Bayesian Structural Equation Modeling Research Articles

Evaluating Local Model Misspecification with Modification Indices in Bayesian Structural Equation Modeling

Model evaluation is a crucial step in SEM, consisting of two broad areas: global and local fit, where local fit indices are used to modify the original model. In the modification process, the modification index (MI) and the standardized expected parameter change (SEPC) are used to select the parameters that can be added to improve the fit. The purpose of this study is to extend the application of MI and SEPC to Bayesian SEM. We present how researchers can estimate posterior distributions of MI and SEPC using a posterior predictive model check (PPMC). We evaluated the effectiveness of these PPMCs with a simulation and found that MI can be used to detect the most relevant added parameters and that SEPC can be used as an effect size. Similar to maximum-likelihood estimation, the SEPC can overestimate the population value. Lastly, we present an example application of these indices.

A tutorial on Bayesian structural equation modelling: Principles and applications.

This paper explores the utilisation of Bayesian structural equation modelling (BSEM) in psychology, highlighting its advantages over frequentist methods for handling complex models and small sample sizes. Basic concepts and fundamental issues relevant to BSEM are introduced, such as prior setting, model convergence, and model fit evaluation and so on. The paper also provides illustrative examples of commonly employed BSEMs, including confirmatory factor analysis (CFA) models, mediation models and multigroup CFA models, accompanied by empirical data and computer codes to facilitate implementation. Our goal is to provide researchers with novel ideas for empirical research and equip them to overcome challenges inherent to traditional methods. As BSEM continues to gain traction in various fields, we anticipate its development will feature improved methods, techniques and reporting standards.

Explaining Protest Participation in Semi-authoritarian Regimes: The Power of Social Networks

AbstractThis study uses the case of ecological protests in the Russian Federation and the social network of 903 263 VKontakte users to investigate the unique characteristics of protesters’ social networks and assess the effects of network topology on the likelihood of participation. By applying Bayesian structural equation modeling to the data, the study finds that social network topology plays a more nuanced and complex role in predicting protest participation beyond simple exposure to information. Notably, individuals situated as brokers in lower-density and higher-closure networks exhibit a higher likelihood of participation, while central nodes participate less. The results highlight the importance of strategic positioning as brokers for increasing the likelihood of participation. The paper discusses these and other findings, contributing to a deeper understanding of the interplay between social network structures and protest participation.

Gamification in Enhancing Student Financial Knowledge, Engagement, and Enjoyment in Financial Education

This study aims to examine the impact of gamification on financial knowledge through student enjoyment and student engagement. This study employed a quantitative approach and used a survey to examine the relationships between variables. The sampling technique used was purposive sampling, with 289 students who have previously received financial education at a public university participating as respondents. The research instrument utilized a questionnaire developed based on prior studies. The results were tested using Variational Bayesian Structural Equation Modeling (VB-SEM) to analyze the influence between variables. The findings showed that gamification had a positive impact on student enjoyment and student engagement. This study demonstrates that gamification does not directly influence students' financial knowledge. Student engagement fully mediated the effect of gamification on students' financial knowledge, whereas enjoyment does not affect students' financial knowledge. This study underscores the importance of fostering student engagement in gamification to enhance financial knowledge. The implications of this research include strengthening student engagement through various strategies, such as apperception, motivation, and interaction during the use of gamification in financial education.

Indulgent Parenting and Adolescents' Maladjustments: The Roles of Cultural Context and Parental Gender.

Background/Objectives. Adolescence is a critical developmental stage marked by the exploration of independence and self-identity. In this study, we aimed to examine the association between indulgent parenting (characterized by high responsiveness and low demandingness) and adolescents' maladjustments across emotional, behavioral, and social domains. Methods. Using a cross-cultural sample of high school students from the U.S. (n = 268) and China (n = 189), we tested the hypotheses that indulgent parenting was associated with adolescents' maladjustments, and that such association varied by cultural context (U.S. vs. China) and parental gender. Results. The results from Bayesian structural equation modeling supported the hypotheses, showing significant associations between indulgent parenting and adolescents' maladjustments and differences in the associations across cultures and parental gender. Conclusions. The findings highlighted the need for culturally informed parenting programs to foster healthy adolescent development.

