The paradata book, edited by Frauke Kreuter, is out! I have a chapter in the book on call scheduling.
One of the problems that I mention is how to define call windows. The goal should be to create homogenous units. For example, I made the following heatmap that shows contact rates by hour for a face-to-face survey. The figure includes contact rates for all cases and for the subset of cases that were determined to be eligibile
I used this heatmap to define contiguous call windows that were homogenous with respect to contact rates. I used ocular inspection to define the call windows.
I think this could be improved. First, clustering techniques might produce more efficient results. I assumed that the call windows had to be contiguous, this might not be true.
Second, along what dimension do we want these windows to be homogenous? Contact rates is really a proxy. We want them to be homogenous with respect to the results of next call on any case, or really our final goal of interviewing the case.
It might be that the heatmap of contact rates gets us much of the way there, but it would be nice to know that for sure.
One of the problems that I mention is how to define call windows. The goal should be to create homogenous units. For example, I made the following heatmap that shows contact rates by hour for a face-to-face survey. The figure includes contact rates for all cases and for the subset of cases that were determined to be eligibile
I used this heatmap to define contiguous call windows that were homogenous with respect to contact rates. I used ocular inspection to define the call windows.
I think this could be improved. First, clustering techniques might produce more efficient results. I assumed that the call windows had to be contiguous, this might not be true.
Second, along what dimension do we want these windows to be homogenous? Contact rates is really a proxy. We want them to be homogenous with respect to the results of next call on any case, or really our final goal of interviewing the case.
It might be that the heatmap of contact rates gets us much of the way there, but it would be nice to know that for sure.
Fascinating! It's amazing how much adding color can help with interpretation. My only comment is on that dimension. It looks like red is high contact rate cells and green is low rate cells. With the proper key, that's easier to understand (or even just by looking at a few cells), and it probably doesn't matter which colors you use, compared to the different between using color v. not. But to make it even more intuitive, you might think of using blue for the low rate cells (heat goes from hot=red to cool=blue, not green). If you're trying to use a stop light analogy, you might want to flip the colors so that green is for the high cells (the good cells) and red as the low cells (the bad cells). I don't know if there's any research to support this, but to me red says "Stop and look...problem here!" and green says "Ok...these are fine, move on".
ReplyDeleteKeep up the great work!
I was thinking that the "hot" colors meant times we wanted to call. But you may be right, it could be interpreted as flagging a problem.
DeleteMy daughter just graduated from "safety town." A key lesson for her was... red means stop.