效应量：估计、报告和解释

鉴于心理学界对效应量（effect size,ES）的日渐重视,本文集中探讨了标准差异型和关联强度型ES指标的计算公式及使用条件,并说明关联强度型指标在SPSS软件中的操作。其次,强调ES估计结果的两个报告原则,即明确指出所计算的是何种ES指标,尽可能地呈现ES的置信区间。在ES的解释方面,建议研究者结合具体情况综合权衡结果的实际重要性,而非机械援引各种所谓＂小＂、＂中＂、＂大＂的ES判定准则。     

The statistical recommendations of the American Psychological Association Publication Manual: Effect sizes,confidence intervals,and meta‐analysis

Estimation based on effect sizes, confidence intervals, and meta‐analysis usually provides a more informative analysis of empirical results than does statistical significance testing, which has long been the conventional choice in psychology. The sixth edition of the American Psychological Association Publication Manual now recommends that psychologists should, wherever possible, use estimation and base their interpretation of research results on point and interval estimates. We outline the Manual's recommendations and suggest how they can be put into practice: adopt an estimation framework, starting with the formulation of research aims as ‘How much?’ or ‘To what extent?’ questions. Calculate from your data effect size estimates and confidence intervals to answer those questions, then interpret. Wherever appropriate, use meta‐analysis to integrate evidence over studies. The Manual's recommendations can assist psychologists improve they way they do their statistics and help build a more quantitative and cumulative discipline.     

与零假设检验有关的统计分析流程

人们熟知的零假设显著性检验,受到一次次质疑与辩护,地位并未动摇,报告检验结果仍然是统计分析的习惯做法。不过,其局限性促使研究者探寻更多的统计方法如区间估计、效应量分析、检验力分析等。本文先介绍假设检验与置信区间的关系;然后讨论检验力与两类错误率和效应量的关系;最后在理顺上述统计方法的基础上,提供一个可操作的统计分析流程。     

Beyond inference by eye: Statistical and graphing practices in JEAB, 1992‐2017

Debates about the utility of p values and correct ways to analyze data have inspired new guidelines on statistical inference by the American Psychological Association (APA) and changes in the way results are reported in other scientific journals, but their impact on the Journal of the Experimental Analysis of Behavior (JEAB) has not previously been evaluated. A content analysis of empirical articles published in JEAB between 1992 and 2017 investigated whether statistical and graphing practices changed during that time period. The likelihood that a JEAB article reported a null hypothesis significance test, included a confidence interval, or depicted at least one figure with error bars has increased over time. Features of graphs in JEAB, including the proportion depicting single‐subject data, have not changed systematically during the same period. Statistics and graphing trends in JEAB largely paralleled those in mainstream psychology journals, but there was no evidence that changes to APA style had any direct impact on JEAB. In the future, the onus will continue to be on authors, reviewers and editors to ensure that statistical and graphing practices in JEAB continue to evolve without interfering with characteristics that set the journal apart from other scientific journals.     

检验能力对统计显著性检验的影响

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,探讨抽样样本总体达到t检验显著性的数目与统计检验能力之间的一致性。模拟实验结果表明,统计检验能力与样本总体t检验显著性的数目具有很高的一致性,但两者同时受到显著性水平α、样本客量n和总体效果大小δ的影响,从而有可能影响统计推断的可靠性。因此,在进行显著性检验过程中,应对统计检验能力进行估计,这将有利于心理学研究成果的积累。     

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。     

平均数差异显著性检验统计检验力和效果大小的估计原理与方法

该文以平均数差异显著性检验为例,对实验数据进行假设检验后,继续对其统计检验力和效果大小进行估计的基本原理和方法作一介绍。     

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。     

An alternative approach to relapse analysis: Using Monte Carlo methods and proportional rates of response

