Deconstructing the construct: A network perspective on psychological phenomena

In psychological measurement, two interpretations of measurement systems have been developed: the reflective interpretation, in which the measured attribute is conceptualized as the common cause of the observables, and the formative interpretation, in which the measured attribute is seen as the common effect of the observables. We advocate a third interpretation, in which attributes are conceptualized as systems of causally coupled (observable) variables. In such a view, a construct like ’depression’ is not seen as a latent variable that underlies symptoms like ’lack of sleep’ or ’fatigue’, and neither as a composite constructed out of these symptoms, but as a system of causal relations between the symptoms themselves (e.g., lack of sleep → fatigue, etc.). We discuss methodological strategies to investigate such systems as well as theoretical consequences that bear on the question in which sense such a construct could be interpreted as real.     

Influence analysis of structural equation models with polytomous variables

The estimation of model parameters in structural equation models with polytomous variables can be handled by several computationally efficient procedures. However, sensitivity or influence analysis of the model is not well studied. We demonstrate that the existing influence analysis methods for contingency tables or for normal theory structural equation models cannot be applied directly to structural equation models with polytomous variables; and we develop appropriate procedures based on the local influence approach of Cook (1986). The proposed procedures are computationally efficient, the necessary bits of the proposed diagnostic measures are readily available following an usual fit of the model. We consider the influence of an individual cell frequency with respect to three cases: when all parameters in an unstructured model are of interest, when the unstructured polychoric correlations are of interest, and when the structural parameters are of interest. We also consider the sensitivity of the parameters estimates. Two examples based on real data are presented for illustration.The work described in this paper was partially supported by a Chinese University of Hong Kong Postdoctoral Fellows Scheme and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (RGC Ref. No. CUHK4186/98P). We are indebted to P.M. Bentler and M.D. Newcomb for providing the data set, Michael Leung for his assistance, and the Editor and the referees for some very valuable comments.     

实验设计:一种非模式的方法

实验设计通常被当作一种种设计模式．这种方法可称之为实验设计的模式方法。但本文认为,这种常用的方法事实上存在着不少缺点。于此．本文尝试提出了一种新的实验设计方法——实验设计的变量方法。实验设计的变量方法的基本思想是．从实验研究的问题出发,根据实验中变量的性质或特点确定实验设计的具体内容。本文最后讨论了实验设计变量方法的优越性。     

潜变量交互效应分析方法

简要回顾了分析显变量交互效应的常用方法。详细讨论了目前分析潜变量交互效应的主要方法,包括用潜变量的因子得分做回归分析、分组线性结构方程模型分析、加入乘积项的结构方程模型分析和两步最小二乘回归分析,并比较和评价了这些方法的优缺点。最后归纳了潜变量交互效应分析方法的研究趋势,并介绍了新近进展(包括LMS方法和GAPI方法)。     

近十年国外罗夏测验攻击变量研究综述

用罗夏测验测量攻击有助于了解个体的攻击性冲动 ,也有助于外显攻击行为的预测和控制。近十年国外文献中有关研究者主要有Holt、Exner、Gacono和Meloy。综述结果表明 ,他们提出的大多数攻击变量都有明确的定义 ,其实证研究也获得了一些支持。但也可以看到 ,目前的研究依然存在不少问题 :(1 )变量的定义有重复、内容有交叉 ;(2 )变量的分类标准不唯一 ;(3 )某些变量的使用和操作欠方便。因而 ,这些变量的定义、分类和使用都还有待于进一步研究支持。     

