Pub Date : 2023-06-21DOI: 10.35566/jbds/v3n1/ogasawara
H. Ogasawara
The proofs of the probability density function (pdf) of the Wishart distribution tend to be complicated with geometric viewpoints, tedious Jacobians and not self-contained algebra. In this paper, some known proofs and simple new ones for uncorrelated and correlated cases are provided with didactic explanations. For the new derivation of the uncorrelated case, an elementary direct derivation of the distribution of the Bartlett-decomposed matrix is provided. In the derivation of the correlated case from the uncorrelated one, simple methods including a new one are shown.
{"title":"On some known derivations and new ones for the Wishart distribution: A didactic","authors":"H. Ogasawara","doi":"10.35566/jbds/v3n1/ogasawara","DOIUrl":"https://doi.org/10.35566/jbds/v3n1/ogasawara","url":null,"abstract":"The proofs of the probability density function (pdf) of the Wishart distribution tend to be complicated with geometric viewpoints, tedious Jacobians and not self-contained algebra. In this paper, some known proofs and simple new ones for uncorrelated and correlated cases are provided with didactic explanations. For the new derivation of the uncorrelated case, an elementary direct derivation of the distribution of the Bartlett-decomposed matrix is provided. In the derivation of the correlated case from the uncorrelated one, simple methods including a new one are shown.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47472307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-27DOI: 10.35566/jbds/v3n1/mccure
Kenneth McClure
Item response modeling is common throughout psychology and education in assessments of intelligence, psychopathology, and ability. The current paper provides a tutorial on estimating the two-parameter logistic and graded response models in a Bayesian framework as well as provide an introduction on evaluating convergence and model fit in this framework. Example data are drawn from depression items in the 2017 Wave of the National Longitudinal Survey of Youth and example code is provided for JAGS and implemented through R using the runjags package. The aim of this paper is to provide readers with the necessary information to conduct Bayesian IRT in JAGS.
{"title":"Bayesian IRT in JAGS: A Tutorial","authors":"Kenneth McClure","doi":"10.35566/jbds/v3n1/mccure","DOIUrl":"https://doi.org/10.35566/jbds/v3n1/mccure","url":null,"abstract":"Item response modeling is common throughout psychology and education in assessments of intelligence, psychopathology, and ability. The current paper provides a tutorial on estimating the two-parameter logistic and graded response models in a Bayesian framework as well as provide an introduction on evaluating convergence and model fit in this framework. Example data are drawn from depression items in the 2017 Wave of the National Longitudinal Survey of Youth and example code is provided for JAGS and implemented through R using the runjags package. The aim of this paper is to provide readers with the necessary information to conduct Bayesian IRT in JAGS.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45345246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Meta-analysis of proportions has been widely adopted across various scientific disciplines as a means to estimate the prevalence of phenomena of interest. However, there is a lack of comprehensive tutorials demonstrating the proper execution of such analyses using the R programming language. The objective of this study is to bridge this gap and provide an extensive guide to conducting a meta-analysis of proportions using R. Furthermore, we offer a thorough critical review of the methods and tests involved in conducting a meta-analysis of proportions, highlighting several common practices that may yield biased estimations and misleading inferences. We illustrate the meta-analytic process in five stages: (1) preparation of the R environment; (2) computation of effect sizes; (3) quantification of heterogeneity; (4) visualization of heterogeneity with the forest plot and the Baujat plot; and (5) explanation of heterogeneity with moderator analyses. In the last section of the tutorial, we address the misconception of assessing publication bias in the context of meta-analysis of proportions. The provided code offers readers three options to transform proportional data (e.g., the double arcsine method). The tutorial presentation is conceptually oriented and formula usage is minimal. We will use a published meta-analysis of proportions as an example to illustrate the implementation of the R code and the interpretation of the results.