Mediating effect of cognitive appraisal and coping on anticipatory grief in family caregivers of patients with cancer: a Bayesian structural equation model study

BackgroundAnticipatory grief is common among family caregivers of cancer patients and may be related to caregiver burden, family resilience, psychological capital, cognitive appraisal, and coping strategies. The purpose of this study was to examine the mediating role of cognitive appraisal and coping strategies in the relationship between caregiver burden, family resilience, psychological capital, and anticipatory grief among caregivers of cancer patients.MethodsThis study surveyed from January to September 2023 among 265 caregivers of lung and breast cancer patients in two public hospitals. They completed measures of caregiver burden, family resilience, psychological capital, cognitive appraisal, coping, and anticipatory grief. AMOS software was used to model the data with Bayesian structural equation modeling.ResultsBayesian structural equation modeling results showed that caregiver burden had a direct effect on anticipatory grief. The chain mediating effects for cognitive appraisal tendency and coping tendency between caregiver burden, family resilience, psychological capital, and anticipatory grief, respectively. Coping tendency acted as a mediator between psychological capital and anticipatory grief.ConclusionsThe relationships between caregiver burden, family resilience, and psychological capital with anticipatory grief are embedded in the mediating effects of cognitive appraisal and coping. Early identification and intervention for caregiver burden, family resilience, psychological capital, cognitive appraisal, and coping methods may prevent anticipatory grief in caregivers of cancer patients.

Assessing the Psychometric Qualities of the Data-Informed School Leadership Survey

Purpose: There is little consensus in the literature regarding a) what it means for a school leader to lead with data, and b) how to measure data-informed leadership in a reliable and valid way. This study examines the psychometric properties of an operational measure intended to assess the extent to which a school leader is a ‘data-informed school leader. The measurement invariance, reliabilities and construct and predictive validities of the Data-Informed School Leadership Survey (DISL Survey) are assessed using various psychometric statistical techniques. Methods: Using data collected from 155 teachers from 7 public schools in a southern state, the following psychometric statistics used to address our purpose: the Many-Facet Rasch (MFR) Model, Bayesian second-order Confirmatory Factor Analysis (CFA), Bayesian Structural Equation Modeling- Multiple Indicators, Multiple Causes analysis (Bayesian SEM-MIMIC), and reliability analysis. Findings: Results show an adequate fit from all MFR, Bayesian CFA, and MIMIC models and a high reliability (Cronbach α = .98). The DISL Survey instrument exhibits sound psychometric properties. Results likewise confirm the value of using MFR modeling and Bayesian methods to examine the psychometric properties of DISL Survey as a means of improving educational leadership measures. Implications for Research and Practice: Data from this study confirm the validity and reliability of the Data-Informed School Leadership Survey (DISL Survey) as an instrument to assess the strengths and weaknesses of Data-Informed School Leadership (DISL) and as a means for providing feedback for improving such leadership. Heretofore a measure for assessing this leadership was non-existent.

The gut microbiota-immune-brain axis in a wild vertebrate: dynamic interactions and health impacts.

