决策中损益值大小效应的发生条件及机制再探 —— 一种齐当别视角及兼对视角本身的一些探讨

通过两个研究,损益值大小效应在1元~1亿元的广阔范围内,以及在只采用选择反应模式的条件下得到了进一步探讨,其机制也在齐当别模型的全新视角下得到了讨论;此外,齐当别模型本身以及模型的经典判断任务也得到了探讨。结果发现：基于选择反应模式时,损益值大小效应在获得情景中稳定存在,并只存在一个风险倾向的拐点,而它在损失情景中则不存在;损益值大小效应的机制能够被齐当别模型很好的解释;齐当别模型的经典判断任务对模型预测效能的检验力不足,但其与新设计的维度内判断任务联合后却有很大改善;齐当别模型对损失情景中的均含0结果值的决策任务的解释力还有待进一步探讨     

Impact of School Psychological Environment Variables on Happiness of Korean Youths

Abstract: This study analyzed the structural relationships between school‐related psychological variables (teacher support, learning flow, friendship) and the happiness of Korean youths, and compared structural equation models for middle and high schools. Questionnaire data were collected from students in middle and high schools in 10 cities and provinces of Korea. The final data set from 870 students was analyzed with Amos 20.0. Goodness of fit indices for the hypothetical model were good, and all paths were significant. That is, a model incorporating indirect and direct impact of school‐related psychological environment variables, such as teacher support, learning flow, and friendship, on the happiness of Korean youths is valid. Second, structural equation models for middle and high schools demonstrated significant differences. Therefore, different types of assistance are needed to increase the happiness of students in middle schools and high schools.     

哪个测验Q矩阵更合理:基于DINA模型测验Q矩阵合理性侦查指标及其比较与应用

本研究对多个测验Q矩阵的相对合理性的比较与选用开展研究,采用Monte Carlo模拟与实证研究相结合的范式,探讨R_square、HCI、-2LL、AIC、BIC、residual、ABS_residual及本研究新开发的BIC2等八项指标在测验Q矩阵合理性侦查效果及其比较。研究发现:八项指标中,除BIC和BIC2两项指标的对测验Q矩阵相对合理性的平均正确识别率在95%以上,其余指标的平均正确识别率不足90%,整体而言,考虑样本容量及参数个数双重加权的BIC和BIC2两项指标的表现总体上优于其它几项指标;各项指标在不同Q矩阵错误类型下其正确识别率也不尽相同。     

基于目标导向行为模型的患者基层首诊决策机制研究

基于目标导向行为模型,采用结构方程模型方法探析患者基层首诊决策过程中的影响因素及其内在作用机制,从认知过程、情感过程、习惯过程和动机过程四方面剖析患者基层首诊行为意愿的形成机理。结果表明患者主观规范、行为态度、知觉行为控制以及正向预期情绪均正向显著影响患者基层首诊行为欲望,行为欲望和过去行为频率显著正向影响患者基层首诊行为意愿。建议强化患者基层首诊行为的价值与必要性,进一步改善患者基层就诊体验,增加患者基层首诊行为频率,激发患者基层首诊行为欲望。

     

健康本源论模型介绍及启示

基于文献分析的方法,梳理健康本源论的发展历程,重点介绍健康本源论模型及模型中的主要概念,探讨健康本源论在实践中的应用及其对健康研究的启示。国外应用健康本源论指导医学实践的经验表明,对健康领域的研究重心从疾病预防转移到健康促进,对于提高个体和社会健康水平具有一定的指导意义。未来我国健康相关研究可从健康本源论视角出发,结合国情及文化,对健康本源论进行本土化调整和发展,为健康领域研究与实践提供借鉴和理论依据。

     

潜在增长模型下抑郁症状变化轨迹的研究进展

潜在增长模型已成为国外纵向研究的有力分析工具。通过对国外抑郁症状纵向研究及潜在增长模型应用研究的整理,发现患者抑郁症状的变化轨迹存在个体差异,但其中又存在相似特征,根据这些特征可以将抑郁症状的变化轨迹主要分为四种,早期症状快速减少、治疗期间症状缓慢平稳减少、早期症状减少后期出现反弹和症状未出现明显变化。通过总结该模型在抑郁症状研究领域的应用成果,了解抑郁症患者抑郁症状变化的情况,以期为我国相关研究的进一步开展提供新思路。

