NETWORK ANALYSIS OF IMPOSTER PHENOMENON, PERFECTIONISM AND SELF-EFFICACY IN HIGH SCHOOL STUDENTS

Abstract

The paper notes that high school education is associated with the orientation of students to high results, which can lead to the actualization of imposter phenomenon and pathological perfectionism, as well as to the formation of mutualistic connections between them that require study. The purpose of the study is to analyze the network structure of imposter phenomenon, perfectionism and self-efficacy connections in high school students. The hypothesis of the study is that imposter phenomenon, perfectionism and self-efficacy form a network structure of interconnected nodes, while there are specifics in the network topology depending on gender. The sample consisted of 120 students from Izhevsk (60 girls, 60 boys) aged from 17 to 19 years (M = 18). The study was conducted using a survey method (formalized questionnaires), data analysis was implemented using network analysis. As a result, the specificity of network functioning depending on gender was revealed (p < 0.001). In girls, the strongest node was pathological perfectionism (s = 1.48), closely connected to the imposter phenomenon (pr = 0.58). In boys, the imposter phenomenon (s = 1.13), normal perfectionism (s = 0.70) and self-efficacy in communication (s = 0.25) entered the core of the network. Network models are characterized by satisfactory and good stability. The results can be useful to educational psychologists who provide psychological support during the preparation of students for final exams. The scientific novelty of the research is connected with the study of psychological phenomena as dynamic complex networks with nodes (features), edges (directional and non-directional connections), the topology of which reflects polydeterministic mutualistic connections. The network approach is an alternative to the approach of a general latent factor (hidden cause - observed symptom), providing an opportunity to study the structures of networks, their formation, as well as to predict the response to the impact at the level of all nodes.