Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 2 |
Since 2006 (last 20 years) | 11 |
Descriptor
Source
Structural Equation Modeling:… | 13 |
Author
Bentler, Peter M. | 3 |
Savalei, Victoria | 2 |
Bang Quan Zheng | 1 |
Boomsma, Anne | 1 |
Depaoli, Sarah | 1 |
Fan, Xitao | 1 |
Ferrer, Emilio | 1 |
Gold, Michael S. | 1 |
Herzog, Walter | 1 |
Jeffrey R. Harring | 1 |
Ji Seung Yang | 1 |
More ▼ |
Publication Type
Journal Articles | 13 |
Reports - Research | 8 |
Reports - Evaluative | 4 |
Reports - Descriptive | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Bang Quan Zheng; Peter M. Bentler – Structural Equation Modeling: A Multidisciplinary Journal, 2022
Chi-square tests based on maximum likelihood (ML) estimation of covariance structures often incorrectly over-reject the null hypothesis: [sigma] = [sigma(theta)] when the sample size is small. Reweighted least squares (RLS) avoids this problem. In some models, the vector of parameter must contain means, variances, and covariances, yet whether RLS…
Descriptors: Maximum Likelihood Statistics, Structural Equation Models, Goodness of Fit, Sample Size
Xiaying Zheng; Ji Seung Yang; Jeffrey R. Harring – Structural Equation Modeling: A Multidisciplinary Journal, 2022
Measuring change in an educational or psychological construct over time is often achieved by repeatedly administering the same items to the same examinees over time and fitting a second-order latent growth curve model. However, latent growth modeling with full information maximum likelihood (FIML) estimation becomes computationally challenging…
Descriptors: Longitudinal Studies, Data Analysis, Item Response Theory, Structural Equation Models
Kim, Su-Young – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Just as growth mixture models are useful with single-phase longitudinal data, multiphase growth mixture models can be used with multiple-phase longitudinal data. One of the practically important issues in single- and multiphase growth mixture models is the sample size requirements for accurate estimation. In a Monte Carlo simulation study, the…
Descriptors: Structural Equation Models, Sample Size, Computation, Monte Carlo Methods
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
Voelkle, Manuel C.; Oud, Johan H. L.; von Oertzen, Timo; Lindenberger, Ulman – Structural Equation Modeling: A Multidisciplinary Journal, 2012
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…
Descriptors: Structural Equation Models, Maximum Likelihood Statistics, Computation, Factor Analysis
Peugh, James; Fan, Xitao – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Growth mixture modeling (GMM) has become a more popular statistical method for modeling population heterogeneity in longitudinal data, but the performance characteristics of GMM enumeration indexes in correctly identifying heterogeneous growth trajectories are largely unknown. Few empirical studies have addressed this issue. This study considered…
Descriptors: Structural Equation Models, Statistical Analysis, Longitudinal Studies, Evaluation Research
Depaoli, Sarah – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Parameter recovery was assessed within mixture confirmatory factor analysis across multiple estimator conditions under different simulated levels of mixture class separation. Mixture class separation was defined in the measurement model (through factor loadings) and the structural model (through factor variances). Maximum likelihood (ML) via the…
Descriptors: Markov Processes, Factor Analysis, Statistical Bias, Evaluation Research
Savalei, Victoria – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Incomplete nonnormal data are common occurrences in applied research. Although these 2 problems are often dealt with separately by methodologists, they often cooccur. Very little has been written about statistics appropriate for evaluating models with such data. This article extends several existing statistics for complete nonnormal data to…
Descriptors: Sample Size, Statistics, Data, Monte Carlo Methods
Song, Hairong; Ferrer, Emilio – Structural Equation Modeling: A Multidisciplinary Journal, 2009
This article presents a state-space modeling (SSM) technique for fitting process factor analysis models directly to raw data. The Kalman smoother via the expectation-maximization algorithm to obtain maximum likelihood parameter estimates is used. To examine the finite sample properties of the estimates in SSM when common factors are involved, a…
Descriptors: Factor Analysis, Computation, Mathematics, Maximum Likelihood Statistics
Herzog, Walter; Boomsma, Anne; Reinecke, Sven – Structural Equation Modeling: A Multidisciplinary Journal, 2007
According to Kenny and McCoach (2003), chi-square tests of structural equation models produce inflated Type I error rates when the degrees of freedom increase. So far, the amount of this bias in large models has not been quantified. In a Monte Carlo study of confirmatory factor models with a range of 48 to 960 degrees of freedom it was found that…
Descriptors: Monte Carlo Methods, Structural Equation Models, Effect Size, Maximum Likelihood Statistics
Savalei, Victoria; Bentler, Peter M. – Structural Equation Modeling: A Multidisciplinary Journal, 2005
This article proposes a new approach to the statistical analysis of pairwisepresent covariance structure data. The estimator is based on maximizing the complete data likelihood function, and the associated test statistic and standard errors are corrected for misspecification using Satorra-Bentler corrections. A Monte Carlo study was conducted to…
Descriptors: Evaluation Methods, Maximum Likelihood Statistics, Statistical Analysis, Monte Carlo Methods
Ximenez, Carmen – Structural Equation Modeling: A Multidisciplinary Journal, 2006
The recovery of weak factors has been extensively studied in the context of exploratory factor analysis. This article presents the results of a Monte Carlo simulation study of recovery of weak factor loadings in confirmatory factor analysis under conditions of estimation method (maximum likelihood vs. unweighted least squares), sample size,…
Descriptors: Monte Carlo Methods, Factor Analysis, Least Squares Statistics, Sample Size
Gold, Michael S.; Bentler, Peter M.; Kim, Kevin H. – Structural Equation Modeling: A Multidisciplinary Journal, 2003
This article describes a Monte Carlo study of 2 methods for treating incomplete nonnormal data. Skewed, kurtotic data sets conforming to a single structured model, but varying in sample size, percentage of data missing, and missing-data mechanism, were produced. An asymptotically distribution-free available-case (ADFAC) method and structured-model…
Descriptors: Monte Carlo Methods, Computation, Sample Size, Comparative Analysis