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1.
When structural equation modeling (SEM) analyses are conducted, significance tests for all important model relationships (parameters including factor loadings, covariances, etc.) are typically conducted at a specified nominal Type I error rate (α). Despite the fact that many significance tests are often conducted in SEM, rarely is multiplicity control applied. Cribbie (2000, 2007) demonstrated that without some form of adjustment, the familywise Type I error rate can become severely inflated. Cribbie also confirmed that the popular Bonferroni method was overly conservative due to the correlations among the parameters in the model. The purpose of this study was to compare the Type I error rates and per-parameter power of traditional multiplicity strategies with those of adjusted Bonferroni procedures that incorporate not only the number of tests in a family, but also the degree of correlation between parameters. The adjusted Bonferroni procedures were found to produce per-parameter power rates higher than the original Bonferroni procedure without inflating the familywise error rate. 相似文献
2.
Using data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998–99 (N = 4447), this analysis employs an opportunity-propensity (O-P) framework (Byrnes, 2003; Byrnes & Miller, 2007; Byrnes & Wasik 2009) to examine the influence of multiple student, teacher, classroom, and school factors on eighth-grade science achievement. Saçkes, Trundle, Bell, and O’Connell (2011) fit an O-P structural equation model (SEM) to the same database to explain science achievement growth from Kindergarten to third grade. We extend this work by fitting an O-P SEM to this database to predict science achievement growth from fifth to eighth grade. This middle school model includes an opportunity variable – science curriculum track placement – that operates only in middle and high school. This variable and the school’s poverty rate are significant predictors of several opportunity factors. We replicate previous findings that propensity factors are the strongest determinants of science achievement, notably prior achievement. However, we find more opportunity factors than previous studies that are also significant. Other things being equal, having a state-certified teacher is the second strongest predictor of achievement within the model. Placement in a science honors course and being enrolled in a low income school are also linked to small but significant impacts on science achievement. 相似文献
3.
We study whether changes in school emphasis on academic success (SEAS) and safe schools (SAFE) may explain the increased science performance in Norway between TIMSS 2007 and 2011. Two-level structural equation modelling (SEM) of merged TIMSS data was used to investigate whether changes in levels of SEAS and SAFE mediate the changes in science performance. Two mediation models were fitted, one using subdomain scores of science as manifest dependent variables and one in which these scores are indicators of a latent science performance variable. The change in the latent science variable was fully mediated by SEAS, but this model did not explain changes in earth science performance, which increased more than the other subdomains. In the model with subdomain scores as manifest dependent variables, SEAS mediated the increased performance of all 4 subdomains of science. SAFE did not explain increased science performance but did have a positive impact on SEAS. 相似文献
4.
Mike W.-L. Cheung 《Structural equation modeling》2013,20(1):157-167
Structural equation modeling (SEM) is now a generic modeling framework for many multivariate techniques applied in the social and behavioral sciences. Many statistical models can be considered either as special cases of SEM or as part of the latent variable modeling framework. One popular extension is the use of SEM to conduct linear mixed-effects modeling (LMM) such as cross-sectional multilevel modeling and latent growth modeling. It is well known that LMM can be formulated as structural equation models. However, one main difference between the implementations in SEM and LMM is that maximum likelihood (ML) estimation is usually used in SEM, whereas restricted (or residual) maximum likelihood (REML) estimation is the default method in most LMM packages. This article shows how REML estimation can be implemented in SEM. Two empirical examples on latent growth model and meta-analysis are used to illustrate the procedures implemented in OpenMx. Issues related to implementing REML in SEM are discussed. 相似文献
5.
中小企业融资问题已成为国际上公认的难题。在我国，金融机构的贷款是中小企业获得资金的主要来源，鉴于我国中小企业在国民经济中的重要作用和地位，金融机构应大力支持中小企业的发展。本在通过对金融机构支持中小企业的现状分析，探讨了金融机构在提高自身的经营业绩效益的同时，应如何进一步加大对中小企业的扶持力度。 相似文献
6.
Structural equation modeling: Back to basics 总被引：1，自引：0，他引：1
Ralph O. Mueller 《Structural equation modeling》2013,20(4):353-369
Major technological advances incorporated into structural equation modeling (SEM) computer programs now make it possible for practitioners who are basically unfamiliar with the purposes and limitations of SEM to use this tool within their research contexts. The current move by program developers to market more user friendly software packages is a welcomed trend in the social and behavioral science research community. The quest to simplify the data analysis step in the research process has—at least with regard to SEM—created a situation that allows practitioners to apply SEM but forgetting, knowingly ignoring, or most dangerously, being ignorant of some basic philosophical and statistical issues that must be addressed before sound SEM analyses should be conducted. This article focuses on some of the almost forgotten topics taken here from each step in the SEM process: model conceptualization, identification and parameter estimation, and data‐model fit assessment and model modification. The main objective is to raise awareness among researchers new to SEM of a few basic but key philosophical and statistical issues. These should be addressed before launching into any one of the new generation of SEM software packages and being led astray by the seemingly irresistible temptation to prematurely start “playing” with the data. 相似文献
7.
