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Stop Splitting Hairs: The Problems with Dichotomizing Continuous Data in Language Research

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  • Hemelstrand, Shawn
  • Inoue, Tomohiro

Abstract

It is common in the language sciences to dichotomize continuous data in order to fit this data to statistical tests. However, several statisticians and methodologists have warned against this practice for years. Many in the language sciences seem unaware of this problem. Because of the lack of modern, robust, and open data simulations related to this issue in the language science literature, our paper provides an empirical investigation of this practice. Across six different simulations, our analysis shows that dichotomization almost universally increases the standard errors, and consequently leads to inaccuracy of tests of statistical significance. Furthermore, effect sizes like are often diminished by the reduction of available information in the data. Our paper concludes by providing suggestions and considerations for future empirical studies.

Suggested Citation

  • Hemelstrand, Shawn & Inoue, Tomohiro, 2025. "Stop Splitting Hairs: The Problems with Dichotomizing Continuous Data in Language Research," OSF Preprints 8usxn, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:8usxn
    DOI: 10.31219/osf.io/8usxn
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    References listed on IDEAS

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    1. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
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