共查询到20条相似文献，搜索用时 31 毫秒
1.
Multigroup exploratory factor analysis (EFA) has gained popularity to address measurement invariance for two reasons. Firstly, repeatedly respecifying confirmatory factor analysis (CFA) models strongly capitalizes on chance and using EFA as a precursor works better. Secondly, the fixed zero loadings of CFA are often too restrictive. In multigroup EFA, factor loading invariance is rejected if the fit decreases significantly when fixing the loadings to be equal across groups. To locate the precise factor loading non-invariances by means of hypothesis testing, the factors’ rotational freedom needs to be resolved per group. In the literature, a solution exists for identifying optimal rotations for one group or invariant loadings across groups. Building on this, we present multigroup factor rotation (MGFR) for identifying loading non-invariances. Specifically, MGFR rotates group-specific loadings both to simple structure and between-group agreement, while disentangling loading differences from differences in the structural model (i.e., factor (co)variances). 相似文献
2.
Manuel C. Voelkle Johan H. L. Oud Timo von Oertzen Ulman Lindenberger 《Structural equation modeling》2013,20(3):329-350
This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary T and N by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time series analysis (T large and N = 1) and conventional SEM (N large and T = 1 or small) by integrating both approaches. The resulting combined model offers a variety of new modeling options including a direct test of the ergodicity hypothesis, according to which the factorial structure of an individual observed at many time points is identical to the factorial structure of a group of individuals observed at a single point in time. Third, we illustrate the flexibility of SEM time series modeling by extending the approach to account for complex error structures. We end with a discussion of current limitations and future applications of SEM-based time series modeling for arbitrary T and N. 相似文献
3.
This Monte Carlo study investigated the impacts of measurement noninvariance across groups on major parameter estimates in latent growth modeling when researchers test group differences in initial status and latent growth. The average initial status and latent growth and the group effects on initial status and latent growth were investigated in terms of Type I error and bias. The location and magnitude of noninvariance across groups was related to the location and magnitude of bias and Type I error in the parameter estimates. That is, noninvariance in factor loadings and intercepts was associated with the Type I error inflation and bias in the parameter estimates of the slope factor (or latent growth) and the intercept factor (or initial status), respectively. As noninvariance became large, the degree of Type I error and bias also increased. On the other hand, a correctly specified second-order latent growth model yielded unbiased parameter estimates and correct statistical inferences. Other findings and implications on future studies were discussed. 相似文献
4.
Catherine Landreth Gladys M. Gardner Bettie C. Eckhardt Anne D. Prugh 《Journal of Experimental Education》2013,81(2):65-91
The theoretical basis for the Analysis of Means Technique is discussed. In addition, a simplified working procedure is outlined, step-by-step for an actual problem. The data for the problem are analyzed by the analysis of means techniques, which compares differences between means instead of population variances estimates. The data are also analyzed by the analysis of variance technique. The conclusions reached by both techniques are the same. The graphical representation of the ANOM is helpful in understanding data. The ANOM technique is usually used in conjunction with the analysis of variance, either as an initial analysis of data or to augment the analysis of variance. 相似文献
5.
The models presented here posit a complex relationship between efficacy in student engagement and intent-to-leave that is mediated by in-class variables of instructional management, student behavior stressors, aspects of burnout, and job satisfaction. Using data collected from 631 teachers, analyses provided support for the two models that predicted teachers' intent-to-leave. To enhance generalizability, this study also tested whether the structural coefficients were invariant across teacher gender and grade level. With one exception, the models appeared largely invariant across gender and grade level. Supplementing the structural models, measurement invariance and equality of latent factor means were also explored. 相似文献
6.
Testing for factorial invariance of the Modified Leadership Scale for Sports: using a Japanese version 总被引：1，自引：1，他引：0
The objective of this study was to provide empirical evidence to support psychometric properties of a modified four-dimensional model of the Leadership Scale for Sports (LSS). The study tested invariance of all parameters (i.e., factor loadings, error variances, and factor variances–covariances) in the four-dimensional measurement model between two groups of student-athletes. For testing multi-group invariance of the proposed scale, 335 middle school and 320 high school student-athletes in Japan participated in this study. The modified version of the LSS consists of 35 items representing training instruction, democratic behaviour, positive feedback, and social support. A chi-square difference test was employed for model comparisons. The results supported configural, metric, scalar and factor variance–covariance invariance in the modified LSS across the two student-athlete groups. 相似文献
7.
