Ordinal Factor Analysis of Graded-Preference Questionnaire Data

We introduce a new comparative response format, suitable for assessing personality and similar constructs. In this “graded-block” format, items measuring different constructs are first organized in blocks of 2 or more; then, pairs are formed from items within blocks. The pairs are presented 1 at a time to enable respondents expressing the extent of preference for 1 item or the other using several graded categories. We model such data using confirmatory factor analysis (CFA) for ordinal outcomes. We derive Fisher information matrices for the graded pairs, and supply R code to enable computation of standard errors of trait scores. An empirical example illustrates the approach in low-stakes personality assessments and shows that similar results are obtained when using graded blocks of size 3 and a standard Likert format. However, graded-block designs might be superior when insufficient differentiation between items is expected (due to acquiescence, halo, or social desirability).     

均匀分布参数基于不完全数据的区间估计

针对不完全样本观测数据,讨论了一类均匀分布总体参数的区间估计问题．利用样本中位数给出了构造置信区间的一个新枢轴量,推导出了枢轴量的概率密度函数表达式,并且在大样本场合,讨论了总体参数的近似置信区间．该方法不仅适用于不完全数据场合,而且还适用于样本中可能存在异常数据的情形,具有稳健性．     

Exploratory Graph Analysis for Factor Retention: Simulation Results for Continuous and Binary Data

Exploratory graph analysis (EGA) is a commonly applied technique intended to help social scientists discover latent variables. Yet, the results can be influenced by the methodological decisions the researcher makes along the way. In this article, we focus on the choice regarding the number of factors to retain: We compare the performance of the recently developed EGA with various traditional factor retention criteria. We use both continuous and binary data, as evidence regarding the accuracy of such criteria in the latter case is scarce. Simulation results, based on scenarios resulting from varying sample size, communalities from major factors, interfactor correlations, skewness, and correlation measure, show that EGA outperforms the traditional factor retention criteria considered in most cases in terms of bias and accuracy. In addition, we show that factor retention decisions for binary data are preferably made using Pearson, instead of tetrachoric, correlations, which is contradictory to popular belief.     

基于固定资产投资风险的控制及概率研究

针对项目投资中的现金流量估算及风险问题,基于概率数理统计分析方法,对投资项目动态决策指标NPV进行估计推断。同时,对寻求内含收益率值的插值逼近收敛方法进行了比较、分析和探讨。     

A Simple Analytic Confidence Interval for CFI Given Nonnormal Data

The comparative fit index (CFI) is one of the most widely-used fit indices in structural equation modeling (SEM). When applying the CFI to model evaluation, although it is universally recognized that the focus should be the population fit, in practice one often considers only the CFI value within a sample and neglects the uncertainty in point estimation. Confidence interval (CI) methods for CFI appeared only recently, but these methods assume multivariate normality, which often fails to hold in practice. In addition, the current methods are applications of the bootstrap and are thus computationally intensive. To better handle nonnormal data and simplify CI construction, in this paper we propose an analytic CI method for CFI without assuming normality. We then carry out simulation studies to compare the new and current methods at various levels of model misfit and nonnormality. Simulation results verify the effectiveness and advantages of the new method.     

应用Fiducial方法推断Logistic模型下的响应刺激量

基于Logistic响应模型,在二元响应数据下,将待估计的响应刺激量变换为模型的一个参数,应用鞍点逼近方法给出了该响应刺激量估计条件分布的高阶近似公式.在此基础上,引入Fiducial模型,并应用Fiducial模型给出了响应刺激量的区间估计,通过蒙特卡罗数值模拟表明,在样本量较小时,给合Fiducial模型,应用鞍点逼近方法,能够较好地估计响应刺激量.     

