共查询到20条相似文献，搜索用时 399 毫秒
1.
《Structural equation modeling》2013,20(3):471-492
We illustrate testing measurement invariance in a second-order factor model using a quality of life dataset (n = 924). Measurement invariance was tested across 2 groups at a set of hierarchically structured levels: (a) configural invariance, (b) first-order factor loadings, (c) second-order factor loadings, (d) intercepts of measured variables, (e) intercepts of first-order factors, (f) disturbances of first-order factors, and (g) residual variances of observed variables. Given that measurement invariance at the factor loading and intercept levels was achieved, the latent factor mean difference on the higher order factor between the groups was also estimated. The analyses were performed on the mean and covariance structures within the framework of the confirmatory factor analysis using the LISREL 8.51 program. Implications of second-order factor models and measurement invariance in psychological research were discussed. 相似文献
2.
Cross-cultural comparisons of latent variable means demands equivalent loadings and intercepts or thresholds. Although equivalence generally emphasizes items as originally designed, researchers sometimes modify response options in categorical items. For example, substantive research interests drive decisions to reduce the number of item categories. Further, categorical multiple-group confirmatory factor analysis (MG-CFA) methods generally require that the number of indicator categories is equal across groups; however, categories with few observations in at least one group can cause challenges. In the current paper, we examine the impact of collapsing ordinal response categories in MG-CFA. An empirical analysis and a complementary simulation study suggested meaningful impacts on model fit due to collapsing categories. We also found reduced scale reliability, measured as a function of Fisher’s information. Our findings further illustrated artifactual fit improvement, pointing to the possibility of data dredging for improved model-data consistency in challenging invariance contexts with large numbers of groups. 相似文献
3.
Wim J.C.M. van de Grift Seyeoung Chun Ridwan Maulana Okhwa Lee Michelle Helms-Lorenz 《School Effectiveness & School Improvement》2017,28(3):337-349
Six observation scales for measuring the skills of teachers and 1 scale for measuring student engagement, assessed in South Korea and The Netherlands, are sufficiently reliable and offer sufficient predictive value for student engagement. A multigroup confirmatory factor analysis shows that the factor loadings and intercepts of the scales are the same, within acceptable boundaries, in both countries. Therefore, we can compare the average scores of teachers in both countries in a reliable and valid way. The 289 Dutch teachers score significantly better on “creating a safe and stimulating learning climate” and “intensive and activating teaching” and almost significantly on “efficient classroom management”. We find no significant differences in “clear and structured instruction”. The 375 South Korean teachers perform significantly better than the Dutch teachers on “teaching learning strategies” and almost significantly on “differentiating instruction”. Furthermore, we find better student engagement in South Korea. 相似文献
4.
Fang Fang Chen 《Structural equation modeling》2013,20(3):464-504
Two Monte Carlo studies were conducted to examine the sensitivity of goodness of fit indexes to lack of measurement invariance at 3 commonly tested levels: factor loadings, intercepts, and residual variances. Standardized root mean square residual (SRMR) appears to be more sensitive to lack of invariance in factor loadings than in intercepts or residual variances. Comparative fit index (CFI) and root mean square error of approximation (RMSEA) appear to be equally sensitive to all 3 types of lack of invariance. The most intriguing finding is that changes in fit statistics are affected by the interaction between the pattern of invariance and the proportion of invariant items: when the pattern of lack of invariance is uniform, the relation is nonmonotonic, whereas when the pattern of lack of invariance is mixed, the relation is monotonic. Unequal sample sizes affect changes across all 3 levels of invariance: Changes are bigger when sample sizes are equal rather than when they are unequal. Cutoff points for testing invariance at different levels are recommended. 相似文献
5.
Suzanne Jak 《Structural equation modeling》2019,26(4):607-622
When modeling latent variables at multiple levels, it is important to consider the meaning of the latent variables at the different levels. If a higher-level common factor represents the aggregated version of a lower-level factor, the associated factor loadings will be equal across levels. However, many researchers do not consider cross-level invariance constraints in their research. Not applying these constraints when in fact they are appropriate leads to overparameterized models, and associated convergence and estimation problems. This simulation study used a two-level mediation model on common factors to show that when factor loadings are equal in the population, not applying cross-level invariance constraints leads to more estimation problems and smaller true positive rates. Some directions for future research on cross-level invariance in MLSEM are discussed. 相似文献
6.