The gut microbiota-immune-brain axis is a feedback network which influences diverse physiological processes and plays a pivotal role in overall health and wellbeing. Although research in humans and laboratory mice has shed light into the associations and mechanisms governing this communication network, evidence of such interactions in wild, especially in young animals, is lacking. We therefore investigated these interactions during early development in a population of common buzzards (Buteo buteo) and their effects on individual condition. In a longitudinal study, we used a multi-marker approach to establish potential links between the bacterial and eukaryotic gut microbiota, a panel of immune assays and feather corticosterone measurements as a proxy for long-term stress. Using Bayesian structural equation modeling, we found no support for feedback between gut microbial diversity and immune or stress parameters. However, we did find strong relationships in the feedback network. Immunity was negatively correlated with corticosterone levels, and microbial diversity was positively associated with nestling body condition. Furthermore, corticosterone levels and eukaryotic microbiota diversity decreased with age while immune activity increased. The absence of conclusive support for the microbiota-immune-brain axis in common buzzard nestlings, coupled with the evidence for stress mediated immunosuppression, suggests a dominating role of stress-dominated maturation of the immune system during early development. Confounding factors inherent to wild systems and developing animals might override associations known from adult laboratory model subjects. The positive association between microbial diversity and body condition indicates the potential health benefits of possessing a diverse and stable microbiota.

DO CHILDHOOD MEMORIES INFLUENCE THE THERAPEUTIC RELATIONSHIP? AN EMPIRICAL BAYESIAN SEM APPROACH.

This study aims to explore the interplay of these concepts in a sample patients diagnosed with schizophrenia and related psychotic disorders. Hence, the potentially mediating role of countertransference in the relationship between recalled parenting styles, childhood trauma and therapeutic working alliance was examined. A total sample of 30 patients (Age: 38.60; SD = 16.37; 50% female) treated for psychotic disorders in an inpatient setting were assessed. Pathways between the variables Recalled Parental Styles, Childhood Trauma, Countertransference and Working Alliance were estimated via frequentist and empirical Bayesian Structural Equation Modeling (EBSEM). Both maximum likelihood and EBSEM derived results suggested direct effects of remembered parental style on childhood trauma, as well as of childhood trauma on negative countertransference. Positive and negative countertransference were associated with working alliance. Furthermore, the findings suggested indirect effects of remembered parental style on negative countertransference via childhood trauma, as well as remembered parental style with working alliance mediated via childhood trauma and negative countertransference. Childhood trauma showed a significant indirect effect on working alliance via negative countertransference. The results provide preliminary support for the idea that traumatic memories from the past influence the therapeutic relationship in the present. In correspondence to this, the clinical significance of concepts like reverie and containment are discussed.

Effects of Office-Yoga and Walking at the Workplace to Improve Health and Wellbeing: A Longitudinal Randomized Controlled Trial

AbstractWorkplace physical activity interventions in office-based settings are considered essential components of occupational health management, aimed at reducing sedentary behavior and its associated adverse effects, while also promoting employees’ health and wellbeing. This study investigates the effectiveness of office-yoga and walking on employees’ prevalence and intensity of musculoskeletal complaints (MSC), as well as on activation, which encompasses vitality and vigilance. Using a three-month longitudinal randomized controlled trial design, 459 office workers from five organizations were randomly assigned to either the office-yoga intervention group, the walking intervention group, or to a waitlist control group. Participants in the intervention groups were instructed to individually engage in brief daily sessions of the prescribed activity at the workplace. Those in the office-yoga group received video tutorials to perform the exercises. Self-reported MSC and activation were assessed weekly. Bayesian structural equation modeling revealed small effects of office-yoga (d = − 0.26) and walking (d = − 0.31) in reducing the prevalence of MSC as well as a program-based effectiveness of walking over office-yoga in reducing the intensity of MSC (d = 0.23). Concerning longitudinal trends, latent growth modeling revealed an intervention-based effectiveness for both office-yoga and walking on prevalence (doffice−yoga = − 0.32; dwalking = − 0.30) and intensity of MSC (doffice−yoga = − 0.42; dwalking = − 0.35). Neither intervention showed improvements in activation. We found no indication of adverse effects of either intervention. While both office-yoga and walking can effectively improve employees’ MSC, our findings suggest that walking is more effective in reducing intensity of MSC compared to office-yoga.

How Does Prior Distribution Affect Model Fit Indices of Bayesian Structural Equation Model?

How Does Prior Distribution Affect Model Fit Indices of Bayesian Structural Equation Model?

