Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures

Eight different variable selection techniques for model-based and non-model-based clustering are evaluated across a wide range of cluster structures. It is shown that several methods have difficulties when non-informative variables (i.e., random noise) are included in the model. Furthermore, the distribution of the random noise greatly impacts the performance of nearly all of the variable selection procedures. Overall, a variable selection technique based on a variance-to-range weighting procedure coupled with the largest decreases in within-cluster sums of squares error performed the best. On the other hand, variable selection methods used in conjunction with finite mixture models performed the worst.     

石河子市剖宫产患者产后抑郁状况调查及影响因素分析

为了了解剖宫产患者产后抑郁的发生情况,对剖宫产患者进行抑郁状况的调查,并对其影响因素进行分析,对抑郁组与非抑郁组的一般情况构成比比较,并采用抑郁自评量表（SDS）、应付方式评定量表（YFFS）、社会支持问卷（ISSI）进行测评。结果显示,应付方式评定量表中的主观支持（P〈0．05）,社会支持问卷中退避倾向性进入回归方程,提示应付方式及社会支持是影响剖宫产患者产后抑郁的重要因素。     

医疗纠纷泛化原因多视角分析

随着社会的发展,医疗纠纷日趋增多,成为各卫生部门的最大难题,是继医疗改革之后的又一备受社会各界关注的热门话题,医患关系成为社会最不和谐的声音。医患关系是在社会政治、经济、文化、法律等因素共同作用,围绕“治病”所形成的道德、诚信、契约、经济利益关系,通过分析现代医患关系的变化,探讨医疗纠纷增多的原因。     

福柯与《临床医学的诞生》

《临床医学的诞生》是20世纪法国著名学者福柯的一部医学史研究专著,主要从空间、语言和死亡三个方面探讨了现代医学也就是临床医学诞生的可能性条件,兼具历史与批判的性质。它揭示了隐藏在医学话语背后的权力关系,反映了福柯瓦解传统、颠覆理性的新型历史观。     

初中生自我调节学习的类型及对学习的影响

自我调节学习是一种学习者主动激励自己并积极使用适当的学习策略的学习,研究利用潜在剖面分析法和辨别分析法对316名初中生自我调节学习的类型进行考察,探讨不同自我调节学习类型初中生的特点,分析自我调节学习类型对学习动机、焦虑、学习成绩的影响。结果表明:(1)初中生自我调节学习分成综合发展型、均衡调节型和策略滞后型3种。(2)综合发展型初中生的自我调节学习水平最高,策略滞后型初中生的自我调节学习水平尤其是学习策略水平最低,而均衡调节型初中生的自我调节学习水平处于综合发展型和策略滞后型之间。(3)自我调节学习类型对学习动机、焦虑、学习成绩有影响。     

多项式回归与一致性研究：应用及分析

本文介绍了多项式回归的分析方法, 讨论并验证了它相比传统的差异分数分析法在检验一致性假设中的优势。针对目前该方法在国内应用方面的缺失状况, 本文从分析基准、理论推导、分析步骤和假设检验等方面完善了多项式回归与响应面分析相结合的应用分析框架。最后, 本文以个人-组织价值观一致性对员工情感性变革承诺的影响研究为例, 对分析框架的具体应用进行了说明。结果表明, 被传统差异分数很好支持的一致性效应并不一定完美, 新的分析框架能够揭示更多的有用信息并给管理实践带来更具体而准确的引导。     

接纳与承诺疗法的理论背景、实证研究与未来发展

接纳与承诺疗法(Acceptance and Commitment Therapy, ACT)于上世纪90年代初由美国治疗师Steven C. Hayes提出。作为基于正念技术的第三代行为治疗理论之一, 该疗法以功能情境主义为哲学基础, 立足于人类认知和语言基本性质的实证研究, 旨在通过平衡接纳与改变来提高心理灵活性。临床研究已证明其在相当广泛的临床问题上都取得了良好效果, 并在与CBT等传统疗法的对比中展现优势。ACT的临床效果和技术细节可做进一步研究, 该疗法与CBT的融合及其在临床领域之外的应用也是未来的研究方向。     

IMAGINATION'S GRIP ON SCIENCE

Abstract: In part because “imagination” is a slippery notion, its exact role in the production of scientific knowledge remains unclear. There is, however, one often explicit and deliberate use of imagination by scientists that can be (and has been) studied intensively by epistemologists and historians of science: thought experiments. The main goal of this article is to document the varieties of thought experimentation, not so much in terms of the different sciences in which they occur but rather in terms of the different functions they fulfil. I argue that thought experimentation (and hence imagination) plays a role not only in theory choice but in singular causal analysis and scientific discovery as well. I pinpoint, moreover, some of the rules governing the use of thought experiments in theory choice and in singular causal analysis, that is, some of the criteria they should meet in order to fulfil those functions successfully.     

Limited information estimation and testing of discretized multivariate normal structural models

Discretized multivariate normal structural models are often estimated using multistage estimation procedures. The asymptotic properties of parameter estimates, standard errors, and tests of structural restrictions on thresholds and polychoric correlations are well known. It was not clear how to assess the overall discrepancy between the contingency table and the model for these estimators. It is shown that the overall discrepancy can be decomposed into a distributional discrepancy and a structural discrepancy. A test of the overall model specification is proposed, as well as a test of the distributional specification (i.e., discretized multivariate normality). Also, the small sample performance of overall, distributional, and structural tests, as well as of parameter estimates and standard errors is investigated under conditions of correct model specification and also under mild structural and/or distributional misspecification. It is found that relatively small samples are needed for parameter estimates, standard errors, and structural tests. Larger samples are needed for the distributional and overall tests. Furthermore, parameter estimates, standard errors, and structural tests are surprisingly robust to distributional misspecification. This research was supported by the Department of Universities, Research and Information Society (DURSI) of the Catalan Government, and by grants BSO2000-0661 and BSO2003-08507 of the Spanish Ministry of Science and Technology.