A modification of the Johnson-Neyman technique comparing two regressions, applied to treatment effects dependent on baseline levels.
Treatment effects can often depend on baseline (pre-treatment) variables. This is ignored in many studies, or else the relationship is removed statistically by analysis of covariance. The latter method, however, assumes that slopes are equal and also loses information on the base-line contribution. Instead, the Johnson-Neyman technique does not assume that slopes are equal and furthermore permits examination of the baseline contribution. The method was devised for education research and is here extended to biological studies. However, the method assumes that variances are equal for the regressions, which might not be always so. The mmodification proposed accommodates inequalities of variance, whether intrinsic to the regressions or resulting from differences in group size. The Johnson--Neyman technique is discussed in relation to alternative analyses and, in appropriate situations, is considered to yield more information. Furthermore, with the refinement described, it involves even fewer assumptions and becomes more powerful.
D001695:Biology; D003201:Computers; D012044:Regression Analysis; D012107:Research Design; D013223:Statistics as Topic