Bayesian Structural Equation Modeling Approach Research Articles

Latent Factors Affecting Smoking Cessation Among TB and HIV Patients: Bayesian Structural Equation Modeling Approach

Latent Factors Affecting Smoking Cessation Among TB and HIV Patients: Bayesian Structural Equation Modeling Approach

Examining the complex relations between childhood adversity, mindfulness, attachment, and various personality outcomes: A Bayesian structural equation modeling approach.

The purpose of our present study was two-fold; first, we wanted to replicate the finding that the relationships between abuse and neglect and these traits are partly indirect through insecure attachment. Second, we wanted to determine whether mindfulness is an additional mediator in these relationships. A sample of 291 undergraduate students participated in the current study (Mage = 21.7, SD = 6.5). Findings from a Bayesian Structural Equation Model supported the prediction that the relationship between childhood abuse and neglect and disordered personality traits was indirect through insecure attachment. More specifically, for borderline traits it was indirect through anxious attachment while interpersonal manipulation and callous affect psychopathic traits it was indirect through avoidant attachment. Importantly, mindfulness was not a significant mediator in the model for any of the outcome variables. Overall, there was support for the idea that insecure attachment was a potential mechanism in the relationship between childhood abuse and neglect and disordered personality traits, though there was no support for mindfulness as a potential mediator. Implications are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Supplemental Material for Examining the Complex Relations Between Childhood Adversity, Mindfulness, Attachment, and Various Personality Outcomes: A Bayesian Structural Equation Modeling Approach

Supplemental Material for Examining the Complex Relations Between Childhood Adversity, Mindfulness, Attachment, and Various Personality Outcomes: A Bayesian Structural Equation Modeling Approach

A New Bayesian Structural Equation Modeling Approach with Priors on the Covariance Matrix Parameter

Bayesian inference for structural equation models (SEMs) is increasingly popular in social and psychological sciences owing to its flexibility to adapt to more complex models and the ability to include prior information if available. However, there are two major hurdles in using the traditional Bayesian SEM in practice: (1) the information nested in the prior distributions is hard to control, and (2) the MCMC iterative procedures naturally lead to Markov chains with serial dependence and the diagnostics of their convergence are often difficult. In this study, we present an alternative procedure for Bayesian SEM aiming to address the two challenges. In the new Bayesian SEM procedure, we specify a prior distribution on the population covariance matrix parameter $\mathbf{\Sigma}$ and obtain its posterior distribution $p(\mathbf{\Sigma}|\text{data})$. We then construct a posterior distribution of model parameters $\boldsymbol{\theta}$ in the hypothetical SEM model by transforming the posterior distribution of $\mathbf{\Sigma}$ to a distribution of model parameter $\boldsymbol{\theta}$. The new procedure eases the practice of Bayesian SEM significantly and has a better control over the information nested in the prior distribution. We evaluated its performance through a simulation study and demonstrate its application through an empirical example.

Occupational Dysfunction as a Mediator between Recovery Process and Difficulties in Daily Life in Severe and Persistent Mental Illness: A Bayesian Structural Equation Modeling Approach.

Background This study is aimed at verifying a hypothetical model of the structural relationship between the recovery process and difficulties in daily life mediated by occupational dysfunction in severe and persistent mental illness (SPMI). Methods Community-dwelling participants with SPMI were enrolled in this multicenter cross-sectional study. The Recovery Assessment Scale (RAS), the World Health Organization Disability Assessment Schedule second edition (WHODAS 2.0), and the Classification and Assessment of Occupational Dysfunction (CAOD) were used for assessment. Confirmatory factor analysis, multiple regression analysis, and Bayesian structural equation modelling (BSEM) were determined to analyze the hypothesized model. If the mediation model was significant, the path coefficient from difficulty in daily life to recovery and the multiplication of the path coefficients mediated by occupational dysfunction were considered as each the direct effect and the indirect effect. The goodness of fit in the model was determined by the posterior predictive P value (PPP). Each path coefficient was validated with median and 95% confidence interval (CI). Results The participants comprised 98 individuals with SPMI. The factor structures of RAS, WHODAS 2.0, and CAOD were confirmed by confirmatory factor analysis to be similar to those of their original studies. Multiple regression analysis showed that the independent variables of RAS were WHODAS 2.0 and CAOD, and that of CAOD was WHODAS 2.0. The goodness of fit of the model in the BSEM was satisfactory with a PPP = 0.27. The standardized path coefficients were, respectively, significant at −0.372 (95% CI: −0.586, −0.141) from “difficulty in daily life” to “recovery” as the direct effect and at −0.322 (95% CI: −0.477, −0.171) mediated by “occupational dysfunction” as the indirect effect. Conclusions An approach for reducing not only difficulty in daily life but also occupational dysfunction may be an additional strategy of person-centered, recovery-oriented practice in SPMI.

