### Understanding "Randomly Selected"

I had the opportunity this morning to meet with a medical researcher who runs many clinical trials. He spoke about the problems of explaining randomization when enrolling persons in a trial. It's hard to be sure they understand the concept of randomization. To be sure, it's even more difficult to be sure they understand the consequences of either enrolling or not enrolling in a trial. But the problem of explaining randomization caught my attention.

This reminds me of the situation that interviewers find themselves in quite frequently. In implementing random selection of a person from within a household, they often find that the person selected is someone other than the informant who aided with the selection. In these cases, the informant may be disappointed that they weren't selected and ask if they can do the interview instead. It's often difficult to explain why we want to speak to the other person, who is not there or maybe not even willing to do the interview.

It certainly takes a skillful respondent to explain the concept of random sampling in that situation. It might be that research into explaining this concept to participants in a clinical trial would help us arm interviewers to respond to these kinds of questions.

1. There is an important difference I think: in clinical trials, it is random assignment to treatment that we have to explain. People get the idea of experimentation quite easily in my experience (it takes me 20 minutes to explain this to first year students). Random selection is however far more complicated to explain, especially within households. The whole idea of sampling takes me 4 hours to explain, and even then I think most students don't get it.
I agree that it is a huge problem to select respondents within households (are there any review papers on this?), but if we don't have a personalized frame, I am not sure how we can solve this.

1. Although not exactly a review paper on within-household selection, there is a paper on POQ (Spring 2005) by Gaziano that compares different techniques of several studies.

2. Well, the clinician we were discussing this seemed to think it was hard for patients to understand.

I'm guessing we can do a better job of training interviewers to answer these types of questions. I'm not sure how.

### "Responsive Design" and "Adaptive Design"

My dissertation was entitled "Adaptive Survey Design to Reduce Nonresponse Bias." I had been working for several years on "responsive designs" before that. As I was preparing my dissertation, I really saw "adaptive" design as a subset of responsive design.

Since then, I've seen both terms used in different places. As both terms are relatively new, there is likely to be confusion about the meanings. I thought I might offer my understanding of the terms, for what it's worth.

The term "responsive design" was developed by Groves and Heeringa (2006). They coined the term, so I think their definition is the one that should be used. They defined "responsive design" in the following way:

1. Preidentify a set of design features that affect cost and error tradeoffs.
2. Identify indicators for these costs and errors. Monitor these during data collection.
3. Alter the design features based on pre-identified decision rules based on the indi…

### An Experimental Adaptive Contact Strategy

I'm running an experiment on contact methods in a telephone survey. I'm going to present the results of the experiment at the FCSM conference in November. Here's the basic idea.

Multi-level models are fit daily with the household being a grouping factor. The models provide household-specific estimates of the probability of contact for each of four call windows. The predictor variables in this model are the geographic context variables available for an RDD sample.

Let $\mathbf{X_{ij}}$ denote a $k_j \times 1$ vector of demographic variables for the $i^{th}$ person and $j^{th}$ call. The data records are calls. There may be zero, one, or multiple calls to household in each window. The outcome variable is an indicator for whether contact was achieved on the call. This contact indicator is denoted $R_{ijl}$ for the $i^{th}$ person on the $j^{th}$ call to the $l^{th}$ window. Then for each of the four call windows denoted $l$, a separate model is fit where each household is assum…

### Goodhart's Law

I enjoy listening to the data skeptic podcast. It's a data science view of statistics, machine learning, etc. They recently discussed Goodhart's Law on the podcast. Goodhart's was an economist. The law that bears his name says that "when a measure becomes a target, then it ceases to be a good measure." People try and find a way to "game" the situation. They maximize the indicator but produce poor quality on other dimensions as a consequence. The classic example is a rat reduction program implemented by a government. They want to motivate the population to destroy rats, so they offer a fee for each rat that is killed. Rather than turn in the rat's body, they just ask for the tail. As a result, some persons decide to breed rats and cut off their tails. The end result... more rats.

I have some mixed feelings about this issue. There are many optimization procedures that require some single measure which can be either maximized or minimized. I think thes…