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The association between self-esteem and dimensions and classes of cross-platform social media use in a sample of emerging adults - Evidence from regression and latent class analyses

机译:新兴成人样本中的自尊和跨平台社交媒体和跨平台社交媒体的关联 - 来自回归和潜在课程分析的证据

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There is growing interest in the role of social media use in young people's mental health, and self-esteem has been hypothesised as a potential link in this association. However, existing studies have tended to use basic indicators of use in isolation and single-platform data, and further, have not controlled for other key variables. To address these limitations, emerging adults completed online questionnaires on social media engagement and self-esteem. In line with the interpersonal-connection-behaviours framework we explored online behaviours that putatively connect and disconnect users, e.g. meeting new people and engaging in social comparisons, respectively. Data were analysed using two methodologies, facilitating examination of the relationship between self-esteem and individual engagement indicators (regression analysis) as well as patterns of use (Latent Class Analysis). Overall levels of use and upward social comparisons independently predicted variance in self-esteem scores, even after controlling for demographic and socioeconomic covariates. Further, membership to meaningful, empirically-derived classes of social media users was predicted by self-esteem. These findings indicate that the association between social media use, social comparisons and self-esteem is robust, and extends to multi-platform data. We argue that such a move away from studies of single-platform data is critical if findings are to be generalised.
机译:社交媒体对年轻人心理健康的作用越来越感兴趣,自尊被假设作为本协会的潜在联系。然而,现有的研究已经倾向于使用隔离和单平台数据的基本指标,并且进一步尚未控制其他关键变量。为了解决这些限制,新兴成人完成了社交媒体参与和自尊的在线问卷。符合人际关系 - 连接行为框架,我们探讨了在线行为,即借助连接和断开用户的行为,例如,结识新人,分别从事社会比较。使用两种方法进行分析数据,促进自尊与个人参与指标(回归分析)与使用模式(潜在课程分析)之间的关系。即使在控制人口统计和社会经济协变者之后,整体使用水平和向上的社会比较也独立地预测了自尊分数的方差。此外,通过自尊来预测成员资格,经验主义派生的社交媒体用户的阶级。这些调查结果表明,社交媒体使用,社会比较和自尊之间的关联是强大的,并扩展到多平台数据。我们认为,如果要概括发现,这种转向单平台数据的研究至关重要。

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