Analyzing the Safety Consequences of Pedestrian Spatial Violation at Mid-Blocks: A Bayesian Structural Equation Modeling Approach

The objective of this study is to understand the impact of a variety of factors on the frequency and severity of pedestrian-vehicle collisions that involve pedestrian spatial violations at mid-blocks. To that end, the historical collision records of the City of Hamilton between 2010 and 2017 were obtained, and collisions that had occurred at mid-blocks were filtered out. A Bayesian structural equation modeling (SEM) framework was developed to investigate the impact of a wide range of factors on such collisions. First, a classical SEM was developed to group the different factors into sets of latent variables. Four latent variables were defined, including location amenities and attractions, pedestrian/road network characteristics, exposure parameters, and location/collision-specific factors. The Bayesian SEM was then implemented to investigate the relationship between the latent variables and collisions. The results showed that the amenities and attractions of a location (e.g., parks, schools, bike-share stations, and bus stops) were the most influential factor on the frequency of collisions that involve spatial violation, followed by pedestrian network characteristics. Pedestrian network characteristics and location/collision-specific factors were found to be the most influential factors on the severity of collisions. The location of bike-share stations, pedestrian network connectivity, exposure to walkers, and the number of lanes were the four observed variables that explained the highest percentage of the variance in each latent group, respectively. The results of this study should assist engineers and planners to develop better design concepts to mitigate collisions that are caused by pedestrian spatial violations in urban areas.

An examination of model fit and measurement invariance of general mental ability and personality measures used in the multilingual context of the Swiss Armed Forces: A Bayesian structural equation modeling approach

ABSTRACT Measurement invariance of psychological test batteries is an essential quality criterion when the test batteries are administered in different cultural and language contexts. The purpose of this study was to examine to what extent measurement model fit and measurement invariance across the two largest language groups in Switzerland (i.e., German and French speakers) can be assumed for selected general mental ability and personality tests used in the Swiss Armed Forces’ cadre selection process. For the model fit and invariance testing, we used Bayesian structural equation modeling (BSEM). Because the sizes of the language group samples were unbalanced, we reran the invariance testing with the subsampling procedure as a robustness check. The results showed that at least partial approximate scalar invariance can be assumed for the constructs. However, comparisons in the full sample and subsamples also showed that certain test items function differently across the language groups. The results are discussed regarding the three following issues: First, we critically discuss the applied criterion and alternative effect size measures for assessing the practical importance of non-invariances. Second, we highlight potential remedies and further testing options, that can be applied, once certain items have been detected to function differently. Third, we discuss alternative modeling and invariance testing approaches to BSEM and outline future research avenues.

Mediation effects of online public attention on the relationship between air pollution and precautionary behavior

This study investigates the mediation effects of online public attention on the relationship between air pollution and precautionary behavior based on a merged real-world data set that includes daily air quality, Internet search and media indices, social media discussions, and product purchases. Using a Bayesian structural equation modeling approach, we show that online public attention to air pollution increases when air pollution increases, and such attention is captured by more media reports, social media discussions, and Internet searches. A comprehensive relationship involving direct and indirect effects between air pollution and precautionary behavior is established. Air pollution has a positive effect on proactive defensive behaviors, reflected in increased purchases of preventive products, and this effect is partially mediated by online media coverage and the public's Internet searches. Air pollution also motivates passive defensive behaviors, reflected in decreased purchases of outdoor sports products, and this effect is partially mediated by social media coverage. These results suggest that governments could improve the quality of policy making by considering the different roles of various forms of online public attention in the public's risk perceptions of and reactions to air pollution.

