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1.
Multigroup structural equation modeling (SEM) plays a key role in studying measurement invariance and in group comparison. However, existing methods for multigroup SEM assume that different samples are independent. This article develops a method for multigroup SEM with correlated samples. Parallel to that for independent samples, the focus here is on the cross-group stability of the within-group structure and parameters. In particular, the method does not require the specification of any between-group relationship. Rescaled and adjusted statistics as well as sandwich-type covariance matrices make the developed method work for possibly nonnormal variables with finite 4th-order moments. The method is applied to a longitudinal data set on the development of entrepreneurial teams across 4 phases. Detailed analysis is provided regarding the stability of the effect of psychological compatibility on team performance, as it is mediated by fairness perception and team cohesion.  相似文献   

2.
We present a multigroup multilevel confirmatory factor analysis (CFA) model and a procedure for testing multilevel factorial invariance in n-level structural equation modeling (nSEM). Multigroup multilevel CFA introduces a complexity when the group membership at the lower level intersects the clustered structure, because the observations in different groups but in the same cluster are not independent of one another. nSEM provides a framework in which the multigroup multilevel data structure is represented with the dependency between groups at the lower level properly taken into account. The procedure for testing multilevel factorial invariance is illustrated with an empirical example using an R package xxm2.  相似文献   

3.
Previous acculturation research has established the influences of acculturation strategies and social support on cross-cultural adaptation. The present study attempted to elaborate these direct associations by proposing that social support and the use of the integration and marginalization strategies might affect psychological adaptation indirectly, via their influences on sociocultural adaptation. Two hundred and twelve Mainland Chinese students studying at a university in Hong Kong completed measures of psychological and sociocultural adaptation, the integration and marginalization strategies, and social support. Analyses using structural equation modeling (SEM) showed that sociocultural adaptation significantly mediated the effects of integration, marginalization, and social support on psychological adaptation. The direct impacts of social support and the two acculturation strategies on psychological adaptation were not significant. A multigroup SEM analysis revealed no gender differences in the full mediation model. Implications of the findings are discussed.  相似文献   

4.
Structural Equation Modeling: A Second Course, edited by Hancock and Mueller, is an important resource for methodologists, applied researchers, and students of structural equation modeling (SEM) alike. This well-written edited volume provides coverage of a number of important issues and techniques not commonly treated in a didactic manner and specifically not covered in most introductory SEM textbooks. Indeed, the topics covered in this volume are topics for which instructors of SEM courses commonly refer students to supplemental journal article readings (Stapleton &; Leite, 2005). This book is particularly valuable in that readers are provided with relevant literature reviews and as such do not have to wrestle with integrating concepts across journal articles with different notation. It is useful in its provision of concrete examples of how to implement each data analytic strategy using common SEM software. In cases where the procedure is not implemented in widely distributed software, chapter authors make clear reference to alternative software and available macros. Hancock and Mueller are to be credited for working carefully with the chapter authors for consistent use of software for their examples, notation, and tone. As such, this volume is much more than a course pack. The following provides a review of the content of each chapter in this edited volume.  相似文献   

5.
Structural equation modeling (SEM) has a long history of representing models graphically as path diagrams. This article presents the freely available semPlot package for R, which fills the gap between advanced, but time-consuming, graphical software and the limited graphics produced automatically by SEM software. In addition, semPlot offers more functionality than drawing path diagrams: It can act as a common ground for importing SEM results into R. Any result usable as input to semPlot can also be represented in any of the 3 popular SEM frameworks, as well as translated to input syntax for the R packages sem (Fox, Nie, & Byrnes, 2013) and lavaan (Rosseel, 2012). Special considerations are made in the package for the automatic placement of variables, using 3 novel algorithms that extend the earlier work of Boker, McArdle, and Neale (2002). The article concludes with detailed instructions on these node-placement algorithms.  相似文献   

6.
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   

7.
In social science research, an indirect effect occurs when the influence of an antecedent variable on the effect variable is mediated by an intervening variable. To compare indirect effects within a sample or across different samples, structural equation modeling (SEM) can be used if the computer program supports model fitting with nonlinear constraints. However, such an option is not routinely available in every popular software program. In this study, the basic idea of generating covariance-equivalent models in SEM is given and a sequential model fitting method is proposed as an alternative without the need to use nonlinear constraints. Under this method, the hypothesized model is transformed into a set of successive covariance-equivalent models so that an indirect effect is reparameterized as a single model parameter in the final transformed model. Real examples are given to illustrate how the proposed method is implemented using EQS, a SEM program that currently does not support the analysis with nonlinear constraints.  相似文献   

