Lhat vary little from usual conditions analysis of

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Lhat vary little from usual conditions. Analysis of Single-System Data There are two basic strategi es for the analysis of SSR data: visual analysis and statistical analysis. Each h as its strengths and limitations, but in some stu di es , it is possible to use both to explore the data more fully. Visual Analysis Visual analysis has been the primary approach used in the evaluation of SSR data from the beginning and is based on the assumption th at only effects that are powerful enough to be obvious to the naked eye should be taken seriously. Acco rding to Parsonson and Baer ( 1978), "Differences between baseline and ex-perimental conditions have to be clearly evident and reliable for a convincing demonstration of stable change to be claimed . . . an effect w ould probably have to be more powerful than that required to produce a statisti- caUy significant change" (p. 112). (Note th at the magnitude of change sou ght visually is conceptually related to effect size in statistical anal ys is.) This search for s tron g effects is consistent with commo n social work sentiment, in th at most cl ien t: and community iss ues with wh ich social workers intervene are quite serious and require very substantial levels of change. The change sought in visual analysis usually is in mean levels of a problem or goal over time (e.g., is the client more or less depressed than during baseline?). Besides level, however, both trend (e.g., has a problem that was getting worse over time stabilized or begun to in1pro ve?) and variability (e. g., has a child's erratic behavior leveled out?) are also often important considerations. Visual analysis relies on graphin g; note the graphs used in earlier di scussions of SSR designs in thi s chapter. Strong, consistent effects should be immediately obvious to the observer, and multiple independent observers should agree that an effect is present to accept that change is real. One common standard for judging the presence of such an effect is the extent of overlap in data between the baseline phase and the intervention phase. If there is no overlap (or almost none when there are many data poin ts), the presence of a real effect usually ca n be accepted with confidence (see the left panel of Figure 14.7).
CHIIPTER 14 • SINGLE · SYSTEM R ESEII RCH 263 Figure 14.7 The data on the left panel show a dear discontinuity at the point of interventi on , with no overla p between ph ases, su ggest ing an intervenlion effect. The data s hown on the right, despite the nearly comp lete overlap between phases, are also convincing, and a dear trend reversed dramatically at the poin t of intervention. I I I I I I I ~~ ~! I I ~ 0 Figure 14 .8 T he da ta on the left pa nel sh ow a tr end in the baseline data that genera ll y con tinues into the intervention phase, suggesting little or no effect. By contras t, there is a clear d is continuity of level at the po int of interve ntion in the data on the right, whi ch suggests an effect eve n though the slopes within phases are simi l ar.

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