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1.
In this study, we contrast two competing approaches, not previously compared, that balance the rigor of CFA/SEM with the flexibility to fit realistically complex data. Exploratory SEM (ESEM) is claimed to provide an optimal compromise between EFA and CFA/SEM. Alternatively, a family of three Bayesian SEMs (BSEMs) replace fixed-zero estimates with informative, small-variance priors for different subsets of parameters: cross-loadings (CL), residual covariances (RC), or CLs and RCs (CLRC). In Study 1, using three simulation studies, results showed that (1) BSEM-CL performed more closely to ESEM; (2) BSEM-CLRC did not provide more accurate model estimation compared with BSEM-CL; (3) BSEM-RC provided unstable estimation; and (4) different specifications of targeted values in ESEM and informative priors in BSEM have significant impacts on model estimation. The real data analysis (Study 2) showed that the differences in estimation between different models were largely consistent with those in Study1 but somewhat smaller.  相似文献   

2.
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   

3.
The aim of this study is to investigate interrelationships between overexcitability and learning patterns from the perspective of personality development according to Dabrowski’s theory of positive disintegration. To this end, Bayesian structural equation modeling (BSEM) is applied which allows for the simultaneous inclusion in the measurement model of all, approximate zero cross-loadings and residual covariances based on zero-mean, small-variance priors, and represents substantive theory better. Our BSEM analysis with a sample of 516 students in higher education yields positive results regarding the validity of the model, in contrast to a frequentist approach to validation, and reveals that overexcitability – the degree and nature of which is characteristic of the potential for advanced personality development, according to Dabrowski’s theory – is substantially related to the way in which information is processed, as well as to the regulation strategies that are used for this purpose and to study motivation. Overexcitability is able to explain variations in learning patterns to varying degrees, ranging from weakly (3.3% for reproduction-directed learning for the female group) to rather strongly (46.1% for meaning-directed learning for males), with intellectual overexcitability representing the strongest indicator of deep learning. This study further argues for the relevance of including emotion dynamics – taking into account their multilevelness – in the study of the learning process.  相似文献   

4.
The article employs exploratory structural equation modeling (ESEM) to evaluate constructs of economic, cultural, and social capital in international large-scale assessment (LSA) data from the Progress in International Reading Literacy Study (PIRLS) 2006 and the Programme for International Student Assessment (PISA) 2009. ESEM integrates the theory-generating approach of exploratory factor analysis (EFA) and theory-testing approach of confirmatory factor analysis (CFA). It relaxes the zero-loading restriction in CFA, allowing items to load on different factors simultaneously, and it provides measurement invariance tests across countries not available in EFA. A main criticism of international LSA studies is the extended use of indicators poorly grounded in theory, like socioeconomic status, that prevent the study of mechanisms underlying associations with student outcomes. This article contributes to addressing this criticism by providing statistical criteria to evaluate the fit of well-defined sociological constructs with the empirical data.  相似文献   

5.
This study is a methodological-substantive synergy, demonstrating the power and flexibility of exploratory structural equation modeling (ESEM) methods that integrate confirmatory and exploratory factor analyses (CFA and EFA), as applied to substantively important questions based on multidimentional students' evaluations of university teaching (SETs). For these data, there is a well established ESEM structure but typical CFA models do not fit the data and substantially inflate correlations among the nine SET factors (median rs = .34 for ESEM, .72 for CFA) in a way that undermines discriminant validity and usefulness as diagnostic feedback. A 13-model taxonomy of ESEM measurement invariance is proposed, showing complete invariance (factor loadings, factor correlations, item uniquenesses, item intercepts, latent means) over multiple groups based on the SETs collected in the first and second halves of a 13-year period. Fully latent ESEM growth models that unconfounded measurement error from communality showed almost no linear or quadratic effects over this 13-year period. Latent multiple indicators multiple causes models showed that relations with background variables (workload/difficulty, class size, prior subject interest, expected grades) were small in size and varied systematically for different ESEM SET factors, supporting their discriminant validity and a construct validity interpretation of the relations. A new approach to higher order ESEM was demonstrated, but was not fully appropriate for these data. Based on ESEM methodology, substantively important questions were addressed that could not be appropriately addressed with a traditional CFA approach.  相似文献   

6.
Bayesian structural equation modeling (BSEM) was used to investigate the latent structure of the Differential Ability Scales—Second Edition core battery using the standardization sample normative data for ages 7–17. Results revealed plausibility of a three‐factor model, consistent with publisher theory, expressed as either a higher‐order (HO) or a bifactor (BF) model. The results also revealed an alternative structure with the best model fit, a two‐factor BF model with Matrices (MA) and Sequential and Quantitative Reasoning (SQ) loading on g only with no respective group factor loading. This was only the second study to use BSEM to investigate the structure of a commercial ability test and the first to use a large normative sample and the specification of both approximate zero cross‐loadings and correlated residual terms. It is believed that the results produced from the current study will advance the field's understanding of not only the factor structure of the DAS‐II core battery but also the potential utility of BSEM in psychometric investigations of intelligence test structures.  相似文献   

