A procedure that makes successive two-way splits in the data to find homogeneous segments that differ from one another. Also called tree analysis. Predictions can be made by forecasting the size and typical behavior for each segment. As its name implies, this procedure is useful for analyzing situations in which interactions are important. On the negative side, it requires much data so that each segment (cell size) is large enough (certainly greater than ten, judging from Einhorn’s [1972] results). The evidence for its utility in forecasting is favorable but limited. Armstrong and Andress (1970) analyzed data from 2,717 gas stations using AID and regression. To keep knowledge constant, exploratory procedures (e.g., stepwise regression) were used. Predictions were then made for 3,000 stations in a holdout sample. The MAPE was much lower for AID than for regression (41% vs. 58%). Also, Stuckert (1958) found trees to be more accurate than regression in forecasting the academic success of about one thousand entering college freshmen. See also segmentation.

• Armstrong, J. S. & J. G. Andress (1970), “Exploratory analysis of marketing data: Trees vs. regression,” Journal of Marketing Research, 7, 487-492.  (Full text)
• Einhorn, H. J. (1972), “Alchemy in the behavioral sciences,” Public Opinion Quarterly, 36, 367-378.
• Stuckert, R. P. (1958), “A configurational approach to prediction,” Sociometry, 21, 225-237.