Standard errors of fit indices using residuals in structural equation modeling

Psychometrika ◽  
2001 ◽  
Vol 66 (3) ◽  
pp. 421-436 ◽  
Author(s):  
Haruhiko Ogasawara
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Standard Errors ◽  
Fit Indices

Structural Equation Modeling: An Alternative to Traditional Validation Techniques

1986 ◽  
Vol 14 (4) ◽  
pp. 345-352
Author(s):  
Margaret E. Bell ◽  
Jean A. Massey
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Statistical Significance ◽  
Equation Modeling ◽  
Fit Indices ◽  
T Values ◽  
Conditions Of Learning ◽  
Nursing Course ◽  
Cardiovascular Nursing

Validation of the sequencing of objectives is an important step in structural design. Prior statistical techniques, such as the reproducibility coefficient, have yielded only summary information. In contrast, structural equation modeling provides both goodness-of-fit indices and effect coefficients for links or paths between time-ordered events, i.e., objectives. Discussed here is the application of structural equation modeling to a set of objectives in a senior-level cardiovascular nursing course. Consistent with the theory-based requirement of structural equation modeling, the objectives were developed using Robert Gagné's conditions of learning. Also discussed is the use of “t” values, which indicate statistical significance of the paths, for testing instructional links in the learning model.

scholarly journals Resilience Analysis of Crisis in Medical Environments: A Structural Equation Modeling based on Crisis Management Components

2022 ◽  
Author(s):  
SAMIRA GHIYASI ◽  
FATEMEH VERDI BAGHDADI ◽  
FARSHAD HASHEMZADEH ◽  
AHMAD SOLTANZADEH
Keyword(s):  
Structural Equation Modeling ◽  
Crisis Management ◽  
Work Experience ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Equation Modeling ◽  
Fit Indices ◽  
Cross Sectional ◽  
Resilience Index ◽  
The Impact

Enhancing the index of crisis resilience is one of the key goals in medical environments. Various parameters can affect crisis resilience. The current study was designed to analyze crisis resilience in medical environments based on the crisis management components. This cross-sectional and descriptive-analytical study was performed in 14 hospitals and medical centers, in 2020. A sample size of 343.5 was determined based on the Cochran's formula. We used a 44-item crisis management questionnaire of Azadian et al. to collect data. The components of this questionnaire included management commitment, error learning, culture learning, awareness, preparedness, flexibility, and transparency. The data was analyzed based on the structural equation modeling approach using IBM SPSS AMOS v. 23.0. The participants’ age and work experience mean were 37.78±8.14 and 8.22±4.47 years. The index of crisis resilience was equal to 2.96±0.87. The results showed that all components of crisis management had a significant relationship with this index (p <0.05). The highest and lowest impact on the resilience index were related to preparedness (E=0.88) and transparency (E=0.60). The goodness of fit indices of this model including RMSEA, CFI, NFI, and NNFI (TLI) was 2.86, 0.071, 0.965, 0.972, and 0.978. The index of crisis resilience in the medical environments was at a moderate level. Furthermore, the structural equation modeling findings indicated that the impact of each component of crisis management should be considered in prioritizing measures to increase the level of resilience.  

Advanced Mediating Effects Tests, Multi-Group Analyses, and Measurement Model Assessments in PLS-Based SEM

2014 ◽  
Vol 10 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Ned Kock
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Measurement Model ◽  
Health Sciences ◽  
Effect Sizes ◽  
Equation Modeling ◽  
Standard Errors ◽  
Mediating Effects ◽  
The Social ◽  
Pls Method

Use of the partial least squares (PLS) method has been on the rise among e-collaboration researchers. It has also seen increasing use in a wide variety of fields of research. This includes most business-related disciplines, as well as the social and health sciences. The use of the PLS method has been primarily in the context of PLS-based structural equation modeling (SEM). This article discusses a variety of advanced PLS-based SEM uses of critical coefficients such as standard errors, effect sizes, loadings, cross-loadings and weights. Among these uses are advanced mediating effects tests, comprehensive multi-group analyses, and measurement model assessments.

scholarly journals New Developments in Factor Score Regression: Fit Indices and a Model Comparison Test

2019 ◽  
Vol 79 (6) ◽  
pp. 1017-1037 ◽  
Author(s):  
Ines Devlieger ◽  
Wouter Talloen ◽  
Yves Rosseel
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Model Comparison ◽  
Type I Error ◽  
Factor Score ◽  
Regression Coefficients ◽  
Equation Modeling ◽  
Type I ◽  
Fit Indices ◽  
Comparison Test

Factor score regression (FSR) is a popular alternative for structural equation modeling. Naively applying FSR induces bias for the estimators of the regression coefficients. Croon proposed a method to correct for this bias. Next to estimating effects without bias, interest often lies in inference of regression coefficients or in the fit of the model. In this article, we propose fit indices for FSR that can be used to inspect the model fit. We also introduce a model comparison test based on one of these newly proposed fit indices that can be used for inference of the estimators on the regression coefficients. In a simulation study we compare FSR with Croon’s corrections and structural equation modeling in terms of bias of the regression coefficients, Type I error rate and power.

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