Moderating factors of video-modeling with other as model: A meta-analysis of single-case studies

Video modeling with other as model (VMO) is a more practical method for implementing video-based modeling techniques, such as video self-modeling, which requires significantly more editing. Despite this, identification of contextual factors such as participant characteristics and targeted outcomes that moderate the effectiveness of VMO has not previously been explored. The purpose of this study was to meta-analytically evaluate the evidence base of VMO with individuals with disabilities to determine if participant characteristics and targeted outcomes moderate the effectiveness of the intervention. Findings indicate that VMO is highly effective for participants with autism spectrum disorder (IRD = .83) and moderately effective for participants with developmental disabilities (IRD = .68). However, differential effects are indicated across levels of moderators for diagnoses and targeted outcomes. Implications for practice and future research are discussed.

► We use nonparametric meta-analytic techniques to examine the effectiveness of video-based modeling with other as model (VMO) for participants with disabilities.
► We examine if variations in magnitude of change are present for participant characteristics including primary disability and age.
► We examine differential effects of video modeling for targeted outcomes.
► VMO is an evidence-based practice for participants with ASD across ages and targeted outcomes.
► Even with smaller effects, such as those found for participants with developmental disabilities, VMO may still be considered due to minimal resource expenditure.