Bias Adjustment in Multilevel Meta-Analysis of Standardized Single-Case Experimental Data

Abstract

Previous research found bias in the estimate of the overall fixed effects and variance components using multilevel meta-analyses of standardized single-case data. Therefore, we evaluate two adjustments in an attempt to reduce the bias and improve the statistical properties of the parameter estimates. The results confirm the existence of bias when estimating an overall effect based on standardized data and bias when estimating variance components. The relative bias decreases as the number of measurement occasions increases. The results indicate that the between-case variance and, to a smaller degree, the between-study variance is overestimated, especially when measurement occasions per phase is only 10. In general, Hedges's bias correction procedure led to better estimations of both fixed effects and variance components.