Diagnosing Global Case Influence on MAR Versus MNAR Model Comparisons

When missingness is suspected to be not at random (MNAR) in longitudinal studies, researchers sometimes compare the fit of a target model that assumes missingness at random (here termed a MAR model) and a model that accommodates a hypothesized MNAR missingness mechanism (here termed a MNAR model). It is well known that such comparisons are only interpretable conditional on the validity of the chosen MNAR model’s assumptions about the missingness mechanism. For that reason, researchers often perform a sensitivity analysis comparing the MAR model to not one, but several, plausible alternative MNAR models. In the social sciences, it is not widely known that such model comparisons can be particularly sensitive to case influence, such that conclusions drawn could depend on a single case. This article describes two convenient diagnostics suited for detecting case influence on MAR–MNAR model comparisons. Both diagnostics require much less computational burden than global influence diagnostics that have been used in other disciplines for MNAR sensitivity analyses. We illustrate the interpretation and implementation of these diagnostics with simulated and empirical latent growth modeling examples. It is hoped that this article increases awareness of the potential for case influence on MAR–MNAR model comparisons and how it could be detected in longitudinal social science applications.     

Fitting Nonlinear Latent Growth Curve Models With Individually Varying Time Points

Individual growth trajectories of psychological phenomena are often theorized to be nonlinear. Additionally, individuals’ measurement schedules might be unique. In a structural equation framework, latent growth curve model (LGM) applications typically have either (a) modeled nonlinearity assuming some degree of balance in measurement schedules, or (b) accommodated truly individually varying time points, assuming linear growth. This article describes how to fit 4 popular nonlinear LGMs (polynomial, shape-factor, piecewise, and structured latent curve) with truly individually varying time points, via a definition variable approach. The extension is straightforward for certain nonlinear LGMs (e.g., polynomial and structured latent curve) but in the case of shape-factor LGMs requires a reexpression of the model, and in the case of piecewise LGMs requires introduction of a general framework for imparting piecewise structure, along with tools for its automation. All 4 nonlinear LGMs with individually varying time scores are demonstrated using an empirical example on infant weight, and software syntax is provided. The discussion highlights some advantages of modeling nonlinear growth within structural equation versus multilevel frameworks, when time scores individually vary.     

Accelerated matrix recovery via random projection based on inexact augmented Lagrange multiplier method

In this paper, a unified matrix recovery model was proposed for diverse corrupted matrices. Resulting from the separable structure of the proposed model, the convex optimization problem can be solved efficiently by adopting an inexact augmented Lagrange multiplier (IALM) method. Additionally, a random projection accelerated technique (IALM+RP) was adopted to improve the success rate. From the preliminary numerical comparisons, it was indicated that for the standard robust principal component analysis (PCA) problem, IALM+RP was at least two to six times faster than IALM with an insignificant reduction in accuracy; and for the outlier pursuit (OP) problem, IALM+RP was at least 6.9 times faster, even up to 8.3 times faster when the size of matrix was 2 000×2 000.     

疏影横斜水清浅 暗香浮动月黄昏——简评蒋冰的书法创作

蒋冰是近年来崛起的一位卓有成就的青年书法家。他的书法创作呈现出一种神清骨秀、高洁端庄、幽独超绝的气质风韵,具有极强的艺术审美价值。这与其深厚的文化底蕴、自然的创作理念、强烈的情感流露和执着的艺术追求密切相关。他的作品给当下书坛带来了一种不同凡响的清新气息。     

影响知识型员工区域间流动三类要素关系分析

本文的研究目的是了解知识型员工的区域间流动的三类要素的关系,即人口统计变量、价值观和区域性因素偏好的关系。通过对625名知识型员工的问卷调查,获得人口统计变量、价值观和区域性因素的偏好的相关数据,对其进行独立样本T检验、单因素方差分析和相关分析,得出了人口统计变量、价值观和区域性因素偏好部分相关的结论。     

