Hierarchical Model of Generalized and Specific Vulnerabilities in Anxiety

This study evaluated a theoretical hierarchical relationship among the general anxiety vulnerability variable of neuroticism, the specific vulnerability variables of anxiety sensitivity and intolerance of uncertainty, and variables reflecting specific anxiety foci including panic symptoms, health anxiety, obsessive-compulsive symptoms and generalized anxiety/worry. Questionnaires assessing these variables were administered to a non-clinical sample of 91 first-year psychology students (64.8% women). Path analysis results were highly consistent with the hypothesized hierarchical model. Neuroticism was found to have a significant direct effect on both anxiety sensitivity and intolerance of uncertainty. Both neuroticism and anxiety sensitivity had direct significant effects on panic symptoms, neuroticism and intolerance of uncertainty both made significant direct contributions to the prediction of worry, and neuroticism made a significant direct contribution to the prediction of obsessive-compulsive symptoms. Contrary to the hypothesized model, anxiety sensitivity but not neuroticism uniquely predicted health anxiety. The results of this study provide initial empirical evidence for a hierarchical relationship among general and specific vulnerabilities, and specific anxiety manifestations.     

Estimation of the MIRID: A program and a SAS-based approach

The MIRID CML program is a program for the estimation of the parameter values of two different componential IRT models: the Rasch—MIRID and the OPLM—MIRID (Butter, 1994; Butter, De Boeck, & Verhelst, 1998). To estimate the parameters of both models, the program uses a CML approach. The model parameters can also be estimated with a MML approach that can be implemented in PROC NLMIXED of SAS Version 8. Both the MIRID CML program and the MML SAS approach are explained and compared in a simulation study. The results showed that they did about equally well in estimating the values of the item parameters but that there were some differences in the estimation of the person parameters, as could be expected from the differential assumptions regarding the distribution of the persons. The SAS MML approach is much slower than the MIRID CML program, but it is more flexible.     

Bayesian Hierarchical Multivariate Formulation with Factor Analysis for Nested Ordinal Data

This article devises a Bayesian multivariate formulation for analysis of ordinal data that records teacher classroom performance along multiple dimensions to assess aspects characterizing good instruction. Study designs for scoring teachers seek to measure instructional performance over multiple classroom measurement event sessions at varied occasions using disjoint intervals within each session and employment of multiple ratings on intervals scored by different raters; a design which instantiates a nesting structure with each level contributing a source of variation in recorded scores. We generally possess little a priori knowledge of the existence or form of a sparse generating structure for the multivariate dimensions at any level in the nesting that would permit collapsing over dimensions as is done under univariate modeling. Our approach composes a Bayesian data augmentation scheme that introduces a latent continuous multivariate response linked to the observed ordinal scores with the latent response mean constructed as an additive multivariate decomposition of nested level means that permits the extraction of de-noised continuous teacher-level scores and the associated correlation matrix. A semi-parametric extension facilitates inference for teacher-level dependence among the dimensions of classroom performance under multi-modality induced by sub-groupings of rater perspectives. We next replace an inverse Wishart prior specified for the teacher covariance matrix over dimensions of instruction with a factor analytic structure to allow the simultaneous assessment of an underlying sparse generating structure. Our formulation for Bayesian factor analysis employs parameter expansion with an accompanying post-processing sign re-labeling step of factor loadings that together reduce posterior correlations among sampled parameters to improve parameter mixing in our Markov chain Monte Carlo (MCMC) scheme. We evaluate the performance of our formulation on simulated data and make an application for the assessment of the teacher covariance structure with a dataset derived from a study of middle and high school algebra teachers.     

Analysis of School Context Effects on Differential Item Functioning Using Hierarchical Generalized Linear Models

This article considers and illustrates a strategy to study effects of school context on differential item functioning (DIF) in large-scale assessment. The approach employs a hierarchical generalized linear modeling framework to (a) detect DIF, and (b) identify school-level correlates of the between-group differences in item performance. To illustrate, I investigated (a) whether any of the civic skills items used in the U.S. Civic Education Study of the International Association for the Evaluation of Educational Achievement displayed ethnic–racial DIF, and (b) the extent to which the ethnic–racial DIF was related to teacher-reported opportunity to learn (OTL) major civic-related topics. The results indicated that 3 of the 13 items displayed ethnic–racial DIF. After adjusting for OTL, two of the three flagged items ceased to exhibit DIF. Implications of this approach are discussed.     

