Conditional estimation of exponential random graph models from snowball sampling designs

A complete survey of a network in a large population may be prohibitively difficult and costly. So it is important to estimate models for networks using data from various network sampling designs, such as link-tracing designs. We focus here on snowball sampling designs, designs in which the members of an initial sample of network members are asked to nominate their network partners, their network partners are then traced and asked to nominate their network partners, and so on. We assume an exponential random graph model (ERGM) of a particular parametric form and outline a conditional maximum likelihood estimation procedure for obtaining estimates of ERGM parameters. This procedure is intended to complement the likelihood approach developed by  Handcock and Gile (2010) by providing a practical means of estimation when the size of the complete network is unknown and/or the complete network is very large. We report the outcome of a simulation study with a known model designed to assess the impact of initial sample size, population size, and number of sampling waves on properties of the estimates. We conclude with a discussion of the potential applications and further developments of the approach.     

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.     

Reciprocal Markov Modeling of Feedback Mechanisms Between Emotion and Dietary Choice Using Experience-Sampling Data

With intensively collected longitudinal data, recent advances in the experience-sampling method (ESM) benefit social science empirical research, but also pose important methodological challenges. As traditional statistical models are not generally well equipped to analyze a system of variables that contain feedback loops, this paper proposes the utility of an extended hidden Markov model to model reciprocal the relationship between momentary emotion and eating behavior. This paper revisited an ESM data set (Lu, Huet, &; Dube, 2011 Lu, J., Huet, C., &; Dubé, L. (2011). Emotional reinforcement as a protective factor for healthy eating in home settings. The American Journal of Clinical Nutrition, 94(1), 254–261. http://dx.doi.org/10.3945/ajcn.110.006361[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) that observed 160 participants' food consumption and momentary emotions 6 times per day in 10 days. Focusing on the analyses on feedback loop between mood and meal-healthiness decision, the proposed reciprocal Markov model (RMM) can accommodate both hidden (“general” emotional states: positive vs. negative state) and observed states (meal: healthier, same or less healthy than usual) without presuming independence between observations and smooth trajectories of mood or behavior changes. The results of RMM analyses illustrated the reciprocal chains of meal consumption and mood as well as the effect of contextual factors that moderate the interrelationship between eating and emotion. A simulation experiment that generated data consistent with the empirical study further demonstrated that the procedure is promising in terms of recovering the parameters.     

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.     

基于多层结构方程模型的情境效应分析—— 兼与多层线性模型比较

多层(嵌套)数据的变量关系研究, 必须借助多层模型来实现。两层模型中, 层一自变量Xij按组均值中心化, 并将组均值 置于层2截距方程式中, 可将Xij对因变量Yij的效应分解为组间和组内部分, 二者之差被称为情境效应, 称为情境变量。多层结构方程模型(MSEM)将多层线性模型(MLM)和结构方程模型(SEM)相结合, 通过设置潜变量和多指标的方法校正了MLM在情境效应分析中出现的抽样误差和测量误差, 同时解决了数据的多层(嵌套)结构和潜变量的估计问题。除了分析原理的说明, 还以班级平均竞争氛围对学生竞争表现的情境效应为例进行分析方法的示范, 并比较MSEM和MLM的异同, 随后展望了MSEM情境效应模型、情境效应无偏估计方法和情境变量研究的拓展方向。     

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。     

Linking dimensions and dynamics in psychopathology research: An example using DSM-5 instruments

The DSM-5 includes two measures to assess dimensions of personality pathology (Personality Inventory for DSM-5; PID-5) and cross-cutting symptoms of psychopathology (DSM-5 Cross-cutting Symptom Measure; CCS). Few studies have evaluated these measures in an ecologically valid context. Participants (N = 248, student sample) completed self-report versions of the DSM-5 measures, and then completed a two-week daily diary of internalizing and externalizing outcomes. The CCS scales evidenced convergent and discriminant validity with these outcomes, PID-5 Negative Affectivity and Detachment were related to internalizing outcomes, and PID-5 Antagonism was related to externalizing outcomes. We also demonstrated the benefit of linking static dimensions to temporally-dynamic processes by examining how PID-5/CCS dimensions moderated the within-person associations among daily cognitive distortions and internalizing and externalizing outcomes.     

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。