본 연구는 청소년이 경험한 부정적 생애경험의 이질성과 중첩성을 고 려하여, 청소년의 부정적 생애경험 유형을 잠재계층분석을 통해 탐색하 고, 각 유형과 우울·불안 간의 관계를 다중회귀분석을 통해 실증적으로 규명하고자 하였다. 이를 위해 한국복지패널 4차년도(2009)와 7차년도 (2012) 자료를 활용하였으며, 최종적으로 520명의 청소년을 연구대상으 로 선정하였다. 분석결과, 청소년기 경험한 부정적 생애경험 유형은 ‘저 역경 집단’, ‘다중역경 집단’, ‘알코올 중심 저역경 집단’의 3개의 유형으 로 분류되었으며, 이 중 다중역경 집단은 다양한 부정적 생애경험에 중 첩적으로 노출된 집단으로, 저역경 집단에 비해 우울·불안 수준이 유의하 게 높은 것으로 나타났다. 반면 저역경 집단과 알코올 중심 저역경 집단 간에는 우울·불안 수준의 유의미한 차이가 나타나지 않았다. 부정적 생애 경험의 조합에 따라 우울·불안의 차이가 나타날 수 있다는 결과에 따라 향후 청소년의 정신건강 증진을 위한 조기 개입과 다차원적 대응 전략을 위한 실천적·정책적 함의를 제시하였다.

This study aims to identify latent classes among shared e-scooter users based on their characteristics and analyze the differences in personal and usage characteristics across these classes. Specifically, the study has the following key objectives: (1) to select variables related to the personal and usage characteristics of shared e-scooter users; (2) to collect data on the personal and usage characteristics of shared e-scooter users; (3) to derive the latent classes of shared e-scooter users; and (4) to test the differences in personal and usage characteristics across the identified latent classes. Variables related to the personal and usage characteristics of shared e-scooter users were selected based on a literature review. Through a survey, data on the personal and usage characteristics of shared e-scooter users were collected. A latent class analysis (LCA) was performed to derive the latent classes of shared e-scooter users. Finally, a chi-square analysis was conducted to test the differences in personal and usage characteristics across the latent classes of shared e-scooter users. The results of this study are as follows. The personal characteristics of shared e-scooter users were identified as age and sex, whereas the usage characteristics were identified as usage frequency, time periods of e-scooter usage, return/rental zones, return/rental places, and types of roads used. Data on sex, age, usage frequency, periods of e-scooter usage, and return/rental locations were collected from 278 shared e-scooter users. Based on information criterion, statistical validation, and the entropy index, four latent classes of shared e-scooter users were identified: “male users with a commuting purpose in business zones,” “male users with a homeward commuting purpose in residential zones,” “female users with a leisure purpose in park/green zones,” and “users in their 20s with a commuting purpose in residential zones.” The results of a chisquare analysis revealed statistically significant differences (p < 0.05) in the personal and usage characteristics across the latent classes. Shared e-scooter user types were classified through Latent Class Analysis (LCA), and differences in personal and usage characteristics were identified across the classes. The preferred usage environments and conditions for each class of shared e-scooter users are determined. Variables related to the return/rental zone and periods of e-scooter usage showed the most significant differences among the classes. These findings can contribute to the development of customized user policies and the improvement of services based on the characteristics of shared e-scooter users.