Multivariate Behavioral Research最新文献

Ideal Point or Dominance Process? Unfolding Tree Approaches to Likert Scale Data with Multi-Process Models. 理想点还是优势过程？用多过程模型展开树方法处理李克特尺度数据。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-05-27 DOI: 10.1080/00273171.2025.2496505

Biao Zeng, Hongbo Wen, Minjeong Jeon

{"title":"Ideal Point or Dominance Process? Unfolding Tree Approaches to Likert Scale Data with Multi-Process Models.","authors":"Biao Zeng, Hongbo Wen, Minjeong Jeon","doi":"10.1080/00273171.2025.2496505","DOIUrl":"https://doi.org/10.1080/00273171.2025.2496505","url":null,"abstract":"This study introduces a new multi-process analytical framework based on the ideal point assumption for analyzing Likert scale data with three newly developed Unfolding Tree (UTree) models. Through simulations, we tested the performance of proposed models and existing Item Response Tree (IRTree) models across various conditions. Subsequently, empirical data were utilized to analyze and compare the UTree models relative to IRTree models, exploring respondents' decision-making processes and underlying latent traits. Simulation results showed that fit indices could effectively discern the correct model underlying the data. When the correct model was employed, both IRTree and UTree accurately retrieved item and individual parameters, with the recovery precision improving as the number of items and sample size increased. Conversely, when an incorrect model was utilized, the mis-specified model consistently returned biased results in estimating individual parameters, which was pronounced when the respondents followed an ideal point response process. Empirical findings highlight that respondents' decisions align with the ideal point process rather than the dominance process. The respondents' choices of extreme response options are more driven by target traits than by extreme response style. Furthermore, evidence indicates the presence of two distinct but moderately correlated target traits throughout the different decision stages.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-32"},"PeriodicalIF":5.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Missing Data Handling via EM and Multiple Imputation in Network Analysis using Glasso and Atan Regularization. 基于Glasso和Atan正则化的网络分析中EM和多重输入的缺失数据处理。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-05-26 DOI: 10.1080/00273171.2025.2503833

Kai Jannik Nehler, Martin Schultze

{"title":"Missing Data Handling via EM and Multiple Imputation in Network Analysis using Glasso and Atan Regularization.","authors":"Kai Jannik Nehler, Martin Schultze","doi":"10.1080/00273171.2025.2503833","DOIUrl":"https://doi.org/10.1080/00273171.2025.2503833","url":null,"abstract":"The existing literature on missing data handling in psychological network analysis using cross-sectional data is currently limited to likelihood based approaches. In addition, there is a focus on convex regularization, with the missing handling implemented using different calculations in model selection across various packages. Our work aims to contribute to the literature by implementing a missing data handling approach based on multiple imputation, specifically stacking the imputations, and evaluating it against direct and two-step EM methods. Standardized model selection across the multiple imputation and EM methods is ensured, and the comparative evaluation between the missing handling methods is performed separately for convex regularization (glasso) and nonconvex regularization (atan). Simulated conditions vary network size, number of observations, and amount of missingness. Evaluation criteria encompass edge set recovery, partial correlation bias, and correlation of network statistics. Overall, missing data handling approaches exhibit similar performance under many conditions. Using glasso with EBIC model selection, the two-step EM method performs best overall, closely followed by stacked multiple imputation. For atan regularization using BIC model selection, stacked multiple imputation proves most consistent across all conditions and evaluation criteria.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-23"},"PeriodicalIF":5.3,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Bayesian Multilevel Latent Class Profile Analysis: Inference and Estimation for Exploring the Diverse Pathways to Academic Proficiency. 贝叶斯多水平潜类分析：探索学术能力不同途径的推论与估计。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-05-22 DOI: 10.1080/00273171.2025.2501341

