Exploring individual change.

摘要: In the analysis of impact clinical interventions, received wisdom has been that posttreatment scores, with pretreatment scores equated by random assignment or statistically partialed out, should be used to evaluate treatment outcomes. However, are not generally more reliable than, nor equivalent to, change even out both. Moreover, there data-analytic methods indicate how individual patients change, in terms response curves over time, rather than only much groups on average. These take researchers back data they ought use for choosing specific models used. To maximize relevance practice, results research always reported at this most disaggregated level, as well as, when appropriate, aggregated statistical levels.