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Ismail Cuhadar; Ömür Kaya Kalkan – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Simulation studies are needed to investigate how many score categories are sufficient to treat ordered categorical data as continuous, particularly for bifactor models. The current simulation study aims to address such needs by investigating the performance of estimation methods in the bifactor models with ordered categorical data. Results support…
Descriptors: Predictor Variables, Structural Equation Models, Sample Size, Evaluation Methods
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Bogaert, Jasper; Loh, Wen Wei; Rosseel, Yves – Educational and Psychological Measurement, 2023
Factor score regression (FSR) is widely used as a convenient alternative to traditional structural equation modeling (SEM) for assessing structural relations between latent variables. But when latent variables are simply replaced by factor scores, biases in the structural parameter estimates often have to be corrected, due to the measurement error…
Descriptors: Factor Analysis, Regression (Statistics), Structural Equation Models, Error of Measurement
Yuan, Ke-Hai; Zhang, Zhiyong; Zhao, Yanyun – Grantee Submission, 2017
The normal-distribution-based likelihood ratio statistic T[subscript ml] = nF[subscript ml] is widely used for power analysis in structural Equation modeling (SEM). In such an analysis, power and sample size are computed by assuming that T[subscript ml] follows a central chi-square distribution under H[subscript 0] and a noncentral chi-square…
Descriptors: Statistical Analysis, Evaluation Methods, Structural Equation Models, Reliability
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van Smeden, Maarten; Hessen, David J. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
In this article, a 2-way multigroup common factor model (MG-CFM) is presented. The MG-CFM can be used to estimate interaction effects between 2 grouping variables on 1 or more hypothesized latent variables. For testing the significance of such interactions, a likelihood ratio test is presented. In a simulation study, the robustness of the…
Descriptors: Multivariate Analysis, Robustness (Statistics), Sample Size, Statistical Analysis
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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
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Fan, Weihua; Hancock, Gregory R. – Journal of Educational and Behavioral Statistics, 2012
This study proposes robust means modeling (RMM) approaches for hypothesis testing of mean differences for between-subjects designs in order to control the biasing effects of nonnormality and variance inequality. Drawing from structural equation modeling (SEM), the RMM approaches make no assumption of variance homogeneity and employ robust…
Descriptors: Robustness (Statistics), Hypothesis Testing, Monte Carlo Methods, Simulation
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Hox, Joop J.; Maas, Cora J. M. – Structural Equation Modeling, 2001
Assessed the robustness of an estimation method for multilevel and path analysis with hierarchical data proposed by B. Muthen (1989) with unequal groups and small sample sizes and in the presence of a low or high intraclass correlation. Simulation results show the effects of varying these conditions on the within-group and between-groups part of…
Descriptors: Estimation (Mathematics), Robustness (Statistics), Sample Size, Simulation