Power,participation and decision making in the primary classroom: children as action researchers 总被引：1，自引：1，他引：0
Sue Cox 《Educational Action Research》2013,21(4):457-468
This article discusses a project focused on children researching their role in decision making in their classrooms and schools, with a view to increasing their involvement. The action research project was carried out by children, their class teachers and university researchers in six Norfolk primary schools from 2004 to 2006. As the project aimed to introduce more participatory approaches to decision making in classrooms, this necessarily had implications for the ways in which adults worked with children as action researchers. The article explores the constraints encountered by both children and teachers in sharing decisions and in carrying out action research, and identifies two dimensions: the teachers’ thinking and action, as well as children’s research and decision making. The teachers struggled with their need to mediate the project aims in the context of the changing nature of their professional role in the current target‐driven school culture. 相似文献
7.
Tiffany A. Whittaker 《Structural equation modeling》2013,20(1):108-130
Latent means methods such as multiple-indicator multiple-cause (MIMIC) and structured means modeling (SMM) allow researchers to determine whether or not a significant difference exists between groups' factor means. Strong invariance is typically recommended when interpreting latent mean differences. The extent of the impact of noninvariant intercepts on conclusions made when implementing both MIMIC and SMM methods was the main purpose of this study. The impact of intercept noninvariance on Type I error rates, power, and two model fit indices when using MIMIC and SMM approaches under various conditions were examined. Type I error and power were adversely affected by intercept noninvariance. Although the fit indices did not detect small misspecifications in the form of noninvariant intercepts, one did perform more optimally. 相似文献
8.
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
9.
Frans J. Oort 《Structural equation modeling》2013,20(3):383-396
In exploratory or unrestricted factor analysis, all factor loadings are free to be estimated. In oblique solutions, the correlations between common factors are free to be estimated as well. The purpose of this article is to show how likelihood-based confidence intervals can be obtained for rotated factor loadings and factor correlations, by applying maximum likelihood factor analysis subject to scaling and rotation constraints. As an illustrative example, an oblique 5-factor model will be fitted to the variance-covariance matrix of the 30 personality facets measured by the Revised NEO Personality Inventory, and confidence intervals will be estimated for all factor loadings and factor correlations, as well as for the associated reliability and validity coefficients. 相似文献
10.
Mark H. Salisbury Michael B. Paulsen Ernest T. Pascarella 《Research in higher education》2010,51(7):615-640
Although interest in study abroad has grown consistently in recent decades, study abroad professionals and higher education
scholars have been unable to explain or rectify the long-standing gender gap in study abroad participation. This study applies
an integrated model of the student-choice construct to explore differences between male and female intent to study abroad.
Results indicate that, not only can various forms of social and cultural capital predict student decisions about curricular
opportunities during college such as study abroad, but gender plays a substantial role in altering the ways in which those
forms of capital shape student decisions differently. These findings present a range of implications for researchers interested
in better understanding the decision making process of college students as well as study abroad professionals and national
policymakers intent on narrowing the gender gap in study abroad participation. 相似文献
11.
A number of psychometricians have suggested that parallel analysis (PA) tends to yield more accurate results in determining the number of factors in comparison with other statistical methods. Nevertheless, all too often PA can suggest an incorrect number of factors, particularly in statistically unfavorable conditions (e.g., small sample sizes and low factor loadings). Because of this, researchers have recommended using multiple methods to make judgments about the number of factors to extract. Implicit in this recommendation is that, when the number of factors is chosen based on PA, uncertainty nevertheless exists. We propose a Bayesian parallel analysis (B-PA) method to incorporate the uncertainty with decisions about the number of factors. B-PA yields a probability distribution for the various possible numbers of factors. We implement and compare B-PA with a frequentist approach, revised parallel analysis (R-PA), in the contexts of real and simulated data. Results show that B-PA provides relevant information regarding the uncertainty in determining the number of factors, particularly under conditions with small sample sizes, low factor loadings, and less distinguishable factors. Even if the indicated number of factors with the highest probability is incorrect, B-PA can show a sizable probability of retaining the correct number of factors. Interestingly, when the mode of the distribution of the probabilities associated with different numbers of factors was treated as the number of factors to retain, B-PA was somewhat more accurate than R-PA in a majority of the conditions. 相似文献
12.
