技术使用和青少年幸福的规范分析：统计有效性和贝叶斯建议
Specification analysis for technology use and teenager well-being: statistical validity and a Bayesian proposal
摘要：
科学中的一个关键问题是评估数据分析选择的稳健性，同时避免选择性报告并提供有效的推断。规范曲线分析是一种旨在防止选择性报告的工具。唉，当用于推断时，由于错误地调整协变量，它可能会产生严重的偏倚和假阳性，并掩盖重要的处理效应异质性。作为我们的激励应用，它导致了一项有影响力的研究，得出结论，技术使用和青少年心理健康之间没有相关联系。我们讨论这些问题，并提出了一个有效的推理策略。贝叶斯规范曲线分析（BSCA）使用贝叶斯模型平均值来合并治疗、结局和亚群之间的协变量和异质性效应。BSCA对青少年的幸福感给出了截然不同的见解，揭示了与技术的关联因设备、性别和评估幸福感的人（青少年或其父母）而异。
英文摘要：
A key issue in science is assessing robustness to data analysis choices, while avoiding selective reporting and providing valid inference. Specification Curve Analysis is a tool intended to prevent selective reporting. Alas, when used for inference it can create severe biases and false positives, due to wrongly adjusting for covariates, and mask important treatment effect heterogeneity. As our motivating application, it led an influential study to conclude there is no relevant association between technology use and teenager mental well-being. We discuss these issues and propose a strategy for valid inference. Bayesian Specification Curve Analysis (BSCA) uses Bayesian Model Averaging to incorporate covariates and heterogeneous effects across treatments, outcomes and sub-populations. BSCA gives significantly different insights into teenager well-being, revealing that the association with technology differs by device, gender and who assesses well-being (teenagers or their parents).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