{"title":"Conducting Meta-analyses of Proportions in R","authors":"Naike Wang","doi":"10.35566/jbds/v3n2/wang","DOIUrl":"https://doi.org/10.35566/jbds/v3n2/wang","url":null,"abstract":"Meta-analysis of proportions has been widely adopted across various scientific disciplines as a means to estimate the prevalence of phenomena of interest. However, there is a lack of comprehensive tutorials demonstrating the proper execution of such analyses using the R programming language. The objective of this study is to bridge this gap and provide an extensive guide to conducting a meta-analysis of proportions using R. Furthermore, we offer a thorough critical review of the methods and tests involved in conducting a meta-analysis of proportions, highlighting several common practices that may yield biased estimations and misleading inferences. We illustrate the meta-analytic process in five stages: (1) preparation of the R environment; (2) computation of effect sizes; (3) quantification of heterogeneity; (4) visualization of heterogeneity with the forest plot and the Baujat plot; and (5) explanation of heterogeneity with moderator analyses. In the last section of the tutorial, we address the misconception of assessing publication bias in the context of meta-analysis of proportions. The provided code offers readers three options to transform proportional data (e.g., the double arcsine method). The tutorial presentation is conceptually oriented and formula usage is minimal. We will use a published meta-analysis of proportions as an example to illustrate the implementation of the R code and the interpretation of the results.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135508171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Latent growth curve models (LGCM) are widely used in longitudinal data analysis, and robust methods can be used to model error distributions for non-normal data. This tutorial introduces how to modellinear, non-linear, and quadratic growth curve models under the Bayesian framework and uses examples to illustrate how to model errors using t, exponential power, and skew-normal distributions. The code of JAGS models is provided and implemented by the R package runjags. Model diagnostics and comparisons are briefly discussed.
{"title":"Robust Bayesian growth curve modeling: A tutorial using JAGS","authors":"Ruoxuan Li","doi":"10.35566/jbds/v3n2/li","DOIUrl":"https://doi.org/10.35566/jbds/v3n2/li","url":null,"abstract":"Latent growth curve models (LGCM) are widely used in longitudinal data analysis, and robust methods can be used to model error distributions for non-normal data. This tutorial introduces how to modellinear, non-linear, and quadratic growth curve models under the Bayesian framework and uses examples to illustrate how to model errors using t, exponential power, and skew-normal distributions. The code of JAGS models is provided and implemented by the R package runjags. Model diagnostics and comparisons are briefly discussed.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135508148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.35566/jbds/v2n2/suzuki
Honoka Suzuki, Oscar Gonzalez
Abstract Ordinal variables, such as those measured on a five-point Likert scale, are ubiquitous in the behavioral sciences. However, machine learning methods for modeling ordinal outcome variables (i.e., ordinal classification) are not as well-developed or widely utilized, compared to classification and regression methods for modeling nominal and continuous outcomes, respectively. Consequently, ordinal outcomes are often treated “naively” as nominal or continuous outcomes in practice. This study builds upon previous literature that has examined the predictive performance of such naïve approaches of treating ordinal outcome variables compared to ordinal classification methods in machine learning. We conducted a Monte Carlo simulation study to systematically assess the relative predictive performance of an ordinal classification approach proposed by Frank and Hall (2001) against naïve approaches according to two key factors that have received limited attention in previous literature: (1) the machine learning algorithm being used to implement the approaches and (2) the class distribution of the ordinal outcome variable. The consideration of these important, practical factors expands our knowledge on the consequences of naïve treatments of ordinal outcomes, which are shown in this study to vary substantially according to these factors. Given the ubiquity of ordinal measures coupled with the growing presence of machine learning applications in the behavioral sciences, these are important considerations for building high-performing predictive models in the field.
