群体归因偏差及矫正策略

转型期社会群体对于社会认知和人的发展的归因偏差主要表现在过度归因于体制和政策、社会认识偏差、人情认知偏差、社会浮躁心态与社会牢骚等方面。因此,应通过克服归因从众心理,克服群体归因极化模式,提高元认知水平和强化自我控制等对策,使个体与群体都能得到良好的发展,更好地促进社会发展。     

利用最小化代价函数方法实现多窗口谱分析

利用最小化代价函数的方法推导了一种谱分析的多窗口。性能分析的结果表明，此方法与离散长球序列多窗口谱分析方法具有相当的估计偏差与方差性能。与离散长球序列多窗口相比，此方法得到的多窗口具有直观的解析表达式而无须求解矩阵的特征分解问题，因而具有较小的计算量。通过对离散白噪声与ＡＲ过程进行的多窗口谱分析对比实验，验证了此方法的有效性和正确性。     

地理距离、方言文化与劳动力空间流动

本文基于百度迁徙大数据研究中国劳动力的空间流动，系统考察地理和文化对劳动力流动的影响。通过构造流动机会比率，基于引力模型和普通最小二乘法的研究表明地理距离和方言距离阻碍劳动力流动。在空间距离上，劳动力偏好邻近城市，地理距离每增加1%，劳动力的流动机会比率降低约0.6%。在空间位置上，劳动力倾向于在方言文化相近地域范围流动，方言距离每增加1%，劳动力的流动机会比率下降2%左右。通过构造两地年均降水量差距和小麦种植适宜度差距作为方言距离的工具变量，以两阶段最小二乘法估计缓解内生性问题，估计显示结论稳健。考虑普通话因素后方言距离的抑制影响依然稳健，但目的地的高普通话普及率显著发挥促进劳动力流动的引力作用。最后，本文得出持续推广普通话、加强交通建设和深化中等教育的政策建议。     

Bias Reduction through First-order Mean Correction, Bootstrapping and Recursive Mean Adjustment

Standard methods of estimation for autoregressive models are known to be biased in finite samples, which has implications for estimation, hypothesis testing, confidence interval construction and forecasting. Three methods of bias reduction are considered here: first-order bias correction, FOBC, where the total bias is approximated by the O(T^-1) bias; bootstrapping; and recursive mean adjustment, RMA. In addition, we show how first-order bias correction is related to linear bias correction. The practically important case where the AR model includes an unknown linear trend is considered in detail. The fidelity of nominal to actual coverage of confidence intervals is also assessed. A simulation study covers the AR(1) model and a number of extensions based on the empirical AR(p) models fitted by Nelson & Plosser (1982). Overall, which method dominates depends on the criterion adopted: bootstrapping tends to be the best at reducing bias, recursive mean adjustment is best at reducing mean squared error, whilst FOBC does particularly well in maintaining the fidelity of confidence intervals.     

Risk of Error and the Kappa Coefficient of a Binary Diagnostic Test in the Presence of Partial Verification

The accuracy of a binary diagnostic test is usually measured in terms of its sensitivity and its specificity, or through positive and negative predictive values. Another way to describe the validity of a binary diagnostic test is the risk of error and the kappa coefficient of the risk of error. The risk of error is the average loss that is caused when incorrectly classifying a non-diseased or a diseased patient, and the kappa coefficient of the risk of error is a measure of the agreement between the diagnostic test and the gold standard. In the presence of partial verification of the disease, the disease status of some patients is unknown, and therefore the evaluation of a diagnostic test cannot be carried out through the traditional method. In this paper, we have deduced the maximum likelihood estimators and variances of the risk of error and of the kappa coefficient of the risk of error in the presence of partial verification of the disease. Simulation experiments have been carried out to study the effect of the verification probabilities on the coverage of the confidence interval of the kappa coefficient.     

跨文化交际障碍中的行为意识因素

鉴于国内的许多跨文化交际研究主要集中在纯语言的探讨和表层文化的分析上,试图从行为意识的角度出发,就思维定势、偏见意识和价值观念三大因素来分析跨文化交际中存在的障碍,从而使学习者排除跨文化交际的障碍,并从跨文化交流中获得益处。     

任务转换范式下情绪注意偏向

相对于其他信息来说,情绪信息往往会引起情绪障碍患者更多的注意并具有一种认知加工上的优先权。在注意实验中,与中性刺激相比,具有情绪意义的刺激更能吸引注意或占用注意资源从而引起注意偏向。通过转换代价和任务规则一致性效应两个指标阐述了任务转换范式下情绪障碍患者的情绪信息注意偏向加工机制,并指出这两种指标分别代表了个体在注意偏向中的两个不同的认知控制加工,为情绪障碍患者的情绪调节提供必要的依据。文中最后还指出了ERPs、f MRI新技术是未来任务转换范式下研究情绪与注意关系的新方向。     

投射偏差视角下的服务类品牌消费--以农村信息消费支付意愿为例

现将投射偏差模型框架引入服务类耐用品消费者均衡中,从理论上阐述投射偏差如何会导致消费者在服务类商品消费时无法达到效用最大化。研究采用实验和实地调研的方法,一共有251名人员参与了实验与调研,分析上网体验对于未来一年宽带的支付意愿影响。研究结果发现,人们在体验上网前后对于信息的认知状态有显著差异,没有上过网的人会基于当前的认知状态而低估未来一年宽带的效用,其支付意愿明显低于上过网的人,投射偏差在服务类耐用品的消费均衡中发挥了重要作用。     

中庸思维影响潜变量测量结果的验证

选取等效的新浪网、时光网、搜狐网和IMDb的口碑评价量表,以大学女生为调查对象,对6部热播电视剧进行口碑评价,得出中庸思维影响潜变量测评的结论.中庸思维的论证是通过研究被调查者内心已经有了一个特定打分倾向后对被测客体会如何打分来进行的,并通过计算条件均值的方法来验证.实证表明,被调查者内心有了一个特定打分倾向之后,依然会不自觉地偏向中心点给被测客体打分,验证了中国文化中中庸思维对潜变量测量的影响.另外,数据呈现出负偏现象,更多地是反映了被测客体的真实表现.     

Notes on estimation of the intraclass correlation under the AB/BA crossover trial

Under the AB/BA crossover trial, we focus our attention on estimation of the intraclass correlation in normal data. We develop both point and interval estimators in closed form for the intraclass correlation. We employ Monte Carlo simulation to study the performance of these estimators in a variety of situations. We note that the estimators developed here for the intraclass correlation remain valid even when there are possibly unexpected carry-over effects.