scholarly journals Assessing Model Fit: Caveats and Recommendations for Confirmatory Factor Analysis and Exploratory Structural Equation Modeling

2015 ◽  
Vol 19 (1) ◽  
pp. 12-21 ◽  
Author(s):  
John L. Perry ◽  
Adam R. Nicholls ◽  
Peter J. Clough ◽  
Lee Crust
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Model Fit ◽  
Equation Modeling ◽  
Exploratory Structural Equation Modeling ◽  
Confirmatory Factor

scholarly journals Assessing the Unidimensionality of Clayton’s Environmental Identity Scale Using Confirmatory Factor Analysis (CFA) and Bifactor Exploratory Structural Equation Modeling (bifactor-ESEM)

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Paulo Moreira ◽  
Ana Loureiro ◽  
Richard Inman ◽  
Pablo Olivos-Jara
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Bifactor Model ◽  
Equation Modeling ◽  
Environmental Identity ◽  
Order Model ◽  
Exploratory Structural Equation Modeling ◽  
Confirmatory Factor

A relevant intrapersonal characteristic for understanding intentions and behavior toward environmental sustainability is the degree to which nature is important for a person’s self-definition. Clayton’s Environmental Identity (EID) scale purports to measure this construct. However, a limited number of prior exploratory studies of this measure have supported different factor structures. Hence, our initial aim was to develop an understanding of the dimensionality of Clayton’s 24-item EID scale by testing competing latent structures using confirmatory factor analysis. We analyzed self-reported data from 458 adults (Mage = 26.7 years; 81% female). Four a priori models (a first-order model, a second-order model, a unidimensional model, and a bifactor model) did not show satisfactory fit to the data. An ancillary analysis using bifactor exploratory structural equation modeling (bifactor-ESEM) indicated a bifactor model with three specific factors had a good fit to the data. The factor loadings of this model and values for bifactor indices (Omega Hierarchical and Explained Common Variance [ECV]) indicated a single mean score across all EID scale items taps into an essentially unidimensional construct and is therefore appropriate to interpret. In sum, our study provides a critical insight into the dimensionality of Clayton’s EID scale that will be valuable when applying this measure for research and intervention purposes.

Evaluating the construct validity of Stress Overload Scale-Short using exploratory structural equation modeling

2017 ◽  
Vol 25 (7) ◽  
pp. 913-921 ◽  
Author(s):  
Wenlong Mu ◽  
Wenjie Duan
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Negative Emotion ◽  
Equation Modeling ◽  
Exploratory Structural Equation Modeling ◽  
Confirmatory Factor ◽  
Personal Vulnerability ◽  
Negative Emotion Symptoms

This study used exploratory structural equation modeling and confirmatory factor analysis to examine the construct validity of the Chinese version of Stress Overload Scale-Short, which included personal vulnerability and event load. The participants included 629 community residents and 495 university students. The results indicated a better goodness-of-fit using exploratory structural equation modeling compared with confirmatory factor analysis. The Stress Overload Scale-Short performed well in distinguishing individuals with more negative emotion symptoms from the general population. A moderation analysis demonstrated that social support moderates the effect of personal vulnerability on negative emotion symptoms. These results facilitated the application of Stress Overload Scale-Short in the current population.

Revisiting the Leadership Scale for Sport

2016 ◽  
Vol 119 (2) ◽  
pp. 435-449 ◽  
Author(s):  
Weisheng Chiu ◽  
Fernando M. Rodriguez ◽  
Doyeon Won
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
National Collegiate Athletic Association ◽  
Equation Modeling ◽  
Factorial Validity ◽  
Factor Solution ◽  
Exploratory Structural Equation Modeling ◽  
Confirmatory Factor

This study examines the factor structure of the shortened version of the Leadership Scale for Sport, through a survey of 201 collegiate swimmers at National Collegiate Athletic Association Division II and III institutions, using both exploratory structural equation modeling and confirmatory factor analysis. Both exploratory structural equation modeling and confirmatory factor analysis showed that a five-factor solution fit the data adequately. The sizes of factor loadings on target factors substantially differed between the confirmatory factor analysis and exploratory structural equation modeling solutions. In addition, the inter-correlations between factors of the Leadership Scale for Sport and the correlations with athletes’ satisfaction were found to be inflated in the confirmatory factor analysis solution. Overall, the findings provide evidence of the factorial validity of the shortened Leadership Scale for Sport.

A Comparison of Reliability Estimation Based on Confirmatory Factor Analysis and Exploratory Structural Equation Models

2021 ◽  
pp. 001316442110089
Author(s):  
Yuanshu Fu ◽  
Zhonglin Wen ◽  
Yang Wang
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Reliability Estimation ◽  
Model Fit ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Reliability Estimates ◽  
Confirmatory Factor ◽  
Composite Reliability

Composite reliability, or coefficient omega, can be estimated using structural equation modeling. Composite reliability is usually estimated under the basic independent clusters model of confirmatory factor analysis (ICM-CFA). However, due to the existence of cross-loadings, the model fit of the exploratory structural equation model (ESEM) is often found to be substantially better than that of ICM-CFA. The present study first illustrated the method used to estimate composite reliability under ESEM and then compared the difference between ESEM and ICM-CFA in terms of composite reliability estimation under various indicators per factor, target factor loadings, cross-loadings, and sample sizes. The results showed no apparent difference in using ESEM or ICM-CFA for estimating composite reliability, and the rotation type did not affect the composite reliability estimates generated by ESEM. An empirical example was given as further proof of the results of the simulation studies. Based on the present study, we suggest that if the model fit of ESEM (regardless of the utilized rotation criteria) is acceptable but that of ICM-CFA is not, the composite reliability estimates based on the above two models should be similar. If the target factor loadings are relatively small, researchers should increase the number of indicators per factor or increase the sample size.

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