心理病理学网络理论、方法与挑战

对于精神障碍这一概念的理解, 传统DSM-ICD分类诊断系统和研究领域标准RDoC均基于潜变量视角, 认为精神障碍的症状由其潜在共同原因所致。这2种观点都忽略了症状间的相互作用。不同于分类和维度视角, Borsboom在2008年对精神障碍的概念化提出了的全新视角——心理病理学网络理论。此理论的核心观点是症状之间的动态因果关系构成了精神障碍。基于心理病理学网络理论的网络分析方法, 主要以结合EBIC的glasso算法估计症状间的偏相关网络, 并通过网络中节点中心性与网络连接性等指标, 来考查精神障碍症状的不同特性。近几年来, 研究者发现心理病理学网络分析方法在对症状间因果关系的推断、核心症状的识别和网络结构的可靠性与可重复性方面仍面临一些挑战。这些挑战为心理病理学网络理论与方法指明了未来可能的发展方向。     

在亚健康状态研究中运用结构方程模型的合理性

亚健康状态是处于健康与疾病之间的一种“非病但非健康”的中间状态,主要表现为躯体、心理和社会适应等方面的不适,目前尚没有公认的概念和测量诊断工具。由于亚健康状态研究数据中含有潜变量、测量误差以及变量间关系需要确定等因素,不能使用传统的统计学方法进行处理。结构方程模型能够同时处理观测变量、潜变量、测量误差及变量间的关系,应用结构方程模型验证亚健康状况测量量表的结构效度和亚健康状态理论模型是非常合理的。     

社会网络分析在心理研究中的应用

个体不是孤立存在的, 其心理与行为的变化常依赖于与他人存在的关系。社会网络分析正是考虑到了这种依赖性, 发展出相应的处理关系变量的方法。在介绍社会网络理论的中心思想、社会网络分析的基本概念、数据表示方法和研究方法以及在个体水平和关系水平上的静态统计模型后, 使用UCINET软件展现了处理关系数据的基本步骤, 并简述社会网络分析在人格心理学、发展与教育心理学和管理心理学中的应用。     

一种新的统计方法和研究思路——结构方程建模述评

结构方程建模(SEM)是一种新的统计方法和研究思路,近年来在心理学和社会科学领域的应用日益增多。本文在非数学化的基础上,结合心理学研究实例介绍了SEM的渊源、发展、基本概念、分析过程以及相应的计算机统计软件LI-SREL。本文认为SEM有四大优点:①引进潜变量使研究更为深入,②遵循一般线性模式进行验证型分析,使研究更有意义,③妥善处理多变量间的复杂关系,④模型具有参数不变性。SEM应该引起国内心理学界的重视。     

二阶潜增长模型标度方法及其可比的一阶潜增长模型

潜增长模型(LGM)是分析纵向数据的一种强有力工具,在心理学和其他社会科学研究领域受到重视。多指标测量的变量,既可以用合成分数建立单变量LGM(一阶LGM),也可以用指标建立潜变量LGM(二阶LGM)。简述了二阶LGM标度方法(包括尺度指标法和效应编码),提出了有可操作性的潜变量标准化标度方法和合成分数的一阶LGM标准化模型。系统总结了二阶LGM标度方法及其可比的一阶LGM建模,并用多指标变量的实测数据进行示例。推荐使用效应编码法对二阶LGM进行标度和标准化。     

社会网络分析在社会心理学中的应用

在美国社会心理学家莫雷诺提出的社会测量法基础之上发展起来的社会网络分析(SocialNetworkAnalysis)作为当今吸引诸多学科注意、运用日益成熟的一种研究方法,为当今社会心理学研究提供了一些新的术语、思路和方法。本首先探讨了社会网络分析的形成与发展;继而在分析社会网络分析基本研究思路的基础之上,比较了两种不同取向的网络分析方法——整体网络分析与自我中心网络分析,以及相关软件,并且介绍了社会网络调查表——SNI技术。最后,针对当今社会心理学研究中的某些问题以及计算机网络支持的社会网络(Computer-Supported Social Net-WOrk。,简称为CSSN)兴起的事实,强调了社会网络分析对当前社会心理学研究的意义,并介绍了我们对于计算机网络支持的社会网络的研究设想和初步研究进展。     

