A Gradual Effects Model for Single-Case Designs. (4th July 2018)

• Record Type: Journal Article
• Authors: Swan, Daniel M.; Pustejovsky, James E.
• Is Part Of: Multivariate behavioral research. Volume 53:Number 4(2018)
• Page Start: 574
• Page End: 593
• Publication Date: 2018-07-04
• Subjects: Effect size -- meta-analysis -- log response ratio -- single-case research -- generalized linear model -- intervention analysis; Psychometrics -- Periodicals; Psychology, Experimental -- Periodicals; Psychology, Experimental; Psychometrics; Periodicals; 150.15195
• DOI: 10.1080/00273171.2018.1466681
• Languages: English
• ISSNs: 0027-3171

Abstract

ABSTRACT: Single-case designs are a class of repeated measures experiments used to evaluate the effects of interventions for small or specialized populations, such as individuals with low-incidence disabilities. There has been growing interest in systematic reviews and syntheses of evidence from single-case designs, but there remains a need to further develop appropriate statistical models and effect sizes for data from the designs. We propose a novel model for single-case data that exhibit nonlinear time trends created by an intervention that produces gradual effects, which build up and dissipate over time. The model expresses a structural relationship between a pattern of treatment assignment and an outcome variable, making it appropriate for both treatment reversal and multiple baseline designs. It is formulated as a generalized linear model so that it can be applied to outcomes measured as frequency counts or proportions, both of which are commonly used in single-case research, while providing readily interpretable effect size estimates such as log response ratios or log odds ratios. We demonstrate the gradual effects model by applying it to data from a single-case study and examine the performance of proposed estimation methods in a Monte Carlo simulation of frequency count data.