Psychological methods最新文献_第6页

Bayesian multilevel compositional data analysis: Introduction, evaluation, and application. 贝叶斯多层次成分数据分析：介绍、评价与应用。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-14 DOI: 10.1037/met0000750

Flora Le, Tyman E Stanford, Dorothea Dumuid, Joshua F Wiley

{"title":"Bayesian multilevel compositional data analysis: Introduction, evaluation, and application.","authors":"Flora Le, Tyman E Stanford, Dorothea Dumuid, Joshua F Wiley","doi":"10.1037/met0000750","DOIUrl":"https://doi.org/10.1037/met0000750","url":null,"abstract":"Multilevel compositional data are data that are repeatedly measured or clustered within groups, and are nonnegative and sum to a constant value. These data arise in various settings, such as intensive, longitudinal studies using ecological momentary assessments and wearable devices. Examples include 24-hr sleep-wake behaviors, sleep architecture, and macronutrients. This article presents an innovative method for analyzing multilevel compositional data using Bayesian inference. We describe the theoretical details of the data and the models, and outline the steps necessary to implement this method. We introduce the R package multilevelcoda to facilitate the application of this method and illustrate using a real data example. An extensive parameter recovery simulation study verified the robust performance of the method. Across all conditions investigated in the simulation study, the fitted models had minimal convergence issues (convergence rate > 99%) and achieved excellent quality parameter estimates and inference, with an average bias of 0.00 (range = -0.09 to 0.05) and coverage of 0.95 (range = 0.93 to 0.97). We conclude the article with recommendations on the use of the Bayesian multilevel compositional data analysis. We hope to promote wider application of this method to gain novel and robust answers to scientific questions. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The use of large language models for qualitative research: The Deep Computational Text Analyser (DECOTA). 在定性研究中使用大型语言模型：深度计算文本分析器（DECOTA）。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-07 DOI: 10.1037/met0000753

Lois Player, Ryan Hughes, Kaloyan Mitev, Lorraine Whitmarsh, Christina Demski, Nicholas Nash, Trisevgeni Papakonstantinou, Mark Wilson

{"title":"The use of large language models for qualitative research: The Deep Computational Text Analyser (DECOTA).","authors":"Lois Player, Ryan Hughes, Kaloyan Mitev, Lorraine Whitmarsh, Christina Demski, Nicholas Nash, Trisevgeni Papakonstantinou, Mark Wilson","doi":"10.1037/met0000753","DOIUrl":"https://doi.org/10.1037/met0000753","url":null,"abstract":"Machine-assisted approaches for free-text analysis are rising in popularity, owing to a growing need to rapidly analyze large volumes of qualitative data. In both research and policy settings, these approaches have promise in providing timely insights into public perceptions and enabling policymakers to understand their community's needs. However, current approaches still require expert human interpretation-posing a financial and practical barrier for those outside of academia. For the first time, we propose and validate the Deep Computational Text Analyser (DECOTA)-a novel machine learning methodology that automatically analyzes large free-text data sets and outputs concise themes. Building on structural topic modeling approaches, we used two fine-tuned large language models and sentence transformers to automatically derive \"codes\" and their corresponding \"themes\", as in inductive thematic analysis. To fully automate the process, we designed and validated a novel algorithm to choose the optimal number of \"topics\" for the structural topic modeling. DECOTA outputs key codes and themes, their prevalence, and how prevalence varies across covariates such as age and gender. Each code is accompanied by three representative quotes. Four data sets previously analyzed using thematic analysis were triangulated with DECOTA's codes and themes. We found that DECOTA is approximately 378 times faster and 1,920 times cheaper than human coding and consistently yields codes in agreement with or complementary to human coding (averaging 91.6% for codes and 90% for themes). The implications for evidence-based policy development, public engagement with policymaking, and psychometric measure development are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Zero inflation in intensive longitudinal data: Why is it important and how should we deal with it? 密集纵向数据中的零通胀：为什么它很重要，我们应该如何应对？

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-07 DOI: 10.1037/met0000754

Sijing S J Shao, Ziqian Xu, Qimin Liu, Kenneth McClure, Ross Jacobucci, Scott E Maxwell, Zhiyong Zhang

