title: Nonlinear Bayesian analysis for single case designs.
abstract: Several authors have suggested the use of multilevel models for the analysis of data from single case designs. Multilevel models are a logical approach to analyzing such data, and deal well with the possible different time points and treatment phases for different subjects. However, they are limited in several ways that are addressed by Bayesian methods. For small samples Bayesian methods fully take into account uncertainty in random effects when estimating fixed effects; the computational methods now in use can fit complex models that represent accurately the behavior being modeled; groups of parameters can be more accurately estimated with shrinkage methods; prior information can be included; and interpretation is more straightforward. The computer programs for Bayesian analysis allow many (nonstandard) nonlinear models to be fit; an example using floor and ceiling effects is discussed here.
authors.affiliation: Educational Psychology, CUNY Graduate Center, 365 Fifth Avenue, New York, NY 10016, United States. Electronic address: drindskopf@gc.cuny.edu.
authors.forename: David
authors.initials: D
authors.lastname: Rindskopf
mesh_terms: D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D006801:Humans; D017711:Nonlinear Dynamics; D012107:Research Design / Q000592:standards*
keywords: Bayesian; Multilevel; Nonlinear; Single case; Single subject