Bayesian Structural Equation Modeling Research Articles

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.

Identifying Dynamic Shifts to Careless and Insufficient Effort Behavior in Questionnaire Responses; a Novel Approach and Experimental Validation

Careless and insufficient effort responding (C/IER) is a situation where participants respond to survey instruments without considering the item content. This phenomena adds noise to data leading to erroneous inference. There are multiple approaches to identifying and accounting for C/IER in survey settings, of these approaches the best performing are model based classification techniques. Classic approaches to accounting for C/IER treat it as a person level phenomena. They first use some method to identify participants who exhibit C/IER, then use list-wise deletion to remove them prior to analysis. We argue that C/IER is actually a state that participants may exhibit for a portion of a survey instrument. In other words, participants start a survey with the intention to follow instructions, but at some point transition to a C/IER state and no longer respond in line with item contents. Accounting for C/IER at the item level, as opposed to the person level preserves data resulting in increased power. In this article we present a Bayesian Dynamic Latent Class Structural Equation Modeling (DLCSEM) approach for simultaneously accounting for C/IER at the item level and estimating a model of interest. We use a simulation study to establish the approaches performance under empirically relevant conditions and to compare it to other methods. We then conducted an experimental validation in which we induce C/IER like responses from human subjects and investigate the approaches ability to identify the point in which participants transition to a C/IER state. We also compare the model to existing approaches. In both the simulation and experimental validation the DLCSEM outperforms the alternative approaches. We conclude that the approach should be used by applied researchers for the pragmatic benefits of the method. Conclusions, limitations, and future directions are discussed.

Application of the integrated threat theory to conservation law enforcement.

Interactions between law enforcement agents in conservation (e.g., rangers) and illegal resource users (e.g., illegal hunters) can be violent and sometimes fatal, which negatively affects conservation efforts and people's well-being. Models from social psychology, such as integrated threat theory (ITT) (intergroup interactions shape intergroup emotions, prejudices and perceived threats leading to hostile attitudes or behaviors between groups), are useful in addressing such interactions. Conservation approaches relying mainly on law enforcement have never been investigated using this framework. Using a structured questionnaire, we collected data from 282 rangers in protected and unprotected areas (n=50) in northern Iran. We applied Bayesian structural equation modeling in an assessment of rangers' affective attitudes (i.e., emotions or feelings that shape attitudes toward a person or object) toward illegal hunters in an ITT framework. Rangers' positive perceptions of illegal hunters were negatively associated with intergroup anxiety (emotional response to fear) and negative stereotypes about a hunter's personality, which mediated the relationship between negative contact and affective attitudes. This suggests that negative contact, such as verbal abuse, may lead rangers to perceive illegal hunters as arrogant or cruel, which likely forms a basis for perceived threats. Rangers' positive contact with illegal hunters, such as playing or working together, likely lowered their perceived realistic threats (i.e., fear of property damage). Perceived realistic threats of rangers were positively associated with negative contacts (e.g., physical harm). The associations we identified suggest that relationships based on positive interactions between rangers and illegal hunters can reduce fear and prejudice. Thus, we suggest that rangers and hunters be provided with safe spaces to have positive interactions, which may help lower tension and develop cooperative conservation mechanisms.

Critical barriers to sustainable construction technology adoption in developing countries – A case study in India

Sustainability of the construction is one of the important sector to improve the economy of developing countries. To improve sustainability, precast buildings are adopted in developing countries. Since the sustainability of the precast buildings is affected by various barriers it is essential to determine the barriers that affect their sustainability. Hence the novel Relative Factor Reliable Index framework is utilized to identify the most critical barrier while constructing precast buildings. Thus a survey is conducted with various participants by creating an online questionnaire using Likert scale with Polychromous Rasch Model. From survey data the existing methods does not find the appropriate barrier due lack representation of dependence between the barriers. So that the survey results are analyzed using Bayesian Belief structural equation modeling Network which utilizes Probabilistic Graphical Model (PGM) and directed polygonal graph to determine the interrelationship between the barriers by assigning weightage. To identify the barrier that affects the sustainability of construction, barriers are ranked with Relative Factor Reliable Index using Relative Importance Index and Z score normalization which statistically validate the barriers to Sustainable construction. From the ranked barriers, the barriers Professional fees for engineers and consultants, Lack of Public awareness about precast buildings, and Ineffective waste disposal are identified as the most critical barriers.

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

The interdependence of substance use, satisfaction with life, and psychological distress: a dynamic structural equation model analysis.

