The Influence of Number of Categories and Threshold Values on Fit Indices in Structural Equation Modeling with Ordered Categorical Data

2018 ◽  
Vol 53 (5) ◽  
pp. 731-755 ◽  
Author(s):  
Yan Xia ◽  
Yanyun Yang
1986 ◽  
Vol 14 (4) ◽  
pp. 345-352
Author(s):  
Margaret E. Bell ◽  
Jean A. Massey

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.


Author(s):  
SAMIRA GHIYASI ◽  
FATEMEH VERDI BAGHDADI ◽  
FARSHAD HASHEMZADEH ◽  
AHMAD SOLTANZADEH

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.  


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