A comparison of mean phase difference and generalized least squares for analyzing single-case data.

The present study focuses on single-case data analysis specifically on two procedures for quantifying differences between baseline and treatment measurements. The first technique tested is based on generalized least square regression analysis and is compared to a proposed non-regression technique, which allows obtaining similar information. The comparison is carried out in the context of generated data representing a variety of patterns including both independent and serially related measurements arising from different underlying processes. Heterogeneity in autocorrelation and data variability was also included, as well as different types of trend, and slope and level changes. The results suggest that the two techniques perform adequately for a wide range of conditions and that researchers can use both of them with certain guarantees. The regression-based procedure offers more efficient estimates, whereas the proposed non-regression procedure is more sensitive to intervention effects. Considering current and previous findings, some tentative recommendations are offered to applied researchers in order to help choosing among the plurality of single-case data analysis techniques.