In this paper we focus on a well-known sequential Monte Carlo algorithm — Lomonosov’s turnip method. Despite the fact that this method was shown to be efficient under some mild conditions, it is known to be inadequate for a stable estimation of the network reliability in a rare-event setting. To overcome this obstacle, we suggest a quite general combination of sequential Monte Carlo and multilevel splitting.
Zhang D.Q., Han X., Jiang C., Liu J., Li Q. Time-dependent reliability analysis through response surface method. Natskår A., Moan T. Structural reliability analysis of a sea fastening structure for sea transport of heavy objects. This article, along with other proposed blogs on research design and writing quality research papers, will assist students, research scholars and academicians in publishing research articles in good journals.
Cronbach’s Alpha: A Tool for Assessing the Reliability of Scales
We can think of reliability as how consistently or dependably we have measured something in a given sample. Common types of reliability that we encounter in human resource management include inter-rater reliability, test-retest reliability, and internal consistency reliability. Conventionally, a measurement tool demonstrates an acceptable level of reliability in a sample when the reliability estimate is .70 or higher, where .00 indicates very low reliability and 1.00 indicates very high reliability. That being said, we should always strive for reliability estimates that are much closer to 1.00.
Moreover, Monte Carlo simulation is performed, which provides the reference solutions. In the last fifteen years the subset sampling method has often been used in reliability problems as a tool for calculating small probabilities. This method is extrapolating from an initial Monte Carlo estimate, for which the probability content of a failure domain found by a suitable higher level of the original limit state function.
Engineering Failure Analysis
Be aware that the Cronbach test is highly dependent upon the number of items in the scale . Professor Sanchez is strengthening concrete by adding fibers of carbon, steel or polymer – ranging in size from nanometers to micrometersHer work focuses on multi-scale experiments and modeling of the durability of these nanostructured materials under a variety of conditions. Nanomodification of cement-based materials shows a promising potential of greatly enhancing the material’s mechanical properties and durability, while providing smart properties such as self-sensing, electrical conductivity and ductility. Internal consistency reliability is typically estimated using a statistic called Cronbach’s alpha, which is the average correlation among all possible pairs of items, adjusting for the number of items. To estimate the Cronbach’s alpha of the BSS, go to the Analyze menu and select Scale → Reliability Analysis…. Select all the bss items and move them from the left window into the right window.
- Here we will show you how you can reduce a series of variables into one using a Reliability Analysis.
- This is achieved using an efficient finite element analysis -based multi-scale reliability framework and sequential optimisation strategy.
- In this example, all three of these items appear to “tap into” our conceptual definition of turnover intentions .
- Natskår A., Moan T. Structural reliability analysis of a sea fastening structure for sea transport of heavy objects.
- The correlation in observations between the two tests is an estimate of test-retest reliability.
- It can be intuitively observed that the samples are distributed in two separate clusters.
In past decades, tremendous research efforts have been expended in assessing reliability and performance of lifeline networks under single catastrophic event, but there are minor researches presenting a multi-hazard model. In this paper, simulating multi-hazard https://wizardsdev.com/en/news/multiscale-analysis/ effect as common cause failure group, an efficient minimal path sets method is applied to multi-hazard reliability analysis of lifeline networks. Recently, a few researchers have incorporate common cause failures in the reliability analysis of the networks.
2.4 Compute Cronbach’s alpha
The FOAM/SOAM is utilized to approximate the limit state functions, with which the NPR index of each multi-CEM component can be computed through HL-RF algorithm. The reliability of the studied structure is quantified by the multi-dimensional volume ratio of the safe domain to the whole convex domain. Two numerical examples and an engineering application are conducted in the end validating the effectiveness of the proposed method.
Viewing that the considered limit state function is nonlinear, the SOAM is adopted to approximate it. Via adjusting the threshold, the reliability analysis results under different limit state functions are obtained. As shown in Figure 11, the results obtained by using the multi-CEM are very close to the results by MCS, which is more accurate than that by using the CAM. This phenomenon once again proves the rationality and advancement the proposed method. In this example, all three of these items appear to “tap into” our conceptual definition of turnover intentions .
Data Availability Statement
A more sophisticated technique for evaluating convergent and discriminant validity is the multi-trait multi-method approach. This technique requires measuring each construct using two or more different methods (e.g., survey and personal observation, or perhaps survey of two different respondent groups such as teachers and parents for evaluating academic quality). This is an onerous and relatively less popular approach, and is therefore not discussed here. An efficient FEA-based multi-scale reliability framework used in this study is extended and combined with a proposed sequential optimisation strategy to produce an efficient, flexible and accurate RBDO framework for fibre-reinforced composite laminate components. The proposed RBDO strategy is demonstrated by finding the optimum design solution for a composite component under the effect of multi-scale uncertainties while meeting a specific stiffness reliability requirement. Performing this using the double-loop approach is computationally expensive because of the number of uncertainties and function evaluations required to assess the reliability.
Convergent validity refers to the closeness with which a measure relates to the construct that it is purported to measure, and discriminant validity refers to the degree to which a measure does not measure other constructs that it is not supposed to measure. Usually, convergent validity and discriminant validity are assessed jointly for a set of related constructs. For instance, if you expect that an organization’s knowledge is related to its performance, how can you assure that your measure of organizational knowledge is indeed measuring organizational knowledge and not organizational performance ?
While when the limit state function is strongly nonlinear, the SOAM is suggested, so as to improve the calculation accuracy. Then by performing regularization on the multidimensional ellipsoid and eigenvalue-decomposition on the characteristic matrix Ωk, a unit sphere space δ can be obtained for each ellipsoidal component . Based on the output from the alpha function, we can conclude that the raw alpha of .83 for all three items exceeds our cutoff of .70 for acceptable internal consistency, and enters into the realm of what we would consider to be good internal consistency. Next, take a look at the output table called Reliability if an item is dropped; this table indicates what would happen to Cronbach’s alpha if you were to drop the item listed in the row in which the item appears and then re-estimate Cronbach’s alpha. For example, if you dropped TurnInt1 and retained all other items, Cronbach’s alpha would drop to approximately .75.
Complex systems are characterized by large numbers of components, cut sets or link sets, or by statistical dependence between the component states. These measures of complexity render the computation of system reliability a challenging task. In this paper, a decomposition approach is described, which, together with a linear programming formulation, allows determination of bounds on the reliability of complex systems with manageable computational effort. The approach also facilitates multi-scale modeling and analysis of a system, whereby varying degrees of detail can be considered in the decomposed system. The paper also describes a method for computing bounds on conditional probabilities by use of linear programming, which can be used to update the system reliability for any given event. According to the sample distribution of the uncertain variables, two typical situations can be considered for the constructed multi-CEM.