     

生命早期环境不可预测性对过度进食的影响:基于生命史理论

在生命史理论的视角下,本研究通过两个研究揭示了生命早期环境不可预测性对过度进食的影响及其作用机制。研究1招募处于生命早期阶段的91名初中生(年龄12～14岁),采用饱食进食(Eating in the absence of hunger,EAH)范式,结果发现生命早期环境不可预测性能够显著正向预测个体饱食状态下的高热量食物选择(即过度进食);研究2招募新冠病毒疾病(COVID-19)爆发背景下301名武汉市居民(高死亡威胁组)和179名其他省市居民(控制组)(年龄18～60岁)为被试,通过问卷法回溯性地测量生命早期环境不可预测性并探究其影响当前过度进食的机制,结果发现生命早期环境不可预测性通过生命史策略的中介作用间接影响过度进食。同时,死亡威胁(新冠病毒疫情)扩大了环境不可预测性通过生命史策略间接影响过度进食的效应,而社会支持则能缓冲这一效应。研究结果为COVID-19背景下和灾后居民的健康进食干预提供了依据。     

情节模拟对预期伴侣反应性及依恋安全感的影响

当依恋对象不在身边时,个体可以通过内部表征的方式通达依恋对象的可得与反应性,获得依恋安全感。然而,现有研究对内部表征通达途径的考察忽略了情节模拟的作用。该研究采用实验组控制组前后测设计,考察是否可以通过依恋相关的情节模拟通达依恋对象的可得与反应性,帮助个体获得依恋安全感。研究招募正处于恋爱关系中且恋爱时长超过6个月的大学生46名。前测评定依恋相关的困境情境;3天后实验组被试进行依恋相关的情节模拟任务,控制组被试进行情境结果的撰写任务,并再次对情境进行评定。结果发现,相比于控制组,情节模拟组预期伴侣的反应性和依恋安全感有更大提升。以上的研究结果表明,依恋相关的情节模拟可以通达伴侣可得与反应性并帮助个体获得依恋安全感。     

A Comparison of Inverse-Wishart Prior Specifications for Covariance Matrices in Multilevel Autoregressive Models

Multilevel autoregressive models are especially suited for modeling between-person differences in within-person processes. Fitting these models with Bayesian techniques requires the specification of prior distributions for all parameters. Often it is desirable to specify prior distributions that have negligible effects on the resulting parameter estimates. However, the conjugate prior distribution for covariance matrices—the Inverse-Wishart distribution—tends to be informative when variances are close to zero. This is problematic for multilevel autoregressive models, because autoregressive parameters are usually small for each individual, so that the variance of these parameters will be small. We performed a simulation study to compare the performance of three Inverse-Wishart prior specifications suggested in the literature, when one or more variances for the random effects in the multilevel autoregressive model are small. Our results show that the prior specification that uses plug-in ML estimates of the variances performs best. We advise to always include a sensitivity analysis for the prior specification for covariance matrices of random parameters, especially in autoregressive models, and to include a data-based prior specification in this analysis. We illustrate such an analysis by means of an empirical application on repeated measures data on worrying and positive affect.     

Exploring Factor Model Parameters across Continuous Variables with Local Structural Equation Models

Using an empirical data set, we investigated variation in factor model parameters across a continuous moderator variable and demonstrated three modeling approaches: multiple-group mean and covariance structure (MGMCS) analyses, local structural equation modeling (LSEM), and moderated factor analysis (MFA). We focused on how to study variation in factor model parameters as a function of continuous variables such as age, socioeconomic status, ability levels, acculturation, and so forth. Specifically, we formalized the LSEM approach in detail as compared with previous work and investigated its statistical properties with an analytical derivation and a simulation study. We also provide code for the easy implementation of LSEM. The illustration of methods was based on cross-sectional cognitive ability data from individuals ranging in age from 4 to 23 years. Variations in factor loadings across age were examined with regard to the age differentiation hypothesis. LSEM and MFA converged with respect to the conclusions. When there was a broad age range within groups and varying relations between the indicator variables and the common factor across age, MGMCS produced distorted parameter estimates. We discuss the pros of LSEM compared with MFA and recommend using the two tools as complementary approaches for investigating moderation in factor model parameters.