{"title":"Zero inflation in intensive longitudinal data: Why is it important and how should we deal with it?","authors":"Sijing S J Shao, Ziqian Xu, Qimin Liu, Kenneth McClure, Ross Jacobucci, Scott E Maxwell, Zhiyong Zhang","doi":"10.1037/met0000754","DOIUrl":"https://doi.org/10.1037/met0000754","url":null,"abstract":"This study addresses the challenge of analyzing intensive longitudinal data (ILD) with zero-inflated autoregressive processes. ILD, characterized by intensive longitudinal measurements, often exhibit excessive zeros and temporal dependencies. Neglecting zero inflation or mishandling it can lead to biased parameter estimates and inaccurate conclusions. To overcome this issue, we propose a novel zero-inflated process change multilevel autoregressive (ZIP-CAR) model that incorporates zero inflation using a Bayesian framework. We compare the performance of the proposed method with existing methods through a simulation study and demonstrate its advantages in accurately estimating parameters and improving statistical power. Additionally, we apply the ZIP-CAR model to a real intensive longitudinal data set on problematic drinking behaviors, highlighting its effectiveness in capturing autoregressive and cross-lag effects while accounting for zero inflation. The results underscore the importance of addressing zero inflation in ILD analysis and provide practical recommendations for researchers. Our proposed model offers a valuable tool for analyzing ILD with zero-inflated autoregressive processes, facilitating a more comprehensive understanding of dynamic behavioral changes over time. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intensive longitudinal mediation in Mplus. Mplus 中的密集纵向调解。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-01 Epub Date: 2022-12-22 DOI: 10.1037/met0000536

Daniel McNeish, David P MacKinnon

{"title":"Intensive longitudinal mediation in Mplus.","authors":"Daniel McNeish, David P MacKinnon","doi":"10.1037/met0000536","DOIUrl":"10.1037/met0000536","url":null,"abstract":"Much of the existing longitudinal mediation literature focuses on panel data where relatively few repeated measures are collected over a relatively broad timespan. However, technological advances in data collection (e.g., smartphones, wearables) have led to a proliferation of short duration, densely collected longitudinal data in behavioral research. These intensive longitudinal data differ in structure and focus relative to traditionally collected panel data. As a result, existing methodological resources do not necessarily extend to nuances present in the recent influx of intensive longitudinal data and designs. In this tutorial, we first cover potential limitations of traditional longitudinal mediation models to accommodate unique characteristics of intensive longitudinal data. Then, we discuss how recently developed dynamic structural equation models (DSEMs) may be well-suited for mediation modeling with intensive longitudinal data and can overcome some of the limitations associated with traditional approaches. We describe four increasingly complex intensive longitudinal mediation models: (a) stationary models where the indirect effect is constant over time and people, (b) person-specific models where the indirect effect varies across people, (c) dynamic models where the indirect effect varies across time, and (d) cross-classified models where the indirect effect varies across both time and people. We apply each model to a running example featuring a mobile health intervention designed to improve health behavior of individuals with binge eating disorder. In each example, we provide annotated Mplus code and interpretation of the output to guide empirical researchers through mediation modeling with this increasingly popular type of longitudinal data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"393-415"},"PeriodicalIF":7.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10419989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cognitive and cultural models in psychological science: A tutorial on modeling free-list data as a dependent variable in Bayesian regression. 心理科学中的认知和文化模型：在贝叶斯回归中将自由表数据建模为因变量的教程。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-01 Epub Date: 2023-03-23 DOI: 10.1037/met0000553

Theiss Bendixen, Benjamin Grant Purzycki

{"title":"Cognitive and cultural models in psychological science: A tutorial on modeling free-list data as a dependent variable in Bayesian regression.","authors":"Theiss Bendixen, Benjamin Grant Purzycki","doi":"10.1037/met0000553","DOIUrl":"10.1037/met0000553","url":null,"abstract":"Assessing relationships between culture and cognition is central to psychological science. To this end, free-listing is a useful methodological instrument. To facilitate its wider use, we here present the free-list method along with some of its many applications and offer a tutorial on how to prepare and statistically model free-list data as a dependent variable in Bayesian regression using openly available data and code. We further demonstrate the real-world utility of the outlined workflow by modeling within-subject agreement between a free-list task and a corollary item response scale on religious beliefs with a cross-culturally diverse sample. Overall, we fail to find a reliable statistical association between these two instruments, an original empirical finding that calls for further inquiry into identifying the cognitive processes that item response scales and free-list tasks tap into. Throughout, we argue that free-listing is an unambiguous measure of cognitive and cultural information and that the free-list method therefore has broad potential across the social sciences aiming to measure and model individual-level and cross-cultural variation in mental representations. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"223-239"},"PeriodicalIF":7.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9367003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic framework for defining R-squared measures in mediation analysis. 中介分析中定义r平方测度的系统框架。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-01 Epub Date: 2023-05-11 DOI: 10.1037/met0000571

