Do the DSM decision trees improve diagnostic ability?

Experiment 1 examined whether the use of the DSM-III-R decision trees increased the accuracy of DSM-III-R diagnoses. Results indicated that the use of the decision trees interacted with the level of DSM-III-R experience to affect diagnostic accuracy. The use of the decision trees resulted in a modest increase in diagnostic accuracy for participants with less DSM-III-R experience; for participants with more DSM-III-R experience, the use of the decision trees had no significant effect on diagnostic accuracy. Experiment 2 examined whether the use of the DSM-III-R decision trees increased the accuracy and confidence and decreased the time of DSM-III-R diagnosis across participants with varying levels of DSM-III-R experience. The primary analyses consisted of a 3 X 2 X 2-multivariate analysis of variance (MANOVA) to determine whether the use of the decision trees increased diagnostic accuracy and diagnostic confidence and decreased diagnostic time. Results indicated (1) the experienced participants made more accurate diagnoses than the less-experienced and no-experience participants; (2) the decision trees, combined with practice, increased class diagnostic accuracy and decreased diagnostic time; and (3) participants were more confident in their diagnosis when they used the decision trees than when they did not use the decision trees. Supplementary analyses consisted of two one-way analysis of variance (ANOVA) procedures and indicated that participants' preference for and knowledge of how to use the decision trees did not significantly affect their diagnostic accuracy.