社会网络视角下的同伴关系与心理健康

近年来, 从社会网络视角考察同伴关系与心理健康的相互作用正成为发展心理学和健康心理学研究的热点。研究者多借助整体网和纵向数据, 通过两种作用机制, 即选择过程(selection process) (强调心理和行为变量对社会网络和同伴关系的影响, 如关系的形成、维持和解除)和影响过程(influence process) (强调社会网络和同伴关系对心理和行为变量的影响), 来分析同伴关系与心理健康协同演进的动态过程。实证研究关注的领域集中在青少年健康风险行为(如吸烟、喝酒、药物滥用)和情绪问题(如抑郁、焦虑、孤独感)。未来研究应该注重拓展社会网络的类型和样本、加强理论建构、增加对积极心理和消极关系的研究, 并有望在互联网领域及社会网络的生物学基础等方面取得进展。     

执行功能可分离性及与年龄关系的潜变量分析

应用潜变量分析研究抑制优势反应、记忆刷新和注意转换三种执行功能的可分离性,以及与年龄的关系。被试为18~85岁的健康成年人142名,完成九项执行功能任务。结果显示：本研究设计的九项执行功能任务有较好的信度;验证性因子分析显示,九项任务与对应执行功能间的因子负荷较高（汇聚性效度好）,而不同执行功能间的相关较低（鉴别性效度好）;在此基础上建立了三种执行功能及其与年龄关系的潜变量分析模型。研究结果表明,执行系统不是单一的认知结构,可以分离为若干相对独立的执行加工过程。     

贝叶斯结构方程模型及其研究现状

在心理学研究中结构方程模型(Structural Equation Modeling, SEM)被广泛用于检验潜变量间的因果效应, 其估计方法有频率学方法(如, 极大似然估计)和贝叶斯方法两类。近年来由于贝叶斯统计的流行及其在结构方程建模中易于处理小样本、缺失数据及复杂模型等方面的优势, 贝叶斯结构方程模型发展迅速, 但其在国内心理学领域的应用不足。主要介绍了贝叶斯结构方程模型的方法基础和优良特性, 及几类常用的贝叶斯结构方程模型及其应用现状, 旨在为应用研究者介绍新的研究工具。     

形成性测量模型：结构方程模型的新视角

形成性测量模型(Formative Model, FM)是指标变异导致潜变量变异的模型, 反映性测量模型(Reflective Model, RM)是潜变量变异导致指标变异的模型。FM在模型界定、识别和估计、信效度评价以及模型应用等方面均与RM存在极大的不同。模型界定错误会使参数估计发生偏差, 影响统计结论的有效性, 应当审慎考虑指标和潜变量之间的关系, 选择恰当的测量模型。进一步揭示两者的区别和误用带来的偏差, 完善FM的识别和估计、信效度评价方法、对变量含义的解释以及高阶FM的理论解释和模型估计是未来的研究方向。     

Using network science to analyze concept maps of psychology undergraduates

Network science is an emerging area of complexity science that uses mathematical techniques to study complex systems and could represent a new way of quantifying and investigating the internal structure of domain‐specific knowledge as approximated by students' concept maps. Students enrolled in introductory psychology constructed concept maps to represent their understanding of a psychology chapter. Concept networks were constructed based on the concept maps generated by students. Network analysis revealed that the structure of concept networks differed across students (i.e., some networks were better connected than others), and network structure significantly predicted quiz scores, such that concept networks with larger average shortest path lengths (a network metric representing the average of the shortest paths between two nodes in a network) were associated with higher quiz scores, after controlling for network size. This paper illustrates how network science techniques can be used to quantify the conceptual structure of a learner's knowledge.     

Analysis of multivariate frequency data by graphical models and generalizations of the multidimensional row-column association model

Models used to analyze cross-classifications of counts from psychological experiments must represent associations between multiple discrete variables and take into account attributes of stimuli, experimental conditions, or characteristics of subjects. The models must also lend themselves to psychological interpretations about underlying structures mediating the relationship between stimuli and responses. To meet these needs, the author extends the graphical latent variable models for nominal and/or ordinal data proposed by C. J. Anderson and J. K. Vermunt (2000) to situations in which dependencies between observed variables are not fully accounted for by the latent variables. The graphical models provide a unified framework for studying multivariate associations that include log-linear models and log-multiplicative association models as special cases.     

Variables latentes et propriétés mentales : pour une épistémologie affirmée pragmatiste et réaliste

The reliability of models with latent variables is questioned by different authors: what is the ontology of mental properties? How to integrate the complex of mental processes with latent variables? All these debates raise the question of the legitimacy of latent variables as methodological approach comparatively to new approaches such as Network analysis. By clearly posing the ontological nature of mental properties as emergent properties of mental processes, and by clearly posing a theory of pragmatist and realistic knowledge, our work seeks to show that latent variables are efficient approaches for inferring mental properties.     

