The Poor Fit of Model Fit for Selecting Number of Factors in Exploratory Factor Analysis for Scale Evaluation

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the ubiquity of correlated residuals and imperfect model specification. Our research focuses on a scale evaluation context and the performance of four standard model fit indices: root mean square error of approximate (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker–Lewis index (TLI), and two equivalence test-based model fit indices: RMSEAt and CFIt. We use Monte Carlo simulation to generate and analyze data based on a substantive example using the positive and negative affective schedule (N = 1,000). We systematically vary the number and magnitude of correlated residuals as well as nonspecific misspecification, to evaluate the impact on model fit indices in fitting a two-factor exploratory factor analysis. Our results show that all fit indices, except SRMR, are overly sensitive to correlated residuals and nonspecific error, resulting in solutions that are overfactored. SRMR performed well, consistently selecting the correct number of factors; however, previous research suggests it does not perform well with categorical data. In general, we do not recommend using model fit indices to select number of factors in a scale evaluation framework.     

Alternative Monte Carlo Approach for General Global Illumination

Ithasbeenwellknownthatglobalilluminationsimu latesthelighttransportamongallthesurfacesandthelightsourcesinavirtualsceneconsideringdiffusereflection/refrac tionandspecularreflection/refraction .Duetosimplicity,generalityanddimensionalindependence,MonteCarlomethodorrandomwalkmethodisoneofthemostimportantmethodsforsolvingthedifficultglobalillumination .Inpartic ular,MonteCarloisthelastresortwhenallotheranalyticalornumericalmethodsfail[1 ] .　　Lighttransportormultiplelightinteractionsamongthesu…     

圆面探测器对圆面源（非同轴）的几何因子的蒙特卡罗计算

用基本Monte Carlo方法计算了圆面探测器对均匀平行圆面源（两圆面中心轴线间距离为C）的几何因子，并且与圆面探测器对均匀同轴圆面源的几何因子进行了比较，探讨了两圆面中心轴线间距离C对几何因子的影响；为了检验本文方法及所编程序的好坏，选取C=0时的数据与多边形近似法进行了比较。并根据讨论的结果，提出了对实验操作的要求。     

运用OpenMP和CUDA优化蒙特卡洛算法

本文采用OpenMP和CUDA技术,对蒙特卡洛算法进行并行化改进,以充分利用多核处理器和GPU的计算能力,通过对比算法改进前后的性能表现,可以看到采用OpenMP和CUDA技术,能够极大提高计算性能。借鉴该方法,我们可以在个人计算机上改进相关软件计算性能。     

卡洛·斯卡帕的建筑设计思想——以意大利斯坦普利亚基金会建筑的修复设计为例

意大利建筑界殿堂级建筑师卡洛·斯卡帕,是斯坦普利亚基金会建筑修复的设计者。斯坦普利亚基金会建筑修复设计体现了他的建筑思想。卡洛·斯卡帕从场所出发,重视建筑与环境之间的关系;从文脉出发,重视建筑的历史性,其作品与周围的文脉、与威尼斯地区的历史要素密切相关。此外,他的水元素的神奇设计手法和空间动线设计以及不规则的建筑思想也在斯坦普利亚基金会建筑修复设计中体现得淋漓尽致。     

Empirical Sample Size Guidelines for Use of Latent Difference Score Mediation

Mediation models are commonly used to identify the mechanisms through which one variable influences another. Among longitudinal mediation methods, latent difference score mediation stands out due to its unique ability to capture nonlinear change over time. However, there is limited information regarding sample size demands to achieve adequate power with this method, resulting in few applications of latent difference score mediation. To address this limitation, the current study presents empirically supported sample size guidelines for 10 common latent difference score mediation structural models and 9 unique population models. The results of this study offer researchers with a set of representative sample estimates that may be used when designing studies or seeking funding.     

Using Monte Carlo Normal Distributions to Evaluate Structural Models With Nonnormal Data

Statistical theories of goodness-of-fit tests in structural equation modeling are based on asymptotic distributions of test statistics. When the model includes a large number of variables or the population is not from a multivariate normal distribution, the asymptotic distributions do not approximate the distribution of the test statistics very well at small sample sizes. A variety of methods have been developed to improve the accuracy of hypothesis testing at small sample sizes. However, all these methods have their limitations, specially for nonnormal distributed data. We propose a Monte Carlo test that is able to control Type I error with more accuracy compared to existing approaches in both normal and nonnormally distributed data at small sample sizes. Extensive simulation studies show that the suggested Monte Carlo test has a more accurate observed significance level as compared to other tests with a reasonable power to reject misspecified models.     

Simulation of scattering in dense medium by Monte Carlo method

We present a Monte Carlo (MC) method to simulate the scattering for medium within randomly distributed particles,discuss the convergence of this method by varying the size parameter ka, volume parameter ηand calculation parameter Ni, then compare this method with the classical iteration method with the same parameters. The calculation results showed that this method has good convergence and accords with the iteration method while consuming less CPU time. At the end of this paper, this method is used to discuss the visual light scatter in the c-Si/α-Si films.     

高职数学中蒙特卡罗方法的应用

计算不规则图形面积问题时常用的方法,一般是先建立不规则图形的函数,再根据定积分公式来求其面积。考虑到高职计算机类学生数学基础知识差但对计算机程序熟悉的特点,采用蒙特卡罗方法,即通过计算机编程可以实现对高职数学中不规则图形面积的计算。实证表明将数学知识点结合学生的专业特点能起到较好的教学效果。     

Comparing BSEM and EUPD Estimates for Two-Group SB-MTMM Experiments

In 2018, two different approaches have been suggested to solve the estimation problems that have been detected during the analysis of the data of the two-group split ballot multi-trait multi-method (SB-MTMM experiments performed in many countries between 2002 and 2010 in the European Social Survey). One group suggested using the Bayesian estimation. The other group suggested a new estimation procedure (EUPD) that makes use of the pooled data across the different countries. In this note, we present a comparison of the results of the two approaches on the same generated data, which are comparable with the data that created problems in the ESS.