Get access

Quantile Regression in the Study of Developmental Sciences

Authors


  • 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.

Abstract

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.

Get access to the full text of this article

Ancillary