7.
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8.
Exploratory structural equation modeling (ESEM) is an approach for analysis of latent variables using exploratory factor analysis to evaluate the measurement model. This study compared ESEM with two dominant approaches for multiple regression with latent variables, structural equation modeling (SEM) and manifest regression analysis (MRA). Main findings included: (1) ESEM in general provided the least biased estimation of the regression coefficients; SEM was more biased than MRA given large cross-factor loadings. (2) MRA produced the most precise estimation, followed by ESEM and then SEM. (3) SEM was the least powerful in the significance tests; statistical power was lower for ESEM than MRA with relatively small target-factor loadings, but higher for ESEM than MRA with relatively large target-factor loadings. (4) ESEM showed difficulties in convergence and occasionally created an inflated type I error rate under some conditions. ESEM is recommended when non-ignorable cross-factor loadings exist.  相似文献   

9.
We present a multigroup multilevel confirmatory factor analysis (CFA) model and a procedure for testing multilevel factorial invariance in n-level structural equation modeling (nSEM). Multigroup multilevel CFA introduces a complexity when the group membership at the lower level intersects the clustered structure, because the observations in different groups but in the same cluster are not independent of one another. nSEM provides a framework in which the multigroup multilevel data structure is represented with the dependency between groups at the lower level properly taken into account. The procedure for testing multilevel factorial invariance is illustrated with an empirical example using an R package xxm2.  相似文献   

10.
Expectancy-value researchers have described the components of subjective task value in multiple ways, leading to multiple competing structural representations of subjective task value data. The purpose of this study was to examine these competing multidimensional factor structures by comparing correlated factor, hierarchical, and bifactor representations of both confirmatory factor analysis and exploratory structural equation modeling (ESEM) models across three theoretical conceptualizations of subjective task value. Results indicate that, in an undergraduate life science learning context (n = 334), the best representation for subjective task value data was a bifactor ESEM model that allowed for the disentangling of general and specific variance of general subjective task value, specific value beliefs, and specific costs. Full measurement invariance of the retained structure across continuing generation and first-generation students was found, and no differential item functioning was found across gender. General subjective task value and specific opportunity cost significantly and positively predicted achievement and specific utility value significantly and negatively predicted achievement, providing support for the criterion-related validity of the general and specific factors for predicting achievement outcomes.  相似文献   

11.
This investigation examines the use of structural equation modeling (SEM) procedures to develop and validate scales to measure environmental responsibility, character development and leadership, and attitudes toward school for environmental education programs servicing middle school children. The scales represent outcomes commonly of interest to environmental education programs and also to after‐school and positive youth development activities. First, we developed the scales using confirmatory factor analysis (CFA) and then we used multi‐group longitudinal CFA to cross‐validate the model with data collected before participation in the environmental education program, immediately after the program, and three months later. The results support a three‐factor model, producing three scales that appear to be valid and reliable.  相似文献   

12.
The authors' purpose was to examine the degree to which low achievement is related to ontogenetic factors (i.e., personal psychological traits expressed as attention and depression) or microsystemic factors (i.e., socioeconomic status, parenting, relationship with peers and teachers), using a total of 721 middle school students in South Korea. Based on the percentile rank, low-achieving students (bottom 15%; n = 323) and high-achieving students (top 15%; n = 398) were grouped, and a multigroup structural equation modeling was employed to determine which ecological factor(s) contribute to predict low achievement of middle school students. Results from multigroup structural equation modeling showed that 3 of the 6 ecological and ontological factors had significant direct or indirect effect on low achievement: socioeconomic status (direct effect), attention (direct effect), parenting (indirect effect). The findings are discussed in terms of the intertwined influences of ecological factors on low achievement, finally leading to the discussion on the limitations and future directions for research.  相似文献   

13.
Recently, advancements in Bayesian structural equation modeling (SEM), particularly software developments, have allowed researchers to more easily employ it in data analysis. With the potential for greater use, come opportunities to apply Bayesian SEM in a wider array of situations, including for small sample size problems. Effective use of Bayseian estimation hinges on selection of appropriate prior distributions for model parameters. Researchers have suggested that informative priors may be useful with small samples, presuming that the mean of the prior is accurate with respect to the population mean. The purpose of this simulation study was to examine model parameter estimation for the Multiple Indicator Multiple Cause model when an informative prior distribution had an incorrect mean. Results demonstrated that the use of incorrect informative priors with somewhat larger variance than is typical, yields more accurate parameter estimates than do naïve priors, or maximum likelihood estimation. Implications for practice are discussed.  相似文献   