JungWun Lee, D Betsy McCoach, Ofer Harel, Hwan Chung

{"title":"Bayesian Multilevel Latent Class Profile Analysis: Inference and Estimation for Exploring the Diverse Pathways to Academic Proficiency.","authors":"JungWun Lee, D Betsy McCoach, Ofer Harel, Hwan Chung","doi":"10.1080/00273171.2025.2501341","DOIUrl":"https://doi.org/10.1080/00273171.2025.2501341","url":null,"abstract":"Multilevel latent class profile analysis (MLCPA) is a recently developed technique for understanding latent class dynamics in longitudinal studies; however, conventional maximum likelihood (ML) estimation may face challenges, particularly with small sample sizes or boundary solutions. As an alternative method, we propose a Bayesian estimation for MLCPA by employing non-informative prior distributions. In addition, we shed light on the underflow problem, which denotes a phenomenon such that the logarithm of the likelihood is negative infinity due to the multilevel structure. We perform extensive numerical studies to compare the behaviors of the MLE and the Bayesian estimates and investigate the accuracies of approximated model selection criteria. The simulation study revealed that Bayesian estimates are preferred to ML estimates when the underlying latent classes are well-separated, while the ML estimates are preferred when the underlying latent classes overlap. Utilizing the Progress Monitoring and Reporting Network data, which includes longitudinal academic performance metrics, our analysis uncovers distinct pathways of latent classes for students, further differentiated by latent groups of schools. These findings shed light on the considerable variations in academic proficiency trajectories and thus may offer new perspectives on academic proficiency patterns, with important implications for policy development and targeted educational interventions.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-19"},"PeriodicalIF":5.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144120383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Three Approaches to Testing for Statistical Suppression. 统计抑制的三种检验方法。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-05-21 DOI: 10.1080/00273171.2025.2483245

Felix B Muniz, David P MacKinnon

{"title":"Three Approaches to Testing for Statistical Suppression.","authors":"Felix B Muniz, David P MacKinnon","doi":"10.1080/00273171.2025.2483245","DOIUrl":"https://doi.org/10.1080/00273171.2025.2483245","url":null,"abstract":"Suppression effects are important for theoretical and applied research because these effects occur when there is an unexpected increase in an effect when it is adjusted for a third variable. This paper investigates three approaches to testing for statistical suppression. The first test was proposed in 1978 and is based on the relationship between the zero-order and semi-partial correlations. The second test comes from a condition that is necessary for suppression proposed in 1997. The third test is an extension of the test for the inconsistent mediated effect. We derive standard errors for the Velicer, and Sharpe and Roberts tests, conduct a statistical simulation study, and apply all three tests to two real data sets and several published correlation matrices. In the simulation study, the test based on inconsistent mediation had the best properties overall. For the data examples, when raw data were available, we constructed bootstrap confidence intervals to assess significance, and for correlations, we compared each test statistic to the normal distribution to assess statistical significance. Each test gave consistent results when applied to the example data sets. Analytical work demonstrated conditions where each test gave conflicting results. The mediation test of suppression based on the sign of the product of the mediated effect and the direct effect had the best overall performance.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-23"},"PeriodicalIF":5.3,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Accounting for Measurement Invariance Violations in Careless Responding Detection in Intensive Longitudinal Data: Exploratory vs. Partially Constrained Latent Markov Factor Analysis. 在密集的纵向数据中粗心响应检测中的测量不变性违规：探索性与部分约束的潜在马尔可夫因子分析。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-05-06 DOI: 10.1080/00273171.2025.2492016

Leonie V D E Vogelsmeier, Joran Jongerling, Esther Ulitzsch

{"title":"Accounting for Measurement Invariance Violations in Careless Responding Detection in Intensive Longitudinal Data: Exploratory vs. Partially Constrained Latent Markov Factor Analysis.","authors":"Leonie V D E Vogelsmeier, Joran Jongerling, Esther Ulitzsch","doi":"10.1080/00273171.2025.2492016","DOIUrl":"https://doi.org/10.1080/00273171.2025.2492016","url":null,"abstract":"Intensive longitudinal data (ILD) collection methods like experience sampling methodology can place significant burdens on participants, potentially resulting in careless responding, such as random responding. Such behavior can undermine the validity of any inferences drawn from the data if not properly identified and addressed. Recently, a confirmatory mixture model (here referred to as fully constrained latent Markov factor analysis, LMFA) has been introduced as a promising solution to detect careless responding in ILD. However, this method relies on the key assumption of measurement invariance of the attentive responses, which is easily violated due to shifts in how participants interpret items. If the assumption is violated, the ability of the fully constrained LMFA to accurately identify careless responding is compromised. In this study, we evaluated two more flexible variants of LMFA-fully exploratory LMFA and partially constrained LMFA-to distinguish between careless and attentive responding in the presence of non-invariant attentive responses. Simulation results indicated that the fully exploratory LMFA model is an effective tool for reliably detecting and interpreting different types of careless responding while accounting for violations of measurement invariance. Conversely, the partially constrained model struggled to accurately detect careless responses. We end by discussing potential reasons for this.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-20"},"PeriodicalIF":5.3,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144005691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Latent State-Trait Models for Experience-Sampling Data in R with the lsttheory Package: A Tutorial. 用lsttheory包在R中估计经验抽样数据的潜在状态-特征模型：教程。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-05-01 Epub Date: 2025-04-25 DOI: 10.1080/00273171.2025.2454904

