共同方法偏差的统计检验与控制方法

共同方法偏差是心理学研究中一个越来越被关注的课题,但国内心理学界对其的介绍、研究还比较少,导致研究中对共同方法偏差的控制还有所欠缺。共同方法偏差的控制方法分为程序控制和统计控制,该文介绍了共同方法偏差的多种统计控制法,如因素分析法、偏相关法、潜在的误差变量控制法、结构方程模型法等,分析了其各自的优缺点,并提出了如何选择采用这些方法的建议.     

共同方法偏差检验：问题与建议

检验共同方法偏差(CMB)已经成为心理学实证研究中的一个环节。本文从数学模型角度分析方法变异(CMV)的影响,并讨论了CMB常用的检验法——Harman单因子法、控制未测量的潜在方法因子(ULMC)法、验证性因子分析(CFA)标签变量法的检验力。Harman单因子法检验力很低,ULMC法检验力中等,CFA标签变量法检验力虽然较高但问题也不少。提出一个好的检验法应当满足的三个特点：符合CMV的数学模型、评价标准不受非CMV来源的影响、对CMV、CMB的变化敏感。最后给出CMB检验的建议。     

决策评估中的结果偏差

结果偏差又称结果效应,它是发生在决策评估中的一种偏差式判断,即当决策结果与决策质量不存在实质性联系时,评估者仍根据结果信息评估决策质量.针对这一判断谬误的产生根源,研究者们提出了认知解释和申辩模型.结果偏差的影响因素主要包括动机、情境因素和评估手段等.基于先前研究存在的问题和不足,今后的研究应着眼于改进研究手段,理清与其他现象的关系并提出有效的应对策略.     

应对行为跨文化测评的构念等同与偏差性研究

本文对COPE量表测评我国大学生应对行为方式的构念等同与偏差性进行深入探讨。采用验证性因素分析的方法测试了原版量表(COPE)及其中文修订本(C-COPE)的不同因素结构模型与465名大学生测评数据的拟合优度。研究结果支持C—COPE的测评维度结构,表明COPE量表对我国人群所进行的应对测评与西方社会的相比确实存在跨文化构念不等同性或偏差性,两者测评结果的跨文化比较因此缺乏必要前提条件。文中对所发生的构念偏差现象进行了详细分析。     

自主学习的测评方法

1　引言　　自主学习一般是指个体自觉确定学习目标、制定学习计划、选择学习方法、监控学习过程、评价学习结果的过程或能力。在过去的 2 0年中 ,无论是自主学习的理论研究还是应用研究 ,都强调并致力于解决自主学习的测量问题。这是因为 ,一方面 ,研究者需要借助自主学习的测量结果来验证自己的理论分析 ,尤其是对自主学习的构成成分的分析 ;另一方面 ,众多的应用研究需要借助自主学习测量来说明自己的教育干预效果。目前 ,国外研究者对自主学习的测评采用两种取向。一种取向是把自主学习看成一种能力 (aptitude) ,是一种相对持久的、能够…     

共同方法变异是“致命瘟疫”吗?——论争、新知与应对

共同方法变异是由构念间相似的测量方法特征引起的系统变异, 可歪曲构念间的关系, 造成共同方法偏差。60年来, 这一问题在社会科学研究中被反复提及, 但它是否严重威胁研究效度尚无定论。虽然实证证据表明, 共同方法变异普遍存在, 数据来源、测量时间、问卷设计等因素可导致共同方法偏差, 使自我报告的横断式调查研究饱受质疑, 但部分学者从测量误差和非共同方法变异的制约作用等角度做出了回应和辩护, 认为无需过度担忧。以测量为中心的新视角强调共同方法变异是测量方法和被测构念交互作用的产物, 应从方法和构念两个维度评估共同方法变异风险。建议研究者树立均衡无偏的态度, 接纳共同方法变异的存在, 纠正对自我报告的偏见, 着重通过改进研究设计做好预先应对。     

