Performance of Model Fit and Selection Indices for Bayesian Structural Equation Modeling with Missing Data

2022 ◽  
pp. 1-19
Author(s):  
Sonja D. Winter ◽  
Sarah Depaoli
Keyword(s):  
Structural Equation Modeling ◽  
Missing Data ◽  
Structural Equation ◽  
Model Fit ◽  
Equation Modeling ◽  
Selection Indices ◽  
Bayesian Structural Equation Modeling

scholarly journals Prior Sensitivity in Bayesian Structural Equation Modeling for Sparse Factor Loading Structures

2020 ◽  
Vol 80 (6) ◽  
pp. 1025-1058 ◽  
Author(s):  
Xinya Liang
Keyword(s):  
Structural Equation Modeling ◽  
Variable Selection ◽  
Structural Equation ◽  
Factor Loading ◽  
Model Fit ◽  
Equation Modeling ◽  
Model Parameters ◽  
Shrinkage Priors ◽  
Bayesian Structural Equation Modeling ◽  
The Impact

Bayesian structural equation modeling (BSEM) is a flexible tool for the exploration and estimation of sparse factor loading structures; that is, most cross-loading entries are zero and only a few important cross-loadings are nonzero. The current investigation was focused on the BSEM with small-variance normal distribution priors (BSEM-N) for both variable selection and model estimation. The prior sensitivity in BSEM-N was explored in factor analysis models with sparse loading structures through a simulation study (Study 1) and an empirical example (Study 2). Study 1 examined the prior sensitivity in BSEM-N based on the model fit, population model recovery, true and false positive rates, and parameter estimation. Seven shrinkage priors on cross-loadings and five noninformative/vague priors on other model parameters were examined. Study 2 provided a real data example to illustrate the impact of various priors on model fit and parameter selection and estimation. Results indicated that when the 95% credible intervals of shrinkage priors barely covered the population cross-loading values, it resulted in the best balance between true and false positives. If the goal is to perform variable selection, a sparse cross-loading structure is required, preferably with a minimal number of nontrivial cross-loadings and relatively high primary loading values. To improve parameter estimates, a relatively large prior variance is preferred. When cross-loadings are relatively large, BSEM-N with zero-mean priors is not recommended for the estimation of cross-loadings and factor correlations.

Factor Structure of the PANAS With Bayesian Structural Equation Modeling in a Chinese Sample

2021 ◽  
pp. 016327872199679
Author(s):  
Minsun Kim ◽  
Ze Wang
Keyword(s):  
High School Students ◽  
Structural Equation Modeling ◽  
Factor Structure ◽  
Structural Equation ◽  
Model Fit ◽  
Self Report ◽  
Equation Modeling ◽  
School Students ◽  
Chinese Sample ◽  
Bayesian Structural Equation Modeling

The Positive and Negative Affect Schedule (PANAS) is the most widely used self-report instrument for assessing affect. However, there are inconsistent findings regarding the factor structure of the PANAS. In this study, we applied Bayesian structural equation modeling (BSEM) to investigate the structure of the PANAS using data from a sample of 893 Chinese middle and high school students. Four models, the orthogonal two-, the oblique two-, the three-, and the bi-factor models were tested with prior specifications including approximately zero cross-loadings and residual covariances. The results indicated that the orthogonal two-factor model specified with informative priors for both cross-loadings and residual correlations has the best model fit. Confirmatory factor analysis with the maximum likelihood estimator (ML-CFA) based on modifications from BSEM analysis showed improved model fit compared to ML-CFA based on frequentist analysis, which is the evidence for the merit of BSEM for addressing misspecifications.

Further Insights Into the German Version of the Multidimensional Assessment of Interoceptive Awareness (MAIA)

2019 ◽  
Vol 35 (3) ◽  
pp. 317-325 ◽  
Author(s):  
Dorota Reis
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Interoceptive Awareness ◽  
Multidimensional Assessment ◽  
New Instrument ◽  
Confirmatory Factor ◽  
Complex Models ◽  
Approximate Zero ◽  
Bayesian Structural Equation Modeling

Abstract. Interoception is defined as an iterative process that refers to receiving, accessing, appraising, and responding to body sensations. Recently, following an extensive process of development, Mehling and colleagues (2012) proposed a new instrument, the Multidimensional Assessment of Interoceptive Awareness (MAIA), which captures these different aspects of interoception with eight subscales. The aim of this study was to reexamine the dimensionality of the MAIA by applying maximum likelihood confirmatory factor analysis (ML-CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). ML-CFA, ESEM, and BSEM were examined in a sample of 320 German adults. ML-CFA showed a poor fit to the data. ESEM yielded a better fit and contained numerous significant cross-loadings, of which one was substantial (≥ .30). The BSEM model with approximate zero informative priors yielded an excellent fit and confirmed the substantial cross-loading found in ESEM. The study demonstrates that ESEM and BSEM are flexible techniques that can be used to improve our understanding of multidimensional constructs. In addition, BSEM can be seen as less exploratory than ESEM and it might also be used to overcome potential limitations of ESEM with regard to more complex models relative to the sample size.

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.

scholarly journals Tonic immobility is a measure of boldness toward predators: an application of Bayesian structural equation modeling

2012 ◽  
Vol 23 (3) ◽  
pp. 619-626 ◽  
Author(s):  
Pim Edelaar ◽  
David Serrano ◽  
Martina Carrete ◽  
Julio Blas ◽  
Jaime Potti ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Tonic Immobility ◽  
Equation Modeling ◽  
Bayesian Structural Equation Modeling
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