Causal Analysis
The rules guiding causal analysis are the same as those used for classification analysis: relevance, exhaustiveness, mutual exclusiveness, and consistency. The basic purpose here is to identify the controlling variables (lehan 1984,74) or those factors that if significantly altered would decisively affect the problem (e.g., jail overcrowding, nutrition status of the elderly, or poverty). From controlling variables it is more feasible to establish objectives and specify performance criteria. Causal or "hierarchy" analysis allows the analyst to distinguish causes from symptoms and to organize data in categories that indicate the differences among the three major kinds of causes, as follows (Dunn 2008, 102-5).
First, possible causes are those that are remote but contribute to the occurrence of the problem. Studies linking elite power structures and official conspiracies to the persistence of poverty are satisfying and make riveting tales, but are actionable only on the macro-revolutionary level. In most societies this means either a long-term solution or a very short one! In other cases, the persistent political influence of certain interest groups leads to consistent policy definitions and solutions (e.g., builders and contractors advocate capital expenditure solutions for roaming dogs). Revision of policy and budget formulation processes to include wider public participation is a medium-term solution to deal with more remote or possible problems like this.