This OSF project contains supplementary material for the article Wenz, S. E. (2019). What Quantile Regression Does and Doesn’t Do: A Commentary on Petscher and Logan (2014). Child Development, 90(4), 1442–1452. https://doi.org/10.1111/cdev.13141 ## Abstract ## Petscher and Logan (2014)’s description of quantile regression might mislead readers to believe it would estimate the relation between an outcome, *y*, and one or more predictors, ***x***, at different quantiles of the unconditional distribution of *y*. However, quantile regression models the conditional quantile function of *y* given ***x*** just as linear regression models the conditional mean function. This article’s contribution is twofold: First, it discusses potential consequences of methodological misconceptions and formulations of Petscher and Logan (2014)’s presentation by contrasting features of quantile regression and linear regression. Secondly, it reinforces the importance of correct understanding of quantile regression in empirical research by illustrating similarities and differences of various quantile regression estimators and linear regression using simulated data. ## References ## Petscher, Y., & Logan, J. A. R. (2014). Quantile Regression in the Study of Developmental Sciences. *Child Development*, 85(3), 861–881. https://doi.org/10.1111/cdev.12190