Relapse is the recovery of a previously suppressed response. Animal models have been useful in examining the mechanisms underlying relapse (e.g., reinstatement, renewal, reacquisition, resurgence). However, there are several challenges to analyzing relapse data using traditional approaches. For example, null hypothesis significance testing is commonly used to determine whether relapse has occurred. However, this method requires several a priori assumptions about the data, as well as a large sample size for between‐subjects comparisons or repeated testing for within‐subjects comparisons. Monte Carlo methods may represent an improved analytic technique, because these methods require no prior assumptions, permit smaller sample sizes, and can be tailored to account for all of the data from an experiment instead of some limited set. In the present study, we conducted reanalyses of three studies of relapse (Berry, Sweeney, & Odum, 2014 ; Galizio et al., 2018 ; Odum & Shahan, 2004 ) using Monte Carlo techniques to determine if relapse occurred and if there were differences in rate of response based on relevant independent variables (such as group membership or schedule of reinforcement). These reanalyses supported the previous findings. Finally, we provide general recommendations for using Monte Carlo methods in studies of relapse.     

《心理统计学》教学内容的新探索

本文在对当前国内外主要心理统计学教材进行比较的基础上,指出与上个世纪八十年代的心理统计学教材内容相比较,在内容上的新探索主要体现在（1）由“假设检验”的内容中发展出“统计检验力”和“效果大小”的统计指标和估计方法;（2）引进一般线性模型来统合方差分析和回归分析这两种统计方法;（3）适度增加一些“多元统计分析”的内容等三个方面.本文对前两个方面的新内容作了简要评述,并对教材内容的编排方面提出了新的思路.     

Replicability and randomization test logic in behavior analysis

Randomization tests are a class of nonparametric statistics that determine the significance of treatment effects. Unlike parametric statistics, randomization tests do not assume a random sample, or make any of the distributional assumptions that often preclude statistical inferences about single‐case data. A feature that randomization tests share with parametric statistics, however, is the derivation of a p‐value. P‐values are notoriously misinterpreted and are partly responsible for the putative “replication crisis.” Behavior analysts might question the utility of adding such a controversial index of statistical significance to their methods, so it is the aim of this paper to describe the randomization test logic and its potentially beneficial consequences. In doing so, this paper will: (1) address the replication crisis as a behavior analyst views it, (2) differentiate the problematic p‐values of parametric statistics from the, arguably, more useful p‐values of randomization tests, and (3) review the logic of randomization tests and their unique fit within the behavior analytic tradition of studying behavioral processes that cut across species.     

Predictors of Irrational Thinking in Regular Slot Machine Gamblers

Previous research has suggested that irrational thinking may play a central role in the maintenance of behavior in slot machine gambling (M. B. Walker, 1992b). The present study is an evaluation of the validity and predictors of irrational thinking in a sample of regular gamblers (N = 20) drawn from the general community. The results were generally consistent with earlier findings; 75% of gambling-related cognitions were found to be irrational. Irrationality was unrelated to the amount of money lost or won during sessions but was positively related to risk taking. The most common irrational cognitions included false beliefs concerning the extent to which outcomes could be controlled or predicted and the attribution of human qualities (personification) to gambling devices. Gender comparisons showed that women were more likely than men to personify the machines. The validity of the speaking-aloud approach and suggestions for future research are discussed.     

A Test by Any Other Name: P Values,Bayes Factors,and Statistical Inference

Procedures used for statistical inference are receiving increased scrutiny as the scientific community studies the factors associated with insuring reproducible research. This note addresses recent negative attention directed at p values, the relationship of confidence intervals and tests, and the role of Bayesian inference and Bayes factors, with an eye toward better understanding these different strategies for statistical inference. We argue that researchers and data analysts too often resort to binary decisions (e.g., whether to reject or accept the null hypothesis) in settings where this may not be required.     

