Specification Analysis for Technology Use and Teenager Well-Being: Statistical Validity and a Bayesian Proposal
本文指出规范曲线分析在推断中可能产生严重偏差和假阳性，并提出贝叶斯规范曲线分析（BSCA）方法，应用于技术使用与青少年福祉关系的研究，发现关联因设备、性别和评估者而异。
Abstract 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 subpopulations. 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).