Survey Methods Musings

In one of my favorite non-survey articles, Rossi and colleagues looked at the relative value of purchase history data and demographic information in predicting the impact of coupons with different values. The purchase history data was more valuable in the prediction. I believe a similar situations applies to surveys, at least in some settings. That is, paradata might be more valuable than sampling frame data. Of course, many of the surveys that I work on have very weak data on the sampling frame. In any event, I fit random intercept logistic regression models predicting contact that include some sampling frame data from an RDD survey. The sampling frame data are generally neighborhood characteristics. I recently made this chart, which shows the predicted vs observed contact rates for households in a particular time slot (call window). The dark circles are the predictions by observed values (household contact rates) for the multi-level model. I also fit a marginal logistic regressio...

I've been thinking about the evaluation of the risk of nonresponse bias a bit lately. Imputation seems to be a natural way to evaluate those risks. In my setup, I impute the unit nonresponders. Then I can use imputation to evaluate the value of the data that I observed (a retrospective view) and to predict the value of different parts of the data that I did not/have not yet observed (a prospective view). Allow me to use a little notation. Y_a is a matrix of observed data collected under protocol a . Y_b is a matrix of observed data collected under protocol b . Y_m is the matrix of data for the nonresponders. It's missing. I could break Y_m into two pieces: Y_m1 and Y_m2. 1) Retrospective. I can delete data that I observed and impute the values plus all the other missing values (i.e. the unit nonresponse). I can impute Y_b and Y_m conditional on Y_a . I can also impute Y_m conditional on Y_b and Y_a . It might be interesting to compare the estimates from these two ...