8.
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   

9.
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   

10.
The Rosenberg Self-Esteem scale (RSE) has been widely used in examinations of sex differences in global self-esteem. However, previous examinations of sex differences have not accounted for method effects associated with item wording, which have consistently been reported by researchers using the RSE. Accordingly, this study examined the multigroup invariance of global self-esteem and method effects associated with negatively worded items on the RSE between males and females. A correlated traits, correlated methods framework for modeling method effects was combined with a standard multigroup invariance routine using covariance structure analysis. Overall, there were few differences between males and females in terms of the measurement of self-esteem and method effects associated with negatively worded items on the RSE. Our findings suggest that, whereas method effects exist on the RSE scale for both males and females, the method effects associated with negatively worded items do not influence the measurement invariance and mean differences in global self-esteem scores between the sexes.  相似文献   

11.
The aim of this article is to consider models incorporating principal components from the perspective of structural equation modeling. These models include the principal component analysis of patterned matrices, multiple analysis of variance based on principal components, and multigroup principal component analysis. We demonstrate that these models can be fit readily using the programs LISREL 8 and Mx. The models and certain extensions are discussed, and several illustrations are given.  相似文献   

12.
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   

13.
This study investigates relations between cognitive, interpersonal, and intrapersonal opportunities, noncognitive outcomes, and student achievement in 1,096 students from 20 high schools (10 Deeper Learning [DL] network, 10 control) who participated in the Study of DL (SDL). DL is an umbrella term used to encompass the cognitive, interpersonal, and intrapersonal skills and knowledge that students need to be successful in school and the workforce. Structural equation modeling (SEM) was used to test a theoretical model that hypothesizes plausible pathways between DL opportunities to interpersonal, intrapersonal, and cognitive outcomes. Further, a multigroup structural equation modeling examined differences in these relations between DL and matched nonnetwork schools. Cognitive and intrapersonal opportunities were related to both interpersonal and intrapersonal outcomes. Further, cognitive opportunities were indirectly related to student achievement through interpersonal outcomes. Similar patterns of relations were found in DL and control schools. This study helps to build a stronger understanding of how types of DL opportunities and strategies can best develop the complex skills that students need to be successful in school and can also be used to inform future intervention designs and research studies.  相似文献   

14.
Measurement bias can be detected using structural equation modeling (SEM), by testing measurement invariance with multigroup factor analysis (Jöreskog, 1971;Meredith, 1993;Sörbom, 1974) MIMIC modeling (Muthén, 1989) or restricted factor analysis (Oort, 1992,1998). In educational research, data often have a nested, multilevel structure, for example when data are collected from children in classrooms. Multilevel structures might complicate measurement bias research. In 2-level data, the potentially “biasing trait” or “violator” can be a Level 1 variable (e.g., pupil sex), or a Level 2 variable (e.g., teacher sex). One can also test measurement invariance with respect to the clustering variable (e.g., classroom). This article provides a stepwise approach for the detection of measurement bias with respect to these 3 types of violators. This approach works from Level 1 upward, so the final model accounts for all bias and substantive findings at both levels. The 5 proposed steps are illustrated with data of teacher–child relationships.  相似文献   

15.
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16.
It is well known that measurement error in observable variables induces bias in estimates in standard regression analysis and that structural equation models are a typical solution to this problem. Often, multiple indicator equations are subsumed as part of the structural equation model, allowing for consistent estimation of the relevant regression parameters. In many instances, however, embedding the measurement model into structural equation models is not possible because the model would not be identified. To correct for measurement error one has no other recourse than to provide the exact values of the variances of the measurement error terms of the model, although in practice such variances cannot be ascertained exactly, but only estimated from an independent study. The usual approach so far has been to treat the estimated values of error variances as if they were known exact population values in the subsequent structural equation modeling (SEM) analysis. In this article we show that fixing measurement error variance estimates as if they were true values can make the reported standard errors of the structural parameters of the model smaller than they should be. Inferences about the parameters of interest will be incorrect if the estimated nature of the variances is not taken into account. For general SEM, we derive an explicit expression that provides the terms to be added to the standard errors provided by the standard SEM software that treats the estimated variances as exact population values. Interestingly, we find there is a differential impact of the corrections to be added to the standard errors depending on which parameter of the model is estimated. The theoretical results are illustrated with simulations and also with empirical data on a typical SEM model.  相似文献   

