The purpose of the study is to investigate empirically the multilevel approach for combining single-case or single-subject experimental designs (SSED) data. More specifically, the authors aim at assessing the value of the approach for numbers of observations, cases and studies that are common in SSED research, by looking at the bias and precision of the parameter estimates, and the validity of statistical inferences. Special attention is given to the problem of standardization: if data are not measured on the same scale in each of the studies, they should be standardized before being combined. An option to make linearly equitable scales with a meaningful zero value comparable, is to divide the scores for each case by (an estimate of) the within phase standard deviation. The value of this approach has to be investigated yet. With the obtained study results, they want to inform applied researchers about possibilities and limitations of the use of the basic multilevel model for combining SSED data. At the same time, the results will give indications about conditions for which the model or the estimation procedures should be further developed (e.g., by using bootstrap procedures instead of maximum likelihood procedures). (Contains 2 tables.)