共查询到20条相似文献，搜索用时 756 毫秒
1.
Meta-analytic structural equation modeling (MASEM) refers to a set of meta-analysis techniques for combining and comparing structural equation modeling (SEM) results from multiple studies. Existing approaches to MASEM cannot appropriately model between-studies heterogeneity in structural parameters because of missing correlations, lack model fit assessment, and suffer from several theoretical limitations. In this study, we address the major shortcomings of existing approaches by proposing a novel Bayesian multilevel SEM approach. Simulation results showed that the proposed approach performed satisfactorily in terms of parameter estimation and model fit evaluation when the number of studies and the within-study sample size were sufficiently large and when correlations were missing completely at random. An empirical example about the structure of personality based on a subset of data was provided. Results favored the third factor structure over the hierarchical structure. We end the article with discussions and future directions. 相似文献
2.
Ordinal variables are common in many empirical investigations in the social and behavioral sciences. Researchers often apply the maximum likelihood method to fit structural equation models to ordinal data. This assumes that the observed measures have normal distributions, which is not the case when the variables are ordinal. A better approach is to use polychoric correlations and fit the models using methods such as unweighted least squares (ULS), maximum likelihood (ML), weighted least squares (WLS), or diagonally weighted least squares (DWLS). In this simulation evaluation we study the behavior of these methods in combination with polychoric correlations when the models are misspecified. We also study the effect of model size and number of categories on the parameter estimates, their standard errors, and the common chi-square measures of fit when the models are both correct and misspecified. When used routinely, these methods give consistent parameter estimates but ULS, ML, and DWLS give incorrect standard errors. Correct standard errors can be obtained for these methods by robustification using an estimate of the asymptotic covariance matrix W of the polychoric correlations. When used in this way the methods are here called RULS, RML, and RDWLS. 相似文献
3.
《Structural equation modeling》2013,20(3):458-469
A method for obtaining an approximate confidence interval for the difference in root mean square error of approximation-a widely used goodness-of-fit measure-of 2 structural equation models is discussed, which is based on an application of the bootstrap methodology. The confidence interval represents a useful tool when studying plausibility of parameter restrictions in nested structural equation models and can be used for examining the difference in fit, accounting for complexity, for any 2 models-whether nested or nonnested-fitted to the same data set. The method is illustrated on a numerical example. 相似文献
4.
《Structural equation modeling》2013,20(4):598-619
Multilevel structural equation modeling (MSEM) has been proposed as an extension to structural equation modeling for analyzing data with nested structure. We have begun to see a few applications in cross-cultural research in which MSEM fits well as the statistical model. However, given that cross-cultural studies can only afford collecting data from a relatively small number of countries, the appropriateness of MSEM has been questioned. Using the data from the International Social Survey Program (1997; N = 15,244 from 27 countries), we first showed how Muth?n's MSEM procedure could be applied to a real data set on cross-cultural research. Given a small country-level sample size (27 countries) we then demonstrated that results on the individual level were quite stable even when using small individual-level sample sizes, whereas the group-level parameter estimates and their standard errors were affected unsystematically by varying individual-level sample sizes. Use of the findings for cross-cultural research and other areas with limited numbers of groups are discussed. 相似文献
5.
This study presents the random-effects rating scale model (RE-RSM) which takes into account randomness in the thresholds over persons by treating them as random-effects and adding a random variable for each threshold in the rating scale model (RSM) ( Andrich, 1978 ). The RE-RSM turns out to be a special case of the multidimensional random coefficients multinomial logit model (MRCMLM) ( Adams, Wilson, & Wang, 1997 ) so that the estimation procedures for the MRCMLM can be directly applied. The results of the simulation indicated that when the data were generated from the RSM, using the RSM and the RE-RSM to fit the data made little difference: both resulting in accurate parameter recovery. When the data were generated from the RE-RSM, using the RE-RSM to fit the data resulted in unbiased estimates, whereas using the RSM resulted in biased estimates, large fit statistics for the thresholds, and inflated test reliability. An empirical example of 10 items with four-point rating scales was illustrated in which four models were compared: the RSM, the RE-RSM, the partial credit model ( Masters, 1982 ), and the constrained random-effects partial credit model. In this real data set, the need for a random-effects formulation becomes clear. 相似文献
6.
James A. Fitzgerald 《Journal of Experimental Education》2013,81(4):364-367
Many mechanistic rules of thumb for evaluating the goodness of fit of structural equation models (SEM) emphasize model parsimony; all other things being equal, a simpler, more parsimonious model with fewer estimated parameters is better than a more complex model Although this is usually good advice, in the present article a heuristic counterexample is demonstrated in which parsimony as typically operationalized in indices of fit may be undesirable. Specifically, in simplex models of longitudinal data, the failure to include correlated uniquenesses relating the same indicators administered on different occasions will typically lead to systematically inflated estimates of stability. Although simplex models with correlated uniquenesses are substantially less parsimonious and may be unacceptable according to mechanistic decision rules that penalize model complexity, it can be argued a priori that these additional parameter estimates should be included. Simulated data . are used to support this claim and to evaluate the behavior of a variety of fit indices and decision rules. The results demonstrate the validity of Bollen and Long’s (1993) conclusion that “test statistics and fit indices are very beneficial, but they are no replacement for sound judgment and substantive expertise” (p. 8). 相似文献
7.
