Skip to main content

Combining surveys with other sources of data

The term "big data" was meant to cover a wide variety of types of data. Surveys were left out of the definition. Bob Groves attempted to remedy this by coining the terms "organic" and "designed" data. These terms were meant to capture the strengths and weaknesses of big data, on the one hand, and survey data, on the other hand. Organic data are not generated for research purposes, but usually inexpensive to obtain (but not necessarily cheap to analyze). Survey data are designed for research but are often expensive to obtain.

I'm finding that these terms might get in the way of thinking about some actual problems. For instance, travel surveys are looking at combining survey data with GPS data. GPS data can be large and complex, i.e. "big data." On the other hand, features of these data are designed by the researchers in travel studies. That is, the researchers ask persons to carry a GPS device or download an app to their smartphones. These data are certainly outside of what would traditionally be thought of as a survey. On the other hand, these data are more useful when combined with survey data, as in this study.

Combining survey data with other sources of data, often measured by new technologies in ways that generate large amounts of data. This is an interesting area for surveys. And one that might not fit neatly into current categories.

Comments

  1. Thanks for the blog article.Thanks Again. Keep writing.

    ReplyDelete
  2. I am glad to say that I have gained some cool info from your blog on testing. By the way sure, I will be implementing your idea on my upcoming projects. Thank you so much
    Regards:
    Selenium Course in Chennai
    Selenium Courses in Chennai

    ReplyDelete
  3. Excellent way of writing and expressing your thoughts and ideas to the readers, very much impressed in your article. Keep doing more, waiting to read your next blog.
    Regards:
    testing training chennai
    Software testing institutes in chennai

    ReplyDelete

Post a Comment

Popular posts from this blog

"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…