[amelia] Re: Another question

Gary, Thanks for your note about panel attrition. Sorry to bother you again, but preparing all of this survey data has foced me to think about some common problems in light of the imputation tecnology and I thought this one would iterest you. The question is what to do with don't know responses in surveys when the DK is potentially meaningful (i.e., it doesn't simply hide a real response). Below is an idea on how imputation may help with this problem. I havent found any discussion of this possility in any literature and was wondering if you had thought of this or thought it was an idea worth pursuing. At this point it is basically my intuition and I would obviously have to pursue a more rigorous exploration if it's a worthwhile idea. Consider, for example, DK responses to a question asking people to place themselves on a left/right scale. There are a variety of reasons why people might answer DK in this situation. I would group these into two main categories. The first are situations in which we expect that there is really an underlying answer but that it is not revealed. This category includes more than just situations in which the person wants to hide the answer. It also includes cases (1) in which the person just des not want to invest the cogntive energy to come up with a response, (2) in which they don't understand the question, or don't know the meaning of the words. Finally, people who answer don't know instead of putting themselves in the middle of the scale also belong here. The key idea here is that if we could probe the person's attitude further (explaining the meaning of the question or assuring them they could reveal it to us, we could obtain a meaningful answer). A second set of reason's for DK responses are when there is not an underlying answer that is being denied us. This can happen because the people answer DK when the respondent is really uncertain where they fit on the scale with which they are presented. Maybe they haven't thought about the issue at all. Maybe in a case like left/right self placement, they understand what the scale means but cant reconcile their conflicting policy views in a way that gives them a placement and are unwilling to say they are middle of the road, because they don't think they are (for example they could be policy extremists on some left and right policies and this doesn't jibe with what they think a centerist on a left/right scale is). In the first set of cases it seems to me that it is perfectly appropriate to impute the values of the DK category, but in the second it is not. In the second case, we would want to include DK as a valid response and model how this response contributes to the dependent variable in the explanatory model. I would suggest that we can adopt an assumption that since these are non-attitudes, we would expect that they can not have an impact on any kind of behavior that we are modelling and that they should be poorly predicted from other variables in the imputation model. If we adopt these assumptions, then it seems to me there is a reasonable way to proceed which is as follows: (1) impute all the DK's (2) in the analysis model interact the imputed variable with a dummy marking the DK's (3) If category 2 dominates the DK's then the interaction should indicate no relationship between variable and the DV for the imputed cases. (4) if category 1 dominates then they interaction will be insignificant. The real trick for this to be true is that the imputation model for people with non attitudes cannot systematically impute values that then predict the dependent variable in the same way that people with real attitudes (whether missing or not) do. Anyway, if you have any thoughts or have done any work on this, I would love to know. Again, ray and I are currently adopting a methodology to reanalyze a large number of elections studies in a lot of countires to provide a more definitive picture of cross national difference in the sources of voting behavior (and especially economic voting) and so we are trying to adopt the best practice on all these issues. Surprisingly, I thought there would be more concensus on this issue, but the DK literature seems to be more concerned with pointing out the trouble they can cause then in proposing remedies. Finally, since we will be estimating models with a fair number of variables and since we are using multinomial models for multiparty elections, inluding a full set of dummies for every categorical or quasi continuous varibel (with a don't know category included) doesn't seem like a feasible approach. Thanks, Randy ___________________________________________ Randy T. Stevenson Albert Thomas Associate Professor of Political Science Dept. of Political Science /MS 24 Rice University 6100 Main St. Houston, Texas 77005 phone: 713 348-2104 fax: 713 348-5273 email: stevenso(a)ruf.rice.edu