共同方法变异的影响及其统计控制途径的模型分析

共同方法变异(common method variance, CMV)指两个变量之间变异的重叠是因为使用同类测量工具而导致, 而不是代表潜在构念之间的真实关系。虽然以往研究显示CMV不一定导致研究结果的偏差, 在实际研究中应当加以考量。特别是在使用测量方法的研究中, 如果数据来源越单一, 测量方法越类似, CMV效应使研究结果产生偏差的可能性越大。CMV效应的控制方法包括过程控制法和统计控制法。在统计控制法的选择和使用上, 需要重点考虑该方法是否分离了三大变异(特质变异、方法变异和误差变异), CMV效应是在测量构念层面还是题目层面, CMV效应是加法效应还是乘法效应。控制潜在方法因子途径是统计控制方法中最重要的一类方法, 理解其模型是正确使用这类方法的前提。未来研究应当关注多个研究的CMV效应和侧重评估某个理论研究中CMV所引起的潜在的效度威胁。     

对应偏差的跨文化研究述评

对应偏差是西方社会心理学研究的一个重要现象,这一现象在其他文化中是否也普遍存在引起广泛关注。     

心理测验在人才测评中的应用

本文探讨了心理测验在人才测评中应用价值和存在的问题,讨论了科学的人才测评与心理测验的关系,期望可以科学的把心理测验应用到人才测评工作中,提高人才测评的有效性和可靠性。     

Determining the number of components from the matrix of partial correlations

A common problem for both principal component analysis and image component analysis is determining how many components to retain. A number of solutions have been proposed, none of which is totally satisfactory. An alternative solution which employs a matrix of partial correlations is considered. No components are extracted after the average squared partial correlation reaches a minimum. This approach gives an exact stopping point, has a direct operational interpretation, and can be applied to any type of component analysis. The method is most appropriate when component analysis is employed as an alternative to, or a first-stage solution for, factor analysis.     

Gender Differences and Cognition Among Older Adults

The more replicated findings about gender difference in cognitive performance suggest female superiority on visuomotor speed and language ability and male superiority on mechanical and visuospatial tasks. Generally, group strengths found in the early school years become more established at adolescence and remain stable through adulthood. The current study tested whether the patterns established in the early years remained among 30 adult subjects. We also utilized a series of exploratory analyses to determine if observed gender differences were impacted by the covariance present between all cognitive tests. Results suggest that although the patterns established in the early years remain stable through time for males, the established patterns for females are altered with age. Our findings are compelling in supporting a male advantage on visuospatial tasks among older adults. These findings are discussed in terms of common variance between test instruments as a possible source of difference. Our finding that the gender effect tended to increase when common variance was controlled argues that this methodology may enhance the ability to detect domain specific effects.     

On Bayesian estimation in unrestricted factor analysis

It is shown that the common and unique variance estimates produced by Martin & McDonald's Bayesian estimation procedure for the unrestricted common factor model have a predictable sum which is always greater than the maximum likelihood estimate of the total variance. This fact is used to justify a suggested simple alternative method of specifying the Bayesian parameters required by the procedure.     

Understanding Beliefs

Beliefs are central to political psychology, but in many ways remain undertheorized. A good starting place for further thought is the typology of Smith, Bruner, and White that separates reality testing from the social and psychological functions of beliefs. The concept of beliefs has several connotations, some of which involve faith and emotions. It is often difficult to grasp others' beliefs, especially when they are foreign to us or morally repugnant. It is even more difficult to determine whether beliefs are powerful in the sense of determining behavior and autonomous in the sense of not being directly derivable from other factors. The Smith, Bruner, and White typology is useful here, helps us understand the operation of biases, and points to the multiple roles that beliefs play in people's lives, including managing trade-offs and generating what looks to others like hypocrisy.     

The illusion of knowledge: When more information reduces accuracy and increases confidence

Intuition suggests that having more information can increase prediction accuracy of uncertain outcomes. In four experiments, we show that more knowledge can decrease accuracy and simultaneously increase prediction confidence. Participants were asked to predict basketball games sampled from a National Basketball Association season. All participants were provided with statistics (win record, halftime score), while half were additionally given the team names. Knowledge of names increased the confidence of basketball fans consistent with their belief that this knowledge improved their predictions. Contrary to this belief, it decreased the participants’ accuracy by reducing their reliance on statistical cues. One of the factors contributing to this underweighting of statistical cues was a bias to bet on more familiar teams against the statistical odds. Finally, in a real betting experiment, fans earned less money if they knew the team names while persisting in their belief that this knowledge improved their predictions.