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A Pragmatist's Guide to Using Prediction in the Social Sciences

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  • Verhagen, Mark D.

Abstract

Prediction is an underutilized tool in the social sciences, often for the wrong reasons. Many social scientists confuse prediction with unnecessarily complicated methods or with narrowly predicting the future. This is unfortunate. When we view prediction as the simple process of evaluating a model’s ability to approximate an outcome of interest, it becomes a more generally applicable and disarmingly simple technique. For all its simplicity, the value of prediction should not be underestimated. Prediction can address enduring sources of criticism plaguing the social sciences, like a lack of assessing a model’s ability to reflect the real world, or the use of overly simplistic models to capture social life. I illustrate these benefits with empirical examples that merely skim the surface of the many and varied ways in which prediction can be applied, staking the claim that prediction is a truly illustrious ‘free lunch’ that can greatly benefit empirical social scientists.

Suggested Citation

  • Verhagen, Mark D., 2021. "A Pragmatist's Guide to Using Prediction in the Social Sciences," SocArXiv tjkcy_v1, Center for Open Science.
  • Handle: RePEc:osf:socarx:tjkcy_v1
    DOI: 10.31219/osf.io/tjkcy_v1
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    References listed on IDEAS

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    1. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    2. Brian Greenhill & Michael D. Ward & Audrey Sacks, 2011. "The Separation Plot: A New Visual Method for Evaluating the Fit of Binary Models," American Journal of Political Science, John Wiley & Sons, vol. 55(4), pages 991-1002, October.
    3. James J. Heckman & John Eric Humphries & Gregory Veramendi, 2018. "Returns to Education: The Causal Effects of Education on Earnings, Health, and Smoking," Journal of Political Economy, University of Chicago Press, vol. 126(S1), pages 197-246.
    4. Michael J. Hanmer & Kerem Ozan Kalkan, 2013. "Behind the Curve: Clarifying the Best Approach to Calculating Predicted Probabilities and Marginal Effects from Limited Dependent Variable Models," American Journal of Political Science, John Wiley & Sons, vol. 57(1), pages 263-277, January.
    5. Thomas Lemieux, 2006. "Increasing Residual Wage Inequality: Composition Effects, Noisy Data, or Rising Demand for Skill?," American Economic Review, American Economic Association, vol. 96(3), pages 461-498, June.
    6. Jake M. Hofman & Duncan J. Watts & Susan Athey & Filiz Garip & Thomas L. Griffiths & Jon Kleinberg & Helen Margetts & Sendhil Mullainathan & Matthew J. Salganik & Simine Vazire & Alessandro Vespignani, 2021. "Integrating explanation and prediction in computational social science," Nature, Nature, vol. 595(7866), pages 181-188, July.
    7. Uri Simonsohn & Joseph P. Simmons & Leif D. Nelson, 2020. "Specification curve analysis," Nature Human Behaviour, Nature, vol. 4(11), pages 1208-1214, November.
    8. 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.
    9. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    10. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
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