Skip to main content

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.

Comments

  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.

    ReplyDelete
    Replies
    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.

      Delete
  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.

    ReplyDelete

Post a Comment

Popular posts from this blog

Tailoring vs. Targeting

One of the chapters in a recent book on surveying hard-to-reach populations looks at "targeting and tailoring" survey designs. The chapter references this paper on the use of the terms among those who design health communication. I thought the article was an interesting one. They start by saying that "one way to classify message strategies like tailoring is by the level of specificity with which characteristics of the target audience are reflected in the the communication." That made sense. There is likely a continuum of specificity ranging from complete non-differentiation across units to nearly individualized. But then the authors break that continuum and try to define a "fundamental" difference between tailoring and targeting. They say targeting is for some subgroup while tailoring is to the characteristics of the individual. That sounds good, but at least for surveys, I'm not sure the distinction holds. In survey design, what would constitute

What is Data Quality, and How to Enhance it in Research

  We often talk about “data quality” or “data integrity” when we are discussing the collection or analysis of one type of data or another. Yet, the definition of these terms might be unclear, or they may vary across different contexts. In any event, the terms are somewhat abstract -- which can make it difficult, in practice, to improve. That is, we need to know what we are describing with those terms, before we can improve them. Over the last two years, we have been developing a course on   Total Data Quality , soon to be available on Coursera. We start from an error classification scheme adopted by survey methodology many years ago. Known as the “Total Survey Error” perspective, it focuses on the classification of errors into measurement and representation dimensions. One goal of our course is to expand this classification scheme from survey data to other types of data. The figure shows the classification scheme as we have modified it to include both survey data and organic forms of d

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 assu