A Bayesian predictive analysis of test scores

In the classical test theory, a high-reliability test always leads to a precise measurement. However, when it comes to the prediction of test scores, it is not necessarily so. Based on a Bayesian statistical approach, we predicted the distributions of test scores for a new subject, a new test, and a new subject taking a new test. Under some reasonable conditions, the predicted means, variances, and covariances of predicted scores were obtained and investigated. We found that high test reliability did not necessarily lead to small variances or covariances. For a new subject, higher test reliability led to larger predicted variances and covariances, because high test reliability enabled a more accurate prediction of test score variances. Regarding a new subject taking a new test, in this study, higher test reliability led to a large variance when the sample size was smaller than half the number of tests. The classical test theory is reanalyzed from the viewpoint of predictions and some suggestions are made.     

辛苦所得与意外所得的非理性消费偏差：基于IAT检测和ERP证据

不同来源的财富因其在人们心理的权重差异会导致对其消费态度与方式的不同。前人运用一系列行为实验已发现意外所得更容易消费、辛苦所得则不容易消费。基于心理账户和内隐社会认知等理论, 运用IAT和ERP考察财富的意外所得与辛苦所得在人们心理引起的内在消费偏差, 进而对内隐消费态度和脑加工机制进行间接检测。IAT研究结果表明, 意外所得与容易消费的联结更加紧密, 辛苦所得与不易消费的联结更加紧密, 从内隐层面验证了前人的行为研究结论; ERP研究结果发现辛苦所得与意外所得这两个不同收入来源可能建立了不同脑加工机制, 且在P3和LPC成分得到反映, P3成分可能是反映辛苦所得组偏好难消费型消费方式的ERP成分, LPC反映了意外所得收入条件下个体偏好易消费型消费方式的特点。     

跨期选择单维占优模型的过程检验

跨期选择需要决策者在眼前与未来的损益之间做出权衡与取舍。主流跨期选择理论认为, 跨期选择是把未来价值折扣到现在, 根据折扣后的价值进行选择的过程; 而单维占优模型则认为决策者把“结果”维度上的差异和“延迟”维度上的差异进行比较(维度间差异比较), 然后根据差异更大的维度(即占优势的维度)进行决策。跨期选择众理论之争无果的原因之一或是研究者未能找到揭示其心理过程的令人信服的证据。为此, 本研究采用过程检验而非结果拟合的方法, 首创了“直观模拟天平”任务, 对维度间差异比较的大小进行测量, 从而为验证单维占优模型提供了支持性的证据。实验1A证明了维度间差异大小中介了选择结果, 被试认为“结果”维度上的差异相对于“延迟”维度上的差异越大, 越倾向于选择延迟长、结果大的选项; 反之, 被试认为“延迟”维度上的差异相对于“结果”维度上的差异越大, 越倾向于选择延迟短、结果小的选项。实验1B证明了维度间差异比较是在选择时实时(而不是在选择后)进行的。实验1C通过调整实验程序消减了共同方法偏差对结果的影响。实验2进一步证明维度间差异比较可以解释众多的跨期选择异象。通过4个实验, 本研究揭示了维度间差异比较构成了跨期选择的重要(尽管可能不是唯一)决策过程, 为支持单维占优模型提供了重要的过程验证证据。     

个体自信度对双人决策的影响

群体决策是重要的社会现象, 个体自信度在群体决策中发挥了重要作用。本文开展了不同难度和信息交流方式下的双人决策实验, 通过分析自信度和个体决策以及决策调整行为的关系, 研究了个体自信度的交流对双人决策的影响。实验结果表明, 个体的自信度与选择的正确率高度正相关; 双人决策过程是个体根据对方的自信度和选择来不断调整自己的选择最终达成一致的过程, 并通过交互过程提高双人决策的正确率; 实验中双人决策的质量明显优于“自信度分享模型”和“更自信者主导决策模型”的预期结果, 表明群体决策不是通过分享自信度进行的贝叶斯优化整合过程, 也不是由更自信的个体完全主导的过程。     

应激对风险和社会决策的影响

应激是有机体在受到真实或者潜在的威胁刺激时所表现出来的全身性非特异性反应, 伴随着紧张和焦虑的心理体验, 交感神经系统的兴奋, 糖皮质激素分泌的增多以及脑干−边缘系统−前额叶神经环路的改变。应激对个体在风险决策行为中的风险寻求和风险回避倾向, 社会决策行为中的利己和利他倾向都会产生重要影响。策略使用异常、习惯化和自动化反应增强、反馈学习过程以及奖惩敏感性的改变是应激影响决策行为的认知基础; 应激激素的分泌, 及杏仁核、前额叶等在决策过程发挥重要作用的脑区活动的改变则为应激作用于决策行为的神经基础。未来研究应重点关注：应激的个体差异与应激对决策影响效应多样性的关系; 综合多种指标对应激进行测量; 考察应激的时序效应; 揭示个体的最佳应激水平; 加强对慢性应激影响决策以及应激对决策影响效应可逆性的研究; 揭示应激影响决策的神经机制。     