新世纪20年国内心理统计方法研究回顾

新世纪头20年, 国内心理学11本专业期刊一共发表了213篇统计方法研究论文。研究范围主要包括以下10类(按论文篇数排序)：结构方程模型、测验信度、中介效应、效应量与检验力、纵向研究、调节效应、探索性因子分析、潜在类别模型、共同方法偏差和多层线性模型。对各类做了简单的回顾与梳理。结果发现, 国内心理统计方法研究的广度和深度都不断增加, 研究热点在相互融合中共同发展; 但综述类论文比例较大, 原创性研究论文比例有待提高, 研究力量也有待加强。     

Count data in psychological applied research

As some authors have noticed in fields other than psychology, level of measurement and distributional characteristics of count data are commonly not taken into account, so that they are analysed as normally distributed continuous variables, and therefore some general linear model is applied. In this work, we review a random sample of 457 articles published in the last four years in journals with the highest impact factor in the Journal Citation Reports (JCR Social Sciences Edition) of the Institute for Scientific Information. The goals are to know how often count variables appear in psychological applied research and which data analyses are used when dealing with count response variables. Examination showed that there is a notable presence of count variables, especially in some topics in psychology and a remarkable misapplication of the general linear model. One deals with causes and consequences of this approach to data analysis and suggests the use of specific models to analyse count data.     

Latent variables should remain as such: Evidence from a Monte Carlo study

Use of subject scores as manifest variables to assess the relationship between latent variables produces attenuated estimates. This has been demonstrated for raw scores from classical test theory (CTT) and factor scores derived from factor analysis. Conclusions on scores have not been sufficiently extended to item response theory (IRT) theta estimates, which are still recommended for estimation of relationships between latent variables. This is because IRT estimates appear to have preferable properties compared to CTT, while structural equation modeling (SEM) is often advised as an alternative to scores for estimation of the relationship between latent variables. The present research evaluates the consequences of using subject scores as manifest variables in regression models to test the relationship between latent variables. Raw scores and three methods for obtaining theta estimates were used and compared to latent variable SEM modeling. A Monte Carlo study was designed by manipulating sample size, number of items, type of test, and magnitude of the correlation between latent variables. Results show that, despite the advantage of IRT models in other areas, estimates of the relationship between latent variables are always more accurate when SEM models are used. Recommendations are offered for applied researchers.     

Network Analysis in Community Psychology: Looking Back,Looking Forward

Network analysis holds promise for community psychology given the field's aim to understand the interplay between individuals and their social contexts. Indeed, because network analysis focuses explicitly on patterns of relationships between actors, its theories and methods are inherently extra‐individual in nature and particularly well suited to characterizing social contexts. But, to what extent has community psychology taken advantage of this network analysis as a tool for capturing context? To answer these questions, this study provides a review of the use network analysis in articles published in American Journal of Community Psychology . Looking back, we describe and summarize the ways that network analysis has been employed in community psychology research to understand the range of ways community psychologists have found the technique helpful. Looking forward and paying particular attention to analytic issues identified in past applications, we provide some recommendations drawn from the network analysis literature to facilitate future applications of network analysis in community psychology.     

多项式加工树模型次序约束的量化分析及其应用

多项式加工树（MPT）模型是一种认知测量模型,能够对潜在认知过程进行测量和检验。已有研究探讨了二链MPT模型次序约束的重新参数化问题,本研究探讨了MPT模型次序约束的量化分析方法并从二链推广到多链,同时归纳出MPT模型参数向量内和参数向量间两参数次序约束量化分析的结论。数据分析结果表明该方法不仅在MPT模型框架下验证了潜在参数次序关系,而且给出了约束的量化指标,为潜在认知测量提供更有意义的解释。     

Categories and continua: a review of taxometric research

The taxometric procedures developed by Paul Meehl and his colleagues have been used in a large body of research seeking to distinguish between categorical and continuous models of latent variables. In this article the authors survey taxometric studies of psychopathological, personality, and other variables and draw conclusions about the taxonicity of latent variables in these domains. In addition, the authors review research on the validation and refinement of taxometric methods and make proposals for improving the application of taxometric research. The authors consider questions that remain about the conceptual status of taxometrics and raise new ones. They show that taxometric methodology has made an accelerating number of contributions to psychological research, has resolved several longstanding controversies, and has challenged some entrenched theoretical assumptions in differential psychology. Moreover, they contend that the research possibilities that it affords have yet to be fully exploited.