{"title":"Relative Predictive Performance of Treatments of Ordinal Outcome Variables across Machine Learning Algorithms and Class Distributions","authors":"Honoka Suzuki, Oscar Gonzalez","doi":"10.35566/jbds/v2n2/suzuki","DOIUrl":"https://doi.org/10.35566/jbds/v2n2/suzuki","url":null,"abstract":"Abstract Ordinal variables, such as those measured on a five-point Likert scale, are ubiquitous in the behavioral sciences. However, machine learning methods for modeling ordinal outcome variables (i.e., ordinal classification) are not as well-developed or widely utilized, compared to classification and regression methods for modeling nominal and continuous outcomes, respectively. Consequently, ordinal outcomes are often treated “naively” as nominal or continuous outcomes in practice. This study builds upon previous literature that has examined the predictive performance of such naïve approaches of treating ordinal outcome variables compared to ordinal classification methods in machine learning. We conducted a Monte Carlo simulation study to systematically assess the relative predictive performance of an ordinal classification approach proposed by Frank and Hall (2001) against naïve approaches according to two key factors that have received limited attention in previous literature: (1) the machine learning algorithm being used to implement the approaches and (2) the class distribution of the ordinal outcome variable. The consideration of these important, practical factors expands our knowledge on the consequences of naïve treatments of ordinal outcomes, which are shown in this study to vary substantially according to these factors. Given the ubiquity of ordinal measures coupled with the growing presence of machine learning applications in the behavioral sciences, these are important considerations for building high-performing predictive models in the field.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45265750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In behavioral studies, the frequency of a particular behavior or event is often collected and the acquired data are referred to as count data. This tutorial introduces readers to Poisson regression models which is a more appropriate approach for such data. Meanwhile, count data with excessive zeros often occur in behavioral studies and models such as zero-inflated or hurdle models can be employed for handling zero-inflation in the count data. In this tutorial, we aim to cover the necessary fundamentals for these methods and equip readers with application tools of JAGS. Examples of the implementation of the models in JAGS from within R are provided for demonstration purposes.
{"title":"A Tutorial on Bayesian Analysis of Count Data Using JAGS","authors":"Sijing Shao","doi":"10.35566/jbds/v2n2/shao","DOIUrl":"https://doi.org/10.35566/jbds/v2n2/shao","url":null,"abstract":"In behavioral studies, the frequency of a particular behavior or event is often collected and the acquired data are referred to as count data. This tutorial introduces readers to Poisson regression models which is a more appropriate approach for such data. Meanwhile, count data with excessive zeros often occur in behavioral studies and models such as zero-inflated or hurdle models can be employed for handling zero-inflation in the count data. In this tutorial, we aim to cover the necessary fundamentals for these methods and equip readers with application tools of JAGS. Examples of the implementation of the models in JAGS from within R are provided for demonstration purposes.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41518390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the prevalence of missing data in social science research, it is necessary to use methods for handling missing data. One framework in which data with missing values can still be used for parameter estimation is the Bayesian framework. In this tutorial, different missing data mechanisms including Missing Completely at Random, Missing at Random, and Missing Not at Random are introduced. Methods for estimating models with missing values under the Bayesian framework for both ignorable and non-ignorable missingness are also discussed. A structural equation model on data from the Advanced Cognitive Training for Independent and Vital Elderly study is used as an illustration on how to fit missing data models in JAGS.
{"title":"Handling Ignorable and Non-ignorable Missing Data through Bayesian Methods in JAGS","authors":"Ziqian Xu","doi":"10.35566/jbds/v2n2/xu","DOIUrl":"https://doi.org/10.35566/jbds/v2n2/xu","url":null,"abstract":"\u0000 \u0000 \u0000With the prevalence of missing data in social science research, it is necessary to use methods for handling missing data. One framework in which data with missing values can still be used for parameter estimation is the Bayesian framework. In this tutorial, different missing data mechanisms including Missing Completely at Random, Missing at Random, and Missing Not at Random are introduced. Methods for estimating models with missing values under the Bayesian framework for both ignorable and non-ignorable missingness are also discussed. A structural equation model on data from the Advanced Cognitive Training for Independent and Vital Elderly study is used as an illustration on how to fit missing data models in JAGS. \u0000 \u0000 \u0000","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49400980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This tutorial introduces readers to latent class analysis (LCA) as a model-based approach to understand the unobserved heterogeneity in a population. Given the growing popularity of LCA, we aim to equip readers with theoretical fundamentals as well as computational tools. We outline some potential pitfalls of LCA and suggest related solutions. Moreover, we demonstrate how to conduct frequentist and Bayesian LCA in R with real and simulated data. To ease learning, the analysis is broken down into a series of simple steps. Beyond the simple LCA, two extensions including mixed-model LCA and growth curve LCA are provided to aid readers’ transition to more advanced models. The complete R code and data set are provided.