案例概念化在临床上的应用

随着心理治疗方法的发展, 案例概念化作为治疗工作的重要组成部分, 日益成为临床关注的热点。案例概念化主要指在一定治疗理论的基础上, 就来访者的问题成因和维持因素提出假设, 为构建治疗方案提供指导蓝图。西方的临床研究表明案例概念化能有效地促进咨询师和来访者对于问题的认识和理解, 提高治疗效果。以认知行为治疗理论为基础, 具体地介绍案例概念化技术在焦虑症和抑郁症的临床应用。     

层次化护理团队在优质护理服务中的应用探讨

将所研究的临床科室的护理人员按照职称进行分层并组成护理团队.1年后比较实施前后护理质量和护理服务质量的相关指标.实施后,护理质量和护理服务质量均有显著提高(P＜0.01).临床科室组建层次化优质护理团队,能为患者提供更为安全、满意的护理服务,推动优质护理服务的开展.     

二分数据的多层线性模型:原理与应用

分类数据的多层线性模型在我国的心理学研究中鲜有使用。本研究旨在将这种模型引入到我国心理学研究之中。论文首先介绍了二分数据的多层线性模型的原理和假设条件、参数估计和假设检验,然后以6187名小学生为被试,采用二分变量的多层线性模型,说明了个体因素和学校因素对儿童攻击行为的影响,并对分析结果进行了解释。     

Realization and the Formulation of Physicalism

Twenty years ago, Richard Boyd suggested that physicalism could be formulated by appeal to a notion of realization, with no appeal to the identity of the non-physical with the physical. In (Melnyk 2003), I developed this suggestion at length, on the basis of one particular account of realization. I now ask what happens if you try to formulate physicalism on the basis of other accounts of realization, accounts due to LePore and Loewer and to Shoemaker. Having explored two new formulations of physicalism, I conclude that my 2003 formulation remains the most promising.     

护士分层级使用和管理模式在优质护理中的应用研究

在优质护理服务中实施连班制的弹性排班实现护士分层级使用和管理模式,以探讨更合理、科学的护理管理模式.根据护士的学历、职称、技能、临床经验;患者的病情、工作强度、技术难度、操作风险对护士进行动态调配.分层次使用和管理,即在病区实行“护士长→护理组长→责任护士→助理护士”4个层级管理结构,分层级不分等级,均衡年轻护士和老护士.同时考虑到护士的业务技术,胜任能力相匹配,实现护士人力资源最优化的配置,满足患者的需求,提供优质护理服务.实施后护理文书书写时间较实施前明显减少,基础护理、健康教育时间较实施前明显增加,护理质量,满意度明显优于实施前(P＜0.05),差异有统计学意义.实施护士分层级使用及管理模式真正体现护士能级对应,使现有的人力资源得到了优化配置,提高满意度,保证护理质量,切实符合临床实际护理工作的需要.护士专业价值得到真正的体现,有利于护理人才的培养与护理梯队建设,护理学科的发展.     

Application of the Generalized Matching Law for Description of Student Behavior in the Classroom

Matching law and theory have applied implications for classroom instruction and student behavior. The Generalized Matching Law (GML) is examined in this exploratory study to assess its utility for quantitatively describing student behavior in the classroom. Data were collected using computer-based observational software. Time-lag sequential analysis was used to identify specific teacher behaviors following specific student behaviors as estimates of reinforcement. Derived rates of student and teacher behaviors were analyzed in a linear regression of the natural logarithmic form of the GML. On average, the GML described over 70% of the variance in two students' behavior in a first-grade regular education classroom. Discussion addresses the potential utility of the GML for observation and intervention in the classroom.     

