EXPLORING INDIVIDUAL CHANGE

In the analysis of the impact of clinical interventions, the received wisdom has been that posttreatment scores, with pretreatment scores eq uated by random assignment or statistically partialed out, should be u sed to evaluate treatment outcomes. However, posttreatment scores are not generally more reliable than, nor equivalent to, change scores, ev en with pretreatment scores partialed out of both. Moreover, there are data-analytic methods that indicate how individual patients change, i n terms of response curves over time, rather than indicate only how mu ch groups change on the average. These methods take researchers back t o the individual data that they ought to use for choosing the specific models of change to be used. To maximize relevance for clinical pract ice, the results of treatment research should always be reported at th is most disaggregated or individual change level, as well as, when app ropriate, at more aggregated statistical levels.