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1.
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   

2.
We propose a maximum likelihood framework for estimating finite mixtures of multivariate regression and simultaneous equation models with multiple endogenous variables. The proposed “semi‐parametric” approach posits that the sample of endogenous observations arises from a finite mixture of components (or latent‐classes) of unknown proportions with multiple structural relations implied by the specified model for each latent‐class. We devise an Expectation‐Maximization algorithm in a maximum likelihood framework to simultaneously estimate the class proportions, the class‐specific structural parameters, and posterior probabilities of membership of each observation into each latent‐class. The appropriate number of classes can be chosen using various information‐theoretic heuristics. A data set entailing cross‐sectional observations for a diverse sample of businesses is used to illustrate the proposed approach.  相似文献   

3.
Structural equation modeling is a common multivariate technique for the assessment of the interrelationships among latent variables. Structural equation models have been extensively applied to behavioral, medical, and social sciences. Basic structural equation models consist of a measurement equation for characterizing latent variables through multiple observed variables and a mean regression-type structural equation for investigating how explanatory latent variables influence outcomes of interest. However, the conventional structural equation does not provide a comprehensive analysis of the relationship between latent variables. In this article, we introduce the quantile regression method into structural equation models to assess the conditional quantile of the outcome latent variable given the explanatory latent variables and covariates. The estimation is conducted in a Bayesian framework with Markov Chain Monte Carlo algorithm. The posterior inference is performed with the help of asymmetric Laplace distribution. A simulation shows that the proposed method performs satisfactorily. An application to a study of chronic kidney disease is presented.  相似文献   

4.
From the time of William James, psychologists have posited individually importance-weighted-average models (IWAMs) in which weighting specific attributes by individual measures of importance improves prediction of the global outcome measures. Because IWAMs cause much confusion, we briefly review a general taxonomic paradigm and structural equation models for testing IWAMs, and demonstrate its application for 2 simulated and 3 diverse “real” data applications (multidimensional measures of self-concept, quality of life, and job satisfaction). Consistent across the real data applications and previous research more generally, there is surprisingly little support for IWAMs when tested appropriately. In these diverse tests of IWAMs we integrate new approaches such as exploratory structural equation modeling (SEM), alternative approaches to constructing latent interactions, application of bifactor models, modeling method and item-wording effects, and the juxtaposition of model evaluation in relation to goodness of fit (typically used in SEM studies) and variance explained (typically used in multiple regression tests of IWAMs).  相似文献   

5.
Recently, analysis of structural equation models with polytomous and continuous variables has received a lot of attention. However, contributions to the selection of good models are limited. The main objective of this article is to investigate the maximum likelihood estimation of unknown parameters in a general LISREL-type model with mixed polytomous and continuous data and propose a model selection procedure for obtaining good models for the underlying substantive theory. The maximum likelihood estimate is obtained by a Monte Carlo Expectation Maximization algorithm, in which the E step is evaluated via the Gibbs sampler and the M step is completed via the method of conditional maximization. The convergence of the Monte Carlo Expectation Maximization algorithm is monitored by the bridge sampling. A model selection procedure based on Bayes factor and Occam's window search strategy is proposed. The effectiveness of the procedure in accounting for the model uncertainty and in picking good models is discussed. The proposed methodology is illustrated with a real example.  相似文献   

6.
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7.
用量子化学从头算RHF方法在6-31G（d）水平下，对吡啶类分子进行构型全优化，用优化得到的量化参数构建回归模型，预测吡啶类化合物的PKa值．实验结果表明用其模型预测的相关系数为0．9548、最大绝对误差为1．139、最小绝对误差为0．03、误差平方和为12．671．实验证明所构造的回归模型在预测吡啶类化合物的PKa值中得到满意的结果．  相似文献   

8.
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   

9.
对于农业研究中多变量线性模型参数的估计,以往常采用经典统计方法。随着计算机技术的进步,贝叶斯统计方法在科学研究的各个领域迅速发展。文章利用贝叶斯统计方法对农业研究中的多变量模型进行参数估计,并与经典统计方法进行比较,验证了贝叶斯方法的有效性。该方法可为农业研究中多变量模型参数的估计提供新的途径和手段。  相似文献   

