生活事件、负性自动思维及应对方式影响大学生抑郁的路径分析

对321名大学生施测青少年生活事件量表(ASLEC)、自动思维问卷(ATQ)、特质应对方式问卷(TCSQ)及流调中心用抑郁量表(CESD),以探讨生活事件、负性自动思维及应对方式对抑郁的影响模式。结果:(1)相关分析发现:生活事件、负性自动思维、消极应对方式均与抑郁呈显著正相关(p<0.01),生活事件与负性自动思维及消极应对方式呈显著正相关(p<0.01)。(2)路经分析发现:有六条显著路径影响抑郁。应对方式对抑郁产生直接的影响;自动思维可以直接影响抑郁,同时也可通过应对方式间接影响抑郁;生活事件对抑郁的直接影响不显著,其影响是经由负性自动思维及应对方式的中介作用而间接实现的。     

运动觉、布局无关运动与路径整合

路径整合是空间导航的一个重要子领域。本研究采用了一种三角形完成任务的新变式来研究路径整合问题，发现在剥夺被试的视觉输入的前提下，去除被试部分运动觉后，其绩效有不同水平的下降；同时还发现在编码过程中与路径布局无关的不同方向旋转间并无显著性差异。此外，由于本研究是在室外空间中所做的现场研究，故得到的结果优于室内所做的经典研究，进而验证了前人的一个猜测。     

The latent variable approach as applied to transitive reasoning

We discuss the limitations of hypothesis testing using (quasi-) experiments in the study of cognitive development and suggest latent variable modeling as a viable alternative to experimentation. Latent variable models allow testing a theory as a whole, incorporating individual differences with respect to developmental processes or abilities in the model. Experiments, in contrast, aim at testing hypotheses that refer to a specific part of a theory; also they ignore individual differences or model the individual differences using age group as a proxy for developmental stage. Drawing on a sample of 409 5–13-year olds, we demonstrate the advantages of latent variable models in the area of transitive reasoning. A comparison of three models showed that the latent variable model that represented fuzzy trace theory had a better fit than the models representing Piaget's theory or linear ordering theory.     

Motor Learning through Induced Variability at the Task Goal and Execution Redundancy Levels

The authors examined the influence of introducing variability at two different levels in the learning of a striking task. Variability at the task goal level was introduced by changing target location, whereas variability at the execution redundancy level was introduced by using an intermediate target that constrained participants to use different paths from trial to trial to strike the same target. After practice, participants were transferred to 2 test conditions: (a) a fixed-target test, where the position of the target was unchanged; and (b) a variable-target test, where the position of the target was varied from trial to trial. The results from the manipulation at the task goal level were consistent with predictions from the specificity of practice hypothesis. In both the fixed- and variable-target tests, the best performance was achieved by the group that had practiced in the condition matching the test condition. At the execution-redundancy level, practicing multiple solutions to achieve the task goal did not improve performance in either the fixed- or variable-target tests. These results show that introducing variability at the task goal and execution redundancy levels has different effects on learning and generalization and that practice schedules that constrain the participant to use redundant solutions may not facilitate learning.     

Recursive Partitioning with Nonlinear Models of Change

In this article, we introduce nonlinear longitudinal recursive partitioning (nLRP) and the R package longRpart2 to carry out the analysis. This method implements recursive partitioning (also known as decision trees) in order to split data based on individual- (i.e., cluster) level covariates with the goal of predicting differences in nonlinear longitudinal trajectories. At each node, a user-specified linear or nonlinear mixed-effects model is estimated. This method is an extension of Abdolell et al.'s (2002) longitudinal recursive partitioning while permitting a nonlinear mixed-effects model in addition to a linear mixed-effects model in each node. We give an overview of recursive partitioning, nonlinear mixed-effects models for longitudinal data, describe nLRP, and illustrate its use with empirical data from the Early Childhood Longitudinal Study—Kindergarten Cohort.     

