Annual Meeting of the American Educational Research Association (AERA) location:Chicago, IL, USA date:16-20 April 2015
The main purpose of this study is to give empirical illustrations of three possible misspecification issues, previously investigated using Monte Carlo simulation studies, in the context of multilevel analysis of single-case experimental designs (SCED). Previous research indicates that it is not always obvious (1) to specify the covariance matrix, (2) to code complex single-case designs or (3) to model dependent effect sizes in alternating treatment designs (ATD). However, this can have far-reaching consequences as misleading study results are obtained on which important policy, research and practice decisions are based. To illustrate the consequences of ignoring existing covariance and to code complex single-case designs, we used a re-analysis of the meta-analysis of SCED studies of Heyvaert, Saenen, Maes, and Onghena (2014) because in their meta-analysis different types of SCED studies are included. The modeling of dependent effect sizes is illustrated by another dataset as we focus on dependency between treatment effects within ATDs. We created this dataset ourselves and collected ATD studies published in 2012 focusing on treatments to increase reading fluency for children with autism.