Temporal relationships in the movement behaviour of adolescents: Testing and methodological considerations of the ActivityStat hypothesis

ABSTRACT The study aimed to investigate the ActivityStat hypothesis by examining the presence and the timeframe of the temporal relationships among physical activity (PA) levels and stationary time (ST) in adolescents. A secondary analysis was performed on data involving 356 adolescents in Dunedin, New Zealand. Participants wore a waist-worn accelerometer for several consecutive days to assess PA levels [i.e. light PA (LPA), and moderate-to-vigorous PA (MVPA)] and ST. Bayesian continuous-time structural equation modelling (CT-SEM) was used to examine the relationship between the behaviours over time and the timeframe during which these relationships occur. Increases in LPA, MVPA, and ST were positively associated with their later behaviours until 2.5, 1.7, and 2 days later, respectively. A cross-behavioural reciprocal and negative relationship between LPA and ST was demonstrated 0.4 days later. A positive relationship between ST and MVPA was observed until about 0.4 days later. This is the first study to investigate the ActivityStat hypothesis using Bayesian CT-SEM in adolescents, examining the multivariate relationships among different behaviours and the associated timeframes. To conclude, evidence of activity synergy was suggested for the within-behavioural relationships, while behavioural compensation was noted for ST. Thus, the findings provide some support for the ActivityStat hypothesis in adolescents.

Banking sector and economic growth in the digital transformation era: insights from maximum likelihood and Bayesian structural equation modeling

PurposeIn the digital era, the banking sector has transformed into a powerful intermediary, effectively connecting surplus and deficit units. This dynamic landscape empowers savers to secure their finances and generate returns, while simultaneously enabling businesses and individuals to access capital for investment and promoting economic growth. This study explores the relationships among banking development dimensions – represented by primary assets and liabilities, bank capital (core capital and required reserves) and economic growth as measured by components of gross domestic product (GDP).Design/methodology/approachThe study consolidated monthly balance sheets from digital banks over a 20-year period, resulting in an aggregate monthly balance sheet that reflects the financial position of all digital banks in the Palestinian economy. The research employs both maximum likelihood and Bayesian structural equation modeling to measure the causal pathways of the consolidated balance sheet with the individual components of GDP.FindingsThe results revealed that bank main assets (investments and loans) and liabilities (deposits) collectively explain for 97% of bank capital. Investments and loans demonstrate significant negative correlations with bank capital, while deposits exhibit a positive impact. This leads to a fundamental conclusion that a substantial proportion of retained earnings within the banking sector is reinvested, fueling expansion and growth. Additionally, the results showed a significant relationship between bank capital and various GDP components, including private consumption, gross investment and net exports (p = 0.000). However, while the relationship between bank capital and government spending was insignificant in the maximum likelihood estimation, Bayesian estimation revealed a slight yet positive impact of bank capital on government spending.Originality/valueThis research stands out due to its unique exploration of the intricate relationship between bank sector development dimensions, primary assets and liabilities and their impact on bank capital in the digital era. It offers fresh insights by dividing this connection into specific dimensions and constructs, utilizing a comprehensive two-decade dataset covering the digital banks records.

