What the network says is, "given the data, AND assuming the independencies represented in the naive Bayes model are correct, I'm 95% accurate". Probably, the assumptions inherent in your naive model are violated to some extent. For example, suppose someone's job type is "Manager" and there are two questions "Salary" and "Tendency to delegate tasks". Your naive model says that within the class of managers, there is no relationship between these two variables, whereas I believe that high-paid managers are even more eager to delegate. So, to answer your second question, my guess would be to perform a factor analysis to group questions into categories, create nodes for those categories, and make questions children of their category. That way, covariances among findings are represented by the mutual dependence of findings via their parent; such a covariance would not be blocked by observation of the job type. regards, Hans. - -------------------------------------------------- Hans van Leijen NOTION / Universiteit Nyenrode The Netherlands Business School Straatweg 25 3621 BG Breukelen The Netherlands Phone: + 31 346 291313 Fax: + 31 346 291250 E-mail: [EMAIL PROTECTED] URL: http://www.nyenrode.nl/int/research_faculty/cscm/notion/notion.html
