阅读模式及其对英语阅读教学的启示

介绍了现代语言心理学关于四种阅读模式的研究，进而从阅读能力，阅读策略，阅读图式，阅读情境四个方面，探讨了英语阅读教学观念和方法的更新。     

A New Perspective on the Effects of Covariates in Mixture Models

In this simulation study, we explored the effect of introducing covariates to a growth mixture model when covariates were also generated by a mixture model. We varied the association between the latent classes underlying the growth trajectories and the covariates, the degree of separation between the latent classes underlying the covariates, the number of covariates included, and amount of missing data in the growth data. We found that adding covariates to the growth mixture model generally hurt class recovery except where the latent classes underlying the growth trajectories and the covariates were the same or very strongly associated, and there was a large degree of separation between the classes underlying the covariates. We found that when covariates were introduced, entropy might no longer be an accurate indicator of the distinctiveness of the growth trajectory classes.     

Bayesian Analysis of Mixture Structural Equation Models With an Unknown Number of Components

Multivariate heterogenous data with latent variables are common in many fields such as biological, medical, behavioral, and social-psychological sciences. Mixture structural equation models are multivariate techniques used to examine heterogeneous interrelationships among latent variables. In the analysis of mixture models, determination of the number of mixture components is always an important and challenging issue. This article aims to develop a full Bayesian approach with the use of reversible jump Markov chain Monte Carlo method to analyze mixture structural equation models with an unknown number of components. The proposed procedure can simultaneously and efficiently select the number of mixture components and conduct parameter estimation. Simulation studies show the satisfactory empirical performance of the method. The proposed method is applied to study risk factors of osteoporotic fractures in older people.     

Automated Latent Growth Curve Model Fitting: A Segmentation and Knot Selection Approach

Latent growth curve models are widely used in the social and behavioral sciences to study complex developmental patterns of change over time. The trajectories of these developmental patterns frequently exhibit distinct segments in the studied variables. Latent growth models with piecewise functions for repeated measurements of variables have become increasingly popular for modeling such developmental trajectories. A major problem with using piecewise models is determining the precise location of the point where the change in the process has occurred and uncovering the related number of segments. The purpose of this paper is to introduce an optimization procedure that can be used to determine both the segments and location of the knots in piecewise linear latent growth models. The procedure is illustrated using empirical data in order to detect the number of segments and change points. The results demonstrate the capabilities of the procedure for fitting latent growth curve models.     

Examining Chi-Square Test Statistics Under Conditions of Large Model Size and Ordinal Data

This study examined the effect of model size on the chi-square test statistics obtained from ordinal factor analysis models. The performance of six robust chi-square test statistics were compared across various conditions, including number of observed variables (p), number of factors, sample size, model (mis)specification, number of categories, and threshold distribution. Results showed that the unweighted least squares (ULS) robust chi-square statistics generally outperform the diagonally weighted least squares (DWLS) robust chi-square statistics. The ULSM estimator performed the best overall. However, when fitting ordinal factor analysis models with a large number of observed variables and small sample size, the ULSM-based chi-square tests may yield empirical variances that are noticeably larger than the theoretical values and inflated Type I error rates. On the other hand, when the number of observed variables is very large, the mean- and variance-corrected chi-square test statistics (e.g., based on ULSMV and WLSMV) could produce empirical variances conspicuously smaller than the theoretical values and Type I error rates lower than the nominal level, and demonstrate lower power rates to reject misspecified models. Recommendations for applied researchers and future empirical studies involving large models are provided.     

企业技术创新悖论及其网络机制干预研究

随着市场环境的日趋复杂多变及竞争的日益激烈,技术创新成为提升企业竞争力、促进企业成长和效率非线性提高的重要途径。组织结构是影响企业技术创新能力的重要因素。企业技术创新管理面临着技术和组织上的两难悖论,即企业越是追求高新技术,就越是导致企业偏重内部结构并导致部门之间的隔离,从而越是阻碍了企业技术创新;高生产率、产品商业化创新和创新信息管理都需要一个严格等级分明的组织,而新技术发明和创意都要求比较灵活机动的、非等级制的组织形式。企业内外的合作网络机制是解决技术创新的两难悖论、提高创新绩效的关键。     

数据挖掘技术在客户关系管理中的应用研究

客户关系管理以客户为中心,深入研究客户行为,通过客户关怀提高客户满意度,进而提高企业的竞争力。而数据挖掘技术则为客户关系管理提供了强有力的技术支持。文章阐述了数据挖掘技术在客户关系管理中应用的研究背景和意义,并介绍了客户关系管理思想、数据挖掘的过程和方法、以及数据挖掘技术在客户关系管理中的应用。     

基于B/S结构的高校设备管理系统设计

对高校设备管理系统进行了分析和设计。首先对系统进行了需求分析;然后分析了系统的设计原则和功能模块,按照功能把系统分为四大模块,并对各模块进行了功能分析;最后采用三层体系结构对系统进行了软件结构设计。     

Endogenous technical advance and the stochastic trend in output: A neoclassical approach

In the recent endogenous growth literature, the trend in output is stochastic, while investment in scientific knowledge is treated as analogous to a capital stock. This paper models both research and development (R&D) and disembodied technical advance using neo-classical equations, which specify the equilibrium stocks as a function of prices and output. The elasticity of R&D in the production function, estimated using factor shares, is time-varying and increasing. Returns to scale are increasing in all factors. The most important aspect of the model is that the equilibrium rate of technical advance is not cyclically invariant, but co-moves with output at business cycle frequencies. This allows the production function to behave as a stochastic trend. Growth accounting demonstrates that long-term variations in the rate of productivity growth have been associated mainly with low-frequency fluctuations in the rate of disembodied technical advance, with secondary effects from the stocks of R&D and physical capital. During the high productivity period 1948–1972, technical advance contributed 1.48 percentage points per year to output, with 0.98 accounted for by R&D. The productivity slowdown of the mid-1970s was accounted by a gradual decline in the effect of R&D, and a more abrupt collapse in disembodied technology. The subsequent productivity revival was associated with two accelerations in disembodied technical advance, the first beginning in the early 1980s, and the second in the mid-1990s. The production function is simulated in a small econometric model. Model simulations find that exogenous shocks to the inflation rate generate significant variations in growth by inducing fluctuations in disembodied technology.