Simulation modelling analysis for small sets of single-subject data collected over time

The behavioural data yielded by single subjects in naturalistic and controlled settings likely contain valuable information to scientists and practitioners alike. Although some of the properties unique to this data complicate statistical analysis, progress has been made in developing specialised techniques for rigorous data evaluation. There are no perfect tests currently available to analyse short autocorrelated data streams, but there are some promising approaches that warrant further development. Although many approaches have been proposed, and some appear better than others, they all have some limitations. When data sets are large enough (similar to 30 data points per phase), the researcher has a reasonably rich pallet of statistical tools from which to choose. However, when the data set is sparse, the analytical options dwindle. Simulation modelling analysis (SMA; described in this article) is a relatively new technique that appears to offer acceptable Type-I and Type-II error rate control with short streams of autocorrelated data. However, at this point, it is probably too early to endorse any specific statistical approaches for short, autocorrelated timeseries data streams. While SMA shows promise, more work is needed to verify that it is capable of reliable Type-I and Type-II error performance with short serially dependent streams of data.