Longitudinal studies with annual follow-up including psychological and social variables in substance use disorder recovery are scarce. We investigated whether levels of substance use, satisfaction with life, and psychological distress fluctuate across five years in relation to having drug-free friends. A prospective naturalistic cohort study of change trajectories in a cohort of people diagnosed with substance use disorder and using multiple substances with quarterly and annual follow-up over five years. Two-hundred-and-eight patients were recruited from substance use disorder treatment in Rogaland, Norway. Out of these, 164 participants fulfilled the inclusion criteria. We used Bayesian two-level dynamic structural equation modelling. The variable 'drug-free friends' was assessed by a self-reporting questionnaire, while psychological distress was assessed using the Symptoms Checklist 90 Revised. Satisfaction with life was assessed using the Satisfaction With Life Scale while drug use was assessed using the Drug Use Disorders Identification Test. The main findings are that higher-than-average psychological distress at a three-month lag credibly predicts higher-than-normal substance use at the concurrent time point t. Substance use and satisfaction with life seem to have synchronous trajectories over time, i.e. as the first decreases the latter increases and vice versa. During the five years after treatment, the participants mainly experienced a decrease in substance use and increase in satisfaction with life. Since the participants experienced positive and negative fluctuations for several years after treatment, it seems crucial to establish a dialogue with treatment professionals in order to create functional solutions for maintaining motivation and aiding recovery.

Bayesian Structural Equation Models of Correlation Matrices

We present a method for Bayesian structural equation modeling of sample correlation matrices as correlation structures. The method transforms the sample correlation matrix to an unbounded vector using the matrix logarithm function. Bayesian inference about the unbounded vector is performed assuming a multivariate-normal likelihood, with a mean based on the transformed model-implied correlation matrix, and a covariance assumed to be of known form. Using Monte Carlo simulation, we examine the performance of the method with normal and ordinal indicators, as well as the capacity of the method to estimate models that account for misspecification. The performance of the approach is often adequate suggesting that the proposed method can be used for Bayesian analysis of correlation structures. We conclude with a discussion of potential applications of the approach, as well as future directions needed to further develop the method.

Applying SEM, Exploratory SEM, and Bayesian SEM to Personality Assessments

Despite the importance of demonstrating and evaluating how structural equation modeling (SEM), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM) work simultaneously, research comparing these analytic techniques is limited with few studies conducted to systematically compare them to each other using correlated-factor, hierarchical, and bifactor models of personality. In this study, we evaluate the performance of SEM, ESEM, and BSEM across correlated-factor, hierarchical, and bifactor structures and multiple estimation techniques (maximum likelihood, robust weighted least squares, and Bayesian estimation) to test the internal structure of personality. Results across correlated-factor, hierarchical, and bifactor models highlighted the importance of controlling for scale coarseness and allowing small off-target loadings when using maximum likelihood (ML) and robust weighted least squares estimation (WLSMV) and including informative priors (IP) when using Bayesian estimation. In general, Bayesian-IP and WLSMV ESEM models provided noticeably best model fits. This study is expected to serve as a guide for professionals and applied researchers, identify the most appropriate ways to represent the structure of personality, and provide templates for future research into personality and other multidimensional representations of psychological constructs. We provide Mplus code for conducting the demonstrated analyses in the online supplement.

Application of Bayesian structural equation modeling in construction and demolition waste management studies: Development of an extended theory of planned behavior.

Sustainable construction and demolition waste management relies heavily on the attitudes and actions of its constituents; nevertheless, deep analysis for introducing the best estimator is rarely attained. The main objective of this study is to perform a comparison analysis among different approaches of Structural Equation Modeling (SEM) in Construction and Demolition Waste Management (C&DWM) modeling based on an Extended Theory of Planned Behaviour (Extended TPB). The introduced research model includes twelve latent variables, six independent variables, one mediator, three control variables, and one dependent variable. Maximum likelihood (ML), partial least square (PLS), and Bayesian estimators were considered in this study. The output of SEM with the Bayesian estimator was 85.8%, and among effectiveness of six main variables on C&DWM Behavioral (Depenmalaydent variables), five of them have significant relations. Meanwhile, the variation based on SEM with ML estimator was equal to 78.2%, and four correlations with dependent variable have significant relationship. At the conclusion, the R-square of SEM with the PLS estimator was equivalent to 73.4% and three correlations with the dependent variable had significant relationships. At the same time, the values of the three statistical indices include root mean square error (RMSE), mean absolute percentage error (MPE), and mean absolute error (MSE) with involving Bayesian estimator are lower than both ML and PLS estimators. Therefore, compared to both PLS and ML, the predicted values of the Bayesian estimator are closer to the observed values. The lower values of MPE, RMSE, and MSE and the higher values of R-square will generate better goodness of fit for SEM with a Bayesian estimator. Moreover, the SEM with a Bayesian estimator revealed better data fit than both the PLS and ML estimators. The pattern shows that the relationship between research variables can change with different estimators. Hence, researchers using the SEM technique must carefully consider the primary estimator for their data analysis. The precaution is necessary because higher error means different regression coefficients in the research model.