Validation of a revised version of the Physical Self-Inventory – Very Short form for youth with intellectual disabilities (PSI-VS-ID-R): A Bayesian structural equation modeling approach

The objective of the current study was to investigate the psychometric properties of scores obtained on a revised version of the Physical Self-Inventory – Very Short form for youth with intellectual disabilities (PSI-VS-ID-R). A sample of 351 youth (M = 15.81 years) with mild to moderate levels of Intellectual Disabilities (ID) from Australia (N = 230) and Canada (N = 121) participated in this study. They respectively completed the English and French versions of the PSI-VS-ID-R, as well as additional measures of self-concept and physical fitness. Bayes structural equation modeling (BSEM) analyses provided support for the validity and reliability of the a priori nine-factor structure of scores obtained on the PSI-VS-ID-R. Moreover, additional BSEM analyses provided support for the comparability of responses obtained on the PSI-VS-ID-R (i.e., lack of differential item functioning or measurement equivalence) irrespective of linguistic versions and of participants’ age, body-mass index, ID level, sex and frequency of sport involvement. Additionally, latent mean level differences in PSI-VS-ID-R’s factors scores were also found across linguistic versions and as a function of youth’s ID level and frequency of sport involvement. Finally, the convergent validity of scores on the PSI-VS-ID-R factors was supported in relation to measures of self-concept and physical fitness.

Psychometric properties of the Chinese version of Five Facet Mindfulness Questionnaire\u2014short form in cancer patients: a Bayesian structural equation modeling approach

BackgroundMindfulness has emerged as an important correlate of well-being in various clinical populations. The present study evaluated the psychometric properties of the 20-item short form of the Five Facet Mindfulness Questionnaire (FFMQ-SF) in the Chinese context.MethodsThe study sample was 127 Chinese colorectal cancer patients who completed the FFMQ-SF and validated physical and mental health measures. Factorial validity of the FFMQ-SF was assessed using Bayesian structural equation modeling (BSEM) via informative priors on cross-loadings and residual covariances. Linear regression analysis examined its convergent validity with the health measures on imputed datasets.ResultsThe five-factor BSEM model with approximate zero cross-loadings and one residual covariance provided an adequate model fit (PPP = 0.07, RMSEA = 0.06, CFI = 0.95). Satisfactory reliability (ω = 0.77–0.85) was found in four of the five facets (except nonjudging). Acting with awareness predicted lower levels of perceived stress, negative affect, anxiety, depression, and illness symptoms (β = − 0.37 to − 0.42) and better quality of life (β = 0.29–0.32). Observing, nonjudging, and nonreacting did not show any significant associations (p > .05) with health measures. Acting with awareness was not significantly correlated (r < 0.15) with the other four facets.ConclusionThe present findings provide partial support for the psychometric properties of the FFMQ-SF in colorectal cancer patients. The nonjudging facet showed questionable validity and reliability in the present sample. Further studies with larger sample sizes are needed to elucidate the viability of FFMQ-SF as a measure of mindfulness facets in cancer patients.

Neo-Eurasianism and the Russian elite: the irrelevance of Aleksandr Dugin’s geopolitics

ABSTRACTThe consistency and effectiveness of Russia’s assertive foreign policy has earned Putin, both domestically and internationally, the image of a powerful and ambitious leader with a strategic plan to re-establish the Russian empire and defend Russia’s core national interests. Speculation among scholars and practitioners regarding the existence of such a “strategic plan” makes Aleksandr Dugin’s conspiratorial neo-Eurasianism project an especially appealing subject of research. This paper explores key ideas of Dugin’s neo-Eurasianism, as described in his Foundations of Geopolitics, and tests them empirically with data from the Survey of Russian Elites: 1993–2016 using a Bayesian Structural Equation Modeling approach. Its main finding is that the theory has limited utility for understanding elites’ foreign policy perceptions and therefore its influence should not be overstated. Moreover, there is no evidence that Dugin’s theory is more salient in the post-Crimean period than in the pre-Crimean period.

Coaches Coaching Psychological Skills-Why Not? A Framework and Questionnaire Development.

