Collective Identities of Class and Gender: Working-Class Women in the Pittston Coal Strike

This article investigates the intersections and tensions between two collective identities, those of class and gender, for working-class women involved in supporting the 1989–1990 strike against Pittston Coal Group in southwestern Virginia. In the case of this year-long (and ultimately successful) strike, women were organized by United Mine Workers of America (UMWA) staff in strike support activities, but they also sought to organize themselves as women. The tensions between their identity as members of the working class and their identity as women are revealed by examining their forms of activism, their relationship with the UMWA, the divisions between groups of activist women, and the articulation of women's involvement in the strike. The experiences of these women are briefly compared with women's activism in the 1984–1985 British Coal strike. The article concludes by arguing that collective identity is best understood as it emerges in response to specific contexts.     

A multistage ranking model

In this paper, we propose a (n–1)² parameter, multistage ranking model, which represents a generalization of Luce's model. We propose then×n item-rank relative frequency matrix (p-matrix) as a device for summarizing a set of rankings. As an alternative to the traditional maximum likelihood estimation, for the proposed model we suggest a method which estimates the parameters from thep-matrix. An illustrative numerical example is given. The proposed model and its differences from Luce's model are briefly discussed. We also show some specialp-matrix patterns possessed by the Thurstonian models and distance-based models.     

三重自我建构的神经机制

三重自我建构理论（the tripartite model of self-construal）认为,自我包含三个方面：个体自我,关系自我和集体自我;三种自我同时存在。研究发现：相比其他信息,三种自我均诱发了更强的N2（熟悉度指标）、P300（高级认知加工指标）成分以及与自我加工有关的MPFC脑区;个体自我存在右脑优势,而集体自我则存在左脑优势。但其具体表征脑区,其在中国文化背景下的文化特异性,以及不同关系类型、群体范畴是否能进一步划分关系和集体自我等问题均有待探讨。     

不同类型集群行为的差异比较

集群行为是个体以群体成员身份参与的以改善群体地位为目标的行动。近年来, 西方心理学在集群行为领域的研究开始呈现差异化分析比较的研究趋势。根据行为模式是否符合社会规范, 可将集群行为分为常规集群行为与违规集群行为。根据参与群体的社会地位, 可将集群行为分为弱势群体集群行为与优势群体集群行为。根据集群行为的成就, 可将结果分为“成功”与“失败”两类。不同类型集群行为所涉及的心理机制、前因变量及发展趋势具有很大差异。今后的研究应探明不同类型集群行为间的内在联系, 重视文化背景因素并加强与其他学科研究成果的结合。     

Optimal scaling of paired comparison and rank order data: An alternative to guttman's formulation

A formulation, which is different from Guttman's is presented. The two formulations are both called the optimal scaling approach, and are proven to provide identical scale values. The proposed formulation has at least two advantages over Guttman's. Namely, (i) the former serves to clarify close relations of the optimal scaling approach to those of Slater and the vector model of preferential choice, and (ii) in addition to the stimulus scale values, it provides scores for the subjects, which indicate the degrees of response consistency (transitivity), relative to the optimum solution. The method is assumption-free and capable of multidimensional analysis.This study was partly supported by the National Research Council Grant (No. A4581) to S. Nishisato. The author is indebted to Dr. Bert F. Green, Jr., Mr. Tomoichi Ishizuka, and anonymous reviewers for their valuable comments on an earlier draft.     

A lower bound to the probability of choosing the optimal passing score for a mastery test when there is an external criterion

Recently there has been interest in the problem of determining an optimal passing score for a mastery test when the purpose of the test is to predict success or failure on an external criterion. For the case of constant losses for the two error types, a method of determining an optimal passing score is readily derived using standard techniques. The purpose of this note is to describe a lower bound to the probability of identifying an optimal passing score based on a random sample ofN examinees.The work upon which this publication is based was performed pursuant to a grant [contract] with the National Institute of Education, Department of Health, Education and Welfare. Points of view or opinions stated do not necessarily represent official NIE position or policy.     

Paired comparison,triple comparison,and ranking experiments as generalized linear models,and their implementation on GLIM

A wide variety of paired comparison, triple comparison, and ranking experiments may be viewed as generalized linear models. These include paired comparison models based on both the Bradley-Terry and Thurstone-Mosteller approaches, as well as extensions of these models that allow for ties, order of presentation effects, and the presence of covariates. Moreover, the triple comparison model of Pendergrass and Bradley, as well as models for complete rankings of more than three items, can also be represented as generalized linear models. All such models can be easily fit by maximum likelihood, using the widely available GLIM computer package. Additionally, GLIM enables the computation of likelihood ratio statistics for testing many hypotheses of interest. Examples are presented that cover a variety of cases, along with their implementation on GLIM.     

Bayesian analysis of order-statistics models for ranking data

In this paper, a class of probability models for ranking data, the order-statistics models, is investigated. We extend the usual normal order-statistics model into one where the underlying random variables follow a multivariate normal distribution. Bayesian approach and the Gibbs sampling technique are used for parameter estimation. In addition, methods to assess the adequacy of model fit are introduced. Robustness of the model is studied by considering a multivariate-t distribution. The proposed method is applied to analyze the presidential election data of the American Psychological Association (APA).The author is grateful to K. Lam, K.F. Lam, the Editor, an associate editor, and three reviewers for their valuable comments and suggestions. This research was substantially supported by the CRCG grant 335/017/0015 of the University of Hong Kong and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU 7169/98H). Upon completion of this paper, I became aware that similar work had been done independently by K.G. Yao and U. Böckenholt (1999).     

Maximum likelihood additivity analysis

A maximum likelihood estimation procedure was developed to fit unweighted and weighted additive models to conjoint data obtained by the categorical rating, the pair comparison or the directional ranking method. The scoring algorithm used to fit the models was found to be both reliable and efficient, and the program MAXADD is capable of handling up to 300 parameters to be estimated. Practical uses of the procedure are reported to demonstrate various advantages of the procedure as a statistical method.The research reported here was supported by Grant A6394 to the author from the Natural Sciences and Engineering Research Council of Canada. Portions of this research were presented at the Psychometric Society meeting in Iowa City, Iowa, in May, 1980.Thanks are due to Jim Ramsay, Justine Sergent and anonymous reviewers for their helpful comments.Two MAXADD programs which perform the computations discussed in this paper may be obtained from the author.