部分因式展法拉普拉斯反变换深层探析

运算电路的解析,常用部分因式展开法进行拉普拉斯反变换,以求取时城解。对象函数分母进行因式分解,可能出现复根情况的讨论,相关课程、文献分析不够深入,给出的求取原函数方法单一,解题容易出错。为此,对复根情况进行了较为深入的探讨,并提出了一种新颖的求解原函数的方法,消除了求解高阶象函数的原函数困惑,并很大提高了求取原函数的速度和准确率。     

Confidence Intervals for a Semiparametric Approach to Modeling Nonlinear Relations among Latent Variables

Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005) Bauer, D. J. 2005. A semiparametric approach to modeling nonlinear relations among latent variables. Structural Equation Modeling, 12: 513–535. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar] proposed an indirect application of finite mixtures of structural equation models where latent components are estimated in the service of more flexibly recovering characteristics of the latent aggregate regression function. This article develops and evaluates delta method and parametric bootstrap approaches for obtaining approximate confidence intervals for Bauer's semiparametric approach to modeling latent nonlinear functions. Coverage rates of these approximate point-wise confidence intervals or nonsimultaneous confidence bands are evaluated by Monte Carlo and recommendations for their use are suggested.     

不完备信息系统中数据推理的粗糙集方法

含空值决策表的分析一直是智能信息处理领域的一个难点，讨论基于赋值容差关系的数据不完全的信息系统中数据处理方法，并用实例说明不完全决策表中属性约简和决策规则的发现方法。     

A Two-Stage Approach to Missing Data: Theory and Application to Auxiliary Variables

A well-known ad-hoc approach to conducting structural equation modeling with missing data is to obtain a saturated maximum likelihood (ML) estimate of the population covariance matrix and then to use this estimate in the complete data ML fitting function to obtain parameter estimates. This 2-stage (TS) approach is appealing because it minimizes a familiar function while being only marginally less efficient than the full information ML (FIML) approach. Additional advantages of the TS approach include that it allows for easy incorporation of auxiliary variables and that it is more stable in smaller samples. The main disadvantage is that the standard errors and test statistics provided by the complete data routine will not be correct. Empirical approaches to finding the right corrections for the TS approach have failed to provide unequivocal solutions. In this article, correct standard errors and test statistics for the TS approach with missing completely at random and missing at random normally distributed data are developed and studied. The new TS approach performs well in all conditions, is only marginally less efficient than the FIML approach (and is sometimes more efficient), and has good coverage. Additionally, the residual-based TS statistic outperforms the FIML test statistic in smaller samples. The TS method is thus a viable alternative to FIML, especially in small samples, and its further study is encouraged.     

学校组织管理气氛结构的验证性因素分析

文章在对国内外有关学校组织气氛理论研究和教育实践调查的基础上,对学校组织管理气氛的结构因素进行了理论构想,通过探索性因素分析和验证性因素分析,发现学校组织管理气氛由民主作风、制度健全、管理系统性、关心体贴、领导威信、人际领导6个因素而构成的.     

新时期反腐倡廉建设科学化探析

反腐倡廉建设作为中国共产党建设的重要组成部分,必须适应党的建设科学化的根本要求。推进反腐倡廉建设科学化有利于提高党的建设科学化水平,有利于贯彻与落实科学发展观,有利于破解当前反腐倡廉建设的实际难题。针对当前腐败群体集团化、腐败方式变异化、腐败手段智能化、腐败行为期权化等新趋势,必须以科学发展观为根本指导,尊重反腐倡廉建设的科学规律;以加强党的制度建设为切入点,完善惩治与预防腐败的科学体系;以现代科学技术作为有效手段,提升反腐倡廉建设的科学程度;以创新廉政制度建设为重要基石,加强反腐倡廉建设的科学保障,切实提高我国反腐倡廉建设的科学化水平。     

高等院校评价教师系统框架之数据来源分析

高校评教是高校教育教学管理中的重要一环。文章针对目前已有的评教系统数据源单一、数据使用不充分、教师反馈不及时、师生互动性欠佳、学生评教积极性不高等现状,提出了一个高校评教系统新框架,以评教结果的使用范围为目的,反推出评教数据来源应具备多次性和多样性。每学期多次评教,实现教师与学生的互动,促进学生评教积极性,促进教师及时改进教学方法。评教人员除了学生之外,还应包括督导专家和往届生。     

Mixture Simultaneous Factor Analysis for Capturing Differences in Latent Variables Between Higher Level Units of Multilevel Data

Given multivariate data, many research questions pertain to the covariance structure: whether and how the variables (e.g., personality measures) covary. Exploratory factor analysis (EFA) is often used to look for latent variables that might explain the covariances among variables; for example, the Big Five personality structure. In the case of multilevel data, one might wonder whether or not the same covariance (factor) structure holds for each so-called data block (containing data of 1 higher level unit). For instance, is the Big Five personality structure found in each country or do cross-cultural differences exist? The well-known multigroup EFA framework falls short in answering such questions, especially for numerous groups or blocks. We introduce mixture simultaneous factor analysis (MSFA), performing a mixture model clustering of data blocks, based on their factor structure. A simulation study shows excellent results with respect to parameter recovery and an empirical example is included to illustrate the value of MSFA.     

