انگیختگی ادراکی ایجاد حافظه های جدید اپیزودیک را بهبود می بخشد

Because of the complex nature of phenomena examined in applied research, there continues to be a need to identify multivariate analytic techniques which are sensitive to interaction effects and which minimize resource and technical demands. One solution to this dilemma involves the use of a family of statistical techniques sometimes referred to as ‘tree-generating’ statistics. The purpose of this paper is to demonstrate the utility of such statistics for public sector service planning. Specifically, an example using an early version of ‘tree-generating’ statistics, Automatic Interaction Detection, is presented to provide a model for the data analysis needed to plan substance abuse programs on a local level, targeting poor, culturally diverse, adult women.

Because of the complex nature of phenomena examined in applied research, there continues to be a need to identify multivariate analytic techniques which are sensitive to interaction effects and which minimize resource and technical demands. Dumas (1989) identified two procedures sensitive to contextual variables and, consequently, ideal for planning efforts: structural equation modeling (e.g., LISREL) and meta-analysis. In applied program planning and evaluation settings, however, few local communities are likely to have numerous, relatively recent, local data sets that are needed for meta-analysis regarding target problems which are known to have substantial regional and temporal variability (e.g., substance abuse), have the resources to gather the large sample size preferred for structural equation modeling, or have the expertise and resources available to utilize either procedure. One solution to this dilemma involves the use of a family of statistical techniques, sometimes referred to as ‘tree-generating’ statistics, as an alternative approach.