomotor functioning. The results further support the employment of generative modeling in the study of individual differences.

Reliability Analysis of Structures by Active Learning Enhanced Sparse Bayesian Regression

Adaptive sampling near a limit state is important for metamodeling-based reliability analysis of structures involving an implicit limit state function. Active learning based on the posterior mean and standard deviation provided by a chosen metamodel is widely used for such adaptive sampling. Most studies on active learning-based reliability estimation methods use the Kriging approach, which provides prediction along with its variance. As with the Kriging approach, sparse Bayesian learning-based regression also provides posterior mean and standard deviation. Due to the sparsity involved in learning, it is expected to be computationally faster than the Kriging approach. Motivated by this, active learning-enhanced adaptive sampling-based sparse Bayesian regression is explored in the present study for reliability analysis. In doing so, polynomial basis functions, which do not involve free parameters, are chosen for the sparse Bayesian regression to avoid computationally expensive parameter tuning. The convergence of the proposed approach is attained based on the stabilization of 10 consecutive failure estimates. The effectiveness of the proposed adaptive sparse Bayesian regression approach is illustrated numerically with five examples.

Data-Based Estimation of the Dynamic Reliability and Performance Indicator of an Industrial Manufacturing System

The aim is to develop a more simple and effective method's performance and dynamic reliability assessment for complex industrial systems. By using the operating data of the industrial system characterized by a strong desynchronization and applying to it prediction algorithms of artificial intelligence applied to the time series, the model will have learned from the behavior of the complex manufacturing system allowing the operator or decision-maker to better orientate the maintenance, production, and quality policies. Furthermore, we propose this approach to avoid tedious mathematical methods related to dynamic reliability calculations and performance evaluation to make forecasts of the company's operation over a long period by identifying future bottlenecks in the system's behavior. The low-performance indicators and irrelevant reliability presented by many third-generation industries are due to the lack of efficient and simple tools for reliability assessment taking into account the dynamic aspect of the different elements of the production chain, maintenance department, production department, and quality department. We propose to develop a model that will abstract from conventional, complex, and inefficient mathematical methods for systems subject to combinatorial explosion problems in the manufacturing industry.

Psychometric Evaluation of the Polish adaptation of a Self-Report Form of the DSM-5 Level of Personality Functioning Scale (LPFS-SR).

This study examined psychometric properties of the Polish adaptation of a Self-Report Form of the DSM-5 Level of Personality Functioning Scale (LPFS-SR). It is a scale designed to measure general impairment, jointly with a detailed assessment of distinguished components of personality functioning characterized in terms of disturbances in self (identity and self-direction) and interpersonal (empathy and intimacy) functioning - Criterion A in the DSM-5 Alternative Model for Personality Disorders (AMPD). The study involved a non-clinical sample of N = 242 adults (52.9% female; Mage = 30.63 years; SDage = 11.81 years). To provide an evaluation of the criterion validity, Personality Inventory for DSM-5 (PID-5), Personality Inventory for ICD-11 (PiCD), Level of Personality Functioning Scale - Brief Form 2.0 (LPFS-BF 2.0), and Big Five Inventory-2 (BFI-2) were administered. Our data supported that identity, self-direction, intimacy, and empathy components of the LPFS-SR can be characterized by a single, global dimension of personality dysfunction, consistent with the assumption that DSM-5 Criterion A is a relatively homogeneous construct. The LPFS-SR showed good reliability estimates and demonstrated conceptually sound associations with the PD severity index and related measures of personality functioning. Moreover, all the LPFS-SR components manifested at least partial distinction from maladaptive personality traits (i.e., Criterion B in the DSM-5 AMPD). These findings provide support for the validity of the Polish adaptation of the LPFS-SR as an operationalization of impairment in the core and common features of personality pathology described in the DSM-5 alternative model.

Does workplace toxicity undermine psychological capital (PsyCap) of the employees? Exploring the moderating role of gratitude

Purpose This paper aims to examine the relationship between workplace toxicity and psychological capital (PsyCap). It also investigates the moderating role of gratitude in the toxicity–PsyCap link. Design/methodology/approach The study is based on explorative-cum-descriptive research design. The sample comprises 411 employees engaged in banking, insurance, IT, automobile and oil and gas companies. The collected data is explored for reliability, validity, multicollinearity and common method variance estimates. Also, the relationship between workplace toxicity and PsyCap and the moderating effect of gratitude are examined using structural equation modelling. Findings The findings report a negative association between toxicity and PsyCap. Also, the study concludes a significant moderating effect of gratitude. The study recommends the institutionalisation of a gratitude-based organisation to reduce the impact of workplace bullying and uncivil behaviour. Originality/value The study is based on primary data and one of the few studies that explore psychological capital as a dependent variable, which is influenced by toxic behaviours at work.

Exploring Approaches for Estimating Parameters in Cognitive Diagnosis Models with Small Sample Sizes

Cognitive diagnostic models (CDMs) are increasingly being used in various assessment contexts to identify cognitive processes and provide tailored feedback. However, the most commonly used estimation method for CDMs, marginal maximum likelihood estimation with Expectation–Maximization (MMLE-EM), can present difficulties when sample sizes are small. This study compares the results of different estimation methods for CDMs under varying sample sizes using simulated and empirical data. The methods compared include MMLE-EM, Bayes modal, Markov chain Monte Carlo, a non-parametric method, and a parsimonious parametric model such as Restricted DINA. We varied the sample size, and assessed the bias in the estimation of item parameters, the precision in attribute classification, the bias in the reliability estimate, and computational cost. The findings suggest that alternative estimation methods are preferred over MMLE-EM under low sample-size conditions, whereas comparable results are obtained under large sample-size conditions. Practitioners should consider using alternative estimation methods when working with small samples to obtain more accurate estimates of CDM parameters. This study aims to maximize the potential of CDMs by providing guidance on the estimation of the parameters.