14.
Within Bayesian estimation, prior distributions are placed on model parameters and these distributions can take on many different levels of informativeness. Although much of the research conducted within this estimation framework uses what are called diffuse (or noninformative) priors, there are certain models and modeling circumstances where it is more optimal to use what are referred to as informative priors. This study focuses on the latter situation and examines the effects of inaccurate informative priors on the growth parameters within the context of growth mixture modeling. Overall, results indicated that growth mixture modeling is relatively robust to the use of inaccurate mean hyperparameters for the growth parameters, as long as the variance hyperparameters are somewhat large.  相似文献   

15.
This article assesses the multidimensionality of the Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS) using bifactor exploratory structural equation modeling (bifactor ESEM). The first study relies on a sample of community adults (N = 2,301), and revealed the superiority of a bifactor ESEM representation, supporting the 6-factor structure of BPNSFS ratings, and the presence of a single continuum of need fulfillment relative to 2 distinct dimensions reflecting need satisfaction and frustration. These results were replicated in a second representative sample of the Hungarian adult population (N = 504), as well as across gender, and found no evidence of differential item functioning as a function of age. Relative to males, females presented higher levels of relatedness satisfaction and lower levels of competence satisfaction. Finally, autonomy frustration decreased with age, whereas competence satisfaction and frustration presented opposite curvilinear tendencies, showing that the fulfillment of this need increased sharply for younger participants, a tendency that became less pronounced with age.  相似文献   

16.
Appropriate model specification is fundamental to unbiased parameter estimates and accurate model interpretations in structural equation modeling. Thus detecting potential model misspecification has drawn the attention of many researchers. This simulation study evaluates the efficacy of the Bayesian approach (the posterior predictive checking, or PPC procedure) under multilevel bifactor model misspecification (i.e., ignoring a specific factor at the within level). The impact of model misspecification on structural coefficients was also examined in terms of bias and power. Results showed that the PPC procedure performed better in detecting multilevel bifactor model misspecification, when the misspecification became more severe and sample size was larger. Structural coefficients were increasingly negatively biased at the within level, as model misspecification became more severe. Model misspecification at the within level affected the between-level structural coefficient estimates more when data dependency was lower and the number of clusters was smaller. Implications for researchers are discussed.  相似文献   

17.
Although methodology articles have increasingly emphasized the need to analyze data from two members of a dyad simultaneously, the most popular method in substantive applications is to examine dyad members separately. This might be due to the underappreciation of the extra information simultaneous modeling strategies can provide. Therefore, the goal of this study was to compare multiple growth curve modeling approaches for longitudinal dyadic data (LDD) in both structural equation modeling and multilevel modeling frameworks. Models separately assessing change over time for distinguishable dyad members are compared to simultaneous models fitted to LDD from both dyad members. Furthermore, we compared the simultaneous default versus dependent approaches (whether dyad pairs’ Level 1 [or unique] residuals are allowed to covary and differ in variance). Results indicated that estimates of variance and covariance components led to conflicting results. We recommend the simultaneous dependent approach for inferring differences in change over time within a dyad.  相似文献   

18.
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   

19.
Multilevel modeling (MLM) is a popular way of assessing mediation effects with clustered data. Two important limitations of this approach have been identified in prior research and a theoretical rationale has been provided for why multilevel structural equation modeling (MSEM) should be preferred. However, to date, no empirical evidence of MSEM's advantages relative to MLM approaches for multilevel mediation analysis has been provided. Nor has it been demonstrated that MSEM performs adequately for mediation analysis in an absolute sense. This study addresses these gaps and finds that the MSEM method outperforms 2 MLM-based techniques in 2-level models in terms of bias and confidence interval coverage while displaying adequate efficiency, convergence rates, and power under a variety of conditions. Simulation results support prior theoretical work regarding the advantages of MSEM over MLM for mediation in clustered data.  相似文献   

20.
Developments concerning report cards have led to a potential shift from reporting traditional grades to reporting multiple competencies within and across subjects. In this study, we analyzed the dimensional structure of the teacher judgments on a competency-based report card on fourth-grade elementary school students (N = 469). With a methodologically innovative approach of combining exploratory structural equation modeling (ESEM) and confirmatory factor analysis (CFA), we found one learning-oriented and one social-oriented generic subject-unspecific factor of competency judgments and single factors for each included subject. All subject factors showed relatively high correlations with the respective traditional grades. Second-order commonalities further indicated a general factor represented almost perfectly by the learning-oriented generic judgments. Our analyses generally justified the use of competency-based report cards in terms of the dimensional structure and the association with traditional grades. Further, generic subject-unspecific competency judgments contribute to disentangling the multidimensionality of teacher judgments.  相似文献   

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