title: Quantile regression in the study of developmental 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.
authors: Florida Center for Reading Research Florida State University.
authors: Yaacov
authors: Y
authors: Petscher
authors: Ohio State University.
authors: Jessica A R
authors: JAR
authors: Logan
mesh_terms: D035843:Biomedical Research / Q000379:methods*; D003627:Data Interpretation, Statistical; D006803:Human Development; D006801:Humans; D012044:Regression Analysis