Buying an electric car: A rational choice or a norm-directed behavior?

The diffusion of electric cars can contribute to more sustainability in the transport sector, but diffusion rates in most countries are still low. We investigated motives for electric car adoption in German households from an environmental psychology perspective. The public debate focuses on rational aspects such as the purchase price or new technological demands (e.g., limited range and a new charging system). Psychological research on energy-related investment decisions in households confirms the relevance of rational motives, but additionally points to the importance of norm-directed motives (moral and social norms). We investigated the relevance of different motives in an online questionnaire with n = 220 members of German households interested in buying a new car. The questionnaire included possible rational and norm-related predictors of electric car adoption. We tested three action models to explain adoption intention: An adjusted technology acceptance model (TAM), an adjusted norm activation model (NAM), and an integrative model with predictors from both models. We analyzed the hypothesized models with path analyses. All models explained a substantial share of variance in adoption intention. The explained share of variance in the NAM was higher than in the TAM and comparably high to the integrative model. The results demonstrate the important role of moral and social motives for households’ investment decisions. Additionally, the technology’s perceived usefulness was an important rational motive. We discuss the context dependency of the results, as household members might have little knowledge about the new technology during the early stages of a technology’s diffusion process. The results strongly suggest broadening political support schemes, such as informational and image campaigns, as a way to more effectively foster electric car diffusion. More comprehensive assessments appear to be necessary in future analyses of electric car adoption as well as energy-related investment decisions.     

Life skills development in physical education: A self-determination theory-based investigation across the school term

ObjectivesGrounded in self-determination theory (SDT), the main aim of this study was to examine the longitudinal associations between teacher autonomy support, students’ basic psychological need satisfaction and life skills development in physical education (PE).DesignThis study employed a two-wave longitudinal research design.MethodStudents (N = 266, Mage = 12.94 years, SD = 0.70) completed measures assessing perceived autonomy-supportive teaching, need satisfaction (autonomy, competence, and relatedness), and life skills development in PE (teamwork, goal setting, social skills, problem solving and decision making, emotional skills, leadership, time management, and interpersonal communication). Data collections took place during week 6 (timepoint 1; T1) and week 15 (timepoint 2; T2) of the autumn school term.ResultsCross-lagged panel analyses showed that T1 teacher autonomy support did not significantly predict students’ three basic psychological needs, total need satisfaction or life skills development at T2. Students’ T1 total need satisfaction positively predicted their development of all eight life skills at T2. Additionally, students’ T1 autonomy satisfaction positively predicted their teamwork, social skills, emotional skills, leadership, and interpersonal communication skills at T2, T1 competence satisfaction positively predicted students’ teamwork skills at T2, and students’ T1 relatedness satisfaction positively predicted their social skills at T2.ConclusionsProviding partial support for SDT, the findings highlighted that satisfaction of students’ three basic psychological needs had some positive effects on students’ life skills development in PE. As such, a climate that satisfies students’ basic psychological needs should help to develop their life skills in PE.     

Idiographic network analysis of discrete mood states prior to treatment

Idiographic network models based on time‐series data have received recent attention for their ability to model relationships among symptoms and behaviours as they unfold in time within a single individual (cf. Epskamp, Borsboom, & Fried, 2018; Fisher, Medaglia, & Jeronimus, 2018). Rather than examine the correlational relationships between variables in a sample of individuals, an idiographic network examines correlations within a single person, averaged over many time points. Because the approach averages over time, the data must be stationary (i.e. relatively consistent over time). If individuals experience varying states over time—different mixtures of symptoms and behaviours in one moment or another—then averaging over categorically different moments may undermine model accuracy. Fisher and Bosley (2019) address these concerns via the application of Gaussian finite mixture modelling to identify latent classes of time points in intraindividual time‐series data from a sample of adults with major depressive disorder and/or generalised anxiety disorder (n = 45). The present paper outlines an extension of this work, wherein network analysis is used to model within‐class covariation of symptoms. To illustrate this approach, network models were constructed for each intraindividual class identified by Fisher and Bosley (137 networks across the 45 participants, mean classes/person = ~3, range = 2–4 classes/person). We examine the relative consistency in symptom organisation between each individual's multiple mood state networks and assess emergent group‐level patterns. We highlight opportunities for enhanced treatment personalisation and review nomothetic patterns relevant to transdiagnostic conceptualisations of psychopathology. We address opportunities for integrating this approach into clinical practice and outline potential shortcomings.     