10.
介绍了正则化方法盲复原模型的原理,对高斯模糊盲复原和运动模糊盲复原的效果进行了比较,得出了正则化方法盲复原模型算法实现简单的结论,将基于变分的偏微分方程模型应用于模糊车牌号数字图像盲复原中,利用改造的代价泛函处理模糊车牌号码,获得较好的实验效果.  相似文献   

11.
Assessing the correctness of a structural equation model is essential to avoid drawing incorrect conclusions from empirical research. In the past, the chi-square test was recommended for assessing the correctness of the model but this test has been criticized because of its sensitivity to sample size. As a reaction, an abundance of fit indexes have been developed. The result of these developments is that structural equation modeling packages are now producing a large list of fit measures. One would think that this progression has led to a clear understanding of evaluating models with respect to model misspecifications. In this article we question the validity of approaches for model evaluation based on overall goodness-of-fit indexes. The argument against such usage is that they do not provide an adequate indication of the “size” of the model's misspecification. That is, they vary dramatically with the values of incidental parameters that are unrelated with the misspecification in the model. This is illustrated using simple but fundamental models. As an alternative method of model evaluation, we suggest using the expected parameter change in combination with the modification index (MI) and the power of the MI test.  相似文献   

12.
Nonrecursive structural equation models generally take the form of feedback loops, involving 2 latent variables that are connected by 2 unidirectional paths, 1 starting with each variable and terminating in the other variable. Nonrecursive models belong to a larger class of path models that require the use of instrumental variables (IVs) to achieve model identification. Prior research has focused on SEM parameter estimation with IVs when indicators were continuous and normally distributed. Much less is known about how estimators function in the presence of categorical indicators, which are commonly used in the social sciences, such as with cognitive and affective instruments. In this study, there was specific interest in comparing the 2-stage least squares (2SLS) estimator and its categorical variant to other recommended estimators. This study compares the performance of several estimation approaches for fitting structural equation models with categorical indicator variables when IVs are necessary to obtain proper model estimates. Across conditions, 1 extension of the nonlinear 2SLS (N2SLS) approach, the nonlinear 3-stage least squares (N3SLS), which accounts for correlated errors among regressors within each model (as does the N2SLS), as well as correlations of errors across models, which N2SLS does not, appears to work the best among methods compared.  相似文献   

13.
Mathematical models are been proposed to simulate the thermal and metallurgical behaviors of the strip occtLrring on the run-out table （ROT） in a hot strip mill. A variational method is utilized for the discretization of the governing transient conduction-convection equation, with heat transfer coefficients adaptively determined by the actual mill data. To consider the thermal effect of phase transformation during cooling, a constitutive equation for describing austenite decomposition kinetics of steel in air and water cooling zones is coupled with the heat transfer model. As the basic required inputs in the numerical simulations, thermal material properties are experimentally measured for three carbon steels and the least squares method is used to statistically derive regression models for the properties, including specific heat and thermal conductivity. The numerical simulation and experimental results show that the setup accuracy of the temperature prediction system of ROT is effectively improved.  相似文献   

14.
A combined criterion involving the regression slopes of pretest-posttest achievement scores and achievement gain scores was used to classify similar types of classrooms. Mathematics achievement differences among 632 fifth graders were analysed in a longitudinal design and explained in a structural equation framework provided by LISREL, separately for four types of classrooms. The results replicated the findings of an earlier study (Schneider & Treiber, 1984) in that the local nature of achievement models could be demonstrated. That is, the structural components of the causal models could not be generalized across the four groups of classrooms. The inclusion of a second grouping criterion (i. e., achievement gain) proved useful in that a better model fit was always obtained for classrooms with high achievement agains. As a global model test ignoring group and classroom membership did mask the differential validity of the achievement model in the various subgroups, the need for multilevel approaches was emphasized.  相似文献   

15.
为了降低迭代正则化中定尺度参数对快速收敛的敏感性、自适应地优化尺度参数并提高其去噪效果,提出了一种变尺度参数的迭代正则化去噪算法.首先,修改了经典的正则化项,并推导出尺度参数公式;然后,通过研究迭代次数与尺度参数序列的变化趋势,得到变尺度参数的初始值;最后,进行正则化去噪.数值实验表明：相对于恒定尺度参数的IRM算法,变尺度参数IRM算法比选取尺度参数偏小的IRM算法迭代次数大大减少;比选取尺度参数偏大的IRM算法去噪效果更为明显,并较好地保持了图像的细节.  相似文献   