A comparative evaluation of factor- and component-based structural equation modelling approaches under (in)correct construct representations

Structural equation modelling (SEM) has evolved into two domains, factor-based and component-based, dependent on whether constructs are statistically represented as common factors or components. The two SEM domains are conceptually distinct, each assuming their own population models with either of the statistical construct proxies, and statistical SEM approaches should be used for estimating models whose construct representations correspond to what they assume. However, SEM approaches have often been evaluated and compared only under population factor models, providing misleading conclusions about their relative performance. This is partly because population component models and their relationships have not been clearly formulated. Also, it is of fundamental importance to examine how robust SEM approaches can be to potential misrepresentation of constructs because researchers may often lack clear theories to determine whether a factor or component is more representative of a given construct. Addressing these issues, this study begins by clarifying several population component models and their relationships and then provides a comprehensive evaluation of four SEM approaches – the maximum likelihood approach and factor score regression for factor-based SEM as well as generalized structured component analysis (GSCA) and partial least squares path modelling (PLSPM) for component-based SEM – under various experimental conditions. We confirm that the factor-based SEM approaches should be preferred for estimating factor models, whereas the component-based SEM approaches should be chosen for component models. Importantly, the component-based approaches are generally more robust to construct misrepresentation than the factor-based ones. Of the component-based approaches, GSCA should be chosen over PLSPM, regardless of whether or not constructs are misrepresented.     

结构方程模型的应用及分析策略

差不多所有心理、教育、社会等概念,均难以直接准确测量,结构方程（SEM,Struc－tulalEquationModelling）提供一个处理测量误差的方法,采用多个指标去反映潜在变量,也令估计整个模型因子间关系,较传统回归方法更为准确合理。本文主要用一系列有关学习动机的虚拟例子,指出每个问题的主要分析策略,以展示SEM在教育及心理学可以应用的研究范畴。文内探讨的方法包括：验证性因素、高阶因子、路径及因果分析、多时段（multiwave）设计、单形模型（SimpleModel）、及多组比较等。     

叙事医学在遗传代谢病门诊的实践路径

通过阐释遗传代谢病门诊医患沟通中患儿家长在生理、心理和社会层面的不同需求,将叙事医学融入日常的诊疗活动中,提出叙事医学在遗传代谢病门诊的实践路径：医生通过倾听实现关注,再现患儿母亲的疾病体验,反思自我医疗行为,共享诊疗信息,建立互信关系,让医学人文渗透到遗传代谢病的临床决策中,让医患之间达成一种平等的伙伴关系,最终提升临床决策质量。另外,还需要进一步通过细读提高叙事意识,建立符合科室实际的患儿家长叙事服务中心等做法,在实践中强化叙事技能,提升医生的叙事医学素养。

     

新医科背景下医学院系治理边界嬗变研究

医学院系是医学教育的关键实体组织和实践主体,各项教育改革任务的推进都离不开院系的底部支撑。以新医科统领的医学教育变革对医学院系治理提出具有针对性的内在要求,医学院系治理边界呈现出整体性、系统性、开放性等价值旨向。面对当前治理实践中客观存在的组织边界路径依赖,医学院系应守正创新、主动赋能,通过多元参与、问题导向的垂直治理,多维激活、包容自反的水平治理,开放议程、价值共享的外部治理,融合创新、精准安全的数字治理,进一步优化组织建构,促进现代化治理能力提升,以组织良治的行动路向助力新医科建设。

     

Maximum Likelihood Methods in Treating Outliers and Symmetrically Heavy-Tailed Distributions for Nonlinear Structural Equation Models with Missing Data

By means of more than a dozen user friendly packages, structural equation models (SEMs) are widely used in behavioral, education, social, and psychological research. As the underlying theory and methods in these packages are vulnerable to outliers and distributions with longer-than-normal tails, a fundamental problem in the field is the development of robust methods to reduce the influence of outliers and the distributional deviation in the analysis. In this paper we develop a maximum likelihood (ML) approach that is robust to outliers and symmetrically heavy-tailed distributions for analyzing nonlinear SEMs with ignorable missing data. The analytic strategy is to incorporate a general class of distributions into the latent variables and the error measurements in the measurement and structural equations. A Monte Carlo EM (MCEM) algorithm is constructed to obtain the ML estimates, and a path sampling procedure is implemented to compute the observed-data log-likelihood and then the Bayesian information criterion for model comparison. The proposed methodologies are illustrated with simulation studies and an example. The research described herein was fully supported by a grant (CUHK 4243/03H) from the Rearch Grants Council of the Hong Kong Special Administration Region. The authors are thankful to the Editor, the Associate Editor, and anonymous reviewers for valuable comments which improve the paper significantly, and are grateful to ICPSR and the relevant funding agency for allowing the use of their data. Requests for reprints should be sent to S. Y. Lee, Department of Statistics, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong.