Statistical analysis in Small-N Designs: using linear mixed-effects modeling for evaluating intervention effectiveness.

We provide a strong case for the application of LMEM to the analysis of training studies as a preferable alternative to visual analysis or other statistical techniques. When applied to a treatment dataset, the evidence supports that the approach holds up under the extreme conditions of small numbers of individuals, with repeated measures training data for both continuous (reaction time) and binomially distributed (accuracy) dependent measures. The approach provides standardized measures of effect sizes that are obtained through readily available and well-supported statistical packages, and provides statistically rigorous estimates of the expected average effect size of training effects, taking into account variability across both items and individuals.