Accuracy of Estimates and Statistical Power for Testing Meditation in Latent Growth Curve Modeling

The latent growth curve modeling (LGCM) approach has been increasingly utilized to investigate longitudinal mediation. However, little is known about the accuracy of the estimates and statistical power when mediation is evaluated in the LGCM framework. A simulation study was conducted to address these issues under various conditions including sample size, effect size of mediated effect, number of measurement occasions, and R ₂ of measured variables. In general, the results showed that relatively large samples were needed to accurately estimate the mediated effects and to have adequate statistical power, when testing mediation in the LGCM framework. Guidelines for designing studies to examine longitudinal mediation and ways to improve the accuracy of the estimates and statistical power were discussed.     

Multivariate Meta-Analysis as Structural Equation Models

Multivariate meta-analysis has become increasingly popular in the educational, social, and medical sciences. It is because the outcome measures in a meta-analysis can involve more than one effect size. This article proposes 2 mathematically equivalent models to implement multivariate meta-analysis in structural equation modeling (SEM). Specifically, this article shows how multivariate fixed-, random- and mixed-effects meta-analyses can be formulated as structural equation models. metaSEM (a free R package based on OpenMx) and Mplus are used to implement the proposed procedures. A real data set is used to illustrate the procedures. Formulating multivariate meta-analysis as structural equation models provides many new research opportunities for methodological development in both meta-analysis and SEM. Issues related to and extensions on the SEM-based meta-analysis are discussed.     

Detecting Misspecification in Mean Structures for Growth Curve Models: Performance of Pseudo R 2s and Concordance Correlation Coefficients

This study examined the performance of 4 correlation-based fit indexes (marginal and conditional pseudo R ²s; average and conditional concordance correlations) in detecting misspecification in mean structures in growth curve models. Their performance was also compared to that of 4 traditional SEM fit indexes. We found that the marginal pseudo R ² and average concordance correlation were able to detect misspecification in the marginal mean structure (average change trajectory). The conditional pseudo R ² and concordance correlation could detect misspecification when it occurred in the conditional mean structure (individual change trajectory) or in both mean structures. Compared to the SEM fit indexes, the correlation-based fit indexes were more robust to sample size but were less robust to data properties such as magnitude of population mean and measurement error. Theoretical and practical implications of the results and directions for future research are discussed.     

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.     

《太阳帝国》中的中国形象分析

英国作家詹·格·巴拉德在《太阳帝国》中塑造了沉默的中国人形象,他对战争阴霾下的上海的描写,是为了借对"他者"的否定和批判肯定自身。巴拉德对中国形象的塑造采取了意识形态化的策略,在他建构的中国形象背后,则是对自身民族身份的认同困惑,还有隐蔽的帝国主义思想和殖民意图。     

中国计算机制造行业的自我选择和出口学习效应——基于企业层面的面板数据研究

采用中国计算机制造企业的面板数据（1998-2007年）,从企业层面研究出口和企业生产率之间的关系。实证研究发现,出口企业比非出口企业的生产率高,这既来源于自我选择效应又来源于出口学习效应。     

海派市民文化的复兴——《中国达人秀》、《舞林大会》及海派清口所体现的新娱乐动向

海派文化的本质是市民文化,体现出娱乐对城市高强度竞争的救赎功能。海派市民文化进入电视时代以后逐渐适应了新的环境,而恢复海派市民文化正脉则得力于世博时代到来后上海所获得的诸多有关世博的优惠政策以及对上海城市身份的正面确认。周立波的海派清口、东方卫视的《中国达人秀》以及《舞林大会》,体现出了海派市民文化复兴的动向。《中国达人秀》是海派娱乐业的纪念性形态,《舞林大会》激活了世人对上海舞厅文化的记忆,周立波的海派清口系列则显示了海派市民文化的刚性力量。2002年上海获得世博会举办权,使得世界性的元素第一次以正面、积极、合乎法理的姿态再度移植到上海。新海派市民文化将在合理、健康、理性、自觉地消解西方文化的基础上融入大量本土文化的元素。世界性的美学均衡性法则已为上海城市及其市民文化带来了生机。     

天南星提取物的抗炎作用及机制研究

目的：观察天南星提取物的抗炎作用并分析其机制。方法：采用二甲苯致小鼠耳廓肿胀、棉球诱发小鼠肉芽肿增生及醋酸致小鼠腹腔毛细血管通透性增加等三种模型,观察天南星提取物对不同炎症模型的抗炎作用。结果：天南星块茎醇提取物、乙酸乙酯提取物,天南星果实石油醚提取物均能明显抑制二甲苯致小鼠耳廓肿胀度（P〈0.01）;能减轻小鼠棉球肉芽肿（P〈0．01）;且能明显降低小鼠毛细血管通透性（P〈0．01,P〈0.05）。结论：天南星上述提取物具有明显的抗炎作用,其抗炎作用可能与降低血管通透性有关。本实验为其开发利用提供了科学的依据。     

证言式网络广告语篇分析

根据E.S Lewis研究,成功的广告应具备四种功能特征,即：引起注意,发生兴趣,产生欲望,付诸行动。而证言式广告主要利用权威人士的影响力,是否更能达到使潜在消费者付诸行动这一目的？网络广告与传统的报纸与电视广告等相比,又具有哪些独特功能之处？通过对两则证言式网络进行分析研究,发现证言式广告通过权威人士等强调商品的优点,更能使广告标准化;而网络广告一方面通过文字,色彩与图案等的合理搭配,另一方面借助于对词语,句式与语篇等语言结构和表达形式的灵活设计,引发广告受众兴趣,最终采取消费行动。     

跨通道信息获得对二语学习中记忆的影响

单词记忆是二语学习中最为基础也是十分关键的环节,本研究探讨了不同通道组合方式对于长短单词记忆的影响。实验选取23名非熟练英语学习者为被试,以GRE和托福词表中的低频真词作为材料,考察被试对于所学单词及其中文解释的再认成绩。结果表明,视觉先于听觉呈现情况下被试对于单词的再认正确率更高,被试对于长词的再认正确率优于短词,出现了反词长效应。研究得到的规律有助于提高英语学习者背单词的效率。