管理胜任力特征分析:结构方程模型检验

管理胜任力特征分析是人事选拔与评价的重要内容之一。本研究在运用基于胜任力的职位分析并总结国内外有关文献的基础上,编制了管理综合素质评价量表,并运用此量表调查了220名中高层管理者,采用因素分析和结构方程模型检验企业高级管理者胜任力特征的结构。结果表明,管理胜任力特征结构由管理素质和管理技能等两个维度构成,但在维度要素及其关键度上,职位层次间存在显著差异。本研究为管理职位的测评选拔提供了新的理论依据。     

潜变量交互效应模型标准化估计中的检验问题

讨论了潜变量交互效应模型是否能直接用统计软件输出的原始估计的t值对模型的标准化估计进行检验的问题,详细介绍了标准化估计的t值计算及其难点,用Bootstrap方法算出标准化估计的标准误和相应的t值（记为t_bs）,并将其与原始估计的t值比较.结果发现,当原始估计t值超过3时,无论用t值还是用t_ bs检验,结果都是显著,即可以使用t值进行检验.而当t值不超过3时,与t_bs很接近,也可以用t值检验,但t值在临界值附近（例如1.5 ～2.5）时,最好还是使用Bootstrap法计算t_bs进行检验.     

Quantifying Parsimony in Structural Equation Modeling

Fitting propensity (FP) is defined as a model's average ability to fit diverse data patterns, all else being equal. The relevance of FP to model selection is examined in the context of structural equation modeling (SEM). In SEM it is well known that the number of free model parameters influences FP, but other facets of FP are routinely excluded from consideration. It is shown that models possessing the same number of free parameters but different structures may exhibit different FPs. The consequences of this fact are demonstrated using illustrative examples and models culled from published research. The case is made that further attention should be given to quantifying FP in SEM and considering it in model selection. Practical approaches are suggested.     

Predicting Health-related Quality of Life: Testing the Contextual Model Using Structural Equation Modeling

This study aims to investigate the utility of the Contextual Model of Health-Related Quality of Life (HRQOL) to explain the relationship among the domains of HRQOL with a diverse, population-based sample of breast cancer survivors (BCS). We employed a cross-sectional design to investigate HRQOL among 703 multiethnic, population-based BCS. The study methodology was guided by the Contextual Model of HRQOL. Structural Equation Modeling (SEM) was conducted to assess the hypothesized model. SEM identified significant relationships among the bio-psychological domain (general health status, cancer-related factors, and psychological factors), the cultural-socio-ecological domain (health care satisfaction, socio-ecological factor, and socio-economic status), and HRQOL. The best fitting model indicates direct pathways from ‘general health status’, ‘years since diagnosis’, ‘health care satisfaction’ and ‘socio-ecological factor’ to ‘HRQOL’ variables. Additionally, ‘socio-ecological factor’ and ‘socio-economic status’ variables were indirectly associated with HRQOL through ‘general health status’. Findings suggest that the Contextual Model of HRQOL adds valid factors to explain overall HRQOL and increases our understanding of the socio-ecological dimensions predicting HRQOL outcomes. The revelation of inter-relations among the dimensions of HRQOL may inform the translational and clinical utility of the HRQOL construct.

Structural Equation Modeling Approaches for Analyzing Partially Nested Data

Study designs involving clustering in some study arms, but not all study arms, are common in clinical treatment-outcome and educational settings. For instance, in a treatment arm, persons may be nested in therapy groups, whereas in a control arm there are no groups. Methodological approaches for handling such partially nested designs have recently been developed in a multilevel modeling framework (MLM-PN) and have proved very useful. We introduce two alternative structural equation modeling (SEM) approaches for analyzing partially nested data: a multivariate single-level SEM (SSEM-PN) and a multiple-arm multilevel SEM (MSEM-PN). We show how SSEM-PN and MSEM-PN can produce results equivalent to existing MLM-PNs and can be extended to flexibly accommodate several modeling features that are difficult or impossible to handle in MLM-PNs. For instance, using an SSEM-PN or MSEM-PN, it is possible to specify complex structural models involving cluster-level outcomes, obtain absolute model fit, decompose person-level predictor effects in the treatment arm using latent cluster means, and include traditional factors as predictors/outcomes. Importantly, implementation of such features for partially nested designs differs from that for fully nested designs. An empirical example involving a partially nested depression intervention combines several of these features in an analysis of interest for treatment-outcome studies.     

不同适应水平小学生有意义事件报告比较——小学生自我调节机制的一项研究

本研究选取发展良好与适应不良的三——五年级小学生为被试,以问卷访谈形式调查他们心目中的正性和负性意义事件。结果表明,两组被试在事件意义体验的人际亲和性、内在一外在性和丰富性方面有明显不同的反映。感受的丰富性表现在正负性意义事件两方面,某种事件负性意义感受的出现是良好适应的表现。     

Structural Equation Modeling of Multivariate Time Series

The covariance structure of a vector autoregressive process with moving average residuals (VARMA) is derived. It differs from other available expressions for the covariance function of a stationary VARMA process and is compatible with current structural equation methodology. Structural equation modeling programs, such as LISREL, may therefore be employed to fit the model.

