Subjects: Psychology >> Developmental Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Network analysis models (or Network Psychometrics) have been widely used in psychology research in recent years. Unlike latent variable models which conceive observable variables as outcomes of unobservable latent factors, network analysis models apply the graph theory to construct a network to depict the associations among observable variables. The observable variables are treated as nodes and the associations between them are treated as edges. As such, network analysis models reveal the relationships among observable variables and the dynamic system resulted from the interactions between these observable variables. With indices reflecting individual nodes’ characteristics (such as centrality) and network structural characteristics (such as small-worldness), network analysis models provide a new perspective for visualization and for studying various psychological phenomena. In the past decade, network analysis models have been applied in the fields of personality, social, and clinical psychology as well as psychiatry. Future research should continue to develop and improve the methods of network analysis models, making them applicable to more types of data and broader research fields.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Substance addiction involves multiple factors, ranging from biological, social, to cultural. But the dominant biological reductionism-based explanations focus primarily on the brain, potentially hindering a more comprehensive and inclusive research of substance addiction and its recovery. We propose that network theory, focusing on feedback loops formed by interactions between myriad psychological disorder variables, will provide a better holistic framework to understand the complexity of substance addiction. Applying network theory to substance addiction may provide new insights in (1) understanding the interrelationships and interactions between symptoms, (2) understanding the systematic integrity and dynamic changes in symptom networks, and (3) integrating multiple levels of factors into a unified theoretical framework. Also, network theory may generate new approaches for future interventions and treatments. In sum, networktheory, as a theoretical model, provide a new perspective for understanding substance addiction and its intervention. We believe this reframing will encourage more empirical research toward various other hypotheses within this framework, thus, promoting the treatment and recovery of substance addiction.
Subjects: Psychology >> Clinical and Counseling Psychology Subjects: Psychology >> Psychological Measurement submitted time 2022-07-28
Abstract：
Substance addiction involves multiple factors, ranging from biological, social, to cultural. But the dominant biological reductionism-based explanations focus primarily on the brain, potentially hindering a more comprehensive and inclusive research of substance addiction and its recovery. We propose that network theory, focusing on feedback loops formed by interactions between myriad psychological disorder variables, will provide a better holistic framework to understand the complexity of substance addiction. Applying network theory to substance addiction may provide new insights in (1) understanding the interrelationships and interactions between symptoms, (2) understanding the systematic integrity and dynamic changes in symptom networks, and (3) integrating multiple levels of factors into a unified theoretical framework. Also, network theory may generate new approaches for future interventions and treatments. In sum, networktheory, as a theoretical model, provide a new perspective for understanding substance addiction and its intervention. We believe this reframing will encourage more empirical research toward various other hypotheses within this framework, thus, promoting the treatment and recovery of substance addiction.
Subjects: Psychology >> Psychological Measurement submitted time 2019-08-13 Cooperative journals: 《心理科学进展》
Abstract： 变量间的网络分析模型近年来被广泛应用于心理学研究。本文目的在于介绍网 络分析的基本原理与常用指标，并进一步介绍此方法在多个领域中的实证研究，旨在推 进研究者对网络分析模型的理解与应用。不同于潜变量模型将潜变量作为观测变量的共 同先导因素， 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，故使观测变量之间的联系不再受到潜变量模型的局限。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描述方式和理解视角。 本文详细介绍了此方 法目前在人格心理学、社会心理学和临床心理学等领域的应用， 进一步讨论了在未来研 究者可以发展和完善网络分析模型的方向，以使之运用到更多的数据类型和更多的研究 领域。
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