Tihomir Asparouhov 《Structural equation modeling》2013,20(4):495-508
This article presents a new method for multiple-group confirmatory factor analysis (CFA), referred to as the alignment method. The alignment method can be used to estimate group-specific factor means and variances without requiring exact measurement invariance. A strength of the method is the ability to conveniently estimate models for many groups. The method is a valuable alternative to the currently used multiple-group CFA methods for studying measurement invariance that require multiple manual model adjustments guided by modification indexes. Multiple-group CFA is not practical with many groups due to poor model fit of the scalar model and too many large modification indexes. In contrast, the alignment method is based on the configural model and essentially automates and greatly simplifies measurement invariance analysis. The method also provides a detailed account of parameter invariance for every model parameter in every group. 相似文献
8.
Christian Geiser Michael Eid Stephen G. West Tanja Lischetzke Fridtjof W. Nussbeck 《Structural equation modeling》2013,20(3):409-436
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
9.
《Structural equation modeling》2013,20(4):475-502
Over the past decade and a half, methodologists working with structural equation modeling (SEM) have developed approaches for accommodating multilevel data. These approaches are particularly helpful when modeling data that come from complex sampling designs. However, most data sets that are associated with complex sampling designs also include observation weights, and methods to incorporate these sampling weights into multilevel SEM analyses have not been addressed. This article investigates the use of different weighting techniques and finds, through a simulation study, that the use of an effective sample size weight provides unbiased estimates of key parameters and their sampling variances. Also, a popular normalization technique of scaling weights to reflect the actual sample size is shown to produce negatively biased sampling variance estimates, as well as negatively biased within-group variance parameter estimates in the small group size case. 相似文献
10.
The purpose of this study was to examine the impact of misspecifying a growth mixture model (GMM) by assuming that Level-1 residual variances are constant across classes, when they do, in fact, vary in each subpopulation. Misspecification produced bias in the within-class growth trajectories and variance components, and estimates were substantially less precise than those obtained from a correctly specified GMM. Bias and precision became worse as the ratio of the largest to smallest Level-1 residual variances increased, class proportions became more disparate, and the number of class-specific residual variances in the population increased. Although the Level-1 residuals are typically of little substantive interest, these results suggest that researchers should carefully estimate and report these parameters in published GMM applications. 相似文献
11.
刘谢进 《楚雄师范学院学报》2006,21(12):47-49
在给定的权回归模型下,讨论了最小二乘估计、最优加权最小二乘估计和线性无偏最小方差估计的性能比较,得出了在随机误差方差矩阵可逆条件下,可算出最优加权最小二乘估计与线性无偏最小方差估计误差方差阵的差表达式,并在一定条件下,两者趋于一致。 相似文献
12.
As a prerequisite for meaningful comparison of latent variables across multiple populations, measurement invariance or specifically factorial invariance has often been evaluated in social science research. Alongside with the changes in the model chi-square values, the comparative fit index (CFI; Bentler, 1990) is a widely used fit index for evaluating different stages of factorial invariance, including metric invariance (equal factor loadings), scalar invariance (equal intercepts), and strict invariance (equal unique factor variances). Although previous literature generally showed that the CFI performed well for single-group structural equation modeling analyses, its applicability to multiple group analyses such as factorial invariance studies has not been examined. In this study we argue that the commonly used default baseline model for the CFI might not be suitable for factorial invariance studies because (a) it is not nested within the scalar invariance model, and thus (b) the resulting CFI values might not be sensitive to the group differences in the measurement model. We therefore proposed a modified version of the CFI with an alternative (and less restrictive) baseline model that allows observed variables to be correlated. Monte Carlo simulation studies were conducted to evaluate the utility of this modified CFI across various conditions including varying degree of noninvariance and different factorial invariance models. Results showed that the modified CFI outperformed both the conventional CFI and the ΔCFI (Cheung & Rensvold, 2002) in terms of sensitivity to small and medium noninvariance. 相似文献
13.
A typical structural equation model is intended to reproduce the means, variances, and correlations or covariances among a set of variables based on parameter estimates of a highly restricted model. It is not widely appreciated that the sample statistics being modeled can be quite sensitive to outliers and influential observations, leading to bias in model parameter estimates. A classic public epidemiological data set on the relation between cigarette purchases and rates of 4 types of cancer among states in the United States is studied with case-weighting methods that reduce the influence of a few cases on the overall results. The results support and extend the original conclusions; the standardized effect of smoking on a factor underlying deaths from bladder and lung cancer is .79. 相似文献
14.
15.
The objective was to offer guidelines for applied researchers on how to weigh the consequences of errors made in evaluating measurement invariance (MI) on the assessment of factor mean differences. We conducted a simulation study to supplement the MI literature by focusing on choosing among analysis models with different number of between-group constraints imposed on loadings and intercepts of indicators. Data were generated with varying proportions, patterns, and magnitudes of differences in loadings and intercepts as well as factor mean differences and sample size. Based on the findings, we concluded that researchers who conduct MI analyses should recognize that relaxing as well as imposing constraints can affect Type I error rate, power, and bias of estimates in factor mean differences. In addition, fit indexes can be misleading in making decisions about constraints of loadings and intercepts. We offer suggestions for making MI decisions under uncertainty when assessing factor mean differences. 相似文献
16.
