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Quantile Regression in the Study of Developmental Sciences
Author(s) -
Petscher Yaacov,
Logan Jessica A. R.
Publication year - 2013
Publication title -
child development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.103
H-Index - 257
eISSN - 1467-8624
pISSN - 0009-3920
DOI - 10.1111/cdev.12190
Subject(s) - quantile regression , linear regression , statistics , cross sectional regression , outcome (game theory) , quantile , regression analysis , econometrics , psychology , regression , regression diagnostic , bayesian multivariate linear regression , mathematics , mathematical economics
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.