This study is part of a program of research investigating coaches delivering psychological skills (PS). Here, 3 studies feature an original conceptualization of coaching PS and the development and validation of 2 questionnaires capturing the coaching of PS. The authors conducted a qualitative investigation to establish a conceptual framework that included the fundamental coaching of PS behaviors (CPS-F) and the needs-supportive coaching of PS (CPS-NS). They then tested the factor structure of 2 subsequently developed questionnaires via a Bayesian structural equation modeling approach to confirmatory factor analysis across 2 samples and ran tests of invariance, concurrent, discriminant, and predictive validity. The CPS-F questionnaire showed an excellent fit for a 3-factor model, whereas the CPS-NS demonstrated an excellent single-factor fit. Significant relationships with theoretically related constructs suggested concurrent, discriminant, and predictive validity. The findings are expected to significantly further research into our understanding of coaches coaching PS.

The new version of the pictorial scale of Perceived Movement Skill Competence in Spanish children: Evidence of validity and reliability.

© 2019 Revista Internacional de Ciencias del Deporte. All Rights Reserved. The purpose of this study was to i) examine reliability and construct validity of the pictorial scale of Perceived Movement Skill Competence (PMSC) aligned to the third version of the Test of Gross Motor Development (TGMD-3) in a sample of Spanish children; and to ii) analyse the validity of the PMSC and children's perceived motor competence (MC) according to gender. A convenience sample of 361 children (55.7% boys) between 4 and 11 years-old participated. The pictorial scale of PMSC (19 items) was administered (a random subsample repeated the PMSC twice). Test-retest reliability using Intraclass Correlation Coefficients and construct validity using a Bayesian Structural Equation Modeling approach were conducted in each subscale and the total PMSC. High to excellent reliability was found. The two-factor model with non-informative cross-loadings and informative cross-loadings were both adequate fitting models. Boys reported a higher perception in ball skills, locomotion and the total PMSC than girls (p < .05). These findings provide evidence of the construct validity and reliability of the two-factor PMSC aligned with the TGMD-3 in locomotion and ball skills. The Spanish version of the pictorial scale of PMSC would be useful for assessing perceived MC in Spanish children, something in particular interest for Physical Education teachers.

Influence of environmental factors on well-being and care burden of elderly family caregivers: a bayesian structural equation modeling approach

In Japan, the magnitude of the care burden and difficulties with continuity of in-home care among elderly caregivers have been reported, but support systems for elderly family caregivers remain insufficient. This study aimed to test a hypothetic model holding that environmental factors influence elderly family caregivers’ well-being and care burden. Participants were 60 family caregivers of elderly people with care needs who were aged ≥60 years (24 men and 36 women; mean age, 75.7 ± 8.9 years). The study was approved by the Kibi International University Ethical Review Board (No. 17-16). Participants underwent a demographic survey, the Comprehensive Environmental Questionnaire (CEQ), a short version of the Japanese version of the Zarit Caregiver Burden Interview, and the Visual Analogue Scale of Happiness, and the generated hypothetical model was assessed by means of Bayesian Structural Equation Modeling (BSEM). When the hypothetical model was assessed using BSEM, environmental factors were found to influence the well-being more strongly (standardized coefficient [sc] = .406) than the care burden (sc = −.377). Factor loading of environmental factors was largest in family environment, followed by secure living environment, and interactive environment. In addition, the gender of the caregiver also influenced the care burden (sc = −.324) and well-being (sc = −.275). The posterior predictive P -value of the final model was 0.170. This study assessed a hypothetic model holding that environmental factors influence elderly family caregivers’ well-being and care burden, and that the gender of the caregiver is a covariate. Women's care burden was strong, while men's well-being was low. Accordingly, by supporting an interactive environment, a secure living environment, and a family environment as mapped by the CEQ, it may be possible to improve the well-being and mitigate the care burden of elderly family caregivers.

The Construction of Patient Loyalty Model Using Bayesian Structural Equation Modeling Approach

The information on the health status of an individual is often gathered based on a health survey. Patient assessment on the quality of hospital services is important as a reference in improving the service so that it can increase a patient satisfaction and patient loyalty. The concepts of health service are often involve multivariate factors with multidimensional sructure of corresponding factors. One of the methods that can be used to model such these variables is SEM (Structural Equation Modeling). Structural Equation Modelling (SEM) is a multivariate method that incorporates ideas from regression, path-analysis and factor analysis. A Bayesian approach to SEM may enable models that reflect hypotheses based on complex theory. Bayesian SEM is used to construct the model for describing the patient loyalty at Puskesmas in Padang City. The convergence test with the history of trace plot, density plot and the model consistency test were performed with three different prior types. Based on Bayesian SEM approach, it is found that the quality of service and patient satisfaction significantly related to the patient loyalty.