多级属性Q矩阵的验证与估计

多级属性是将诊断测验中传统的二值(即两种水平, 通常定义为0和1)属性定义为多值(多个水平可以为0, 1, …), 它不但可以描述学生对于知识属性是否掌握, 而且可以描述学生在属性上的掌握程度, 这样使得诊断测验能提供给被试更丰富的知识掌握详情。本文将适用于二级属性Q矩阵的统计量(S统计量)拓展到多级属性下的Q矩阵验证和估计, 在两种常见的条件下, 设计了两种估计算法：联合估计算法和在线估计算法。模拟实验结果表明：联合估计算法适用于对专家界定的初始Q矩阵进行验证, 当初始Q矩阵中包含较少的错误时, 通过联合估计算法有很大可能恢复正确的Q矩阵; 在线估计算法适用于对“新项目”进行属性向量和项目参数的在线标定, 基于一定数量的“基础项目”, 在线估计算法对于新项目的估计也能达到较满意的成功率。实证数据分析则进一步展示了该方法的使用。     

Concurrent second-order schedules of reinforcement

Responses on one key (the main key) of a two-key chamber produced food according to a second-order variable-interval schedule with fixed-interval schedule components. A response on a second key (the changeover key) alternated colors on the main key and provided a second independent second-order variable-interval schedule with fixed-interval components. The fixed-interval component on one variable-interval schedule was held constant at 8 sec, while the fixed interval on the other variable-interval schedule was varied from 0 to 32 sec. Under some conditions, a brief stimulus terminated each fixed interval and generated fixed-interval patterns; in other conditions, the brief stimulus was omitted. Relative response rate and relative time deviated substantially from scheduled relative reinforcement rate and, to a lesser extent, from obtained relative reinforcement rate under both brief-stimulus and no-stimulus conditions. Matching was observed with equal components on both schedules; with unequal components, increasingly greater proportions of time and responses than the matching relation would predict were spent on the variable-interval schedule containing the shorter component. Preference for the shorter fixed interval was typically more extreme under brief-stimulus than under no-stimulus schedules. The results limit the extension of the matching relation typically observed under simple concurrent variable-interval schedules to concurrent second-order variable-interval schedules.     

Bayesian Factor Analysis as a Variable-Selection Problem: Alternative Priors and Consequences

Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor-loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, Muthén & Asparouhov proposed a Bayesian structural equation modeling (BSEM) approach to explore the presence of cross loadings in CFA models. We show that the issue of determining factor-loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov's approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike-and-slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set is used to demonstrate our approach.     

A caveat on the Savage–Dickey density ratio: The case of computing Bayes factors for regression parameters

The Savage–Dickey density ratio is a simple method for computing the Bayes factor for an equality constraint on one or more parameters of a statistical model. In regression analysis, this includes the important scenario of testing whether one or more of the covariates have an effect on the dependent variable. However, the Savage–Dickey ratio only provides the correct Bayes factor if the prior distribution of the nuisance parameters under the nested model is identical to the conditional prior under the full model given the equality constraint. This condition is violated for multiple regression models with a Jeffreys–Zellner–Siow prior, which is often used as a default prior in psychology. Besides linear regression models, the limitation of the Savage–Dickey ratio is especially relevant when analytical solutions for the Bayes factor are not available. This is the case for generalized linear models, non-linear models, or cognitive process models with regression extensions. As a remedy, the correct Bayes factor can be computed using a generalized version of the Savage–Dickey density ratio.     

Sex differences in facial emotion perception ability across the lifespan

Perception of emotion in the face is a key component of human social cognition and is considered vital for many domains of life; however, little is known about how this ability differs across the lifespan for men and women. We addressed this question with a large community sample (N?=?100,257) of persons ranging from younger than 15 to older than 60 years of age. Participants were viewers of the television show “Tout le Monde Joue”, and the task was presented on television, with participants responding via their mobile devices. Applying latent variable modeling, and establishing measurement invariance between males and females and across age, we found that, for both males and females, emotion perception abilities peak between the ages of 15 and 30, with poorer performance by younger adults and declining performance after the age of 30. In addition, we show a consistent advantage by females across the lifespan, which decreases in magnitude with increasing age. This large scale study with a wide range of people and testing environments suggests these effects are largely robust. Implications are discussed.