高等医学院校医学专业英语阅读测试内容效度实证分析

本文从内容效度的主要影响因素入手,基于近5年来某高等医学院校医学专业英语阅读测试试题为样本,以语篇输入视角,进行历时性研究与分析,客观评价某高等医学院校医学专业英语阅读测试的内容效度,提出相应建议,以期对提高医学专业英语测试水平有所裨益。     

意愿调查法中的偏好不确定性研究综述

对非市场物品和服务,如环境改善和资源保护,进行经济价值评估是学术界面临的一大难题。意愿调查法（Contingent Valuation Method, CVM）作为非市场物品经济价值评估的一种重要方法,其方法论在不断地接受检验并得到改进,近年来一个重要进展是其分析框架由确定偏好假设向不确定偏好假设转变。为系统认识国际上对CVM中受访者偏好不确定性的研究进展、推动国内相关研究,本文在简单介绍产生不确定结果的原因和不确定偏好假设下标准CVM不足的基础上,系统梳理了不确定偏好的测度方法和不确定偏好下价值计算方法,分析了各种方法的优劣以及考虑不确定性对价值估计值大小和估计效率的影响。总体来看,不确定偏好假设已基本形成共识,个人支付意愿应为随机变量也得到广泛支持。多数学者认为借助一定的测度技术获取的不确定性信息是有价值的,而如何更准确地测度和利用偏好不确定程度以提高资源环境等非市场物品价值估计效率应是今后研究的重点。最后,针对既有研究存在的问题,从加强个人偏好及不确定性的异质性研究、改进问卷设计等方面提出对未来研究工作的建议。     

The effect of toxicity on COVID-19 news network formation in political subcommunities on Reddit: An affiliation network approach

Political polarization remains perhaps the “greatest barrier” to effective COVID-19 pandemic mitigation measures in the United States. Social media has been implicated in fueling this polarization. In this paper, we uncover the network of COVID-19 related news sources shared to 30 politically biased and 2 neutral subcommunities on Reddit. We find, using exponential random graph modeling, that news sources associated with highly toxic – “rude, disrespectful” – content are more likely to be shared across political subreddits. We also find homophily according to toxicity levels in the network of online news sources. Our findings suggest that news sources associated with high toxicity are rewarded with prominent positions in the resultant network. The toxicity in COVID-19 discussions may fuel political polarization by denigrating ideological opponents and politicizing responses to the COVID-19 pandemic, all to the detriment of mitigation measures. Public health practitioners should monitor toxicity in public online discussions to familiarize themselves with emerging political arguments that threaten adherence to public health crises management. We also recommend, based on our findings, that social media platforms algorithmically promote neutral and scientific news sources to reduce toxic discussion in subcommunities and encourage compliance with public health recommendations in the fight against COVID-19.     

基于反向随机投影的神经网络改进算法

提出基于反向随机局部投影的神经网络效率改进算法，通过降低深度学习中的网络规模，重点解决了从“局部连接”到“全连接”和随机节点抽取时输入端节点信息丢失的问题，从而提升网络的效率。在算法中设置缩减参数，提升了算法的可伸展性，以适用于不同数据集的学习。通过数据集ISO-LET进行实验，结果表明，基于反向随机局部投影的神经网络效率改进算法的准确率、效率分别平均提升了3.48%和105.21%?在迭代20次的实验中进行了缩减参数调节实验，当参数设置为1.4时其准确率则优于传统全连接网络2.61%，效率提升了272.78%，具有明显的优势。     

Theoretical solution of a spherically isotrophic hollow sphere for dynamic thermoelastic problems

The separation of variables method was successfully, used to resolve the spherically symmetric dynamic thermoelastic problem for a spherically isotropic elastic hollow sphere. Use of the integral transform can be avoided by means of this method, which is also appropriate for an arbitrary thickness hollow sphere subjected to arbitrary thermal and mechanical loads. Numerical results are presented to show the dynamic stress responses in the uniformly heated hollow spheres. Project supported by the National Natural Science Foundation of China (Nos. 10172075, 10002016)