In structural equation modeling (SEM), researchers need to evaluate whether item response data, which are often multidimensional, can be modeled with a unidimensional measurement model without seriously biasing the parameter estimates. This issue is commonly addressed through testing the fit of a unidimensional model specification, a strategy previously determined to be problematic. As an alternative to the use of fit indexes, we considered the utility of a statistical tool that was expressly designed to assess the degree of departure from unidimensionality in a data set. Specifically, we evaluated the ability of the DETECT “essential unidimensionality” index to predict the bias in parameter estimates that results from misspecifying a unidimensional model when the data are multidimensional. We generated multidimensional data from bifactor structures that varied in general factor strength, number of group factors, and items per group factor; a unidimensional measurement model was then fit and parameter bias recorded. Although DETECT index values were generally predictive of parameter bias, in many cases, the degree of bias was small even though DETECT indicated significant multidimensionality. Thus we do not recommend the stand-alone use of DETECT benchmark values to either accept or reject a unidimensional measurement model. However, when DETECT was used in combination with additional indexes of general factor strength and group factor structure, parameter bias was highly predictable. Recommendations for judging the severity of potential model misspecifications in practice are provided. 相似文献
8.
Shaun McQuitty 《Structural equation modeling》2013,20(3):244-252
LISREL 8 invokes a ridge option when maximum likelihood or generalized least squares are used to estimate a structural equation model with a nonpositive definite covariance or correlation matrix. The implications of the ridge option for model fit statistics, parameter estimates, and standard errors are explored through the use of 2 examples. The results indicate that maximum likelihood estimates are quite stable with the ridge option, though fit statistics and standard errors vary considerably and therefore cannot be trusted. As a result of these findings, the application of the ridge method to structural equation models is not recommended. 相似文献
9.
Mike W.-L. Cheung 《Structural equation modeling》2013,20(3):429-454
Multivariate meta-analysis has become increasingly popular in the educational, social, and medical sciences. It is because the outcome measures in a meta-analysis can involve more than one effect size. This article proposes 2 mathematically equivalent models to implement multivariate meta-analysis in structural equation modeling (SEM). Specifically, this article shows how multivariate fixed-, random- and mixed-effects meta-analyses can be formulated as structural equation models. metaSEM (a free R package based on OpenMx) and Mplus are used to implement the proposed procedures. A real data set is used to illustrate the procedures. Formulating multivariate meta-analysis as structural equation models provides many new research opportunities for methodological development in both meta-analysis and SEM. Issues related to and extensions on the SEM-based meta-analysis are discussed. 相似文献
10.
Response accuracy and response time data can be analyzed with a joint model to measure ability and speed of working, while accounting for relationships between item and person characteristics. In this study, person‐fit statistics are proposed for joint models to detect aberrant response accuracy and/or response time patterns. The person‐fit tests take the correlation between ability and speed into account, as well as the correlation between item characteristics. They are posited as Bayesian significance tests, which have the advantage that the extremeness of a test statistic value is quantified by a posterior probability. The person‐fit tests can be computed as by‐products of a Markov chain Monte Carlo algorithm. Simulation studies were conducted in order to evaluate their performance. For all person‐fit tests, the simulation studies showed good detection rates in identifying aberrant patterns. A real data example is given to illustrate the person‐fit statistics for the evaluation of the joint model. 相似文献
11.
Herbert W. Marsh 《Journal of Educational Measurement》1993,30(2):157-183
The purpose of this investigation is to evaluate structural equation models (SEMs) for measures of the same construct collected on multiple occasions (one-variable, multiwave panel studies). Simplex models hypothesize that a measure at any one wave is substantially influenced by the measure at the 0immediately preceding wave; correlations between the same construct measured on different occasions are predicted to decline systematically as the number of intervening occasions increases. Alternatively, a one-factor model posits that a person's score at any one time is a function of some underlying "true" score and a random disturbance that is idiosyncratic to the time; no temporal ordering of correlations is assumed. Both the simplex and one-factor models can befit when there is only a single indicator of each construct at each wave (e.g., scale scores), but there are serious limitations to such models. Stronger models are possible when the same set of multiple indicators (e.g., the items that make up the scale) is measured at each wave. In Study 1, based on students' evaluations of teaching effectiveness collected over an 8-year period, one-factor models fit the data well, whereas simplex models did not. In Study 2, based on personality variables collected over a 4-year period during adolescence, one-factor models again provided an excellent fit to the data, whereas the simplex model did marginally poorer. The results challenge an overreliance on simplex models and demonstrate that a one-factor model is a potentially useful alternative that should be considered in multiwave studies. 相似文献
12.
