On Fraas and Newman's goodness‐of‐fit test for structural equation models

1995 ◽  
Vol 2 (2) ◽  
pp. 152-154 ◽  
Author(s):  
Louis M. Hsu
Keyword(s):  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Equation Models ◽  
Goodness Of Fit Test

scholarly journals Local Fit Evaluation of Structural Equation Models Using Graphical Criteria

2017 ◽  
Author(s):  
Felix Thoemmes ◽  
Yves Rosseel ◽  
Johannes Textor
Keyword(s):  
Structural Equation Model ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Equation Models ◽  
Equation Model ◽  
Model Fit ◽  
Global Approach ◽  
Goodness Of Fit Test ◽  
Single Index ◽  
Model Misfit

Evaluation of model fit is critically important for every structural equation model and sophisticated methods have been developed for this task. Among them are the χ2 goodness-of-fit test, decomposition of the χ2, derived measures like the popular RMSEA or CFI, or inspection of residuals or modification indices. Many of these methods provide a global approach to model fit evaluation: A single index is computed that quantifies the fit of the entire SEM to the data. In contrast, graphical criteria like d-separation or trek-separation allow to derive implications that can be used for local fit evaluation, an approach that is hardly ever applied. We provide an overview of local fit evaluation from the viewpoint of SEM practitioners. In the presence of model misfit, local fit evaluation can potentially help in pinpointing where the problem with the model lies. For models that do fit the data, local tests can identify the parts of the model that are corroborated by the data. Local tests can also be conducted before a model is fitted at all, and they can be used even for models that are globally under-identified. We discuss appropriate statistical local tests, and provide applied examples. We also present novel software in R that automates this type of local fit evaluation.

scholarly journals A Comparative Analysis of the Anxiety in Offenders of the Law Based on Structural Equational Models

2020 ◽  
Vol 14 (2) ◽  
pp. 65
Author(s):  
Catalina Quintero-López ◽  
Víctor Daniel Gil-Vera ◽  
Alejandra Bustamante-Hernández ◽  
Luis Eduardo De Ángel-Martínez
Keyword(s):  
Antisocial Personality Disorder ◽  
Latent Variable ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Equation Models ◽  
Negative Impact ◽  
Antisocial Personality ◽  
Goodness Of Fit Test ◽  
Men And Women ◽  
The Law

Anxiety affects men and women and have a negative impact on their lives. This paper presents two structural equation models (SEM) to evaluate the variables (physiological and cognitive), that most influenced the anxiety in men and women offenders of the law. Was used a representative sample of 60 offenders of the law (30 mens and 30 womens) of the Specialized Attention Center (SAC) “Carlos Lleras Restrepo” in Medellin, Colombia with diagnosis of Antisocial Personality Disorder (APD). The results of Bartlett's and KMO tests, indicated that the factorial analysis is adequate, all the constructs are statistically significant. The goodness-of-fit test indicated that the model fits well with the data. This paper concludes that, of the two constructs considered: physiological and cognition, in the men the construct that most influences the latent variable physiological are the “Palpitations or tachycardia”. The construct that most influences the latent variable cognitive is the “a feeling of instability”. In the women, the construct that most influences the latent variable physiological is the “dizziness or vertigo”. The construct that most influences the latent variable cognitive is “be afraid”.

scholarly journals Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics

1981 ◽  
Vol 18 (3) ◽  
pp. 382-388 ◽  
Author(s):  
Claes Fornell ◽  
David F. Larcker
Keyword(s):  
Statistical Analysis ◽  
Measurement Error ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Equation Models ◽  
Structural Equations ◽  
Testing System

Several issues relating to goodness of fit in structural equations are examined. The convergence and differentiation criteria, as applied by Bagozzi, are shown not to stand up under mathematical or statistical analysis. The authors argue that the choice of interpretative statistic must be based on the research objective. They demonstrate that when this is done the Fornell-Larcker testing system is internally consistent and that it conforms to the rules of correspondence for relating data to abstract variables.

The Impact of BIB Spiraling-Induced Missing Data Patterns on Goodness-of-Fit Tests in Factor Analysis

1995 ◽  
Vol 20 (1) ◽  
pp. 69-82 ◽  
Author(s):  
David Kaplan
Keyword(s):  
Factor Analysis ◽  
Missing Data ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Covariance Matrices ◽  
Equation Modeling ◽  
Continuous Variables ◽  
Goodness Of Fit Test ◽  
New Approach ◽  
The Impact

This article considers the impact of missing data arising from balanced incomplete block (BIB) spiraled designs on the chi-square goodness-of-fit test in factor analysis. Specifically, data arising from BIB designs possess a unique pattern of missing data that can be characterized as missing completely at random (MCAR). Standard approaches to factor analyzing such data rest on forming pairwise available case (PAC) covariance matrices. Developments in statistical theory for missing data show that PAC covariance matrices may not satisfy Wishart distribution assumptions underlying factor analysis, thus impacting tests of model fit. One approach, advocated by Muthén, Kaplan, and Hollis (1987) for handling missing data in structural equation modeling, is proposed as a possible solution to these problems. This study compares the new approach to the standard PAC approach in a Monte Carlo framework. Results show that tests of goodness-of-fit are very sensitive to PAC approaches even when data are MCAR, as is the case for BIB designs. The new approach is shown to outperform the PAC approach for continuous variables and is comparatively better for dichotomous variables.

scholarly journals COVID-19 Concerns and Personality of Commerce Workers: Its Influence on Burnout

2021 ◽  
Vol 13 (22) ◽  
pp. 12908
Author(s):  
Ana María Rodríguez-López ◽  
Susana Rubio-Valdehita
Keyword(s):  
Structural Equation ◽  
Goodness Of Fit ◽  
Online Survey ◽  
Structural Equation Models ◽  
Burnout Syndrome ◽  
Hierarchical Regression ◽  
Fit Indices ◽  
Cross Sectional ◽  
Sociodemographic Variables ◽  
Cross Sectional Design

We analyze burnout in a sample of commercial workers in Spain and its relationship with sociodemographic variables, personality, and concern about the influence of the COVID-19 pandemic on their jobs through a cross-sectional design. Participants (n = 614) answered an online survey, including questions about sociodemographic data, concern, NEO-FFI (personality), and MBI (burnout syndrome). The survey took place from October 2020 to May 2021. We assessed the relationships between sociodemographic variables, pandemic concern, and personality as predictors of burnout by hierarchical regression analysis and then tested using SEM (structural equation models). The proposed model showed adequate goodness-of-fit indices. The results of the present study suggest that the COVID-19 pandemic had little effect to the development of burnout syndrome in commerce employees. However, in agreement with previous literature, the present study shows that personality has a significant role in predicting burnout. Neuroticism, introversion, conscientiousness, and agreeableness were strong predictors for burnout dimensions. In addition, we found that personality directly affected the pandemic concern: individuals with high levels of Neuroticism and low levels of extraversion, agreeableness, and conscientiousness have more pandemic concerns. In conclusion, personality is an important factor that affects the level of workers’ concern about the influence of the pandemic on their job and the development of burnout syndrome. Furthermore, although we found significant differences between groups formed by various sociodemographic characteristics, the conclusion regarding this type of variable is that their ability to predict burnout is deficient.

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