Particular attention is given to assumptions concerning the process before the first observation. An application to a repeated time series is used to demonstrate the effect of these assumptions on the structure of the reproduced covariance matrix.     

A Propensity Score Adjustment for Multiple Group Structural Equation Modeling

In the behavioral and social sciences, quasi-experimental and observational studies are used due to the difficulty achieving a random assignment. However, the estimation of differences between groups in observational studies frequently suffers from bias due to differences in the distributions of covariates. To estimate average treatment effects when the treatment variable is binary, Rosenbaum and Rubin (1983a) proposed adjustment methods for pretreatment variables using the propensity score. However, these studies were interested only in estimating the average causal effect and/or marginal means. In the behavioral and social sciences, a general estimation method is required to estimate parameters in multiple group structural equation modeling where the differences of covariates are adjusted. We show that a Horvitz–Thompson-type estimator, propensity score weighted M estimator (PWME) is consistent, even when we use estimated propensity scores, and the asymptotic variance of the PWME is shown to be less than that with true propensity scores. Furthermore, we show that the asymptotic distribution of the propensity score weighted statistic under a null hypothesis is a weighted sum of independent χ² ₁ variables. We show the method can compare latent variable means with covariates adjusted using propensity scores, which was not feasible by previous methods. We also apply the proposed method for correlated longitudinal binary responses with informative dropout using data from the Longitudinal Study of Aging (LSOA). The results of a simulation study indicate that the proposed estimation method is more robust than the maximum likelihood (ML) estimation method, in that PWME does not require the knowledge of the relationships among dependent variables and covariates.     

A Bayesian Modeling Approach for Generalized Semiparametric Structural Equation Models

In behavioral, biomedical, and psychological studies, structural equation models (SEMs) have been widely used for assessing relationships between latent variables. Regression-type structural models based on parametric functions are often used for such purposes. In many applications, however, parametric SEMs are not adequate to capture subtle patterns in the functions over the entire range of the predictor variable. A different but equally important limitation of traditional parametric SEMs is that they are not designed to handle mixed data types—continuous, count, ordered, and unordered categorical. This paper develops a generalized semiparametric SEM that is able to handle mixed data types and to simultaneously model different functional relationships among latent variables. A structural equation of the proposed SEM is formulated using a series of unspecified smooth functions. The Bayesian P-splines approach and Markov chain Monte Carlo methods are developed to estimate the smooth functions and the unknown parameters. Moreover, we examine the relative benefits of semiparametric modeling over parametric modeling using a Bayesian model-comparison statistic, called the complete deviance information criterion (DIC). The performance of the developed methodology is evaluated using a simulation study. To illustrate the method, we used a data set derived from the National Longitudinal Survey of Youth.     

Evaluating Small Sample Approaches for Model Test Statistics in Structural Equation Modeling

Through Monte Carlo simulation, small sample methods for evaluating overall data-model fit in structural equation modeling were explored. Type I error behavior and power were examined using maximum likelihood (ML), Satorra-Bentler scaled and adjusted (SB; Satorra & Bentler, 1988, 1994), residual-based (Browne, 1984), and asymptotically distribution free (ADF; Browne, 1982, 1984) test statistics. To accommodate small sample sizes the ML and SB statistics were adjusted using a k-factor correction (Bartlett, 1950); the residual-based and ADF statistics were corrected using modified x² and F statistics (Yuan & Bentler, 1998, 1999). Design characteristics include model type and complexity, ratio of sample size to number of estimated parameters, and distributional form. The k-factor-corrected SB scaled test statistic was especially stable at small sample sizes with both normal and nonnormal data. Methodologists are encouraged to investigate its behavior under a wider variety of models and distributional forms.     

Hypothesis Testing and Model Comparison in Two-level Structural Equation Models

One basic and important problem in two-level structural equation modeling is to find a good model for the observed sample data. This article demonstrates the use of the well-known Bayes factor in the Bayesian literature for hypothesis testing and model comparison in general two-level structural equation models. It is shown that the proposed methodology is flexible, and can be applied to situations with a wide variety of nonnested models. Moreover, some problems encountered in using existing methods for goodness-of-fit assessment of the proposed model can be alleviated. An illustrative example with some real data from an AIDS care study is presented.     

Structural Equation Modeling with Small Samples: Test Statistics

Structural equation modeling is a well-known technique for studying relationships among multivariate data. In practice, high dimensional nonnormal data with small to medium sample sizes are very common, and large sample theory, on which almost all modeling statistics are based, cannot be invoked for model evaluation with test statistics. The most natural method for nonnormal data, the asymptotically distribution free procedure, is not defined when the sample size is less than the number of nonduplicated elements in the sample covariance. Since normal theory maximum likelihood estimation remains defined for intermediate to small sample size, it may be invoked but with the probable consequence of distorted performance in model evaluation. This article studies the small sample behavior of several test statistics that are based on maximum likelihood estimator, but are designed to perform better with nonnormal data. We aim to identify statistics that work reasonably well for a range of small sample sizes and distribution conditions. Monte Carlo results indicate that Yuan and Bentler's recently proposed F-statistic performs satisfactorily.     

