Speakers
Description
Effect sizes are commonly used in meta-analysis, as they provide a tool to summarize the results from each primary study in a common metric. In psychology and related fields, meta-analyses often involve integrating continuous variables measured with different scales across studies, which leads to using standardized mean differences as the effect size index. One of these indices is the standardized mean change (SMC), which quantifies within-group treatment effects when the primary studies have examined the effectiveness of a treatment program using a repeated measures design, and the dependent variable has been measured on a quantitative scale. However, different procedures have been proposed to calculate this effect size, and some of them make assumptions which are hardly verifiable in practice, namely homoscedasticity and a specific value for the correlation between measurement points. This presentation will explore the potential impact on the results if some of these assumptions are violated, using a range of simulated scenarios.
Funding: MICIU/AEI /10.13039/501100011033/ and FEDER funds, European Union, grant no. PID2022-137328NB-I00
