Month: March 2022

You use it when you are measuring something that you expect to stay constant in your sample. Face validity refers to whether an indicator seems to be a reasonable measure of its underlying construct “on its face”. For instance, the frequency of one’s attendance at religious services seems to make sense as an indication of a person’s religiosity without a lot of explanation. However, if we were to suggest how many books were checked out of an office library as a measure of employee morale, then such a measure would probably lack face validity because it does not seem to make much sense. Interestingly, some of the popular measures used in organizational research appears to lack face validity.

By substituting a multi-fidelity model for the high-fidelity model (HFM) used with MCS, similar accuracy to the high-fidelity model could be achieved at a fraction of the cost. This includes defining each construct and identifying their constituent domains and/or dimensions. Next, we select (or create) items or indicators for each construct based on our conceptualization of these construct, as described in the scaling procedure in Chapter 5. Each item is reworded in a uniform manner using simple and easy-to-understand text. Following this step, a panel of expert judges (academics experienced in research methods and/or a representative set of target respondents) can be employed to examine each indicator and conduct a Q-sort analysis.

Multiscale reliability analysis of composite structures based on computer vision

The understanding of the ground state in not exactly soluble models of spinless fermions in one dimension at small coupling is one of the results. And via the transfer matrix theory it has led to the understanding of nontrivial critical behavior in two-dimensional models that are not exactly soluble (like Ising next-nearest-neighbor or Ashkin–Teller model). Content validity is an assessment of how well a set of scale items matches with the relevant content domain of the construct that it is trying https://wizardsdev.com/en/news/multiscale-analysis/ to measure. Of course, this approach requires a detailed description of the entire content domain of a construct, which may be difficult for complex constructs such as self-esteem or intelligence. As with face validity, an expert panel of judges may be employed to examine content validity of constructs. Critical infrastructures, such as water-, sewage-, gas- and power-distribution systems and highway transportation networks are usually complex systems consisting of numerous structural components.

• The paper provides a enough description of the proposed method so that the results can be replicated.
• As shown in Table 6, the factor loadings of the items corresponding to the six factors of pride, enjoyment, anger, anxiety, hopelessness, and boredom exceeded 0.6, indicating high representativeness of the items.
• A method for generating a graphical sample is developed, which integrates the stochasticity of the fibre shape, misalignment, arrangement, volume fraction, matrix voids, and stacking sequences of the laminates.
• As a unique subject, physical education places a dual changes on students, demanding both mental and physical engagement.
• We then simply traverse the Max-tree once and add the grey-level contribution of each Chk to the appropriate bin Figure 10.

An iterative solution algorithm with a parameterized LP formulation is proposed for this purpose. Example applications to connectivity problems of an electric power substation and a network demonstrate the methodologies developed in this paper. Conceptualized the research idea, data collection and analysis, and wrote the manuscript; Q.Z., S.S.

System reliability updating

This is an onerous and relatively less popular approach, and is therefore not discussed here. In order to address the challenge suffered by the traditional approaches due to imprecise probability information, non-probabilistic approaches have been developed to describe the uncertain parameters and then conduct the reliability analysis. The non-probabilistic reliability approach has been well developed since the concept was introduced in [33], [34], and it has been applied to a variety of engineering problems. In composite structures, [36] proposed a non-probabilistic method to predict the buckling load of laminated composite plates and shells by using a convex model considering scatters in elastic module. However, convex models usually provide conservative estimations of the reliability as they give too much weight to the worst case. To enhance more accurate estimates of the upper and lower bounds, [37] proposed an interval analysis method based on interval mathematics, leading to narrower bounds than convex models with less computational cost.

Wang and Goh [27] demonstrated the use of a CNN as a surrogate for random field finite element analyses as a computationally efficient technique for reliability problems involving spatially variable soil properties. The CNN was trained using the maximum entropy method to improve the efficiency of the geotechnical reliability analysis [28]. Chi et al. used deep learning (DL) to forecast conditional probability distributions in the production of renewable energies and energy consumption, further integrated with statistics to investigate integrated energy systems [29]. Xiang et al. [30] proposed a deep reinforcement learning-based sampling method for structural reliability assessment with advantages in highly nonlinear reliability problems.

Quantitative characterization and modeling of composite microstructures by Voronoi cells

How the coupling between the different scales is handled strongly affects the efficiency of the overall model and optimization. This work proposes a new iterative methodology that combines a low-dimensional macroscopic design space with gradient information to perform accurate optimization and a high-dimensional lower-scale space where design variables uncertainties are modeled and upscaled. An inverse problem is solved at each iteration of the optimization process to identify the lower-scale configuration that meets the macroscopic properties in terms of some statistical description. The proposed approach is tested on the optimization of a composite plate subjected to buckling with uncertain ply angles. A particular orthonormal basis is constructed with Fourier chaos expansion for the metamodel upscaling, which provides a very efficient closed-form expression of the lamination parameters statistics. The results demonstrate a drastic improvement in the reliability compared to the deterministic optimized design and a significant computational gain compared to the approach of directly optimizing ply angles via a genetic algorithm.

Items that do not meet the expected norms of factor loading (same-factor loadings higher than 0.60, and cross-factor loadings less than 0.30) should be dropped at this stage. The remaining scales are evaluated for reliability using a measure of internal consistency such as Cronbach alpha. Scale dimensionality may also be verified at this stage, depending on whether the targeted constructs were conceptualized as being unidimensional or multi-dimensional.

Chapter 7 Scale Reliability and Validity

The integrated approach to measurement validation discussed here is quite demanding of researcher time and effort. Nonetheless, this elaborate multi-stage process is needed to ensure that measurement scales used in our research meets the expected norms of scientific research. Because inferences drawn using flawed or compromised scales are meaningless, scale validation and measurement remains one of the most important and involved phase of empirical research.

The control performance under uncertainties is investigated through the reliability assessment by the proposed approach and the Monte Carlo Simulation (MCS) approach, respectively. The numerical study also investigates the influence of the uncertainties in the system parameters and the earthquake excitations on the system failure probability. The results of the numerical examples demonstrate that the proposed approach can efficiently estimate the system reliability and the failure probability of an actively-controlled structure. A research instrument is created comprising all of the refined construct items, and is administered to a pilot test group of representative respondents from the target population. Data collected is tabulated and subjected to correlational analysis or exploratory factor analysis using a software program such as SAS or SPSS for assessment of convergent and discriminant validity.

Column generation methods for probabilistic logic

You can calculate internal consistency without repeating the test or involving other researchers, so it’s a good way of assessing reliability when you only have one data set. The most common way to measure parallel forms reliability is to produce a large set of questions to evaluate the same thing, then divide these randomly into two question sets. To record the stages of healing, rating scales are used, with a set of criteria to assess various aspects of wounds. The results of different researchers assessing the same set of patients are compared, and there is a strong correlation between all sets of results, so the test has high interrater reliability.

The questionnaire can accurately capture each student’s emotional states and needs, offering personalized guidance and support. Moreover, it enables insight into students’ emotional requirements during physical education classes, assisting them in better recognizing, understanding, and managing negative emotions. This ultimately enhances their mental well-being and enriches their experiences in physical activities. Additionally, the questionnaire allows for the comprehension of changes in student emotions.