有中介的调节模型的拓展及其效应量

传统的有中介的调节(mediated moderation, meMO)模型关于误差方差齐性的假设经常被违背, 应用研究中也缺乏测量meMO效应大小的指标。对于单层数据, 本文借助于两层建模的思想, 提出了一种可用于处理方差非齐性的两层有中介的调节(2meMO)模型; 给出了用于测量meMO分析中总调节效应、直接调节效应和有中介调节效应大小的效应量。通过Monte Carlo模拟研究, 比较了meMO和2meMO模型在参数和效应量估计上的表现。并通过实际案例解释了2meMO模型的应用以及效应量的计算和解释。     

The Effect of Researchers' Focus on Interpretation of Diversity Data

Abstract

The tendency for intercultural researchers to focus primarily on cultural differences instead of both differences and similarities may reflect the emphasis of current statistical methodology toward cultural distance instead of cultural overlap. The authors proposed the cultural similarity index as a way of assessing the extent of communalities between 2 groups. The authors (a) analyzed research (T. Cox, S. Lobel, & P. L. McLeod, 1991; Y. F. Niemann & J. Dovidio, 1998) that placed a primary emphasis on differences, (b) presented alternative insights gained from a focus on similarities, and (c) explored the implications of a research focus on both cultural differences and similarities.     

Effect sizes in ANCOVA and difference-in-differences designs

It is common practice in both randomized and quasi-experiments to adjust for baseline characteristics when estimating the average effect of an intervention. The inclusion of a pre-test, for example, can reduce both the standard error of this estimate and—in non-randomized designs—its bias. At the same time, it is also standard to report the effect of an intervention in standardized effect size units, thereby making it comparable to other interventions and studies. Curiously, the estimation of this effect size, including covariate adjustment, has received little attention. In this article, we provide a framework for defining effect sizes in designs with a pre-test (e.g., difference-in-differences and analysis of covariance) and propose estimators of those effect sizes. The estimators and approximations to their sampling distributions are evaluated using a simulation study and then demonstrated using an example from published data.     

方差分析的统计检验力和效果大小的常用方法比较

本文对用方差分析统计检验力和效果大小进行估计的几种不同方法作了简要的介绍和比较。     

赌博游戏中的near-miss效应

near-miss效应是指在赌博中, 与一般的输钱和赢钱相比, “几乎赢(near-miss)”的输钱会诱发个体更高的生理唤醒和更强的赌博动机, 从而导致个体持续赌博的一种现象, 是导致赌博成瘾的主要诱因之一。针对这种现象的研究范式大致有三种：老虎机/类老虎机任务、轮盘任务和刮刮乐彩票任务。这种现象的理论解释目前主要有认知曲解假说、控制幻觉理论和受挫假说。near-miss效应的脑机制和病理研究才刚刚起步, 所涉及到的脑功能区域主要包括脑岛、腹侧纹状体等。未来的研究应在near-miss效应发生机制的理论模型建构、研究范式多样化、研究技术多模态化、病理机制和临床干预等方面进一步展开。     

运算动量效应的理论解释及其发展性预测因素

了解运算偏差的形成与发展对探索算数运算系统的内在机制具有重要意义,早期的算数运算能力是儿童理解和进行复杂数学运算的基础。运算动量偏差是指个体在进行基本数学运算时倾向于高估加法运算结果而低估减法运算结果的一种运算偏差,主要包括三种理论解释,即注意转移假说、启发式解释和压缩解释。鉴于运算动量效应在成年群体中相对稳定却在不同发展阶段儿童中存在不一致的证据,数学能力的提高与空间注意的成熟可结合不同的理论解释来阐明儿童发展过程中运算动量效应的变化趋势。未来可以进一步整合多种研究任务以揭示运算动量效应的发展轨迹,考察数量表征系统与运算动量效应间的关联,探究运算动量效应在不同运算符号中的稳定性,探讨不同因素共同作用对运算动量效应的影响,并设计有关数学能力的干预措施以减少运算动量效应这一运算偏差。     

从效应量应有的性质看中介效应量的合理性

效应量的作用有两个方面, 一是弥补了统计检验的不足, 二是使得效应有可比性。结合统计显著性和效应量, 才能得出适当的统计结论。效应量应当具有一些基本性质, 包括与测量单位无关、单调性、不受样本容量的影响。国际上流行的中介效应量κ平方就是因为缺乏单调性而引发质疑和研究, 从而被彻底终结了其作为中介效应量的合法性。R平方型中介效应量同样有缺乏单调性的问题。文末讨论了如何报告中介效应量以及有待研究的问题。