Hongyun Liu, Ke-Hai Yuan, Hui Li

{"title":"A systematic framework for defining R-squared measures in mediation analysis.","authors":"Hongyun Liu, Ke-Hai Yuan, Hui Li","doi":"10.1037/met0000571","DOIUrl":"10.1037/met0000571","url":null,"abstract":"R-squared measures of explained variance are easy to understand, naturally interpretable, and widely used by substantive researchers. In mediation analysis, however, despite recent advances in measures of mediation effect, few effect sizes have good statistical properties. Also, most of these measures are only available for the simplest three-variable mediation model, especially for R²-type measures. By decomposing the mediator into two parts (i.e., the part related to the predictor and the part unrelated to the predictor), this article proposes a systematic framework to develop new effect-size measures of explained variance in mediation analysis. The framework can be easily extended to more complex mediation models and provides more delicate R² measures for empirical researchers. A Monte Carlo simulation study is conducted to examine the statistical properties of the proposed R² effect-size measure. Results show that the new R2 measure performs well in approximating the true value of the explained variance of the mediation effect. The use of the proposed measure is illustrated with empirical examples together with program code for its implementation. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"306-321"},"PeriodicalIF":7.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9796970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving content validity evaluation of assessment instruments through formal content validity analysis. 通过正式的内容有效性分析改进评估工具的内容有效性评估。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-01 Epub Date: 2023-01-02 DOI: 10.1037/met0000545

Andrea Spoto, Massimo Nucci, Elena Prunetti, Michele Vicovaro

{"title":"Improving content validity evaluation of assessment instruments through formal content validity analysis.","authors":"Andrea Spoto, Massimo Nucci, Elena Prunetti, Michele Vicovaro","doi":"10.1037/met0000545","DOIUrl":"10.1037/met0000545","url":null,"abstract":"Content validity is defined as the degree to which elements of an assessment instrument are relevant to and representative of the target construct. The available methods for content validity evaluation typically focus on the extent to which a set of items are relevant to the target construct, but do not afford precise evaluation of items' behavior, nor their exhaustiveness with respect to the elements of the target construct. Formal content validity analysis (FCVA) is a new procedure combining methods and techniques from various areas of psychological assessment, such as (a) constructing Boolean classification matrices to formalize relationships among an assessment instrument's items and target construct elements, and (b) computing interrater agreement indices. We discuss how FCVA can be extended through the implementation of a Bayesian procedure to improve the interrater agreement indices' accuracy (Bayesian formal content validity analysis [B-FCVA]). With respect to extant methods, FCVA and B-FCVA can provide a great amount of information about content validity while not demanding much more work for authors and experts. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"203-222"},"PeriodicalIF":7.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10468941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-level covariance approach to the disaggregation of between-person effect and within-person effect. 对人际效应和人内效应进行分解的跨层次协方差方法。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-01 Epub Date: 2023-01-09 DOI: 10.1037/met0000548

Kazuki Hori, Yasuo Miyazaki

{"title":"Cross-level covariance approach to the disaggregation of between-person effect and within-person effect.","authors":"Kazuki Hori, Yasuo Miyazaki","doi":"10.1037/met0000548","DOIUrl":"10.1037/met0000548","url":null,"abstract":"In longitudinal studies, researchers are often interested in investigating relations between variables over time. A well-known issue in such a situation is that naively regressing an outcome on a predictor results in a coefficient that is a weighted average of the between-person and within-person effect, which is difficult to interpret. This article focuses on the cross-level covariance approach to disaggregating the two effects. Unlike the traditional centering/detrending approach, the cross-level covariance approach estimates the within-person effect by correlating the within-level observed variables with the between-level latent factors; thereby, partialing out the between-person association from the within-level predictor. With this key device kept, we develop novel latent growth curve models, which can estimate the between-person effects of the predictor's change rate. The proposed models are compared with an existing cross-level covariance model and a centering/detrending model through a real data analysis and a small simulation. The real data analysis shows that the interpretation of the effect parameters and other between-level parameters depends on how a model deals with the time-varying predictors. The simulation reveals that our proposed models can unbiasedly estimate the between- and within-person effects but tend to be more unstable than the existing models. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"340-373"},"PeriodicalIF":7.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10495378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating time-varying treatment effects in longitudinal studies. 纵向研究中时变治疗效果的估计。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-01 Epub Date: 2023-05-11 DOI: 10.1037/met0000574

