Skip to main content

What is the right periodicity?

It seems that intensive measurement is on the rise. There are a number of different kinds of things that are difficult to recall sufficiently over longer periods of time where it might be preferred to ask the question more frequently with a shorter reference period. For example, the number of alcoholic drinks consumed by day. More accurate measurements might be achieved if the questions was asked daily about the previous 24 hour period.

But what is the right period of time? And how do you determine that? This might be an interesting question. The studies I've seen tend to guess at what the correct periodicity is. I think it's probably the case that it would require some experimentation to determine that, including experimentation in the lab.

There are a couple of interesting wrinkles to this problem.

1. How do you set the periodicity when you measure several things that might have different periodicity? Ask the questions at the most frequent periodicity?

2. How does nonresponse/attrition fit into this? If some people will only respond at a certain rate, what should you do? Is it better to force the issue with them, i.e. make an ultimatum that they participate at the rate we desire or not at all; or better to allow them to participate at their preferred rate?

I'm sure the answers vary across the substantive areas of interest. But it does seem like an interesting set of problems in the evolving world of survey research.

Comments

Popular posts from this blog

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…

Training for Paradata

Paradata are messy data. I've been working with paradata for a number of years, and find that there are all kinds of issues. The data aren't always designed with the analyst in mind. They are usually a by-product of a process. The interviewers aren't focused (and rightly so) on generating high-quality paradata. In many situations, they sacrifice the quality of the paradata in order to obtain an interview.

The good thing about paradata is that analysis of paradata is usually done in order to inform specific decisions. How should we design the next survey? What is the problem with this survey? The analysis is effective if the decisions seem correct in retrospect. That is, if the predictions generated by the analysis lead to good decisions.


If students were interested in learning about paradata analysis, then I would suggest that they gain exposure to methods in statistics, machine learning, operations research, and an emerging category "data science." It seems like…

Balancing Response through Reduced Response Rates

A case can be made that balanced response -- that is, achieving similar response rates across all the subgroups that can be defined using sampling frame and paradata -- will improve the quality of survey data. A paper that I was co-author on used simulation with real survey data to show that actions that improved the balance of response usually led to reduced bias in adjusted estimates. I believe the case is an empirical one. We need more studies to speak more generally about how and when this might be true.

On the other hand, I worry that studies that seek balance by reducing response rates (for high-responding groups) might create some issues. I see two types of problems. First, low response rates are generally easier to achieve. It takes skills and effort to achieve high response rates. The ability to obtain high response rates, like any muscle, might be lost if it is not used. Second, if these studies justify the lower response rate by saying that estimates are not significantly c…