I am as new to IRT as a freshly hatched chick. I recognize this is a more general IRT question, but maybe    I am exploring the results from an instrument in which all items have a five-point Likert response set. The responses reflect ordinal phases of policy adoption, in which 1 represents no consideration of the policy and 5 represents full implementation. IRT here models respondent policy responsiveness ("ability"), policy implementation "difficulty," and item discrimination with a graded response model.    My variables have two factors. After sussing out the factors, I have run PROC IRT for each factor separately in order to look at the ICCs and Item Information Curves. As I expect, the legend of the Item Characteristic Curves panels shows values 1-5, corresponding to the values of the responses, with colored lines.   I have noticed that for items in which the Item Information Curve peaks over a negative value of the latent trait, the peaks of the curves for the responses are generally ordered in a way that makes sense-- at a low level of the trait, the most frequent response is "1," or no consideration.   However, for items in which the IIC peaks over a positive value of the trait, the response curves are generally reversed--at a low level of the trait, the most frequent response is "5," or full implementation. On the one hand, this makes sense in that "easy" policies should be more frequently implemented by less "able" respondents. On the other hand, it does not make sense to me that "easy" policies should be less likely to be implemented by more "able" respondents.    There's clearly something I don't understand here or something very wrong with my conceptualization of what the data reflect. I'm hoping it's the former. Can you shed any light? ... View more

I have dataset with a many survey responses and many variables. One variable a string variable named Q3 in which the values look like this: 1,2 1,3,12 1,2,3,12,7,8,9 2,3,9 ... (yes, the values are not entirely in order, though this is probably not a big deal)   I would like to parse this into a set of binary dummy variables, one for each possible value in Q_3, say Q3_1, Q3_2, Q3_3, ... Q3_12. For example, the values of Q3_9 for the data above would be: 0 0 1 1   I've played with some do loops using the SCAN function, but I have not been able to make it work. Can't find a forum question or help doc that addresses quite this situation.    ... View more