公平感对社会认知的影响:经济因素的调节作用

目的及方法:基于中国综合社会调查2010年数据,采用多层结构方程模型,探究中国民众公平感对社会认知的作用,及经济因素在其中的调节效应。研究结果:第一,公平感越高,则社会冲突感越低,外在政治效能感越高,越倾向于对社会不平等进行内部归因,即对于社会的整体认知更加积极; 第二,经济因素能调节公平感与社会不平等内归因的关系,具体而言,经济水平越低,则公平感对于社会不平等的内部归因正向预测作用越大。     

基于多层验证性因素分析的各种信度系数方法

近年来,心理学研究的复现性受到广泛关注,许多实证研究难以重复验证,信度较低。大量研究使用多层技术,但只报告整体信度,导致研究可重复危机。基于各种信度系数和验证性因素分析的理论,以二层模型为例,总结多层信度计算方法,通过文献综述检索应用情况,并用MPLUS进行实例演示,最后讨论单层信度估计存在的偏差及分层估计的好处。总之,对多层数据进行分层信度估计是很有必要的,可消除因测量工具缺乏信度而导致的研究不可重复。     

Accommodating Small Sample Sizes in Three-Level Models When the Third Level is Incidental

Small samples sizes are a pervasive problem when modeling clustered data. In two-level models, this problem has been well studied, and several resources provide guidance for modeling such data. However, a recent review of small-sample clustered data methods has noted that no studies have investigated methods for modeling three-level data with small sample sizes. Furthermore, strategies for two-level models do not necessarily translate to the three-level context. Moreover, three-level models are prone to small samples because the “small sample” designation is primarily based on the sample size of the highest level, and large samples are increasingly difficult to amass as one progresses up a hierarchy. In this study, we focus on the case when the third level is incidental, meaning that the third level is important to consider but there are no explicit research questions at the third level. This study performs a simulation study to examine the performance of seven methods for modeling three-level data with a small sample at the third level. A motivating educational psychology example is also provided to demonstrate how the choice of method can greatly affect results.     

Double Decomposition of Level-1 Variables in Multilevel Models: An Analysis of the Flynn Effect in the NSLY Data

This paper introduces an extension of cluster mean centering (also called group mean centering) for multilevel models, which we call “double decomposition (DD).” This centering method separates between-level variance, as in cluster mean centering, but also decomposes within-level variance of the same variable. This process retains the benefits of cluster mean centering but allows for context variables derived from lower level variables, other than the cluster mean, to be incorporated into the model. A brief simulation study is presented, demonstrating the potential advantage (or even necessity) for DD in certain circumstances. Several applications to multilevel analysis are discussed. Finally, an empirical demonstration examining the Flynn effect (Flynn, 1987 Flynn, J. R. (1987). Massive IQ gains in 14 nations: What IQ tests really measure. Psychological Bulletin, 101(2), 171. https://doi.org/10.1037/h0090408.[Crossref], [Web of Science ®] , [Google Scholar]), our motivating example, is presented. The use of DD in the analysis provides a novel method to narrow the field of plausible causal hypotheses regarding the Flynn effect, in line with suggestions by a number of researchers (Mingroni, 2014 Mingroni, M. A. (2014). Future efforts in Flynn effect research: Balancing reductionism with holism. Journal of Intelligence, 2(4), 122. https://doi.org/10.3390/jintelligence2040122.[Crossref] , [Google Scholar]; Rodgers, 2015 Rodgers, J. L. (2015). Methodological issues associated with studying the Flynn effect: Exploratory and confirmatory efforts in the past, present, and future. Journal of Intelligence, 3(4), 111. https://doi.org/10.3390/jintelligence3040111. [Crossref] , [Google Scholar]).     

The Mobilizing Effect of Right‐Wing Ideological Climates: Cross‐Level Interaction Effects on Different Types of Outgroup Attitudes

The present research investigated a multilevel person‐context interactionist framework for the relationship between right‐wing ideologies and prejudice across two large, representative samples (Study 1: European Social Survey: N = 56,752; Study 2: World Values Survey: N = 74,042). Across three different operationalizations of right‐wing ideology, two contextual levels (regional and national) of right‐wing climate, and three types of outgroup attitudes (i.e., age‐, ethnicity‐, and gender‐based), the analyses consistently revealed cross‐level interactions, showing a strong association between right‐wing attitudes and negative outgroup attitudes at the individual level in contexts with a low right‐wing climate, whereas this relationship is weaker and often even absent in contexts with a high right‐wing climate. These cross‐level interactions remained significant after controlling for statistical artefacts (i.e., restriction of range and outliers). The authors propose norm setting as the mobilizing mechanism through which a right‐wing climate develops and curbs the influence of individual right‐wing social‐ideological attitudes on outgroup attitudes.     

