Factor analysis with (mixed) observed and latent variables in the exponential family

We develop a general approach to factor analysis that involves observed and latent variables that are assumed to be distributed in the exponential family. This gives rise to a number of factor models not considered previously and enables the study of latent variables in an integrated methodological framework, rather than as a collection of seemingly unrelated special cases. The framework accommodates a great variety of different measurement scales and accommodates cases where different latent variables have different distributions. The models are estimated with the method of simulated likelihood, which allows for higher dimensional factor solutions to be estimated than heretofore. The models are illustrated on synthetic data. We investigate their performance when the distribution of the latent variables is mis-specified and when part of the observations are missing. We study the properties of the simulation estimators relative to maximum likelihood estimation with numerical integration. We provide an empirical application to the analysis of attitudes.     

Item Randomized-Response Models for Measuring Noncompliance: Risk-Return Perceptions,Social Influences,and Self-Protective Responses

Randomized response (RR) is a well-known method for measuring sensitive behavior. Yet this method is not often applied because: (i) of its lower efficiency and the resulting need for larger sample sizes which make applications of RR costly; (ii) despite its privacy-protection mechanism the RR design may not be followed by every respondent; and (iii) the incorrect belief that RR yields estimates only of aggregate-level behavior but that these estimates cannot be linked to individual-level covariates. This paper addresses the efficiency problem by applying item randomized-response (IRR) models for the analysis of multivariate RR data. In these models, a person parameter is estimated based on multiple measures of a sensitive behavior under study which allow for more powerful analyses of individual differences than available from univariate RR data. Response behavior that does not follow the RR design is approached by introducing mixture components in the IRR models with one component consisting of respondents who answer truthfully and another component consisting of respondents who do not provide truthful responses. An analysis of data from two large-scale Dutch surveys conducted among recipients of invalidity insurance benefits shows that the willingness of a respondent to answer truthfully is related to the educational level of the respondents and the perceived clarity of the instructions. A person is more willing to comply when the expected benefits of noncompliance are minor and social control is strong. The authors are grateful to the reviewers whose suggestions helped to improve the clarity of the paper substantially. The authors also wish to thank the Dutch Ministry of Social Affairs and Employment for making the reported data available. This research was supported in parts by grants from the Social Sciences and Humanities Research Council of Canada and the Canadian Foundation of Innovation.     

Categorical Ontology of Levels and Emergent Complexity: An Introduction

An overview of the following three related papers in this issue presents the Emergence of Highly Complex Systems such as living organisms, man, society and the human mind from the viewpoint of the current Ontological Theory of Levels. The ontology of spacetime structures in the Universe is discussed beginning with the quantum level; then, the striking emergence of the higher levels of reality is examined from a categorical—relational and logical viewpoint. The ontological problems and methodology aspects discussed in the first two papers are followed by a rigorous paper based on Category Theory, Algebraic Topology and Logic that provides a conceptual and mathematical basis for a Categorical Ontology Theory of Levels. The essential links and relationships between the following three papers of this issue are pointed out, and further possible developments are being considered.     

“揭开黑箱”——变革型领导中介变量的研究现状与展望

变革型领导作用机制的研究目前是国内外学者关注的一个热点.大家比较一致的结论是心理授权在变革型领导行为与领导有效性之间起到中介作用,其中,心理授权中工作意义和自我效能两个维度起主要作用.学者们还发现信任和组织公平在变革型领导与员工工作绩效之间能起到显著的中介作用;另外,领导一成员交换关系和工作特征等作为变革型领导行为过程中的中介变量也得到验证.在未来,基于目标设置的变革型领导作用机制的构思将为今后研究提供一条新思路.     

