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Bentler, Peter M. – Measurement: Interdisciplinary Research and Perspectives, 2016
The latent factor in a causal indicator model is no more than the latent factor of the factor part of the model. However, if the causal indicator variables are well-understood and help to improve the prediction of individuals' factor scores, they can help to interpret the meaning of the latent factor. Aguirre-Urreta, Rönkkö, and Marakas (2016)…
Descriptors: Causal Models, Factor Analysis, Prediction, Scores
Bentler, Peter M.; Molenaar, Peter C. M. – Multivariate Behavioral Research, 2012
Molenaar (2003, 2011) showed that a common factor model could be transformed into an equivalent model without factors, involving only observed variables and residual errors. He called this invertible transformation the Houdini transformation. His derivation involved concepts from time series and state space theory. This article verifies the…
Descriptors: Structural Equation Models, Algebra, Statistical Analysis, Models
Mair, Patrick; Satorra, Albert; Bentler, Peter M. – Multivariate Behavioral Research, 2012
This article develops a procedure based on copulas to simulate multivariate nonnormal data that satisfy a prespecified variance-covariance matrix. The covariance matrix used can comply with a specific moment structure form (e.g., a factor analysis or a general structural equation model). Thus, the method is particularly useful for Monte Carlo…
Descriptors: Structural Equation Models, Data, Monte Carlo Methods, Probability
Bentler, Peter M.; Satorra, Albert – Psychological Methods, 2010
When using existing technology, it can be hard or impossible to determine whether two structural equation models that are being considered may be nested. There is also no routine technology for evaluating whether two very different structural models may be equivalent. A simple nesting and equivalence testing (NET) procedure is proposed that uses…
Descriptors: Structural Equation Models, Testing, Simulation, Sampling
Li, Libo; Bentler, Peter M. – Psychological Methods, 2011
MacCallum, Browne, and Cai (2006) proposed a new framework for evaluation and power analysis of small differences between nested structural equation models (SEMs). In their framework, the null and alternative hypotheses for testing a small difference in fit and its related power analyses were defined by some chosen root-mean-square error of…
Descriptors: Structural Equation Models, Statistical Analysis, Comparative Analysis
Jennrich, Robert I.; Bentler, Peter M. – Psychometrika, 2012
Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford ("Psychometrika" 47:41-54, 1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler ("Psychometrika" 76:537-549, 2011) introduced an exploratory form of bi-factor…
Descriptors: Factor Structure, Factor Analysis, Models, Comparative Analysis
Bentler, Peter M.; de Leeuw, Jan – Psychometrika, 2011
When the factor analysis model holds, component loadings are linear combinations of factor loadings, and vice versa. This interrelation permits us to define new optimization criteria and estimation methods for exploratory factor analysis. Although this article is primarily conceptual in nature, an illustrative example and a small simulation show…
Descriptors: Factor Analysis, Models, Computation, Methods
Bentler, Peter M.; Liang, Jiajuan; Tang, Man-Lai; Yuan, Ke-Hai – Educational and Psychological Measurement, 2011
Maximum likelihood is commonly used for the estimation of model parameters in the 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…
Descriptors: Structural Equation Models, Maximum Likelihood Statistics, Computation, Mathematics
Treiblmaier, Horst; Bentler, Peter M.; Mair, Patrick – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Recently there has been a renewed interest in formative measurement and its role in properly specified models. Formative measurement models are difficult to identify, and hence to estimate and test. Existing solutions to the identification problem are shown to not adequately represent the formative constructs of interest. We propose a new two-step…
Descriptors: Structural Equation Models, Measurement, Predictor Variables, Identification
Tong, Xiaoxiao; Bentler, Peter M. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Recently a new mean scaled and skewness adjusted test statistic was developed for evaluating structural equation models in small samples and with potentially nonnormal data, but this statistic has received only limited evaluation. The performance of this statistic is compared to normal theory maximum likelihood and 2 well-known robust test…
Descriptors: Structural Equation Models, Maximum Likelihood Statistics, Robustness (Statistics), Sample Size
Bae, Jungok; Bentler, Peter M.; Lee, Yae-Sheik – Language Assessment Quarterly, 2016
Content is related to other aspects of writing, but exactly how they are related has remained unclear or has not received sufficient critical attention. Consequently, in most writing assessments, content has been treated as just one among several relatively distinct but equal elements. However, in this study, the authors have quantified these…
Descriptors: Writing Evaluation, Content Analysis, Writing Skills, Story Telling
Mair, Patrick; Wu, Eric; Bentler, Peter M. – Structural Equation Modeling: A Multidisciplinary Journal, 2010
The REQS package is an interface between the R environment of statistical computing and the EQS software for structural equation modeling. The package consists of 3 main functions that read EQS script files and import the results into R, call EQS script files from R, and run EQS script files from R and import the results after EQS computations.…
Descriptors: Structural Equation Models, Computer Software, Statistical Analysis, Simulation
Bentler, Peter M. – Psychometrika, 2009
As pointed out by Sijtsma ("in press"), coefficient alpha is inappropriate as a single summary of the internal consistency of a composite score. Better estimators of internal consistency are available. In addition to those mentioned by Sijtsma, an old dimension-free coefficient and structural equation model based coefficients are…
Descriptors: Structural Equation Models, Reliability, Psychometrics
Satorra, Albert; Bentler, Peter M. – Psychometrika, 2010
A scaled difference test statistic T[tilde][subscript d] that can be computed from standard software of structural equation models (SEM) by hand calculations was proposed in Satorra and Bentler (Psychometrika 66:507-514, 2001). The statistic T[tilde][subscript d] is asymptotically equivalent to the scaled difference test statistic T[bar][subscript…
Descriptors: Structural Equation Models, Scaling, Computer Software, Statistical Analysis
Bentler, Peter M.; Satorra, Albert; Yuan, Ke-Hai – Structural Equation Modeling: A Multidisciplinary Journal, 2009
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…
Descriptors: Smoking, Structural Equation Models, Cancer, Correlation