To infer longitudinal relationships among latent factors, traditional analyses assume that the measurement model is invariant across measurement occasions. Alternative to placing cross-occasion equality constraints on parameters, approximate measurement invariance (MI) can be analyzed by specifying informative priors on parameter differences between occasions. This study evaluated the estimation of structural coefficients in multiple-indicator autoregressive cross-lagged models under various conditions of approximate MI using Bayesian structural equation modeling. Design factors included factor structures, conditions of non-invariance, sizes of structural coefficients, and sample sizes. Models were analyzed using two sets of small-variance priors on select model parameters. Results showed that autoregressive coefficient estimates were more accurate for the mixed pattern than the decreasing pattern of non-invariance. When a model included cross-loadings, an interaction was found between the cross-lagged estimates and the non-invariance conditions. Implications of findings and future research directions are discussed. 相似文献
17.
为了提高行程时间预测的可靠性,构建了自回归综合移动平均与广义自回归条件异方差性(ARIMAGARCH)模型进行城市主干道行程时间动态置信区间预测,其中ARIMA模型作为GARCH模型的均值方程用于捕获行程时间均值,GARCH模型用于捕获行程时间条件方差.运用昆山市交通监测系统中采集的实际交通流数据进行验证和评估.结果表明,相较于传统的ARIMA模型,提出的方法虽然不能显著提升行程时间均值的预测性能,但是在行程时间波动性预测方面具有较大的优势.该方法可捕获行程时间异方差,从而能够预测出比ARIMA模型预测的固定置信区间更能反映行程时间观测值波动性的动态置信区间. 相似文献
18.
David Woodruff 《Journal of Educational Measurement》1991,28(3):191-196
In Woodruff (1990), I derived estimates for the conditional standard error of measurement in prediction (CSEMP), the conditional standard error of estimation (CSEE), and the conditional standard error of prediction (CSEP). My original estimates assume that the conditional residual error score variances and the conditional residual true score variances, obtained from the regression of an observed score onto a parallel observed score, obey the same step-up rules as do the marginal error score variance and the marginal true score variance. The present article derives alternative estimates for the various test score conditional variances that do not depend on these assumptions. 相似文献
19.
Minor cross-loadings on non-targeted factors are often found in psychological or other instruments. Forcing them to zero in confirmatory factor analyses (CFA) leads to biased estimates and distorted structures. Alternatively, exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM) have been proposed. In this research, we compared the performance of the traditional independent-clusters-confirmatory-factor-analysis (ICM-CFA), the nonstandard CFA, ESEM with the Geomin- or Target-rotations, and BSEMs with different cross-loading priors (correct; small- or large-variance priors with zero mean) using simulated data with cross-loadings. Four factors were considered: the number of factors, the size of factor correlations, the cross-loading mean, and the loading variance. Results indicated that ICM-CFA performed the worst. ESEMs were generally superior to CFAs but inferior to BSEM with correct priors that provided the precise estimation. BSEM with large- or small-variance priors performed similarly while the prior mean for cross-loadings was more important than the prior variance. 相似文献
20.
The knowledge explosion in combination with an overcrowded curriculum at all educational levels is causing many educators to place greater emphasis on attitude conceptualization. This paper confronts two interrelated problems, that problem dealing with the psychological concept of attitudes and the problem of attitude measurement. Conceptually, attitudes are explored from an affective, cognitive, behavioral, and biologic dimension. The result is a comprehensive attitude concept. The problem of attitude measurement is not that attitudes exist, nor that they are specific or general, but lies in the way that they are organized. With the current emphasis on computerized research and data analysis, it is astonishing that multiple factor analysis has been so infrequently used for attitude validation and instrumentation. As a measurement and analytic technique, multiple factor analysis provides the intrinsic power to isolate and identify attitudinal factors. Multiple factor analysis is a measurement technique designed to assess construct validity. As such, it unites psychometrics with psychological theory. Factor analysis as a computational technique and as a scale construction technique is explored. The principal component method of factor analysis is reviewed. Multiple factor analysis assists in the process of attitude scale construction in the following ways: 1.) determines the content (factorial) validity of a series of attitude statements by ascertaining whether they measure a single unitary characteristic or a complex of characteristics as reflected in an item intercorrelation matrix; 2.) contributes to the determination of construct validity by ascertaining the smallest number of factors that can be postulated to account for item intercorrelations 3.) provides the statistical research strategy upon which predictive and assessment instruments can be empirically determined from an unrotated factor matrix; and, 4.) serves in general as an objective basic research tool through which psychological traits underlying human attitudes can be derived. 相似文献