初二学生知觉到的教师期望、自我价值感与自我效能的关系研究

以 2 55名初中二年级学生为被试 ,采用验证性因素分析和多元回归方法对教师期望、自我价值感及自我效能的关系进行研究。结果表明 :(1 )人口统计学变量 (性别和班别 )对教师期望、自我价值感及自我效能有影响。 (2 )教师期望与自我价值感和自我效能相关显著 :积极效应与自我价值感及自我效能的各维度呈显著正相关 ,消极效应与自我价值感及自我效能各维度呈显著负相关。 (3 )在控制了人口统计学变量后 ,教师期望与自我价值感仍对自我效能各维度具有较强的预测作用。 (4)自我价值感在教师期望对自我效能各维度的影响上起到了中介作用     

罗夏测验在西方近10年的最新发展

近10年来, 罗夏综合系统不断发展, 新增了XA%与WDA%、知觉−思维指数PTI、GHR:PHR等多个指标, 修改了8个群变量, 删除了精神分裂SCZI指标。这些新调整虽然不是本质性的, 但使得罗夏综合系统内容更加完善, 信度和效度不断提高, 更加精确化和标准化, 成为西方不可或缺的心理测验工具。尽管罗夏综合系统不断发展, 但仍然存在着操作过于复杂、功能还需要进一步完善等问题。罗夏综合系统作为一个代表性的投射测验, 与其它客观测验组成了一个完整的心理测量体系, 会发挥越来越大的作用。     

一种多级评分的认知诊断模型：P-DINA模型的开发

当前绝大多数认知诊断计量模型仅适用于0-1评分数据资料, 大大限制了认知诊断在实际中的应用, 也限制了认知诊断的进一步推广和发展。本文对具有较好发展前景的DINA模型进行拓展, 开发出适合多种评分(含0-1二级评分和多级评分)数据资料的P-DINA模型, 同时采用MCMC算法实现模型参数的估计, 并对该模型性能进行研究。结果表明：(1)本文开发的P-DINA模型无论是在无结构型属性层级关系下还是在结构型属性层级关系下, 参数估计的精度均较高, 参数估计的稳健性较强, 说明开发的P-DINA模型基本合理、可行。(2)P-DINA模型可采用MCMC算法实现参数估计, 且参数估计的精度较高。(3)整体来看, 无结构型属性层级关系和结构型属性层级关系下, P-DINA模型在项目参数的估计精度上两者基本相当; 但在被试属性判准率(MMR和PMR)上无结构型属性层级关系表现的稍差一些。(4)无结构型属性阶层关系下：模型诊断的属性个数越多, 参数 估计的精度越差、属性诊断的正确率(MMR和PMR)越低, 但参数 的估计精度越好; 若想保证属性模式判准率在80%以上, 建议诊断的属性个数不宜超过7个。总之, 本研究为拓展认知诊断在教育学和心理学中的应用提供了一种新方法、新模型。     

Notes on selective influence, probabilistic causality, and probabilistic dimensionality

The paper provides conceptual clarifications for the issues related to the dependence of jointly distributed systems of random entities on external factors. This includes the theory of selective influence as proposed in Dzhafarov [(2003a). Selective influence through conditional independence. Psychometrika, 68, 7-26] and generalized versions of the notions of probabilistic causality [Suppes, P., & Zanotti, M. (1981). When are probabilistic explanations possible? Synthese, 48, 191-199] and dimensionality in the latent variable models [Levine, M. V. (2003). Dimension in latent variable models. Journal of Mathematical Psychology, 47, 450-466]. One of the basic observations is that any system of random entities whose joint distribution depends on a factor set can be represented by functions of two arguments: a single factor-independent source of randomness and the factor set itself. In the case of random variables (i.e., real-valued random entities endowed with Borel sigma-algebras) the single source of randomness can be chosen to be any random variable with a continuous distribution (e.g., uniformly distributed between 0 and 1).     

中西医结合的现状与思考

根据中西医结合的现状,分析中、西医学的可结合性;从中西医学的“共通”点,根据临床结合已取得较好成果的客观事实,从能结合宏观与微观的“黑箱方法”这一角度,通过分析人健康状态时的输入变量、输出变量和疾病状态时的输入变量、输出变量之间的关系,浅析中西医结合切入点,并提及未来医学发展的整体性方向。