We estimated the invariance of educational achievement (EA) and learning attitudes (LA) measures across nations. A multi-group confirmatory factor analysis was used to estimate the invariance of educational achievement and learning attitudes across 55 nations (Programme for International Student Assessment [PISA] 2006 data, N?=?354,203). The constructs had the same meaning (factor loadings) but different scales (intercepts). Our conclusion is that comparisons of the relationships between educational achievement and learning attitudes across countries need to take into consideration two sources of variability: individual differences of students and group differences of educational systems. The lack of scalar invariance in EA and LA measures means that the relationships between EA and LA may have a different meaning at the level of nations and at the student level within countries. In other words, as PISA measures are not invariant in scalar sense, the comparisons across countries with nationally aggregated scores are not justified. 相似文献
13.
Measurement invariance with respect to groups is an essential aspect of the fair use of scores of intelligence tests and other psychological measurements. It is widely believed that equal factor loadings are sufficient to establish measurement invariance in confirmatory factor analysis. Here, it is shown why establishing measurement invariance with confirmatory factor analysis requires a statistical test of the equality over groups of measurement intercepts. Without this essential test, measurement bias may be overlooked. A re-analysis of a study by Te Nijenhuis, Tolboom, Resing, and Bleichrodt (2004) on ethnic differences on the RAKIT IQ test illustrates that ignoring intercept differences may lead to the conclusion that bias of IQ tests with respect to minorities is small, while in reality bias is quite severe. 相似文献
14.
We examine the power associated with the test of factor mean differences when the assumption of factorial invariance is violated. Utilizing the Wald test for obtaining power, issues of model size, sample size, and total versus partial noninvariance are considered along with variation of actual factor mean differences. Results of a population study show that power is profoundly affected by true factor mean differences but is relatively unaffected by the degree of factor loading noninvariance. Inequality of sample size has a profound effect on power probabilities with power decreasing as sample sizes become increasingly disparate. Sample size variations operate such that power is uniformly lower when the group with the smaller generalized variance is associated with the smaller sample size. An increase in the number of variables yields uniformly larger power probabilities. No substantial differences are found between total and partial noninvariance. Results are related to work in the area of robustness of Hotelling's T 2 statistic and discussed in terms of asymptotic covariability of factor means and factor loadings. Implications for practice are considered. 相似文献
15.
In the exploratory factor analysis, when the number of factors exceeds the true number of factors, the likelihood ratio test statistic no longer follows the chi-square distribution due to a problem of rank deficiency and nonidentifiability of model parameters. As a result, decisions regarding the number of factors may be incorrect. Several researchers have pointed out this phenomenon, but it is not well known among applied researchers who use exploratory factor analysis. We demonstrate that overfactoring is one cause for the well-known fact that the likelihood ratio test tends to find too many factors. 相似文献
16.
Maximum likelihood is commonly used for estimation of model parameters in analysis of two-level structural equation models. Constraints on model parameters could be encountered in some situations such as equal factor loadings for different factors. Linear constraints are the most common ones and they are relatively easy to handle in maximum likelihood analysis. Nonlinear constraints could be encountered in complicated applications. In this paper we develop an EM-type algorithm for estimating model parameters with both linear and nonlinear constraints. The empirical performance of the algorithm is demonstrated by a Monte Carlo study. Application of the algorithm for linear constraints is illustrated by setting up a two-level mean and covariance structure model for a real two-level data set and running an EQS program. 相似文献
17.
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
18.
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
19.
《学校用计算机》2013,30(3):103-120
Abstract Making decisions about which technologies, if any, are appropriate for your classroom or educational institution has never been easy. These difficult decisions are, however, increasing in their complexity as new technologies continually enter the marketplace, expectations of parents and students rise for more technology in the classroom, budget constraints require institutions to do more with less, and the benefits of distance education place mounting pressures on educators to provide resources beyond the boundaries of the traditional classroom. So how do you as an educational leader make the challenging decisions related to distance education and other technologies in your school? This article provides a framework for results-focused decision making that can help you make complicated choices in even the most challenging of circumstances. 相似文献
20.
Laura Castro-Schilo Kevin J. Grimm Keith F. Widaman 《Structural equation modeling》2016,23(6):798-818
We introduce an approach for ensuring empirical identification of the correlated trait–correlated method (CT–CM) model under a variety of conditions. A set of models are referred to as augmented correlated trait–correlated method (ACT–CM) models because they are based on systematically augmenting the multitrait–multimethod matrix put forth by Campbell and Fiske (1959). We show results from a Monte Carlo simulation study in which data characteristics lead to an empirically underidentified standard CT–CM model, but a well-identified fully augmented correlated trait–correlated method (FACT–CM) model. This improved identification occurs even for a model in which equality constraints are imposed on loadings on each trait factor and loadings on each method factor—a specific case shown to lead to an empirically underidentified CT–CM model. 相似文献