Julia Norget, Alexa Weiss, Axel Mayer

{"title":"Estimating Latent State-Trait Models for Experience-Sampling Data in R with the lsttheory Package: A Tutorial.","authors":"Julia Norget, Alexa Weiss, Axel Mayer","doi":"10.1080/00273171.2025.2454904","DOIUrl":"10.1080/00273171.2025.2454904","url":null,"abstract":"As the popularity of the experience-sampling methodology rises, there is a growing need for suitable analytical procedures. These studies often aim to separate fleeting situation-specific influences from more enduring ones. Latent state-trait (LST) models can make this differentiation. This tutorial discusses multiple-indicator wide-format LST models suitable for experience-sampling data. We outline second-order and first-order model specifications, their advantages and disadvantages, and make the assumptions of first-order specifications explicit. These LST models are very flexible, allowing for various different models and for testing invariance assumptions. However, their specification is tedious and error-prone. This tutorial introduces a new user-friendly browser app and R-function for experience sampling models in the R-package lsttheory. Extending on existing models, the software also allows to add covariates, which can further explain the stable components. Throughout the tutorial, we answer exemplary research questions about well-being in everyday life with data from a five-day experience-sampling study. An autoregressive model with indicator-specific traits was most appropriate for the data and revealed relatively high consistency, implying that well-being depends more strongly on the person than the current situation. Of the Big Five, extraversion, emotional stability and agreeableness are predictive of trait well-being. We conclude with recommendations about model fit and comparisons.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"620-640"},"PeriodicalIF":5.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Causal Estimands and Multiply Robust Estimation of Mediated-Moderation. 中介调节的因果估计与多重稳健估计。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-05-01 Epub Date: 2025-01-13 DOI: 10.1080/00273171.2024.2444949

Xiao Liu, Mark Eddy, Charles R Martinez

{"title":"Causal Estimands and Multiply Robust Estimation of Mediated-Moderation.","authors":"Xiao Liu, Mark Eddy, Charles R Martinez","doi":"10.1080/00273171.2024.2444949","DOIUrl":"10.1080/00273171.2024.2444949","url":null,"abstract":"When studying effect heterogeneity between different subgroups (i.e., moderation), researchers are frequently interested in the mediation mechanisms underlying the heterogeneity, that is, the mediated moderation. For assessing mediated moderation, conventional methods typically require parametric models to define mediated moderation, which has limitations when parametric models may be misspecified and when causal interpretation is of interest. For causal interpretations about mediation, causal mediation analysis is increasingly popular but is underdeveloped for mediated moderation analysis. In this study, we extend the causal mediation literature, and we propose a novel method for mediated moderation analysis. Using the potential outcomes framework, we obtain two causal estimands that decompose the total moderation: (i) the mediated moderation attributable to a mediator and (ii) the remaining moderation unattributable to the mediator. We also develop a multiply robust estimation method for the mediated moderation analysis, which can incorporate machine learning methods in the inference of the causal estimands. We evaluate the proposed method through simulations. We illustrate the proposed mediated moderation analysis by assessing the mediation mechanism that underlies the gender difference in the effect of a preventive intervention on adolescent behavioral outcomes.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"460-486"},"PeriodicalIF":5.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107499/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evidence That Growth Mixture Model Results Are Highly Sensitive to Scoring Decisions. 生长混合模型结果对评分决策高度敏感的证据。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-05-01 Epub Date: 2025-01-15 DOI: 10.1080/00273171.2024.2444955