结构方程模型及其在数学教育研究中的一个案例 总被引：2，自引：0，他引：2
结构方程模型(SEM)是基于变量的协方差矩阵来分析变量间关系的一种统计方法,它融合了因素分析和路径分析两种统计技术,主要特色是能在估计结构关系时有效控制测量误差的影响,尤其是在测量工具编制上更具有独特的贡献。以编制初中生数学信念量表的具体案例,说明结构方程模型这种方法在教育心理研究中的应用。 相似文献
8.
The purpose of this study is to examine how the dimensions of strategic enrolment management (SEM) tie to the success metrics in the area of enrolment, retention and graduation from senior and programme management perspectives of a self-financed institution in Hong Kong. The literature on SEM has demonstrated that managing enrolment is a global concern and requires institution-wide effort. For successful SEM, it has to be a performance-based, outcome-oriented system which requires significant data to determine its effectiveness, success or failure, growth or decline. Though the focus of most SEM research is about the implementation of SEM in achieving enrolment and retention goals, there are far fewer studies that look critically at the perceptions of success tying SEM to success metrics. This study fills the research gap regarding the perceptions of tying SEM to the success. Thus, success metrics are vital in assessing the achievement of enrolment, retention and graduation goals. This study employs a combination of survey results and a formal content analysis methodology from a series of in-depth face-to-face interviews with both senior and programme managers at a self-financed institution in Hong Kong. The research identifies the perceived importance of different components related to enrolment, retention and graduation successes and examines differences in these perceptions between senior management and programme management. New success metrics (availability of an Honours degree programme, the employability ratio and student learning outcomes) are found in both enrolment and graduation stages which are quantified as perceived SEM success in a self-financed institution in Hong Kong. 相似文献
9.
Structural equation modeling (SEM) is a versatile statistical modeling tool. Its estimation techniques, modeling capacities, and breadth of applications are expanding rapidly. This module introduces some common terminologies. General steps of SEM are discussed along with important considerations in each step. Simple examples are provided to illustrate some of the ideas for beginners. In addition, several popular specialized SEM software programs are briefly discussed with regard to their features and availability. The intent of this module is to focus on foundational issues to inform readers of the potentials as well as the limitations of SEM. Interested readers are encouraged to consult additional references for advanced model types and more application examples. 相似文献
10.
李霞 《信阳师范学院学报(哲学社会科学版)》2001,(5)
目前结构方程模型（ＳＥＭ）已成为心理学、社会学、组织行为 学各领域有力的研究 手段之一，它是理论发展的重要工具，促使研究者细致、认真地考虑研究的理论构思与变量 结构，使研究更为严密并富有理论与实际意义。 相似文献
11.
12.
Joyce L. Y. Kwan 《Structural equation modeling》2013,20(2):225-238
In social science research, a common topic in multiple regression analysis is to compare the squared multiple correlation coefficients in different populations. Existing methods based on asymptotic theories (Olkin & Finn, 1995) and bootstrapping (Chan, 2009) are available but these can only handle a 2-group comparison. Another method based on structural equation modeling (SEM) has been proposed recently. However, this method has three disadvantages. First, it requires the user to explicitly specify the sample R2 as a function in terms of the basic SEM model parameters, which is sometimes troublesome and error prone. Second, it requires the specification of nonlinear constraints, which is not available in some popular SEM software programs. Third, it is for a 2-group comparison primarily. In this article, a 2-stage SEM method is proposed as an alternative. Unlike all other existing methods, the proposed method is simple to use, and it does not require any specific programming features such as the specification of nonlinear constraints. More important, the method allows a simultaneous comparison of 3 or more groups. A real example is given to illustrate the proposed method using EQS, a popular SEM software program. 相似文献
13.
Joerg Evermann 《Structural equation modeling》2013,20(4):677-702
Multiple-group analysis in covariance-based structural equation modeling (SEM) is an important technique to ensure the invariance of latent construct measurements and the validity of theoretical models across different subpopulations. However, not all SEM software packages provide multiple-group analysis capabilities. The sem package for the R system, which holds an important position as the only open-source SEM software, does not currently offer multigroup analysis. This article offers an alternative to true multigroup modeling that is easy to understand and apply in the R software. It is limited, however, by the constraint that groups require equal sample size. 相似文献
14.