{"title":"A Tutorial on Bayesian Latent Class Analysis Using JAGS","authors":"Meng Qiu","doi":"10.35566/jbds/v2n2/qiu","DOIUrl":"https://doi.org/10.35566/jbds/v2n2/qiu","url":null,"abstract":"This tutorial introduces readers to latent class analysis (LCA) as a model-based approach to understand the unobserved heterogeneity in a population. Given the growing popularity of LCA, we aim to equip readers with theoretical fundamentals as well as computational tools. We outline some potential pitfalls of LCA and suggest related solutions. Moreover, we demonstrate how to conduct frequentist and Bayesian LCA in R with real and simulated data. To ease learning, the analysis is broken down into a series of simple steps. Beyond the simple LCA, two extensions including mixed-model LCA and growth curve LCA are provided to aid readers’ transition to more advanced models. The complete R code and data set are provided. \u0000 ","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48376382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bayesian statistics have been widely used given the development of Markov chain Monte Carlo sampling techniques and the growth of computational power. A major challenge of Bayesian methods that has not yet been fully addressed is how we can appropriately evaluate the convergence of the random samples to the target posterior distributions. In this paper, we focus on Gelman and Rubin's diagnostic (PSRF), Brooks and Gleman's diagnostic (MPSRF), and Geweke's diagnostics, and compare the Type I error rate and Type II error rate of seven convergence criteria: MPSRF>1.1, any upper bound of PSRF is larger than 1.1, more than 5% of the upper bounds of PSRFs are larger than 1.1, any PSRF is larger than 1.1, more than 5% of PSRFs are larger than 1.1, any Geweke test statistic is larger than 1.96 or smaller than -1.96, and more than 5% of Geweke test statistics are larger than 1.96 or smaller than -1.96. Based on the simulation results, we recommend the upper bound of PSRF if we only can choose one diagnostic. When the number of estimated parameters is large, between the diagnostic per parameter (i.e., PSRF) or the multivariate diagnostic (i.e., MPSRF), we recommend the upper bound of PSRF over MPSRF. Additionally, we do not suggest claiming convergence at the analysis level while allowing a small proportion of the parameters to have significant convergence diagnosis results.
{"title":"The Performances of Gelman-Rubin and Geweke's Convergence Diagnostics of Monte Carlo Markov Chains in Bayesian Analysis","authors":"H. Du, Zijun Ke, Ge Jiang, Sijia Huang","doi":"10.35566/jbds/v2n2/p3","DOIUrl":"https://doi.org/10.35566/jbds/v2n2/p3","url":null,"abstract":"\u0000Bayesian statistics have been widely used given the development of Markov chain Monte Carlo sampling techniques and the growth of computational power. A major challenge of Bayesian methods that has not yet been fully addressed is how we can appropriately evaluate the convergence of the random samples to the target posterior distributions. In this paper, we focus on Gelman and Rubin's diagnostic (PSRF), Brooks and Gleman's diagnostic (MPSRF), and Geweke's diagnostics, and compare the Type I error rate and Type II error rate of seven convergence criteria: MPSRF>1.1, any upper bound of PSRF is larger than 1.1, more than 5% of the upper bounds of PSRFs are larger than 1.1, any PSRF is larger than 1.1, more than 5% of PSRFs are larger than 1.1, any Geweke test statistic is larger than 1.96 or smaller than -1.96, and more than 5% of Geweke test statistics are larger than 1.96 or smaller than -1.96. Based on the simulation results, we recommend the upper bound of PSRF if we only can choose one diagnostic. When the number of estimated parameters is large, between the diagnostic per parameter (i.e., PSRF) or the multivariate diagnostic (i.e., MPSRF), we recommend the upper bound of PSRF over MPSRF. Additionally, we do not suggest claiming convergence at the analysis level while allowing a small proportion of the parameters to have significant convergence diagnosis results.\u0000","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49559557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"A New Bayesian Structural Equation Modeling Approach with Priors on the Covariance Matrix Parameter","authors":"Haiyan Liu, Wen Qu, Zhiyong Zhang, Hao Wu","doi":"10.35566/jbds/v2n2/p2","DOIUrl":"https://doi.org/10.35566/jbds/v2n2/p2","url":null,"abstract":"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.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44073394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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