When using the popular structural equation modeling (SEM) methodology, the issues of sample size, method of parameter estimation, assessment of model fit, and capitalization on chance are of great importance in the process of evaluating the results of an empirical study. We focus first on implications of the large‐sample theory underlying applications of the methodology. The utility for applied contexts of the asymptotically distribution‐free parameter estimation and model testing method is discussed next. We then argue for wider use of a recently developed, non conventional model‐fit assessment strategy in SEM. We conclude by discussing the issue of capitalization on chance, primarily in situations in which exploratory and confirmatory analyses are conducted on the same data set. 相似文献
13.
Willem E. Saris Albert Satorra William M. van der Veld 《Structural equation modeling》2013,20(4):561-582
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
14.
15.
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
16.
Effects of sample size,estimation methods,and model specification on structural equation modeling fit indexes 总被引：2，自引：0，他引：2
A Monte Carlo simulation study was conducted to investigate the effects on structural equation modeling (SEM) fit indexes of sample size, estimation method, and model specification. Based on a balanced experimental design, samples were generated from a prespecified population covariance matrix and fitted to structural equation models with different degrees of model misspecification. Ten SEM fit indexes were studied. Two primary conclusions were suggested: (a) some fit indexes appear to be noncomparable in terms of the information they provide about model fit for misspecified models and (b) estimation method strongly influenced almost all the fit indexes examined, especially for misspecified models. These 2 issues do not seem to have drawn enough attention from SEM practitioners. Future research should study not only different models vis‐à‐vis model complexity, but a wider range of model specification conditions, including correctly specified models and models specified incorrectly to varying degrees. 相似文献
17.
This article proposes 2 classes of ridge generalized least squares (GLS) procedures for structural equation modeling (SEM) with unknown population distributions. The weight matrix for the first class of ridge GLS is obtained by combining the sample fourth-order moment matrix with the identity matrix. The weight matrix for the second class is obtained by combining the sample fourth-order moment matrix with its diagonal matrix. Empirical results indicate that, with data from an unknown population distribution, parameter estimates by ridge GLS can be much more accurate than those by either GLS or normal-distribution-based maximum likelihood; and standard errors of the parameter estimates also become more accurate in predicting the empirical ones. Rescaled and adjusted statistics are proposed for overall model evaluation, and they also perform much better than the default statistic following from the GLS method. The use of the ridge GLS procedures is illustrated with a real data set. 相似文献
18.
Hypotheses about aberrant test-response behavior and hence invalid person-measurement have hitherto included factors like ability, gender, language, test-anxiety, and motivation, but these have not previously been collectively investigated with real data, or with multilevel models. This study analyzes the effect of these factors on person aberrance using a real mathematics assessment data set under the framework of a two-level (person and classroom) hierarchical model. The results suggest that higher-scoring pupils, and, to a lesser extent, second-language learners are significantly more often aberrant. But more importantly, we find that the classroom makes a significant contribution to person aberrance and conclude that studies that investigate the sources of person aberrance with real data should model the classroom as well as individual levels. 相似文献
19.
Hong Jiao Akihito Kamata Shudong Wang Ying Jin 《Journal of Educational Measurement》2012,49(1):82-100
The applications of item response theory (IRT) models assume local item independence and that examinees are independent of each other. When a representative sample for psychometric analysis is selected using a cluster sampling method in a testlet‐based assessment, both local item dependence and local person dependence are likely to be induced. This study proposed a four‐level IRT model to simultaneously account for dual local dependence due to item clustering and person clustering. Model parameter estimation was explored using the Markov Chain Monte Carlo method. Model parameter recovery was evaluated in a simulation study in comparison with three other related models: the Rasch model, the Rasch testlet model, and the three‐level Rasch model for person clustering. In general, the proposed model recovered the item difficulty and person ability parameters with the least total error. The bias in both item and person parameter estimation was not affected but the standard error (SE) was affected. In some simulation conditions, the difference in classification accuracy between models could go up to 11%. The illustration using the real data generally supported model performance observed in the simulation study. 相似文献
20.
Linear factor analysis (FA) models can be reliably tested using test statistics based on residual covariances. We show that the same statistics can be used to reliably test the fit of item response theory (IRT) models for ordinal data (under some conditions). Hence, the fit of an FA model and of an IRT model to the same data set can now be compared. When applied to a binary data set, our experience suggests that IRT and FA models yield similar fits. However, when the data are polytomous ordinal, IRT models yield a better fit because they involve a higher number of parameters. But when fit is assessed using the root mean square error of approximation (RMSEA), similar fits are obtained again. We explain why. These test statistics have little power to distinguish between FA and IRT models; they are unable to detect that linear FA is misspecified when applied to ordinal data generated under an IRT model. 相似文献