scholarly journals Comparison of Model Fit Indices Used in Structural Equation Modeling Under Multivariate Normality

2015 ◽  
Vol 14 (1) ◽  
pp. 152-167 ◽  
Author(s):  
Sengul Cangur ◽  
Ilker Ercan
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Model Fit ◽  
Multivariate Normality ◽  
Equation Modeling ◽  
Fit Indices ◽  
Model Fit Indices

scholarly journals Professional Fulfillment: Concept and Instrument Proposition

Psico-USF ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 27-39
Author(s):  
Ligia Carolina Oliveira-Silva ◽  
Juliana Barreiros Porto ◽  
John Arnold
Keyword(s):  
Factor Analysis ◽  
Structural Equation ◽  
Model Fit ◽  
Career Goals ◽  
Equation Modeling ◽  
Fit Indices ◽  
Confirmatory Factor ◽  
Model Fit Indices ◽  
The Right ◽  
Professional Fulfillment

Abstract This paper aims to propose a concept and an instrument of professional fulfillment (PF), which is theoretically defined as the perception of having attained or being on the right track for attaining one’s most important career goals. The Professional Fulfillment Scale (PFS) was developed in order to operationalize PF, being tested across two studies. Regarding Study 1, in which 406 workers took part, results from exploratory factor analysis evidenced construct validity for PFS. In Study 2, in which 270 workers took part, results from confirmatory factor analysis and structural equation modeling presented satisfactory model fit indices for PFS. We conclude that both the professional fulfillment concept and the scale are useful for mapping people’s importance and achievement of career goals and evaluation of progress, working as a diagnostic tool for career management.

scholarly journals Analisis Faktor-Faktor Yang Mempengaruhi Kesiapan Kerja Pada Siswa SMK Negeri 1 Sinjai

2018 ◽  
Vol 6 (2) ◽  
pp. 105-115
Author(s):  
Muhammad Ihsan
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Model Fit ◽  
Equation Modeling

Tujuan penelitian ini adalah: 1) Untuk mengetahui bagaimana hasil dari faktor-faktor yang mempengaruhi kesiapan kerja siswa SMK Negeri 1 Sinjai, 2) Untuk mengetahui hasil presentasi  dari analisis faktor-faktor yang mempengaruhi kesiapan kerja pada siswa SMK Negeri 1 Sinjai, 3) Untuk mengetahui faktor-faktor apa sajakah yang mempengaruhi penerimaan kesiapan kerja siswa SMK Negeri 1 Sinjai. Penelitian ini menggunakan metode deskriptif eksploratif. Populasi dalam penelitian ini adalah seluruh siswa kelas 3 SMK Negeri 1 Sinjai sejumlah 341 siswa dan sampel sebanyak 200 siswa. Analisis data dilakukan dengan pendekatan Structural Equation Modeling (SEM), yaitu SEM berbasis covariance. Berdasarkan hasil dari analisis data yang dilakukan, diperoleh kesimpulan bahwa: 1) Pengujian variabel-variabel pada model I-E-O, yang berpengaruh pada kesiapan kerja siswa SMK Negeri 1 Sinjai, dengan menggunakan pendekatan SEM, disimpulkan model fit dengan data yang ada. 2) Faktor kemampuan memiliki nilai koefisien sebesar 5,537437. Faktor kemampuan merupakan faktor terkuat yang mempengaruhi kesiapan kerja pada siswa SMK Negeri 1 Sinjai. Faktor ini terdiri atas prestasi belajar, tingkat intelegensi, pengalaman praktek, kedisiplinan, ekspektansi masuk dunia kerja, bakat. 3) Faktor-faktor yang mempengaruhi kesiapan kerja siswa SMK Negeri 1 Sinjai, dipengaruhi oleh faktor kemampuan, faktor akademis, faktor prilaku dan potensi diri, faktor bawaan/warisan.

scholarly journals Cutoff criteria for overall model fit indexes in generalized structured component analysis

2020 ◽  
Vol 8 (4) ◽  
pp. 189-202
Author(s):  
Gyeongcheol Cho ◽  
Heungsun Hwang ◽  
Marko Sarstedt ◽  
Christian M. Ringle
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Component Analysis ◽  
Model Fit ◽  
Equation Modeling ◽  
Generalized Structured Component Analysis ◽  
Root Mean Square Residual ◽  
Fit Indexes ◽  
Cutoff Criteria

AbstractGeneralized structured component analysis (GSCA) is a technically well-established approach to component-based structural equation modeling that allows for specifying and examining the relationships between observed variables and components thereof. GSCA provides overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean square residual (SRMR). While these indexes have a solid standing in factor-based structural equation modeling, nothing is known about their performance in GSCA. Addressing this limitation, we present a simulation study’s results, which confirm that both GFI and SRMR indexes distinguish effectively between correct and misspecified models. Based on our findings, we propose rules-of-thumb cutoff criteria for each index in different sample sizes, which researchers could use to assess model fit in practice.

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.

A New Dimension of Organizational Justice: Procedural Voice

2009 ◽  
Vol 105 (2) ◽  
pp. 411-426 ◽  
Author(s):  
Denise Jepsen ◽  
John Rodwell
Keyword(s):  
Structural Equation Modeling ◽  
Organizational Justice ◽  
Latent Variable ◽  
Structural Equation ◽  
Factor Model ◽  
Model Fit ◽  
Equation Modeling ◽  
Wide Range

Dimensionality of the Colquitt justice measures was investigated across a wide range of service occupations. Structural equation modeling of data from 410 survey respondents found support for the 4-factor model of justice (procedural, distributive, interpersonal, and informational), although significant improvement of model fit was obtained by including a new latent variable, “procedural voice,” which taps employees' desire to express their views and feelings and influence results. The model was confirmed in a second sample ( N = 505) in the same organization six months later.

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.  

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