title: Exploring function estimators as an alternative to regression in psychology.
abstract: Most forms of regression analysis make assumptions about the relationships between the variables being modeled. As a consequence, it can be difficult to know which form of analysis is most appropriate for a given data set. In this article, we explore the idea that function estimators might provide a better alternative in many situations. Function estimators discover the best function to link dependent and independent variables, no matter what form this takes. Four studies demonstrate that one type of function estimator (a neural network) not only performs the same tasks as linear regression and nonlinear regression, but often performs these tasks better and with more flexibility. Moreover, neural networks allow a useful secondary analysis in which useful groups of people can be identified. We recommend that function estimators be used in preference to regression-based techniques for many analyses. The Matlab script used to write this article may be downloaded from www.psychonomic.org/archive/.
authors.affiliation: Department of Psychology, University of Bath, Bath, England. i.walker@bath.ac.uk
authors.forename: Ian
authors.initials: I
authors.lastname: Walker
authors.forename: Sarah
authors.initials: S
authors.lastname: Milne
mesh_terms: D000293:Adolescent; D000328:Adult; D003625:Data Collection / Q000706:statistics & numerical data; D007722:Health Knowledge, Attitudes, Practice; D006801:Humans; D016014:Linear Models; D008297:Male; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011589:Psychology, Experimental / Q000706:statistics & numerical data*; D012044:Regression Analysis; D016353:Self-Examination / Q000706:statistics & numerical data; D012680:Sensitivity and Specificity; D011795:Surveys and Questionnaires; D013736:Testicular Neoplasms / Q000453:epidemiology / Q000517:prevention & control