Flowering time responses to warming drive reproductive fitness in a changing Arctic.

The Arctic is warming at an alarming rate, leading to earlier spring conditions and plant phenology. It is often unclear to what degree changes in reproductive fitness (flower, fruit, seed production) are a direct response to warming versus an indirect response through shifting phenology. This study aims to quantify the relative importance of these direct and indirect pathways and project the net effects of warming on plant phenology and reproductive fitness under current and future climate scenarios. We used two long-term datasets on twelve tundra species in the Canadian Arctic as part of the International Tundra Experiment (ITEX). Phenology and reproductive fitness were recorded annually on tagged individual plants at both Daring Lake, Northwest Territories (64.87, -111.58) and Alexandra Fiord, Nunavut (78.83, -75.80). Plant species encompass a wide taxonomic diversity across a range of plant functional types with circumpolar/boreal distributions. We use Hierarchical Bayesian Structural Equation models to compare the direct and indirect effects of climate warming on phenology and reproductive fitness across species, sites and years. We find that warming, both experimental and ambient, drives earlier flowering across species, which leads to higher numbers of flowers and fruits produced, reflecting directional phenotypic selection for earlier flowering phenology. Furthermore, this indirect effect of climate warming mediated through phenology was generally ~2-3x stronger than the direct effect of climate on reproductive fitness. Under future climate predictions, individual plants showed a ~2 to 4.5-fold increase in their reproductive fitness (flower counts) with advanced flowering phenology. Our results suggest that, on average, the benefits of early flowering, such as increased development time and subsequent enhanced reproductive fitness, may outweigh its risks. Overall, this work provides important insights into population-level consequences of phenological shifts in a warming Arctic over multi-decadal time scales.

Bayesian network and structural equation modeling of dependencies between country-level sustainability risks and logistics performance

Bayesian network and structural equation modeling of dependencies between country-level sustainability risks and logistics performance

A Comparison of Methods for Synthesizing Results from Previous Research to Obtain Priors for Bayesian Structural Equation Modeling

Bayesian estimation of latent variable models provides some unique advantages to researchers working with small samples and complex models when compared with the more commonly used maximum likelihood approach. A key aspect of Bayesian modeling involves the selection of prior distributions for the parameters of interest. Prior research has demonstrated that using default priors, which are typically noninformative, may yield biased and inefficient estimates. Therefore, it is recommended that data analysts obtain useful, informative priors from prior research whenever possible. The goal of the current simulation study was to compare several methods designed to combine results from prior studies that will yield informative priors for regression coefficients in structural equation models. These methods include noninformative priors, Bayesian synthesis, pooled analysis, aggregated priors, standard meta-analysis, power priors, and the meta-analytic predictive methods. Results demonstrated that power priors and meta-analytic predictive priors, used in conjunction with Bayesian estimation, may yield the most accurate estimates of the latent structure coefficients. Implications for practice and suggestions for future research are discussed.

A comprehensive sustainable development framework; community capitals and village-cooperative initiative.

Considering the cooperative sector capabilities in organizing rural people, primarily focusing on empowering manpower and adopting a sustainable approach to basic resources (water and soil), village-cooperative initiative has emerged as a new concept by registering and forming 4565 new cooperatives in Iran's rural regions. The present research which was conducted in two qualitative and quantitative phases designed a new model by integrating the new village-cooperative approach along with the sustainable livelihood's framework theory. The study sample of the qualitative phase included 32 theorists of the village-cooperative initiative. To analyze the data, the grounded theory and three-step process of open coding, axial coding and selective coding were used using MAXqda18 software. Finally, a paradigm model was designed whose most important components included causal conditions (shocks and seasonal conditions), contextual conditions (trends and governmental support), intervening conditions (control and market development), central categories (sustainable livelihoods with a village- cooperative approach), strategies (coping and adaptation) and consequences (creating jobs, establishing cooperatives, supporting smart agriculture and strengthening social capital). The statistical quantitative phase population consisted of cooperatives members in the village-cooperative initiative (N=405), being selected through Cochran's formula with proportional sampling method summing up to198 people from 5 provinces. The data collection tool was a researcher-made questionnaire whose validity and reliability were confirmed. The Bayesian structural equation modeling was used to analyze the data. The results of the research quantitative phase showed that the variables of financial capital, physical capital, social capital, coping and adaptation strategies, human capital and natural capital were the most effective variables on village-cooperative initiative members' sustainable livelihoods, respectively. Finally, a hybrid model based on the qualitative and quantitative studies was designed and suggestions were made; for instance, the creation of suitable grounds for off-farm activities such as rural industries and ecotourism.