Hello!
Please excuse the setup before the question (which is at the bottom for
those in a hurry).
I am a graduate student interested in creating an adaptive version of a job
analysis survey. Briefly, a job analysis survey is a 300+ item
multidimensional questionnaire given a wide range of workers to determine
the knowledge, skills and behaviors needed to perform their job. They have
underlying dimensions (via factor analysis) such as leadership, written/oral
communication, supervising others, etc. Since the surveys are so long and
time consuming, they are expensive to administer and disliked by workers. In
addition, many of the questions are either slightly redundant or not
applicable to certain jobs.
What I plan to do use existing data from the paper-and-pencil version to
create a Bayesian network for an adaptive version which will administer only
relevant items based on prior information about how other workers in similar
jobs responded.
My question centers around local independence, which is one assumption of
Item Response Theory. Local independence states that the responses to items
within the survey are statistically independent when holding constant the
underlying dimension. Specifically, I was wondering if the network nodes
created for one of the dimensions of a multidimensional survey could be
related to other nodes in other dimensions. Therefore, responses to items
about written/oral communication could be related to items about leadership,
etc.
Question 1: If one was to create a network of a multidimensional survey, do
the network nodes (and particular survey items associated with the nodes)
need to be locally independent (i.e., need to be unrelated to nodes and
items of other dimensions)?
Question 2: Would anyone know of quality sources which may help someone
create adaptive multidimensional surveys using Bayesian networks?
Thanks!
Scott T. Bublitz
NC State University
[EMAIL PROTECTED]