Subjects: Psychology >> Social Psychology submitted time 2024-02-18
Abstract： Previous studies have discovered that the concept of prosocial behavior among adolescents is composed of four dimensions: commonweal-social rule, altruism, relationship, and personal trait. Utilizing this four-dimensional structure, the Prosocial Behavior Scale for Adolescents (PBSA) measurements revealed gender and grade-level differences in the importance attributed to each dimension. Furthermore, numerous prior studies on the development of adolescent prosocial behavior have yielded inconsistent results. In this study, we employed a network analysis approach to explore the network of adolescent prosocial behavior, uncovering the relationships among different dimensions and individual questionnaire items, revealing its core dimensions, and shedding light on differences across gender and grade.
Conducted in 2017, this study included 9,160 students from 15 schools spanning eight provinces or municipalities, namely Beijing, Fujian, Henan, Jiangsu, Shandong, Shaanxi, Sichuan, and Chongqing, with ages ranging from 10 to 17 and covering elementary, middle, and high school students. We utilized the PBSA, consisting of 15 items based on the four-dimensional concept, to assess and analyze the network of adolescent prosocial behavior. The network analysis process followed the standardization guidelines published by Epskamp et al, utilizing qgraph in the R programming for network estimation and computation of centrality indices. Finally, we performed comparisons of dimension networks and item networks across different genders and grades.
In the overall network of adolescent prosocial behavior, as well as in the grade- and gender-based networks, the commonweal-social rule dimension consistently exhibited the highest centrality, followed by altruism, relationship, and traits dimensions. Compared to the prosocial behavior network in females, the male prosocial behavior network showed higher centrality in the commonweal-social rule and relationship dimensions, occupying more central positions within the network. When comparing prosocial behavior networks across different grades, the commonweal-social rule dimension occupied the most central position in all grades. Moreover, its centrality was highest in the middle school group. The centrality of the altruism dimension was highest in the high school group, while the relationship and personal trait dimensions held the highest centrality in the elementary school group. As for the network structure, no differences were found in the gender-based dimension networks. However, differences were identified in the grade-based dimension networks, with high school students exhibiting significantly weaker network strength than middle and elementary school students. Similarly, no notable differences were observed in the item networks based on gender, but differences were found in the item networks based on grade.
Taken together, the current study has found that, in the overall sample as well as among different genders and grades, the commonweal-social rule consistently serves as a core dimension within the network structure of prosocial behavior. There were significant grade differences in both dimension networks and item prosocial behavior networks, along with subtle gender differences in item networks. These results provide a new perspective for deepening our understanding of adolescent prosocial behavior and expanding the research domain of prosocial behavior. These findings suggest that future interventions targeting the commonweal-social rule and altruism dimensions could potentially boost overall prosocial behavior in adolescents. The middle school stage may be a critical period for promoting commonweal-social rule prosocial behavior.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： With the further development of computers and big data technology, human society and its cultural forms are undergoing profound changes. The production and interaction of cultural symbols have become increasingly complex, and cultural members and their social networks have left numerous texts and behavior footprints, which makes it necessary to describe, predict, and even change the culture, so that computable cultural symbols and their interaction process have gradually become the research object of cultural psychology. In this vein, Computational Cultural Psychology (CCP), which employs big data and computation tools to understand cultural symbols and their interaction processes, has emerges rapidly, making large-scale or even full sample cultural analysis possible. The key variables of CCP are mainly about individualism and collectivism, and the analysis technologies include feature dictionaries, machine learning, social networks analysis, and simulation.New research avenues of CCP involve the cultural change effect from the temporal perspective and cultural geography effect from the spatial perspective. For the former, Google Ngram Viewer, Google News, Google Search, name archives, pop songs, and micro-blogs were used to analyze the cultural changes after the long-term historical development and the short-term economic transformation. For the latter, both social media (e.g., Twitter, Facebook, and Weibo) and large-scale survey were used to analyze the cultural differences of various countries or regions in different geographic spaces, as well as the relationship between culture and environment, such as cultural diversity along the "Belt and Road", person - environment fit and cultural value mismatch across different regions in a country or all over the world.It should be noted that there are several limitations in CCP, including decoding distortion, sample bias, semasiological variation, and privacy risk, although new methods and paradigms are provided. In future directions, theoretical interpretation of variables, cultural dynamics, interdisciplinary integration, and ecological validity should be seriously concerned. In particular, accurate definition and theoretical interpretation of big data measurement are needed; a variety of big data corpus (e.g., historical archives) should be used for the evolutionary analysis of dynamic cultures; deep integration, but not conflict, should be encouraged between culture psychology and the sciences of computer, communication, and history; and the "scenarios" of big data should be considered in promoting the ecological validity of cultural psychology.Taken together, a review of the emergence of CCP, as well as the empirical research on the big data analysis of cultural change and cultural geography, is helpful in understanding the advantages, limitations, and future direction of this new field, which sheds light on theoretical and methodological innovation of cultural psychology.
Subjects: Psychology >> Social Psychology Subjects: Psychology >> Industrial Psychology submitted time 2022-11-07
Abstract： With the integrated development of big data technology and cultural psychology, computational cultural psychology came into being as a novel interdisciplinary research field, which makes large-scale cultural analysis possible. The key variables of computational cultural psychology are mainly about individualism and collectivism, and the big data technologies (e.g., feature dictionaries, machine learning, social networks analysis, and simulation) have been used to analyze the cultural change effect from the temporal perspective and cultural geography effect from the spatial perspective. It should be noted that there are several limitations in Computational Cultural Psychology, including decoding distortion, sample bias, semasiological variation, and privacy risk, although new method and paradigm are provided. In future directions, theoretical interpretation of variables, cultural dynamics, interdisciplinary integration, and ecological validity should be seriously concerned.
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