Applications of structural equation modeling in plant functional trait research

1.Plant functional traits, which encompass morphological, physiological, and ecological characteristics, are key to plant adaptation, growth, and development. In recent years, the structural equation model (SEM) has gained widespread use as a powerful statistical tool for studying plant functional traits and conducting research in this field. Its ability to distinguish between direct and indirect effects makes the SEM a robust method for investigating the complex relationships among environment components, traits and ecosystem functions. 2.Here, we review and discuss four commonly used SEMs: (1) the covariance-based structural equation model, (2) the piecewise structural equation model, (3) the Bayesian structural equation model, and (4) the partial least squares structural equation model. We also explore their applications in three typical ecosystems—forest, grassland, and wetland ecosystems—and investigate these forms of SEM in the context of their use in trait-ecosystem function research. 3.Our specific objectives were to: (i) compare the advantages and disadvantages of these four types of SEMs; (ii) analyze the current state of research on SEM applications in plant functional traits across diverse ecosystems; and (iii) highlight new approaches and potential research areas for the future application of SEM in plant functional traits. 4.In this paper, several key findings were obtained: (i) the selection of SEM type is influenced by the different spatial scales of the study. (ii) latent and composite variables were less commonly utilized in recent SEM studies. (iii) while SEMs have proven effective in distinguishing between direct and indirect effects to unravel the complex relationships among multiple variables, indirect effects deserve more attention in general studies. We propose that future applications of SEMs in plant functional traits should incorporate a broader spectrum of traits as well as the trade-offs between them. Larger and more diverse databases of plant functional traits would help make SEM analyses more accurate across different scales.

Macro-scale relationship between body mass and timing of bird migration

Clarifying migration timing and its link with underlying drivers is fundamental to understanding the evolution of bird migration. However, previous studies have focused mainly on environmental drivers such as the latitudes of seasonal distributions and migration distance, while the effect of intrinsic biological traits remains unclear. Here, we compile a global dataset on the annual cycle of migratory birds obtained by tracking 1531 individuals and 177 populations from 186 species, and investigate how body mass, a key intrinsic biological trait, influenced timings of the annual cycle using Bayesian structural equation models. We find that body mass has a strong direct effect on departure date from non-breeding and breeding sites, and indirect effects on arrival date at breeding and non-breeding sites, mainly through its effects on migration distance and a carry-over effect. Our results suggest that environmental factors strongly affect the timing of spring migration, while body mass affects the timing of both spring and autumn migration. Our study provides a new foundation for future research on the causes of species distribution and movement.

BAYESIAN NETWORKS AND STRUCTURAL EQUATION MODELLING TO INVESTIGATE THE PASSENGERS’ PERCEPTIONS IN HIGH-SPEED RAIL SYSTEMS

Ensuring sustainability in the global world today depends on perception management as well as financial management. In order to manage the perceptions, which are inherently latent variables as they are measured indirectly through their indicators, they must be accurately handled and modelled comprehensively. In the present study, a hybrid technique combining Bayesian Networks (BN) and Structural Equation Modelling (SEM), which are regarded as causal models, was used to investigate the perceptions of High-Speed Rail System (HSRS) passengers. In order to provide insight into the customer retention strategy for HSRS, the analyses were performed on the survey data gathered from the frequent users of HSRS operating between 2 cities of Turkey. After the measurement model of the perception variables through SEM was established, the relationships between the variables were learned using BN knowledge extraction algorithms. As a result, relationships from image to trust and loyalty, from trust to perceived value, from perceived value to satisfaction, and from satisfaction to loyalty were determined. Final interpretations were made in terms of risk management with the help of the probabilistic predictive ability of the BN by setting evidence on the satisfaction levels of the perceptions.

Differences in access to privacy information can partly explain digital inequalities in privacy literacy and self-efficacy

ABSTRACT Users of digital technologies are still held legally responsible for managing their personal data. However, not all users have the same opportunities for privacy management as researchers find systematic sociodemographic differences in, for example, users’ privacy literacy and self-efficacy – two important predictors of privacy management. The present study argues that one reason for such digital inequalities is how easily people have access to privacy information in their everyday lives (e.g. through their profession or social contacts). Analysing data of a representative sample of German internet users (N = 3978) by means of a Bayesian structural equation model, we find that men and more educated persons report easier access to privacy information which, in turn, positively relates to both privacy literacy and self-efficacy. Older persons feel less confident about privacy protection while men and more highly educated persons have higher privacy literacy levels. In conclusion, the present study reveals that casual access to privacy information can be important in learning and being confident about privacy management, but it cannot fully explain digital inequalities in online privacy self-efficacy and literacy.

