There are Non‐Causal Explanations of Particular Events

A defence of non‐causal explanations of events is presented in cases where explanation is understood as modal explanation. In such cases the source of modal information is crucial. All explanations implicitly use contrast classes, and relative to a particular contrast we can privilege some difference makers over others. Thinking about changes in these privileged “actual” difference makers is then the source of modal information for any given explanandum. If an actual difference maker is non‐causal, then we have a principled definition of a non‐causal explanation of an event regardless of how much causal information is also contained in the explanans. A demarcation of explanation into causal and non‐causal in this way recovers ordinary language about explanation as well as reflecting genuine differences in practice, such as the in‐principle evidential base for any modal claim.     

特征概率影响多维和少维类别的分类学习和特征学习

探讨了特征概率对多维和少维类别的分类学习和特征学习的效果及策略的影响。结果表明高特征概率条件下,多维比少维类别的分类学习更容易,而且学到更多的特征知识,多维条件下人们更倾向于整体性加工策略,而少维条件下人们倾向于分析性加工策略。低特征概率条件下,多维比少维类别的分类学习和特征学习都困难,且两种条件下人们都倾向于采取分析性加工策略。     

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).     

正、负性情绪的跨文化心理测量:PANAS维度结构检验

对中国大学生(N=201)和美国大学生(N=321)进行PANAS测评,以检验该量表中、英文版的因素效度。探索性因素分析会聚性地验证了该量表的PA和NA两维度结构具有跨文化一致性,中、英文量表两因子的累积方差贡献率分别为51.31％和44．25％,接近Watson等的研究结果。然而研究结果也显示测项偏差问题,因此中文量表的PA分量表的测项组成不等同于原量表。中、英文量表的PA与NA分量表同样具有较高内在一致性信度,表明其符合心理测量学要求。     

从青少年学生的评价看教师的人格结构

本研究以班为单位,要求1594名大、中、小学生采用《青少年学生评价教师人格特质形容词捡核表》对自己理想中的教师人格特质进行评定,结果显示学生们对教师人格特质的评价维度是：高尚／进取、慈爱/公正、客观／求真、外向／开朗、退缩/粗暴、内向／怪异、损人俐己,前四个维度是学生们喜欢的,后三个维度是学生们不喜欢的。学生们对教师人格的评价存在着年龄、性别和学科上的差异。     

Conditional covariance structure of generalized compensatory multidimensional items

Some nonparametric dimensionality assessment procedures, such as DIMTEST and DETECT, use nonparametric estimates of item pair conditional covariances given an appropriately chosen subtest score as their basic building blocks. Such conditional covariances given some subtest score can be regarded as an approximation to the conditional covariances given an appropriately chosen unidimensional latent composite, where the composite is oriented in the multidimensional test space direction in which the subtest score measures best. In this paper, the structure and properties of such item pair conditional covariances given a unidimensional latent composite are thoroughly investigated, assuming a semiparametric IRT modeling framework called a generalized compensatory model. It is shown that such conditional covariances are highly informative about the multidimensionality structure of a test. The theory developed here is very useful in establishing properties of dimensionality assessment procedures, current and yet to be developed, that are based upon estimating such conditional covariances.In particular, the new theory is used to justify the DIMTEST procedure. Because of the importance of conditional covariance estimation, a new bias reducing approach is presented. A byproduct of likely independent importance beyond the study of conditional covariances is a rigorous score information based definition of an item's and a score's direction of best measurement in the multidimensional test space.This paper is based on a chapter of the first author's doctoral dissertation, written at the University of Illinois and supervised by the second author. Part of this research has been presented at the annual meeting of the National Council on Measurement in Education, San Francisco, April 1995.The authors would like to thank Jeff Douglas, Xuming He and Ming-mei Wang for their comments and suggestions. The research of the first author was partially supported by an ETS/GREB Psychometric Fellowship, and by Educational Testing Service Research Allocation Project 884-01. The research of the second author was partially supported by NSF grant DMS 97-04474.     

Approximating a symmetric matrix

We examine the least squares approximationC to a symmetric matrixB, when all diagonal elements get weightw relative to all nondiagonal elements. WhenB has positivityp andC is constrained to be positive semi-definite, our main result states that, whenw1/2, then the rank ofC is never greater thanp, and whenw1/2 then the rank ofC is at leastp. For the problem of approximating a givenn×n matrix with a zero diagonal by a squared-distance matrix, it is shown that the sstress criterion leads to a similar weighted least squares solution withw=(n+2)/4; the main result remains true. Other related problems and algorithmic consequences are briefly discussed.     

中学生社会适应量表的编制

本研究编制的中学生社会适应性量表是针对预初(小学6年级)到高三7个年级的学生,有效样本3595名(男1728名,女1867名)。通过量表的编制、初测、修改和再测,确定了量表的4个维度:内容特质、预测控制、心理调节和动力支持,并获得了较高的信度和效度。内在一致性信度达到0.97,相容效度和效标效度也达到0.65和0.68左右,均比较理想。通过量表编制发现中学生的社会适应在总体上存在性别差异,即女生显著高于男生。中学生的社会适应会随着年级的提高而发生变化,但不同步增加。中学生的社会适应性是一个动态的发展过程,它也是中学生人格发展的一部分,并与个体的学习压力以及经历的生活事件有密切的关系。     

Conditional Covariance Theory and Detect for Polytomous Items

This paper extends the theory of conditional covariances to polytomous items. It has been proven that under some mild conditions, commonly assumed in the analysis of response data, the conditional covariance of two items, dichotomously or polytomously scored, given an appropriately chosen composite is positive if, and only if, the two items measure similar constructs besides the composite. The theory provides a theoretical foundation for dimensionality assessment procedures based on conditional covariances or correlations, such as DETECT and DIMTEST, so that the performance of these procedures is theoretically justified when applied to response data with polytomous items. Various estimators of conditional covariances are constructed, and special attention is paid to the case of complex sampling data, such as those from the National Assessment of Educational Progress (NAEP). As such, the new version of DETECT can be applied to response data sets not only with polytomous items but also with missing values, either by design or at random. DETECT is then applied to analyze the dimensional structure of the 2002 NAEP reading samples of grades 4 and 8. The DETECT results show that the substantive test structure based on the purposes for reading is consistent with the statistical dimensional structure for either grade. This research was supported by the Educational Testing Service and the National Assessment of Educational Progress (Grant R902F980001), US Department of Education. The opinions expressed herein are solely those of the author and do not necessarily represent those of the Educational Testing Service. The author would like to thank Ting Lu, Paul Holland, Shelby Haberman, and Feng Yu for their comments and suggestions. Requests for reprints should be sent to Jinming Zhang, Educational Testing Service, MS 02-T, Rosedale Road, Princeton, NJ 08541, USA. E-mail: jzhang@ets.org