This research was supported by Grant P50HD052120 from the National Institute of Child Health and Human Development, and Grant R305F100005 from the Institute of Education Sciences. The content is solely the responsibility of the authors and does not necessarily represent the view of the National Institute of Child Health and Human Development or the Institute of Education Sciences.
Quantile Regression in the Study of Developmental Sciences
Version of Record online: 13 DEC 2013
© 2013 The Authors. Child Development © 2013 Society for Research in Child Development, Inc.
Volume 85, Issue 3, pages 861–881, May/June 2014
How to Cite
Petscher, Y. and Logan, J. A. R. (2014), Quantile Regression in the Study of Developmental Sciences. Child Development, 85: 861–881. doi: 10.1111/cdev.12190
- Issue online: 10 MAY 2014
- Version of Record online: 13 DEC 2013
- National Institute of Child Health and Human Development. Grant Number: P50HD052120
- Institute of Education Sciences. Grant Number: R305F100005
Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome's distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression.