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Direct linkMerkle, Edgar C.; Fitzsimmons, Ellen; Uanhoro, James; Goodrich, Ben – Grantee Submission, 2021
Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model estimation is complicated by the fact that we typically have multiple interdependent response variables and multiple latent variables (which may also be called random effects or…
Descriptors: Bayesian Statistics, Structural Equation Models, Psychometrics, Factor Analysis
Jaikaew, Pimpilai; Damrongpanit, Suntonrapot – Universal Journal of Educational Research, 2018
The research was designed to examine the effects of question setting using different conditions into 10 sets on the validity of structural equation modeling for factors affecting job morale. The data was collected from 690 personnel working in regional Statistical Offices around Thailand by using cluster random sampling. The tool used in…
Descriptors: Structural Equation Models, Questionnaires, Reliability, Multivariate Analysis
Bai, Haiyan; Sivo, Stephen A.; Pan, Wei; Fan, Xitao – International Journal of Research & Method in Education, 2016
Among the commonly used resampling methods of dealing with small-sample problems, the bootstrap enjoys the widest applications because it often outperforms its counterparts. However, the bootstrap still has limitations when its operations are contemplated. Therefore, the purpose of this study is to examine an alternative, new resampling method…
Descriptors: Sampling, Structural Equation Models, Statistical Inference, Comparative Analysis
Macho, Siegfried; Ledermann, Thomas – Psychological Methods, 2011
The phantom model approach for estimating, testing, and comparing specific effects within structural equation models (SEMs) is presented. The rationale underlying this novel method consists in representing the specific effect to be assessed as a total effect within a separate latent variable model, the phantom model that is added to the main…
Descriptors: Structural Equation Models, Computation, Comparative Analysis, Sampling
Jones-Farmer, L. Allison; Pitts, Jennifer P.; Rainer, R. Kelly – Structural Equation Modeling: A Multidisciplinary Journal, 2008
Although SAS PROC CALIS is not designed to perform multigroup comparisons, it is believed that SAS can be "tricked" into doing so for groups of equal size. At present, there are no comprehensive examples of the steps involved in performing a multigroup comparison in SAS. The purpose of this article is to illustrate these steps. We demonstrate…
Descriptors: Goodness of Fit, Structural Equation Models, Measurement Techniques, Interpersonal Communication
Peer reviewedLee, Sik-Yum; Song, Xin-Yuan – Psychometrika, 2003
Proposed a new nonlinear structural equation model with fixed covariates to deal with some complicated substantive theory and developed a Bayesian path sampling procedure for model comparison. Illustrated the approach with an illustrative example using data from an international study. (SLD)
Descriptors: Bayesian Statistics, Comparative Analysis, Sampling, Structural Equation Models
Zhang, Duan; Willson, Victor L. – Structural Equation Modeling: A Multidisciplinary Journal, 2006
Both structural equation models and hierarchical linear models (HLMs) have been commonly used in multilevel analysis. This study utilized simulated data to investigate the power difference among 3 multilevel models: HLM, deviation structural equation models, and a hybrid approach of HLM and structural equation models. Two factors were examined:…
Descriptors: Comparative Analysis, Structural Equation Models, Interaction, Simulation
