title: A demonstration of how to do a meta-analysis that combines single-case designs with between-groups experiments: The effects of choice making on challenging behaviors performed by people with disabilities.
abstract: OBJECTIVE
  This paper demonstrates how to conduct a meta-analysis that includes both between-group and single-case design (SCD) studies. The example studies whether choice-making interventions decrease challenging behaviors performed by people with disabilities.
  
  
  METHODS
  We used a between-case d-statistic to conduct a meta-analysis of 15 between-group and SCD studies of 70 people with a disability, who received a choice intervention or control. We used robust variance estimation to adjust for dependencies caused by multiple effect sizes per study, and conducted moderator, sensitivity, influence, and publication bias analyses.
  
  
  RESULTS
  The random-effects average was d = 1.02 (standard error of 0.168), so the 95% confidence interval (CI) suggests choice-making reduces challenging behaviors by 0.65 to 1.38 standard deviations. Studies that provided choice training produced a significantly larger intervention effect.
  
  
  CONCLUSIONS
  Choice-making reduces challenging behaviors performed by people with disabilities.
authors.affiliation: a Department Psychological Sciences, School of Social Sciences, Humanities and Arts, University of California , Merced, Merced , CA , USA.
authors.forename: Nicole A M
authors.initials: NAM
authors.lastname: Zelinsky
authors.affiliation: a Department Psychological Sciences, School of Social Sciences, Humanities and Arts, University of California , Merced, Merced , CA , USA.
authors.forename: William
authors.initials: W
authors.lastname: Shadish
mesh_terms: D006233:Persons with Disabilities / Q000523:psychology; D006801:Humans; D015201:Meta-Analysis as Topic; D000066530:Neurological Rehabilitation / Q000379:methods* / Q000592:standards; D012107:Research Design / Q000592:standards
keywords: Meta-analysis; challenging behaviors; choice; disabilities; robust variance estimation