title: Regression models and effect size measures for single case designs.
abstract: A regression modelling approach for the analysis of single case designs (SCDs) is described in this paper. The approach presented addresses two key issues in the analysis of SCDs. The first issue is that of serial dependence among the observations in SCDs. The second issue is that of an effect size measure appropriate for SCDs. As with traditional between-subjects experimental designs, effect size measures are critical in assessing the impact of interventions in SCDs. Although effect size measures when there is level change without trend are straightforward to obtain and have been well studied, the situation is different when there are changes in both level and trend. An effect size measure that combines changes in levels and slopes and that is comparable to the d-type effect size measure obtained in between-subjects designs is presented. Finally, an inferential procedure for assessing the effect of the intervention based on the effect size measure is provided and illustrated.
authors.affiliation: a University of Connecticut , NEAG School of Education , Storrs , CT , USA.
authors.forename: Hariharan
authors.initials: H
authors.lastname: Swaminathan
authors.forename: H Jane
authors.initials: HJ
authors.lastname: Rogers
authors.forename: Robert H
authors.initials: RH
authors.lastname: Horner
authors.forename: George
authors.initials: G
authors.lastname: Sugai
authors.forename: Keith
authors.initials: K
authors.lastname: Smolkowski
mesh_terms: D006801:Humans; D012044:Regression Analysis; D012107:Research Design / Q000706:statistics & numerical data*
keywords: Autocorrelation; Effect size; Generalised least Squares Estimators; Regression models; Single case designs