On Nonequivalence of Several Procedures of Structural Equation Modeling

The normal theory based maximum likelihood procedure is widely used in structural equation modeling. Three alternatives are: the normal theory based generalized least squares, the normal theory based iteratively reweighted least squares, and the asymptotically distribution-free procedure. When data are normally distributed and the model structure is correctly specified, the four procedures are asymptotically equivalent. However, this equivalence is often used when models are not correctly specified. This short paper clarifies conditions under which these procedures are not asymptotically equivalent. Analytical results indicate that, when a model is not correct, two factors contribute to the nonequivalence of the different procedures. One is that the estimated covariance matrices by different procedures are different, the other is that they use different scales to measure the distance between the sample covariance matrix and the estimated covariance matrix. The results are illustrated using real as well as simulated data. Implication of the results to model fit indices is also discussed using the comparative fit index as an example. The work described in this paper was supported by a grant from the Research Grants Council of Hong Kong Special Administrative Region (Project No. CUHK 4170/99M) and by NSF grant DMS04-37167.     

OpenMx 2.0: Extended Structural Equation and Statistical Modeling

The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly written CSOLNP. Entire new methodologies such as item factor analysis and state space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.     

Robust Structural Equation Modeling with Missing Data and Auxiliary Variables

The paper develops a two-stage robust procedure for structural equation modeling (SEM) and an R package rsem to facilitate the use of the procedure by applied researchers. In the first stage, M-estimates of the saturated mean vector and covariance matrix of all variables are obtained. Those corresponding to the substantive variables are then fitted to the structural model in the second stage. A sandwich-type covariance matrix is used to obtain consistent standard errors (SE) of the structural parameter estimates. Rescaled, adjusted as well as corrected and F-statistics are proposed for overall model evaluation. Using R and EQS, the R package rsem combines the two stages and generates all the test statistics and consistent SEs. Following the robust analysis, multiple model fit indices and standardized solutions are provided in the corresponding output of EQS. An example with open/closed book examination data illustrates the proper use of the package. The method is further applied to the analysis of a data set from the National Longitudinal Survey of Youth 1997 cohort, and results show that the developed procedure not only gives a better endorsement of the substantive models but also yields estimates with uniformly smaller standard errors than the normal-distribution-based maximum likelihood.     

Structural Equation Modeling: Basic Concepts and Applications in Personality Assessment Research

Structural equation modeling (SEM) has become an increasingly used methodological strategy in psychology. Nevertheless, many psychologists continue to be unclear about how to apply this analytic tool in their research. This article reviews SEM from a conceptual perspective, particularly focusing on confirmatory factor analysis. Additionally, the relation between SEM and other analytic techniques (e.g., exploratory factor analysis) are addressed. A confirmatory factor analytic example is presented and reviewed in detail. Finally, limitations of SEM and other considerations are discussed.     

Structural Equation Modeling With AMOS,EQS, and LISREL: Comparative Approaches to Testing for the Factorial Validity of a Measuring Instrument

Using a confirmatory factor analytic (CFA) model as a paradigmatic basis for all comparisons, this article reviews and contrasts important features related to 3 of the most widely-used structural equation modeling (SEM) computer programs: AMOS 4.0 (Arbuckle, 1999), EQS 6 (Bentler, 2000), and LISREL 8 (Joreskog & Sorbom, 1996b). Comparisons focus on (a) key aspects of the programs that bear on the specification and testing of CFA models-preliminary analysis of data, and model specification, estimation, assessment, and misspecification; and (b) other important issues that include treatment of incomplete, nonnormally-distributed, or categorically-scaled data. It is expected that this comparative review will provide readers with at least a flavor of the approach taken by each program with respect to both the application of SEM within the framework of a CFA model, and the critically important issues, previously noted, related to data under study.     

The Eating Attitudes Test-26 Revisited using Exploratory Structural Equation Modeling

Most previous studies have failed to replicate the original factor structure of the 26-item version of the Eating Attitudes Test (EAT-26) among community samples of adolescents. The main objective of the present series of four studies (n?=?2178) was to revisit the factor structure of this instrument among mixed gender community samples of adolescents using both exploratory structural equation modeling (ESEM) and confirmatory factor analysis (CFA). First, results from the ESEM analyses provided satisfactory goodness-of-fit statistics and reliability coefficients for a six-factor model of the EAT with 18 items (EAT-18) closely corresponding to the original seven-factor structure proposed for the 40-item version of the EAT. Second, these analyses were satisfactorily replicated among a new sample of community adolescents using CFA. The results confirmed the factor loading and intercept invariance of this model across gender and age groups (i.e., early and late adolescence), as well as the complete invariance of the EAT-18 measurement model between ethnicities (i.e., European versus African origins) and across weight categories (i.e., underweight, normal weight and overweight). Finally, the last study provided support for convergent validity of the EAT-18 with the Eating Disorder Inventory and with instruments measuring global self-esteem, physical appearance, social physique anxiety and fear of negative appearance evaluation.