James Soland, Veronica Cole, Stephen Tavares, Qilin Zhang

{"title":"Evidence That Growth Mixture Model Results Are Highly Sensitive to Scoring Decisions.","authors":"James Soland, Veronica Cole, Stephen Tavares, Qilin Zhang","doi":"10.1080/00273171.2024.2444955","DOIUrl":"10.1080/00273171.2024.2444955","url":null,"abstract":"Interest in identifying latent growth profiles to support the psychological and social-emotional development of individuals has translated into the widespread use of growth mixture models (GMMs). In most cases, GMMs are based on scores from item responses collected using survey scales or other measures. Research already shows that GMMs can be sensitive to departures from ideal modeling conditions and that growth model results outside of GMMs are sensitive to decisions about how item responses are scored, but the impact of scoring decisions on GMMs has never been investigated. We start to close that gap in the literature with the current study. Through empirical and Monte Carlo studies, we show that GMM results-including convergence, class enumeration, and latent growth trajectories within class-are extremely sensitive to seemingly arcane measurement decisions. Further, our results make clear that, because GMM latent classes are not known a priori, measurement models used to produce scores for use in GMMs are, almost by definition, misspecified because they cannot account for group membership. Misspecification of the measurement model then, in turn, biases GMM results. Practical implications of these results are discussed. Our findings raise serious concerns that many results in the current GMM literature may be driven, in part or whole, by measurement artifacts rather than substantive differences in developmental trends.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"487-508"},"PeriodicalIF":5.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Non-Stationarity in Time-Series Analysis: Modeling Stochastic and Deterministic Trends. 时间序列分析中的非平稳性：随机和确定性趋势建模。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-05-01 Epub Date: 2025-01-15 DOI: 10.1080/00273171.2024.2436413

Oisín Ryan, Jonas M B Haslbeck, Lourens J Waldorp

{"title":"Non-Stationarity in Time-Series Analysis: Modeling Stochastic and Deterministic Trends.","authors":"Oisín Ryan, Jonas M B Haslbeck, Lourens J Waldorp","doi":"10.1080/00273171.2024.2436413","DOIUrl":"10.1080/00273171.2024.2436413","url":null,"abstract":"Time series analysis is increasingly popular across scientific domains. A key concept in time series analysis is stationarity, the stability of statistical properties of a time series. Understanding stationarity is crucial to addressing frequent issues in time series analysis such as the consequences of failing to model non-stationarity, how to determine the mechanisms generating non-stationarity, and consequently how to model those mechanisms (i.e., by differencing or detrending). However, many empirical researchers have a limited understanding of stationarity, which can lead to the use of incorrect research practices and misleading substantive conclusions. In this paper, we address this problem by answering these questions in an accessible way. To this end, we study how researchers can use detrending and differencing to model trends in time series analysis. We show via simulation the consequences of modeling trends inappropriately, and evaluate the performance of one popular approach to distinguish different trend types in empirical data. We present these results in an accessible way, providing an extensive introduction to key concepts in time series analysis, illustrated throughout with simple examples. Finally, we discuss a number of take-home messages and extensions to standard approaches, which directly address more complex time-series analysis problems encountered by empirical researchers.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"556-588"},"PeriodicalIF":5.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Evaluating Contextual Models for Intensive Longitudinal Data in the Presence of Noise. 在存在噪声的情况下评估密集纵向数据的情境模型。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-05-01 Epub Date: 2024-12-15 DOI: 10.1080/00273171.2024.2436420

Anja F Ernst, Eva Ceulemans, Laura F Bringmann, Janne Adolf

{"title":"Evaluating Contextual Models for Intensive Longitudinal Data in the Presence of Noise.","authors":"Anja F Ernst, Eva Ceulemans, Laura F Bringmann, Janne Adolf","doi":"10.1080/00273171.2024.2436420","DOIUrl":"10.1080/00273171.2024.2436420","url":null,"abstract":"Nowadays research into affect frequently employs intensive longitudinal data to assess fluctuations in daily emotional experiences. The resulting data are often analyzed with moderated autoregressive models to capture the influences of contextual events on the emotion dynamics. The presence of noise (e.g., measurement error) in the measures of the contextual events, however, is commonly ignored in these models. Disregarding noise in these covariates when it is present may result in biased parameter estimates and wrong conclusions drawn about the underlying emotion dynamics. In a simulation study we evaluate the estimation accuracy, assessed in terms of bias and variance, of different moderated autoregressive models in the presence of noise in the covariate. We show that estimation accuracy decreases when the amount of noise in the covariate increases. We also show that this bias is magnified by a larger effect of the covariate, a slower switching frequency of the covariate, a discrete rather than a continuous covariate, and constant rather than occasional noise in the covariate. We also show that the bias that results from a noisy covariate does not decrease when the number of observations increases. We end with a few recommendations for applying moderated autoregressive models based on our simulation.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"423-443"},"PeriodicalIF":5.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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