Suicide Risk Assessment and Risk Formulation Part II: Suicide Risk Formulation and the Determination of Levels of Risk

The suicide risk formulation (SRF) is dependent on the data gathered in the suicide risk assessment. The SRF assigns a level of suicide risk that is intended to inform decisions about triage, treatment, management, and preventive interventions. However, there is little published about how to stratify and formulate suicide risk, what are the criteria for assigning levels of risk, and how triage and treatment decisions are correlated with levels of risk. The salient clinical issues that define an SRF are reviewed and modeling is suggested for an SRF that might guide clinical researchers toward the refinement of an SRF process.     

贝叶斯网在认知诊断属性层级结构确定中的应用

K. K. Tatsuoka和她同事研究的规则空间模型(RSM)是一种在国内外有较大影响的认知诊断模型, 但是Tatsuoka的RSM是由学科专家先从已编制的测验中抽取出属性, 然后给出测验的关联Q阵, 再由该Q阵导出属性间的层级关系。已有研究证明, 这种做法所得到的属性间的层级关系难以保证是正确的, 甚至难以保证属性间的层级关系是唯一确定的。这里利用贝叶斯网进行结构学习, 从被试的属性掌握模式中挖掘出属性间的层级关系, 学习所得到的层级关系可以用来验证由RSM中的方法得到的层级关系。模拟实验和实证研究的结果都显示了该方法所得到的属性层级关系是有参考价值的, 可以为命题或测量专家带来有用的信息。     

我国近十年来心理学研究中HLM方法的应用述评

以2002-2011年中国期刊网收录的50例应用多层线性模型(HLM)的心理学期刊论文为研究对象,从样本描述、模型发展与规范、数据准备、估计方法与假设检验4个角度进行文献计量和内容分析,对我国心理学研究中HLM方法的使用现状进行评估。结果表明,HLM方法主要用于管理、发展和教育心理学,绝大多数应用都是两层模型且层2样本量较大。HLM方法在广泛应用的同时仍存在忽略前提假设检验、分析过程中的重要信息和结果报告不完整等问题,随后提供了4条建议。     

Hierarchical causes in the cosmological argument

I would like to thank Linda Zagzebski for her very helpful comments on an earlier version of this paper.     

Generalized Change and the Meaning of Rationality Postulates

The standard theory of belief revision was developed to describe how a rational agent should change his beliefs in the presence of new information. Many interesting tools were created, but the concept of rationality was usually assumed to be related to classical logics. In this paper, we explore the fact that the logical tools used can be extended to other sorts of logics, as proved in (Hansson and Wassermann, 2002), to describe models that are closer to the rationality of a real agent. This revised version was published online in August 2006 with corrections to the Cover Date.     

谱聚类算法在不同属性层级结构诊断评估中的应用

聚类分析已成功用于认知诊断评估(CDA)中,使用广泛的聚类分析方法为K-means算法,有研究已证明K-means在CDA中具有较好的聚类效果。而谱聚类算法通常比K-means分类效果更佳,本研究将谱聚类算法引进CDA,探讨了属性层级结构、属性个数、样本量和失误率对该方法的影响。研究发现：（1）谱聚类算法要比K-means提供更好的聚类结果,尤其在实验条件较苛刻时,谱聚类算法更加稳健;（2）线型结构聚类效果最好,收敛型和发散型相近,独立型结构表现较差;（3）属性个数和失误率增加后,聚类效果会下降;（4）样本量增加后,聚类效果有所提升,但K-means方法有时会有反向结果出现。     

Hierarchical schemas and goals in the control of sequential behavior

Traditional accounts of sequential behavior assume that schemas and goals play a causal role in the control of behavior. In contrast, M. Botvinick and D. C. Plaut argued that, at least in routine behavior, schemas and goals are epiphenomenal. The authors evaluate the Botvinick and Plaut account by contrasting the simple recurrent network model of Botvinick and Plaut with their own more traditional hierarchically structured interactive activation model (R. P. Cooper & T. Shallice, 2000). The authors present a range of arguments and additional simulations that demonstrate theoretical and empirical difficulties for both Botvinick and Plaut's model and their theoretical position. The authors conclude that explicit hierarchically organized and causally efficacious schema and goal representations are required to provide an adequate account of the flexibility of sequential behavior.