权力操作范式的分析与比较

从认知的视角研究权力已是目前权力研究的主流,但权力的认知研究结果和外部效度均受制于其实验操作范式.对国外现有权力操作范式进行分析,可在促进其完善的同时,更对建构适合中国背景下的权力操作方式有一定的借鉴意义.从对现有主流权力操作范式,即奖惩法、回忆法、信息控制法、具体数值法、BATNA法、语义唤起等方法介绍,以及对其实证应用与检验结果中,可以明显对比出各种范式的优势与不足.现有学者较少意识到该点,使用各范式时不加区分,这引起了各范式在适用研究、多等级操作、相关概念操作等方面的混乱.未来研究仍需从角色分配、区分自我权力与他人权力操作、操作检验等角度,改进现有权力操作范式.     

A multilevel framework for understanding relationships among traits,states, situations and behaviours

A conceptual and analytic framework for understanding relationships among traits, states, situations, and behaviours is presented. The framework assumes that such relationships can be understood in terms of four questions. (1) What are the relationships between trait and state level constructs, which include psychological states, the situations people experience and behaviour? (2) What are the relationships between psychological states, between states and situations and between states and behaviours? (3) How do such state level relationships vary as a function of trait level individual differences? (4) How do the relationships that are the focus of questions 1, 2, and 3 change across time? This article describes how to use multilevel random coefficient modelling (MRCM) to examine such relationships. The framework can accommodate different definitions of traits and dispositions (Allportian, processing styles, profiles, etc.) and different ways of conceptualising relationships between states and traits (aggregationist, interactionist, etc.). Copyright © 2007 John Wiley & Sons, Ltd.     

Factor analysis for clustered observations

Classical factor analysis assumes a random sample of vectors of observations. For clustered vectors of observations, such as data for students from colleges, or individuals within households, it may be necessary to consider different within-group and between-group factor structures. Such a two-level model for factor analysis is defined, and formulas for a scoring algorithm for estimation with this model are derived. A simple noniterative method based on a decomposition of the total sums of squares and crossproducts is discussed. This method provides a suitable starting solution for the iterative algorithm, but it is also a very good approximation to the maximum likelihood solution. Extensions for higher levels of nesting are indicated. With judicious application of quasi-Newton methods, the amount of computation involved in the scoring algorithm is moderate even for complex problems; in particular, no inversion of matrices with large dimensions is involved. The methods are illustrated on two examples.Suggestions and corrections of three anonymous referees and of an Associate Editor are acknowledged. Discussions with Bob Jennrich on computational aspects were very helpful. Most of research leading to this paper was carried out while the first author was a visiting associate professor at the University of California, Los Angeles.     

Empowerment praxis in community coalitions

Community coalitions address a wide variety of community problems, espousing a community development processes that promotes individual and collective self-determination. They offer a promising venue for the study of empowerment of individuals and organizations. This study utilizes data from members of 35 community coalitions organized for the prevention of alcohol and other drug problems to address the following questions: What individual characteristics are related to the psychological empowerment of coalition members? What organizational characteristics are related to the collective empowering of members? What organization characteristics are related to a coalition being organizationally empowered to succeed in achieving its objectives? At the individual level, psychological empowerment was most strongly related to individuals' participation levels, sense of community, and perceptions of a positive organizational climate. At the group level, the strongest predictors of collective empowering (our operationalization of the empowering organization) were net benefits of participation, commitment, and positive organization climate. Psychological empowerment and positive organizational climate were the two predictors of organizational effectiveness (the empowered organization). Implications and limitations of these findings are discussed.     

Reinforcer pathology II: Reward magnitude,reward delay,and demand for alcohol collectively relate to college students' alcohol related problems

The reinforcer pathologies model of addiction posits that two characteristic patterns of operant behavior characterize addiction. Specifically, individuals suffering from addiction have elevated levels of behavioral economic demand for their substances of abuse and have an elevated tendency to devalue delayed rewards (reflected in high delay discounting rates). Prior research has demonstrated that these behavioral economic markers are significant predictors of many of college students' alcohol-related problems. Delay discounting, however, is a complex behavioral performance likely undergirded by multiple behavioral processes. Emerging analytical approaches have isolated the role of participants' sensitivity to changes in reinforcer magnitude and changes in reinforcer delay. The current study uses these analytic approaches to compare participants' discounting of money versus alcohol, and to build regression models that leverage these new insights to predict a wider range of college students' alcohol related problems. Using these techniques, we were able to 1) demonstrate that individuals differed in their sensitivity to magnitudes of alcohol versus money, but not sensitivity to delays to those commodities and 2) that we could use our behavioral economic measures to predict a range of students' alcohol related problems.