标签位置和内容对女性网购影响的眼动研究

本研究参照广告效果评估的研究方法,考察标签位置（上,下）和标签内容（新款, 秒杀, 热销）对35名女性被试浏览网店服装图片过程中眼动特征以及主观购买意愿的影响。早期眼动指标（首次注视时间）和晚期眼动指标（注视时间比和注视次数比）均表明,标签位置受到标签内容的调节;晚期眼动指标（注视时间比和注视次数比）表明,标签内容也受到标签位置的调节。主观购买意愿表明,标签内容为新款和热销时,被试购买意愿显著高于秒杀情况。以上结果说明在浏览网店服装时,服装标签内容和标签位置共同调节女性被试对服装图片的加工过程,但调节方式在加工早期和加工晚期并不完全相同。     

实质取样对因果强度估计的影响

使用单个目标原因和单个效果的图形集中呈现范式协变关系探讨实质取样增量（12.5%< 50%< 87.5% <100%）对大学生被试因果强度估计的影响。实验结果显示：1）实质取样增加在Power-PC=.50和1协变关系上都导致高估计,在Power-PC=0的协变关系上导致低估计;2）相同实质取样增量对被试在Power-PC=.50（1）协变关系上强度估计的影响符合一次（二次）函数假设。结果说明高实质取样促使被试做出符合理性预期的强度估计,混合假设可以解释被试的强度估计偏离Power-PC值的现象。     

中学生物理力学问题的分类及表征研究

采用自由分类和标签分类法,以16道中学物理力学应用题为实验材料,探讨不同学业水平中学生对物理问题的分类及表征差异,以及题目陈述方式(有无示意图)对分类的影响.结果发现: (1)自由分类实验中,学优生侧重于按问题的深层特征(物理原理表征)进行分类,学困生侧重于问题的表面特征(文字表征);题目陈述方式对优困生的分类时间和分类表征也有影响. (2)标签分类实验中,学优生的正确分类题目数量显著高于学困生,有示意图下学优生的正确分类题目数量显著高于无示意图下.     

Rank‐based permutation approaches for non‐parametric factorial designs

Inference methods for null hypotheses formulated in terms of distribution functions in general non‐parametric factorial designs are studied. The methods can be applied to continuous, ordinal or even ordered categorical data in a unified way, and are based only on ranks. In this set‐up Wald‐type statistics and ANOVA‐type statistics are the current state of the art. The first method is asymptotically exact but a rather liberal statistical testing procedure for small to moderate sample size, while the latter is only an approximation which does not possess the correct asymptotic α level under the null. To bridge these gaps, a novel permutation approach is proposed which can be seen as a flexible generalization of the Kruskal–Wallis test to all kinds of factorial designs with independent observations. It is proven that the permutation principle is asymptotically correct while keeping its finite exactness property when data are exchangeable. The results of extensive simulation studies foster these theoretical findings. A real data set exemplifies its applicability.     

More efficient parameter estimates for factor analysis of ordinal variables by ridge generalized least squares

Data in psychology are often collected using Likert‐type scales, and it has been shown that factor analysis of Likert‐type data is better performed on the polychoric correlation matrix than on the product‐moment covariance matrix, especially when the distributions of the observed variables are skewed. In theory, factor analysis of the polychoric correlation matrix is best conducted using generalized least squares with an asymptotically correct weight matrix (AGLS). However, simulation studies showed that both least squares (LS) and diagonally weighted least squares (DWLS) perform better than AGLS, and thus LS or DWLS is routinely used in practice. In either LS or DWLS, the associations among the polychoric correlation coefficients are completely ignored. To mend such a gap between statistical theory and empirical work, this paper proposes new methods, called ridge GLS, for factor analysis of ordinal data. Monte Carlo results show that, for a wide range of sample sizes, ridge GLS methods yield uniformly more accurate parameter estimates than existing methods (LS, DWLS, AGLS). A real‐data example indicates that estimates by ridge GLS are 9–20% more efficient than those by existing methods. Rescaled and adjusted test statistics as well as sandwich‐type standard errors following the ridge GLS methods also perform reasonably well.