17.
Several papers have been devoted to the use of structural equation modeling (SEM) software in fitting autoregressive moving average (ARMA) models to a univariate series observed in a single subject. Van Buuren (1997) went beyond specification and examined the nature of the estimates obtained with SEM software. Although the results were mixed, he concluded that these parameter estimates resemble true maximum likelihood (ML) estimates. Molenaar (1999) argued that the negative findings for pure moving average models might be due to the absence of invertibility constraints in Van Buuren's simulation experiment. The aim of this article is to (a) reexamine the nature of SEM estimates of ARMA parameters; (b) replicate Van Buuren's simulation experiment in light of Molenaar's comment; and (c) examine the behavior of the log-likelihood ratio test. We conclude that estimates of ARMA parameters obtained with SEM software are identical to those obtained by univariate stochastic model preliminary estimation, and are not true ML estimates. Still, these estimates, which may be viewed as moment estimates, have the same asymptotic properties as ML estimates for pure autoregressive (AR) processes. For pure moving average (MA) processes, they are biased and less efficient. The estimates from SEM software for mixed processes seem to have the same asymptotic properties as ML estimates. Furthermore, the log-likelihood ratio is reliable for pure AR processes, but this is not the case for pure MA processes. For mixed processes, the behavior of the log-likelihood ratio varies, and in this case these statistics should be handled with caution.  相似文献   

18.
Recently, advancements in Bayesian structural equation modeling (SEM), particularly software developments, have allowed researchers to more easily employ it in data analysis. With the potential for greater use, come opportunities to apply Bayesian SEM in a wider array of situations, including for small sample size problems. Effective use of Bayseian estimation hinges on selection of appropriate prior distributions for model parameters. Researchers have suggested that informative priors may be useful with small samples, presuming that the mean of the prior is accurate with respect to the population mean. The purpose of this simulation study was to examine model parameter estimation for the Multiple Indicator Multiple Cause model when an informative prior distribution had an incorrect mean. Results demonstrated that the use of incorrect informative priors with somewhat larger variance than is typical, yields more accurate parameter estimates than do naïve priors, or maximum likelihood estimation. Implications for practice are discussed.  相似文献   

19.
Multilevel modeling is a statistical approach to analyze hierarchical data that consist of individual observations nested within clusters. Bayesian method is a well-known, sometimes better, alternative of Maximum likelihood method for fitting multilevel models. Lack of user friendly and computationally efficient software packages or programs was a main obstacle in applying Bayesian multilevel modeling. In recent years, the development of software packages for multilevel modeling with improved Bayesian algorithms and faster speed has been growing. This article aims to update the knowledge of software packages for Bayesian multilevel modeling and therefore to promote the use of these packages. Three categories of software packages capable of Bayesian multilevel modeling including brms, MCMCglmm, glmmBUGS, Bambi, R2BayesX, BayesReg, R2MLwiN and others are introduced and compared in terms of computational efficiency, modeling capability and flexibility, as well as user-friendliness. Recommendations to practical users and suggestions for future development are also discussed.  相似文献   

20.
Propensity score (PS) analysis aims to reduce bias in treatment effect estimates obtained from observational studies, which may occur due to non-random differences between treated and untreated groups with respect to covariates related to the outcome. We demonstrate how to use structural equation modeling (SEM) for PS analysis to remove selection bias due to latent covariates and estimate treatment effects on latent outcomes. Following the discussion of the design and analysis stages of PS analysis with SEM, an example is presented which uses the Mplus software to analyze data from the 1999 School and Staffing Survey (SASS) and 2000 Teacher Follow-up Survey (TFS) to estimate the effects teacher’s participation in a network of teachers on the teacher’s perception of workload manageability.  相似文献   

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