16.
Structural equation models are typically evaluated on the basis of goodness-of-fit indexes. Despite their popularity, agreeing what value these indexes should attain to confidently decide between the acceptance and rejection of a model has been greatly debated. A recently proposed approach by means of equivalence testing has been recommended as a superior way to evaluate the goodness of fit of models. The approach has also been proposed as providing a necessary vehicle that can be used to advance the inferential nature of structural equation modeling as a confirmatory tool. The purpose of this article is to introduce readers to key ideas in equivalence testing and illustrate its use for conducting model–data fit assessments. Two confirmatory factor analysis models in which a priori specified latent variable models with known structure and tested against data are used as examples. It is advocated that whenever the goodness of fit of a model is to be assessed researchers should always examine the resulting values obtained via the equivalence testing approach.  相似文献   

17.
In this work, we have investigated text readability in Bangla language. Text readability is an indicator of the suitability of a given document with respect to a target reader group. Therefore, text readability has huge impact on educational content preparation. The advances in the field of natural language processing have enabled the automatic identification of reading difficulty of texts and contributed in the design and development of suitable educational materials. In spite of the fact that, Bangla is one of the major languages in India and the official language of Bangladesh, the research of text readability in Bangla is still in its nascent stage. In this paper, we have presented computational models to determine the readability of Bangla text documents based on syntactic properties. Since Bangla is a digital resource poor language, therefore, we were required to develop a novel dataset suitable for automatic identification of text properties. Our initial experiments have shown that existing English readability metrics are inapplicable for Bangla. Accordingly, we have proceeded towards new models for analyzing text readability in Bangla. We have considered language specific syntactic features of Bangla text in this work. We have identified major structural contributors responsible for text comprehensibility and subsequently developed readability models for Bangla texts. We have used different machine-learning methods such as regression, support vector machines (SVM) and support vector regression (SVR) to achieve our aim. The performance of the individual models has been compared against one another. We have conducted detailed user survey for data preparation, identification of important structural parameters of texts and validation of our proposed models. The work posses further implications in the field of educational research and in matching text to readers.  相似文献   

18.
Abstract

School socio-economic compositional (SEC) effects have been influential in educational research predicting a range of outcomes and influencing public policy. However, some recent studies have challenged the veracity of SEC effects when applying residualised-change and fixed effects models and simulating potential measurement errors in hierarchical regression models. We review the residualised change and fixed effects methods in critical studies and find limitations in their capacity to demonstrate null compositional effects. We show this with an adjusted residualised change model finding significant SEC effects. We show structural equation models can address concerns that measurement errors inflate SEC effects by comparing hierarchical regression models to structural equation models. We find that structural equation models can detect SEC effects free from measurement error. We conclude that the reviewed critiques of SEC effects were due to methods unlikely to detect compositional effects. Future research would benefit from the identification of mediators of SEC effects.  相似文献   

19.
考虑到基于2范数的正则化算法存在对结构识别结果过度光滑的效果,提出了基于模态振与L1正则化的损伤识别方法。以—2D简支梁有限元模型为数值算例,比较了使用不同振型数不同损伤程度对损伤识别效果的影响。数值模拟结果表明,对于多损伤工况,当损伤结构的振型数和无损结构的振型数乘积数大于6时,能较好地进行损伤定位,并能对损伤程度给出定性的描述。  相似文献   

20.
Valuable methods have been developed for incorporating ordinal variables into structural equation models using a latent response variable formulation. However, some model parameters, such as the means and variances of latent factors, can be quite difficult to interpret because the latent response variables have an arbitrary metric. This limitation can be particularly problematic in growth models, where the means and variances of the latent growth parameters typically have important substantive meaning when continuous measures are used. However, these methods are often applied to grouped data, where the ordered categories actually represent an interval-level variable that has been measured on an ordinal scale for convenience. The method illustrated in this article shows how category threshold values can be incorporated into the model so that interpretation is more meaningful, with particular emphasis given to the application of this technique with latent growth models.  相似文献   

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