Wen Wei Loh, Dongning Ren

{"title":"Estimating time-varying treatment effects in longitudinal studies.","authors":"Wen Wei Loh, Dongning Ren","doi":"10.1037/met0000574","DOIUrl":"10.1037/met0000574","url":null,"abstract":"Longitudinal study designs are frequently used to investigate the effects of a naturally observed predictor (treatment) on an outcome over time. Because the treatment at each time point or wave is not randomly assigned, valid inferences of its causal effects require adjusting for covariates that confound each treatment-outcome association. But adjusting for covariates which are inevitably time-varying is fraught with difficulties. On the one hand, standard regression adjustment for variables affected by treatment can lead to severe bias. On the other hand, omitting time-varying covariates from confounding adjustment precipitates spurious associations that can lead to severe bias. Thus, either including or omitting time-varying covariates for confounding adjustment can lead to incorrect inferences. In this article, we introduce an estimation strategy from the causal inference literature for evaluating the causal effects of time-varying treatments in the presence of time-varying confounding. G-estimation of the treatment effect at a particular wave proceeds by carefully adjusting for only pre-treatment instances of all variables while dispensing with any post-treatment instances. The introduced approach has various appealing features. Effect modification by time-varying covariates can be investigated using covariate-treatment interactions. Treatment may be either continuous or noncontinuous with any mean model permitted. Unbiased estimation requires correctly specifying a mean model for either the treatment or the outcome, but not necessarily both. The treatment and outcome models can be fitted with standard regression functions. In summary, g-estimation is effective, flexible, robust, and relatively straightforward to implement. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"240-253"},"PeriodicalIF":7.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9796967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling careless responding in ambulatory assessment studies using multilevel latent class analysis: Factors influencing careless responding. 用多水平潜在类分析模拟门诊评估研究中的粗心反应：粗心反应的影响因素。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-01 Epub Date: 2023-05-11 DOI: 10.1037/met0000580

Kilian Hasselhorn, Charlotte Ottenstein, Tanja Lischetzke

{"title":"Modeling careless responding in ambulatory assessment studies using multilevel latent class analysis: Factors influencing careless responding.","authors":"Kilian Hasselhorn, Charlotte Ottenstein, Tanja Lischetzke","doi":"10.1037/met0000580","DOIUrl":"10.1037/met0000580","url":null,"abstract":"As the number of studies using ambulatory assessment (AA) has been increasing across diverse fields of research, so has the necessity to identify potential threats to AA data quality such as careless responding. To date, careless responding has primarily been studied in cross-sectional surveys. The goal of the present research was to identify latent profiles of momentary careless responding on the occasion level and latent classes of individuals (who differ in the distribution of careless responding profiles across occasions) on the person level using multilevel latent class analysis (ML-LCA). We discuss which of the previously proposed indices seem promising for investigating careless responding in AA studies, and we show how ML-LCA can be applied to model careless responding in intensive longitudinal data. We used data from an AA study in which the sampling frequency (3 vs. 9 occasions per day, 7 days, n = 310 participants) was experimentally manipulated. We tested the effect of sampling frequency on careless responding using multigroup ML-LCA and investigated situational and respondent-level covariates. The results showed that four Level 1 profiles (\"careful,\" \"slow,\" and two types of \"careless\" responding) and four Level 2 classes (\"careful,\" \"frequently careless,\" and two types of \"infrequently careless\" respondents) could be identified. Sampling frequency did not have an effect on careless responding. On the person (but not the occasion) level, motivational variables were associated with careless responding. We hope that researchers might find the application of an ML-LCA approach useful to shed more light on factors influencing careless responding in AA studies. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"374-392"},"PeriodicalIF":7.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9498811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0