The Number Of Levels effect is this: attributes with more levels in general tend to achieve higher importance than attributes defined on fewer levels. The authors (Wittink, Huber, Zandan and Johnson) present research which seeks to identify the cause. Behavioral versus algorithmic explanations are investigated. Their findings support the algorithmic hypothesis. The authors conclude that ACA is less susceptible to the number of levels effect than traditional full-profile conjoint methods in part due to the utility balance of the graded pairs.