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Specification analysis for technology use and teenager well‐being: Statistical validity and a Bayesian proposal

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  • Christoph Semken
  • David Rossell

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).

Suggested Citation

  • Christoph Semken & David Rossell, 2022. "Specification analysis for technology use and teenager well‐being: Statistical validity and a Bayesian proposal," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1330-1355, November.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:5:p:1330-1355
    DOI: 10.1111/rssc.12578
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    References listed on IDEAS

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    1. David Rossell & Oriol Abril & Anirban Bhattacharya, 2021. "Approximate Laplace approximations for scalable model selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 853-879, September.
    2. Jiahua Chen & Zehua Chen, 2008. "Extended Bayesian information criteria for model selection with large model spaces," Biometrika, Biometrika Trust, vol. 95(3), pages 759-771.
    3. Garret Christensen & Edward Miguel, 2018. "Transparency, Reproducibility, and the Credibility of Economics Research," Journal of Economic Literature, American Economic Association, vol. 56(3), pages 920-980, September.
    4. Daniel J. Benjamin & James O. Berger & Magnus Johannesson & Brian A. Nosek & E.-J. Wagenmakers & Richard Berk & Kenneth A. Bollen & Björn Brembs & Lawrence Brown & Colin Camerer & David Cesarini & Chr, 2018. "Redefine statistical significance," Nature Human Behaviour, Nature, vol. 2(1), pages 6-10, January.
      • Daniel Benjamin & James Berger & Magnus Johannesson & Brian Nosek & E. Wagenmakers & Richard Berk & Kenneth Bollen & Bjorn Brembs & Lawrence Brown & Colin Camerer & David Cesarini & Christopher Chambe, 2017. "Redefine Statistical Significance," Artefactual Field Experiments 00612, The Field Experiments Website.
    5. Hunt Allcott & Luca Braghieri & Sarah Eichmeyer & Matthew Gentzkow, 2020. "The Welfare Effects of Social Media," American Economic Review, American Economic Association, vol. 110(3), pages 629-676, March.
    6. Ying-Yeh Chen & Suk-Yin Ho & Pei-Chen Lee & Chia-Kai Wu & Susan Shur-Fen Gau, 2017. "Parent-child discrepancies in the report of adolescent emotional and behavioral problems in Taiwan," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-12, June.
    7. C. Glenn Begley & Lee M. Ellis, 2012. "Raise standards for preclinical cancer research," Nature, Nature, vol. 483(7391), pages 531-533, March.
    8. Uri Simonsohn & Joseph P. Simmons & Leif D. Nelson, 2020. "Specification curve analysis," Nature Human Behaviour, Nature, vol. 4(11), pages 1208-1214, November.
    9. Cookson, J. Anthony, 2018. "When saving is gambling," Journal of Financial Economics, Elsevier, vol. 129(1), pages 24-45.
    10. Valen E. Johnson & David Rossell, 2010. "On the use of non‐local prior densities in Bayesian hypothesis tests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 143-170, March.
    11. Nial Friel & Jason Wyse, 2012. "Estimating the evidence – a review," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 288-308, August.
    12. Susan Athey & Guido Imbens, 2015. "A Measure of Robustness to Misspecification," American Economic Review, American Economic Association, vol. 105(5), pages 476-480, May.
    13. Uri Simonsohn & Joseph P. Simmons & Leif D. Nelson, 2020. "Publisher Correction: Specification curve analysis," Nature Human Behaviour, Nature, vol. 4(11), pages 1215-1215, November.
    14. Adam Slez, 2019. "The Difference Between Instability and Uncertainty: Comment on Young and Holsteen (2017)," Sociological Methods & Research, , vol. 48(2), pages 400-430, May.
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