The Bayesian structural equation model integrates the principles of Bayesian statistics, providing a more flexible and comprehensive modeling framework. In exploring complex relationships between variables, handling u...The Bayesian structural equation model integrates the principles of Bayesian statistics, providing a more flexible and comprehensive modeling framework. In exploring complex relationships between variables, handling uncertainty, and dealing with missing data, the Bayesian structural equation model demonstrates unique advantages. Therefore, Bayesian methods are used in this paper to establish a structural equation model of innovative talent cognition, with the measurement of college students’ cognition of innovative talent being studied. An in-depth analysis is conducted on the effects of innovative self-efficacy, social resources, innovative personality traits, and school education, aiming to explore the factors influencing college students’ innovative talent. The results indicate that innovative self-efficacy plays a key role in perception, social resources are significantly positively correlated with the perception of innovative talents, innovative personality tendencies and school education are positively correlated with the perception of innovative talents, but the impact is not significant.展开更多
Bayesian structural equation model(BSEM)integrates the advantages of the Bayesian methods into the framework of structural equation modeling and ensures the identification by assigning priors with small variances.Prev...Bayesian structural equation model(BSEM)integrates the advantages of the Bayesian methods into the framework of structural equation modeling and ensures the identification by assigning priors with small variances.Previous studies have shown that prior specifications in BSEM influence model parameter estimation,but the impact on model fit indices is yet unknown and requires more research.As a result,two simulation studies were carried out.Normal distribution priors were specified for factor loadings,while inverse Wishart distribution priors and separation strategy priors were applied for the variance-covariance matrix of latent factors.Conditions included five sample sizes and 24 prior distribution settings.Simulation Study 1 examined the model-fitting performance of BCFI,BTLI,and BRMSEA proposed by Garnier-Villarreal and Jorgensen(Psychol Method 25(1):46-70,2020)and the PPp value.Simulation Study 2 compared the performance of BCFI,BTLI,BRMSEA,and DIC in model selection between three data generation models and three fitting models.The findings demonstrated that prior settings would affect Bayesian model fit indices in evaluating model fitting and selecting models,especially in small sample sizes.Even under a large sample size,the highly improper factor loading priors resulted in poor performance of the Bayesian model fit indices.BCFI and BTLI were less likely to reject the correct model than BRMSEA and PPp value under different prior specifications.For model selection,different prior settings would affect DIC on selecting the wrong model,and BRMSEA preferred the parsimonious model.Our results indicate that the Bayesian approximate fit indices perform better when evaluating model fitting and choosing models under the BSEM framework.展开更多
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,Inter...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.展开更多
文摘The Bayesian structural equation model integrates the principles of Bayesian statistics, providing a more flexible and comprehensive modeling framework. In exploring complex relationships between variables, handling uncertainty, and dealing with missing data, the Bayesian structural equation model demonstrates unique advantages. Therefore, Bayesian methods are used in this paper to establish a structural equation model of innovative talent cognition, with the measurement of college students’ cognition of innovative talent being studied. An in-depth analysis is conducted on the effects of innovative self-efficacy, social resources, innovative personality traits, and school education, aiming to explore the factors influencing college students’ innovative talent. The results indicate that innovative self-efficacy plays a key role in perception, social resources are significantly positively correlated with the perception of innovative talents, innovative personality tendencies and school education are positively correlated with the perception of innovative talents, but the impact is not significant.
基金supported by the MOE(Ministry of Education)Project of Humanities and Social Science of China[23YJA190007]the Natural Science Foundation of Guangdong Province[2022A1515010367]the Key Research and Development Plan of Yunnan Province,China[202203AC100003].
文摘Bayesian structural equation model(BSEM)integrates the advantages of the Bayesian methods into the framework of structural equation modeling and ensures the identification by assigning priors with small variances.Previous studies have shown that prior specifications in BSEM influence model parameter estimation,but the impact on model fit indices is yet unknown and requires more research.As a result,two simulation studies were carried out.Normal distribution priors were specified for factor loadings,while inverse Wishart distribution priors and separation strategy priors were applied for the variance-covariance matrix of latent factors.Conditions included five sample sizes and 24 prior distribution settings.Simulation Study 1 examined the model-fitting performance of BCFI,BTLI,and BRMSEA proposed by Garnier-Villarreal and Jorgensen(Psychol Method 25(1):46-70,2020)and the PPp value.Simulation Study 2 compared the performance of BCFI,BTLI,BRMSEA,and DIC in model selection between three data generation models and three fitting models.The findings demonstrated that prior settings would affect Bayesian model fit indices in evaluating model fitting and selecting models,especially in small sample sizes.Even under a large sample size,the highly improper factor loading priors resulted in poor performance of the Bayesian model fit indices.BCFI and BTLI were less likely to reject the correct model than BRMSEA and PPp value under different prior specifications.For model selection,different prior settings would affect DIC on selecting the wrong model,and BRMSEA preferred the parsimonious model.Our results indicate that the Bayesian approximate fit indices perform better when evaluating model fitting and choosing models under the BSEM framework.
基金Dr.Xu and Dr.Feng contributed equally to this work.Dr.Xu's work was partially supported by the National Natural Science Foundation of China(71704052 and 72074072)the Natural Science Foundation of Hunan Province,China(2018JJ3263)+5 种基金the Research Foundation of Education Bureau of Hunan Province,China(18B334)Dr.Feng's work was partially supported by the National Natural Science Foundation of China(71802166)the Humanities and Social Science Foundation of the Ministry of Education of China(20YJC630055)Dr.Li's work was partially supported by the LamWoo Research Fund(LWI20005)Faculty Research Grant(DB20A3 and DB21A7)Direct Grant(DR21B3).
文摘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.
