差数显著性t检验与元分析方法的模拟对比

利用计算机程序构造被试总体、模拟实验研究程序进行抽样研究 ,探讨差数显著性t检验方法与元分析方法在检验实验结果数据和进行实际应用方面的差异。模拟实验结果表明 ,差数显著性t检验与总体效果大小和样本容量有明显关系 ,但与随机抽样分布样本数目基本无关 ;抽样分布样本效果大小的平均值可以作为总体效果大小的估计值 ,它与样本容量和抽样样本数目有密切关系 ;本研究提供的实际应用数据结果可以作为研究者进行元分析方法实验研究的参考依据     

差数显著性t检验与元分析的对比研究

利用计算机构造被试总体、模拟实验研究程序进行抽样研究,探讨显著性ｔ检验方法与元分析方法在检验实验结果数据方面的差异。在模拟实验过程中,ｔ验受到显著性水平、样本容量和总体效果大小的影响,因此最终影响了统计推断的可靠性,建议：在进行显著性检验过程中,应对统计检验能力进行估计;元分析方法以样本为元素对总体进行推断,因此具有很高的准确性和可靠性,它将很有可能成为今后心理学研究的重要统计工具。     

传统统计方法面临的挑战:元分析方法

利用计算机构造实验组和控制组总体,进行传统统计方法与元分析比较实验研究。在实验组总体高出控制组总体０．５０个标准差的提下,传统ｔ检验的统计检验能力（Ｐ）仅为为４１％;而利用元分析的方法进行统计分析,其结果与计算机构造模型有很高的一致性从而提出元分析方法在进入心理学实验研究和证实理论方面的有效性和可靠性,以及传统统计方法面临挑战的事实。     

社会信念的自信判断:自我一致性模型的验证

采用重复测验的自信判断范式,检验自我一致性模型。项目一致性与项目共识性的分析表明:被试从大量共享的信念总体中抽取样本进行正误判断,自信判断则基于各个样本信念的一致性,并反映新样本做相同判断的可能性;建立在代表性样本上的判断有更高的一致性或共识性,信念建构的反应时更短且自信更高;项目一致性与项目共识性存在交互关系:高一致性的判断也是高共识性的判断。结果验证了自我一致性模型在中国文化背景下的存在。     

多元总体均值差异显著性检验的研究

对总体均值差异的显著性检验是心理学研究经常涉及到的问题,在一些研究中,我们发现,研究者往往不加分析地假定∑=σ^2I,即样本呈正态分布、指标之间相互独立,且方差相等,因而分别使用方差分析和Scheffe方法来进行检验。但这一假定在实际中往往并不符合,因此说,这其实是统计方法的误用。本文通过理论推导和例证对多元Hotelling T^2统计量在心理学研究中的应用进行了探索。     

心理学研究中应用统计方法应注意的几个问题

心理统计是认识心理现象数量特征的重要工具,在心理学研究中或多或少地存在着统计的误用。本文从心理学研究过程的内在逻辑出发,探讨了在心理研究中应用统计应该注意的问题和可能遇到的误用现象:有偏样本与小样本的使用,潜在变量的缺失,欺骗性的统计图表,量表信度与统计显著性检验的考量,事后解释的谬误,统计关系与因果关系等。针对这些问题提出避免统计误用的方法与建议。     

论心理与教育研究中的抽样问题

抽样的问题常常是任河一项实证研究不可或缺的一个基本环节,因此很受人们的重视。由于抽样总是与被研究的对象(如人、动物等等)密不可分,就使许多人认为抽样似乎就一定是对人、动物而言的。这种现念使研究结果经常出现矛盾,造成了许多误解。本人试图扼要谈谈抽样的理论基础(或曰方法论基础),以及出现矛盾的原因,并介绍一些改进的办法。要谈抽样,首先要明确几个概念:总体、个体、样本。总体是具有某种特征的一类事物的全     

抽样组合中元素数量对青少年组合成绩的影响

先前抽样组合问题研究表明达到形式运算阶段青少年的抽样组合思维成绩表现并不一致,作者分析猜想可能的原因是组合元素数量增加会降低被试的抽样组合成绩。现在实验考察高中一年级学生的抽样组合思维能力,以组合问题中的总体元素数量和样本元素数量为自变量,设置了五选三、七选三和七选四的三种抽样组合问题条件。实验结果发现,随着总体元素数量和样本元素数量的增加,被试的组合成绩明显下降。这表明,青少年的抽样组合思维能力虽已获得,但随组合元素数量增加而表现出倒退,并没达到成熟的一般性;其发展水平可能存在初级水平到高级水平的区分。     

16PF问卷网络与纸笔施测方式的比较

为检验网络测试与纸笔测试方式是否具有同等的信效度,16PF问卷被用于对两个样本分别进行网络(n=213)和纸笔(n=2801)施测;并从网络测试样本中随机抽取47人,随后进行纸笔重测。在α系数、测题同质性和次级因素结构三项检验中,各个人格因素的表现各有优劣,未得出统一的结论,但可以确定网络测试的信效度较纸笔测试没有明显下降。配对t检验的结果显示,两种施测方式下同一批被试的结果在部分因素上有显著性差异,不能将纸笔测试获得的常模直接用于网络测试。     

Fisher与Neyman–Pearson的分歧与心理统计中的假设检验争议

假设检验思想的提出者Fisher与Neyman–Pearson在统计模型的方法论基础、两类错误的性质、显著性水平的理解、以及假设检验的功能等方面存在诸多分歧, 使得心理统计中最常用的原假设显著性检验模式呈现出隐含的各种矛盾, 从而引发了应用上的争议。心理统计不仅需要检讨现有检验模型的模糊之处和提出其他补充性的统计推论方式,更应注重反思心理统计的教育传统, 以建立更加开放和多元的统计应用视野, 使心理统计为更好地心理学研究服务。     