Event-related Factors, Altruism, and Substance Use in Traumatization of Hurricane Student Volunteers: A Bayesian Model for the Follow-up Running Head: Bayesian Analysis of Disaster Traumatization

In surging disaster research, trauma psychologists called for more longitudinal investigation on factors related to resilience/lower traumatization for populations exposed to collective trauma. Little research has employed a Bayesian approach, a means with advantages in small samples and dichotomized endpoints. The present study addressed these needs with a two-wave survey on hurricane volunteers to demonstrate pathways to traumatization after deadly disasters. A survey was conducted at three months (Wave-1) and six months (Wave-2) after hurricane Katrina and Rita (H-KR) (N=201). Standardized instruments were used to assess posttraumatic stress symptoms (PTSS) altruism, substance use for coping, and event-related factors in Wave-1 and posttraumatic stress disorder (PTSD) in Wave-2. Bayesian structural equation modeling (Bayesian-SEM) was performed to evaluate the role of altruism and using substances to cope with Wave-2 PTSD. Traumatization was identified in 18% of participants, showing a significant increase in Wave-1 and a 12% decrease, albeit non-significant, in Wave-2. Supported by all Model fit indices, the final solution of Bayesian-SEM showed no direct overtime effect of altruism and substance use, but the indirect effects through the enhancing role of Waves-1 PTSS, on Wave-2 PTSD. Contrary to cross-sectional studies, no protection from peritraumatic positive emotions was observed. These findings emphasize the importance of longitudinal post-disaster research. Given the new evidence on volunteers' traumatization, altruism, and substance use during times of crisis with limited resources, further investigation among volunteers is crucial. The absence of identified protective factors in volunteers raises concerns for future implications in trauma psychology theory, research, and practice.

Influence of General Health on Quality of Life by the Caregiver and Burden Care as a Mediator: Bayesian Estimator Structural Equation Analysis

Objectives: To test structural equation models of General Health on quality of life by the caregiver and burden care as a mediator. Methods: The quality of life scale was administered to 297 caregivers of persons with schizophrenia. The models were examined using maximum likelihood confirmatory factor analysis (ML-CFA) and Bayesian structural equation modeling (BSEM). The path analysis with direct and indirect effects was discussed in the model. Results: The results reveal that the models fit well for evidence-based. The BIC 1412.998 < 3161.387, DIC 1365.247 < 3094.673, and WAIC 1370.802 < 3094.651 were the lowest BIC of the models and displayed a simple and parsimonious factor-loading pattern. Conclusions: Overall, both of the scales demonstrated excellent measurement properties supporting its relevance to comprehensively measure caregivers' impact and burden on persons with schizophrenia. Psychiatric and mental health care teams and clinical researchers could use the quality of life scale to generate strategies to support the family caregiver and measure the multidisciplinary treatment team interventions' outcomes.

Investment by maternal grandmother buffers children against the impacts of adverse early life experiences

Exogenous shocks during sensitive periods of development can have long-lasting effects on adult phenotypes including behavior, survival and reproduction. Cooperative breeding, such as grandparental care in humans and some other mammal species, is believed to have evolved partly in order to cope with challenging environments. Nevertheless, studies addressing whether grandparental investment can buffer the development of grandchildren from multiple adversities early in life are few and have provided mixed results, perhaps owing to difficulties drawing causal inferences from non-experimental data. Using population-based data of English and Welsh adolescents (sample size ranging from 817 to 1197), we examined whether grandparental investment reduces emotional and behavioral problems in children resulting from facing multiple adverse early life experiences (AELEs), by employing instrumental variable regression in a Bayesian structural equation modeling framework to better justify causal interpretations of the results. When children had faced multiple AELEs, the investment of maternal grandmothers reduced, but could not fully erase, their emotional and behavioral problems. No such result was observed in the case of the investment of other grandparent types. These findings indicate that in adverse environmental conditions the investment of maternal grandmothers can improve child wellbeing.