Bayesian SEM for Specification Search Problems in Testing Factorial Invariance

ABSTRACTSpecification search problems refer to two important but under-addressed issues in testing for factorial invariance: how to select proper reference indicators and how to locate specific non-invariant parameters. In this study, we propose a two-step procedure to solve these issues. Step 1 is to identify a proper reference indicator using the Bayesian structural equation modeling approach. An item is selected if it is associated with the highest likelihood to be invariant across groups. Step 2 is to locate specific non-invariant parameters, given that a proper reference indicator has already been selected in Step 1. A series of simulation analyses show that the proposed method performs well under a variety of data conditions, and optimal performance is observed under conditions of large magnitude of non-invariance, low proportion of non-invariance, and large sample sizes. We also provide an empirical example to demonstrate the specific procedures to implement the proposed method in applied research. The importance and influences are discussed regarding the choices of informative priors with zero mean and small variances. Extensions and limitations are also pointed out.

Bayesian Factor Analysis as a Variable-Selection Problem: Alternative Priors and Consequences

ABSTRACTFactor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor-loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, Muthén & Asparouhov proposed a Bayesian structural equation modeling (BSEM) approach to explore the presence of cross loadings in CFA models. We show that the issue of determining factor-loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov's approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike-and-slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set is used to demonstrate our approach.

Dimensionality of the 9-item Utrecht Work Engagement Scale revisited: A Bayesian structural equation modeling approach.

The aim of this study was to reexamine the dimensionality of the widely used 9-item Utrecht Work Engagement Scale using the maximum likelihood (ML) approach and Bayesian structural equation modeling (BSEM) approach. Three measurement models (1-factor, 3-factor, and bi-factor models) were evaluated in two split samples of 1,112 health-care workers using confirmatory factor analysis and BSEM, which specified small-variance informative priors for cross-loadings and residual covariances. Model fit and comparisons were evaluated by posterior predictive p-value (PPP), deviance information criterion, and Bayesian information criterion (BIC). None of the three ML-based models showed an adequate fit to the data. The use of informative priors for cross-loadings did not improve the PPP for the models. The 1-factor BSEM model with approximately zero residual covariances displayed a good fit (PPP>0.10) to both samples and a substantially lower BIC than its 3-factor and bi-factor counterparts. The BSEM results demonstrate empirical support for the 1-factor model as a parsimonious and reasonable representation of work engagement.

The Prowess and Pitfalls of Bayesian Structural Equation Modeling

Muthén and Asparouhov introduced an approach for conducting Bayesian inference in the context of structural equation models that they termed Bayesian structural equation modeling (BSEM). In this article, we provide an overview of the BSEM technique, illustrate how this technique relates to confirmatory and exploratory factor analysis, and highlight several key problems with using the BSEM approach as it is currently advocated. Utilizing data from a large-scale study of entrepreneurial self-efficacy, we develop a modified approach for applying the BSEM technique in a manner that is more consistent with accepted principles of reflective measurement, factor analysis, and model selection. We devise a series of recommendations to guide future use of the BSEM technique to help ensure that mainstream use of this approach heralds the coming of a new day in measurement development rather than a false dawn.

The Estimation Process in Bayesian Structural Equation Modeling Approach

Structural equation modelling (SEM) is a multivariate method that incorporates ideas from regression, path-analysis and factor analysis. A Bayesian approach to SEM may enable models that reflect hypotheses based on complex theory. The development and application of Bayesian approaches to SEM has, however, been relatively slow but with modern technology and the Gibbs sampler, is now possible. The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameters can be computed from these samples. This study shows that the conditional distributions required in the Gibbs sampler are familiar distributions, hence the algorithm is very efficient. A goodness of fit statistic for assessing the proposed model is presented. An illustrative example with a real data is presented.