大学生心理健康原因浅析及教育途径

通过对大学生心理健康原因分析,提出了开展大学生心理健康教育的有效途径,以促进大学生心理健康教育的进一步发展,从而让大学生们树立心理健康教育的新理念。     

Alternatives to Multilevel Modeling for the Analysis of Clustered Data

Multilevel modeling has grown in use over the years as a way to deal with the nonindependent nature of observations found in clustered data. However, other alternatives to multilevel modeling are available that can account for observations nested within clusters, including the use of Taylor series linearization for variance estimation, the design effect adjusted standard errors approach, and fixed effects modeling. Using 1,000 replications of 12 conditions with varied Level 1 and Level 2 sample sizes, the author compared parameter estimates, standard errors, and statistical significance using various alternative procedures. Results indicate that several acceptable procedures can be used in lieu of or together with multilevel modeling, depending on the type of research question asked and the number of clusters under investigation. Guidelines for applied researchers are discussed.     

按生产要素分配的理论基础

生产要素是人类进行物质资料生产所必须具备的因素，它包括劳动力、土地、资本、技术、信息以及管理才能等。按生产要素分配与劳动价值论在理论上具有一致性。生产方式是按生产要素分配的决定因素，价值的创造与实现是按生产要素分配的内在根据，市场经济使按生产要素分配成为必要和必然的选择。在弄清按生产要素分配的理论基础的前提下，要采取有效举措，努力探索按生产要素分配的科学途径。     

Structural Models for Binary Repeated Measures: Linking Modern Longitudinal Structural Equation Models to Conventional Categorical Data Analysis for Matched Pairs

The current widespread availability of software packages with estimation features for testing structural equation models with binary indicators makes it possible to investigate many hypotheses about differences in proportions over time that are typically only tested with conventional categorical data analyses for matched pairs or repeated measures, such as McNemar’s chi-square. The connection between these conventional tests and simple longitudinal structural equation models is described. The equivalence of several conventional analyses and structural equation models reveals some foundational concepts underlying common longitudinal modeling strategies and brings to light a number of possible modeling extensions that will allow investigators to pursue more complex research questions involving multiple repeated proportion contrasts, mixed between-subjects × within-subjects interactions, and comparisons of estimated membership proportions using latent class factors with multiple indicators. Several models are illustrated, and the implications for using structural equation models for comparing binary repeated measures or matched pairs are discussed.     

Case Diagnostics for Factor Analysis of Ordered Categorical Data With Applications to Person-Fit Measurement

Mahalanobis distance (M-distance) case diagnostics are a useful tool for assessing response pattern inconsistency in factor analysis; however, the derivations of these statistics assume continuous variables, which limits their utility in ordinal self- or rater-report data. This research generalizes M-distance diagnostics to categorical factor analysis. We prove that the residual-based M-distance d_r is equivalent to the person-fit index lco, which motivates the use of the new categorical M-distance d_r* as a person-fit index. d_r* is compared and contrasted with z_h, a commonly used item response theory person-fit index. A simulation study is used to show that a simple transformation of d_r* satisfies established criteria for person-fit measures. A sample of responses to the Rosenberg Self-Esteem Scale is used to determine parameters for a simulation study, and real data are analyzed to contrast the use of d_r and d_r* as indexes of person-fit in continuous and categorical factor analysis.     

浅谈教育学院图书馆读者文献需求特点及对策

新世纪福建教育学院的发展决定了院图书馆读者的文献需求特点,为满足不同信息用户的需求,应从开发文献信息资源、扩大服务领域、提高文献信息队伍等三方面入手.