Précis of statistical significance: rationale, validity, and utility

The null-hypothesis significance-test procedure (NHSTP) is defended in the context of the theory-corroboration experiment, as well as the following contrasts: (a) substantive hypotheses versus statistical hypotheses, (b) theory corroboration versus statistical hypothesis testing, (c) theoretical inference versus statistical decision, (d) experiments versus nonexperimental studies, and (e) theory corroboration versus treatment assessment. The null hypothesis can be true because it is the hypothesis that errors are randomly distributed in data. Moreover, the null hypothesis is never used as a categorical proposition. Statistical significance means only that chance influences can be excluded as an explanation of data; it does not identify the nonchance factor responsible. The experimental conclusion is drawn with the inductive principle underlying the experimental design. A chain of deductive arguments gives rise to the theoretical conclusion via the experimental conclusion. The anomalous relationship between statistical significance and the effect size often used to criticize NHSTP is more apparent than real. The absolute size of the effect is not an index of evidential support for the substantive hypothesis. Nor is the effect size, by itself, informative as to the practical importance of the research result. Being a conditional probability, statistical power cannot be the a priori probability of statistical significance. The validity of statistical power is debatable because statistical significance is determined with a single sampling distribution of the test statistic based on H0, whereas it takes two distributions to represent statistical power or effect size. Sample size should not be determined in the mechanical manner envisaged in power analysis. It is inappropriate to criticize NHSTP for nonstatistical reasons. At the same time, neither effect size, nor confidence interval estimate, nor posterior probability can be used to exclude chance as an explanation of data. Neither can any of them fulfill the nonstatistical functions expected of them by critics.     

An overall statistic for testing symmetry in social interactions

The present work focuses on the skew-symmetry index as a measure of social reciprocity. This index is based on the correspondence between the amount of behaviour that individuals address toward their partners and what they receive in return. Although the skew-symmetry index enables researchers to describe social groups, statistical inferential tests are required. This study proposes an overall statistical technique for testing symmetry in experimental conditions, calculating the skew-symmetry statistic (Phi) at group level. Sampling distributions for the skew-symmetry statistic were estimated by means of a Monte Carlo simulation to allow researchers to make statistical decisions. Furthermore, this study will allow researchers to choose the optimal experimental conditions for carrying out their research, as the power of the statistical test was estimated. This statistical test could be used in experimental social psychology studies in which researchers may control the group size and the number of interactions within dyads.     

新世纪20年国内假设检验及其关联问题的方法学研究

新世纪20年来国内假设检验方法学研究内容可分为如下几类: 零假设显著性检验的不足、p值的使用问题、心理学研究的可重复性问题、效应量、检验力、等效性检验、其他与假设检验关联的研究。零假设显著性检验已经发展成一套组合流程: 为了保证检验力和节省成本, 实验研究需要做先验检验力分析预估样本容量, 但问卷超过160人在传统统计中就没有必要这样做。当拒绝零假设时, 应当结合效应量做出结论。当不拒绝零假设时, 需要报告后验检验力; 如果效应量中或大而检验力不够高, 则可增加被试再行分析, 但这一过程应主动披露, 报告最后的实际p值并对可能犯的第一类错误率做出评估。     

Statistical requirements for properly investigating a null hypothesis

Issues involved in the evaluation of null hypotheses are discussed. The use of equivalence testing is recommended as a possible alternative to the use of simple t or F tests for evaluating a null hypothesis. When statistical power is low and larger sample sizes are not available or practical, consideration should be given to using one-tailed tests or less conservative levels for determining criterion levels of statistical significance. Effect sizes should always be reported along with significance levels, as both are needed to understand results of research. Probabilities alone are not enough and are especially problematic for very large or very small samples. Pre-existing group differences should be tested and properly accounted for when comparing independent groups on dependent variables. If confirmation of a null hypothesis is expected, potential suppressor variables should be considered. If different methods are used to select the samples to be compared, controls for social desirability bias should be implemented. When researchers deviate from these standards or appear to assume that such standards are unimportant or irrelevant, their results should be deemed less credible than when such standards are maintained and followed. Several examples of recent violations of such standards in family social science, comparing gay, lesbian, bisexual, and transgender families with heterosexual families, are provided. Regardless of their political values or expectations, researchers should strive to test null hypotheses rigorously, in accordance with the best professional standards.     

Virtual experiments in megastudies: A case study of language and emotion

A recent dramatic increase in the number and scope of chronometric and norming lexical megastudies offers the ability to conduct virtual experiments—that is, to draw samples of items with properties that vary in critical linguistic dimensions. This paper introduces a bootstrapping approach, which enables testing of research hypotheses against a range of samples selected in a uniform, principled manner and evaluates how likely a theoretically motivated pattern is in a broad distribution of possible outcome patterns. We apply this approach to conflicting theoretical and empirical accounts of the relationship between the psychological valence (positivity) of a word and its speed of recognition. To this end, we conduct three sets of multiple virtual experiments with a factorial and a regression design, drawing data from two lexical decision megastudies. We discuss the influence that criteria for stimuli selection, statistical power, collinearity, and the choice of dataset have on the efficacy and outcomes of the bootstrapping procedure.     

A graphical test for the significance of differences between frequencies from different samples

For testing the significance of differences between frequencies from different samples, an ellipse can easily be constructed on the basis of a formula developed on the assumption that both observed samples are random samples from the same parent population and that the best estimate of the true proportion is the weighted mean proportion of the two samples. The ellipse provides a very rapid method for testing pairs of frequencies.The opinions expressed in this paper are those of the authors and are not to be construed as those of the Navy Department.