变量间的网络分析模型及其应用

变量间的网络分析模型近年来被广泛应用于心理学研究。不同于将潜变量作为观测变量的共同先导因素的潜变量模型, 网络分析模型将观测变量作为初级指标, 采用图论的方法建立观测变量之间的关系网络, 其中变量为网络的节点, 而变量间的关系是节点之间的连线。因此网络分析可以突显观测变量之间的联系以及观测变量相互影响而形成的系统。通过变量网络中基于各个节点特征的指标(如中心性)以及基于整体结构特征的指标(如小世界性), 网络分析为研究各种心理现象提供了新的可视化的描述方式和理解视角。近10年来, 网络分析的方法已在人格心理学、社会心理学和临床心理学等领域得到一定的应用。未来研究应继续发展和完善网络分析模型的理论和方法, 使之运用到更多的数据类型和更广的研究领域中。     

辱虐管理的应对：基于人际拒绝的多动力模型分析

随着辱虐管理产生破坏性影响的作用机制不断得到丰富和完善, 受辱虐下属选择特定应对方式的原因以及有效性成为未来辱虐管理的研究方向。基于人际拒绝的多动力模型, 梳理了在不同质量LMX的关系情境中受辱虐下属的应对方式, 并提出特定应对方式可能引起辱虐管理的恶性循环的假设模型。建议未来研究通过丰富下属的应对行为变量、增加人格和环境的调节机制以完善受辱虐下属的应对模型, 并以此为基础探究辱虐管理的消极互动机制, 为打破辱虐管理恶性循环提供理论支持。     

面孔识别的自我群体偏向

面孔认知研究表明, 人们识别和再认自己所属群体(如种族、性别、年龄)面孔的成绩显著好于识别其他群体面孔的成绩。近年来围绕面孔识别的这种自我群体偏向进行了实验研究, 研究者提出了知觉经验说和社会认知论两种理论来解释其认知加工机制, 而类别化-个性化模型和双路径模型则是将两种理论分别加以整合提出的新解释。研究者还对面孔识别自我群体偏向的神经机制进行了研究, 探讨了评价者、评价对象和评价任务等因素对自我群体偏向的影响。提升面孔识别自我群体偏向研究的生态效度, 构建整合性理论模型以及加强跨文化和本土研究是未来研究的重要方向。     

时变效应模型及在密集追踪数据分析中的应用

密集追踪数据通常蕴含了心理过程的详细变化信息,反映了某些心理的复杂变化过程。时变效应模型用函数替代恒定的系数,可描述密集追踪数据中随时间推移心理的动态变化过程和时变效应,是分析复杂心理过程的有效方法。在介绍时变效应模型的原理后,通过模拟研究考察模型的表现,结果显示:(1)样本量增加可降低函数估计的误差;(2)惩罚样条法的节点数选择与函数的复杂度有关,函数越复杂,所需节点越多;(3)样本量与节点数对函数估计误差的交互效应不显著。进一步应探讨测量次数、数据分布形态、数据缺失等如何影响模型的表现。     

中学教师工作狂类型与工作绩效的关系研究：基于潜在剖面分析

以229名中学教师对研究对象,采用潜在剖面分析方法探索其工作狂类型,并进一步揭示这些类型与工作绩效间的关系。结果表明:（1）教师按工作狂得分可分为三类：重度工作狂、中等偏高工作狂和中等偏低工作狂;（2）不同类别工作狂会对工作绩效产生不同程度的影响。     

父母消极教养方式对中国性少数心理健康的影响：基于Bifactor模型

中国性少数群体普遍存在严重的心理健康问题,家庭是其最大的压力来源。在性少数压力模型的背景下,本研究基于Bifactor模型探究父母消极教养方式对中国性少数（LGB）心理健康的特殊影响。采用父母教养方式问卷、抑郁-焦虑-压力量表和金赛量表对649名LGB进行网络问卷调查,结果发现：（1）父母消极教养方式的Bifactor模型存在部分差异;（2）对于LGB心理健康,父母消极教养方式的一般因子和拒绝特殊因子具有损害作用,过度保护特殊因子具有保护作用。     

权力感对道德困境判断的影响：基于CNI模型的分析

采用道德困境判断的CNI（consequence, norm, inaction）模型,通过两个实验考察权力感对道德困境判断的影响。实验1探讨个人权力感对道德困境判断的影响,结果显示高个人权力感显著增加被试对规则的敏感,促使个体做出道义论的道德判断。实验2考察回忆任务启动的权力感对道德困境判断的影响,发现相对于低权力感启动的被试,高权力感启动的被试在进行判断时更关注行为的结果,对规则更不敏感,这说明启动的权力感促进功利主义的道德判断,抑制道义论的道德判断。结果表明,不同类型的权力感对道德困境判断的影响存在差异。