Specification analysis for technology use and teenager well-being: statistical validity and a Bayesian proposal

TL;DR

This paper critiques existing specification curve analysis methods for bias and false positives, proposing Bayesian Model Averaging to improve inference on technology use and teenager well-being, revealing nuanced effects.

Contribution

It introduces Bayesian Specification Curve Analysis (BSCA), a novel approach that accounts for covariates and heterogeneity, providing more valid and detailed insights into the technology-well-being relationship.

Findings

Reveals device-specific effects on teenager well-being

Shows gender differences in technology impact

Highlights discrepancies based on who assesses well-being

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 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).