title: Binary recursive partitioning: background, methods, and application to psychology.
abstract: Binary recursive partitioning (BRP) is a computationally intensive statistical method that can be used in situations where linear models are often used. Instead of imposing many assumptions to arrive at a tractable statistical model, BRP simply seeks to accurately predict a response variable based on values of predictor variables. The method outputs a decision tree depicting the predictor variables that were related to the response variable, along with the nature of the variables' relationships. No significance tests are involved, and the tree's 'goodness' is judged based on its predictive accuracy. In this paper, we describe BRP methods in a detailed manner and illustrate their use in psychological research. We also provide R code for carrying out the methods.
authors.affiliation: Department of Psychology, Wichita State University, Kansas 67260, USA. edgar.merkle@wichita.edu
authors.forename: Edgar C
authors.initials: EC
authors.lastname: Merkle
authors.forename: Victoria A
authors.initials: VA
authors.lastname: Shaffer
mesh_terms: D000328:Adult; D003625:Data Collection / Q000706:statistics & numerical data; D003663:Decision Trees; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008318:Malpractice / Q000706:statistics & numerical data; D019300:Medical Errors; D008875:Middle Aged; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D011584:Psychology / Q000706:statistics & numerical data*; D012044:Regression Analysis