结构方程模型(SEM)的原理及操作 总被引：10，自引：0，他引：10
孙连荣 《宁波大学学报(教育科学版)》2005,27(2):31-34,43
结构方程模型(SEM)是应用线性方程系统表示观测变量与潜在变量之间及潜在变量之间关系的一种统计方法。当前，SEM及相应的LISREL软件已成为心理学等社会学科中广泛应用的一种分析思想和技术。文章简要介绍了SEM的特点、原理及LISREL的操作方法。 相似文献
15.
中国农村工资性就业教育收益率的估计 总被引：1，自引：0，他引：1
根据CHIP农村住户调查数据,估算了1995、2002、2007年中国农村工资性就业的教育收益率。OLS估计表明1995-2007年农村全体打工者的教育收益率在3%-4%之间,呈轻微的下降趋势。在纠正了能力偏误和样本选择偏差之后,从2002年到2007年,全体打工者的教育收益率从3.2%下降到2.6%,本地打工者的教育收益率从3.5%下降到2.0%,外出打工者的教育收益率从不显著上升到4.5%。此外,教育水平的提高有助于促进农村劳动力从农业生产转向工资性就业,特别是转向本地的工资性就业。 相似文献
16.
《Structural equation modeling》2013,20(4):620-641
The Bollen-Stine bootstrap can be used to correct for standard error and fit statistic bias that occurs in structural equation modeling (SEM) applications due to nonnormal data. The purpose of this article is to demonstrate the use of a custom SAS macro program that can be used to implement the Bollen-Stine bootstrap with existing SEM software. Although this article focuses on missing data, the macro can be used with complete data sets as well. A series of heuristic analyses are presented, along with detailed programming instructions for each of the commercial SEM software packages. 相似文献
17.
Xitao Fan 《Structural equation modeling》2013,20(1):65-79
This article illustrates the relation between structural equation modeling (SEM) and canonical correlation analysis (CCA). The representation of CCA in SEM may provide some important interpretive information that is not available from conventional CCA, that is, statistical tests for the canonical function and index coefficients, and statistical tests for individual canonical functions. Hierarchically, the relation between the two analytic approaches suggests that SEM stands to be a more general analytic approach. For researchers interested in these techniques, an understanding of the interrelation among them can be helpful to our choice of analytic method. 相似文献
18.
三台县城区大气环境质量分析与评价 总被引：1，自引：0，他引：1
根据2006～2007年三台县城区SO2、NO2、PM10浓度的监测数据,综合运用大气环境质量指数评价法和污染负荷分析法对三台县城区的大气质量进行分析与评价。结果表明：三台县城区主要大气污染物依次为PM10、SO2和NO2,污染程度相对较低,2007年较之2006年大气质量总体上有较大改善。SO2和PM10在2006～2007年间污染负荷均呈下降趋势,而NO2则呈现上升趋势,说明三台县城区大气污染正由煤烟型逐步向燃气型的趋势过渡。 相似文献
19.
ABSTRACTIn this paper, we draw from elements of a study that sought to examine how teenage South African girls, both white and black African, articulate their relationship with online sexually explicit materials (SEM). The study contributes to the literature by resisting the dominant discursive practices underlined by the construction of sexuality as an exclusive realm of danger for teenage girls. Challenging this static version of femininity, we focus on the ways in which teenage girls, aged between 13 and 18 years old in two elite private schools, use online SEM to expand their sexual knowledge and engage in pleasurable forms of sexuality. By drawing on individual interviews, focus group discussions and open-ended visual elicitation research methods, we show how girls embrace online SEM in ways that expand the definition of femininity beyond fearing sexuality whilst demonstrating the entanglement with gender inequalities. Girls’ relationship with online SEM, whilst tenuous, disrupts normative assumptions around femininity. However, an ambiguous relationship with online SEM is evident as their challenges to dominant femininity are mediated by concerns about respect and innocence, as well as by persistent evidence of male power within online SEM. Implications for school-based sexuality education concludes the paper. 相似文献
20.
Michael Willoughby Nathan Vandergrift Clancy Blair Douglas A. Granger 《Structural equation modeling》2013,20(1):125-145
AbstractThis study introduces a novel application of structural equation modeling (SEM) for the analysis of cortisol data that are collected using a pre–post–post design. By way of an extended example, an SEM model is developed that permits an examination of both the overall level of cortisol, as well as changes in cortisol (reactivity and regulation), as predictors of cognitive (executive) and behavioral functioning in 3- to 5-year-old children (N = 171) attending Head Start. The SEM model makes use of the parameterization of latent curve models. Throughout the extended example, the strengths of using an SEM approach for the analysis of cortisol data that are collected using pre–post–post designs is highlighted. 相似文献