Skip to main content

Costs of Paradata... Analysis

One of the hidden costs of paradata are the time spent analyzing these data. Here, we've spent a lot of time trying to find standard ways to convert these data into useful information. But many times, we end up doing specialized analyses. Searching for an explanation of some issue. And, sometimes, this analysis doesn't lead to clear-cut answers.

In any event, paradata aren't just collected, they are also managed and analyzed. So there are costs for generating information from these data. We could probably think of this in a total survey error perspective. "Does this analysis reduce total error more than increasing the number of interviews?" In practice, such a question is difficult to answer. What is the value of the analysis we never did? And how much would it have cost?

There might be two extreme policies in this regard. One is "paralysis by analysis." Continually seeking information and delaying decisions. The other extreme is "flying by the seat of the pants," making uninformed decisions frequently. Most of the time, we choose a policy somewhere between these two extremes and hope that we made a nearly optimal choice.

Perhaps the whole problem goes away as we become more adept at manipulating large, complicated data structures. Maybe. I can at least say that hasn't happened yet.
Labels:

Comments

  1. AbirAugust 26, 2015 at 5:12 AM

    Thanks

    ReplyDelete
  2. UnknownAugust 30, 2015 at 11:00 AM

    From starting to finish a survey takes lot of time. However, after a survey using the collected information wisely is challenging. It's good to learn how this thing can be done after a successful survey or, you can try PanXpan (an analytics software) that has a Survey Response Analysis module which can help with this. It's a cost effective software. It's free to try and after that only costs $10 a month.

    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
36 comments
Read more

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
Post a Comment
Read more

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
4 comments
Read more