Question Writing is an Art

As a survey researcher, I like to participate in surveys with enough regularity to keep current on any trends in methodology. As a web designer, an aspect of successful design is a seamlessness with the visitor’s expectations. So if the survey design realm has moved toward submit buttons on the upper right hand corner of individual pages, your idea (no matter how clever) to put a submit button on the upper left can result in a disconnect on the part of the user that will effect their behavior on the page. In fact, the survey design world has evolved quite a bit in the last few years, and it is easy to design something that reflects poorly on the quality of your research endeavor. But these design concerns are less of an issue than they have been, because most researchers are using templates.

Yet there is still value in keeping current.

And sometimes we encounter questions that lend themselves to an explanation of the importance of question writing. These questions are a gift for a field that is so difficult to describe in terms of knowledge and skills!

Here is a question I encountered today (I won’t reveal the source):

How often do you purchase potato chips when you eat out at any quick service and fast food restaurants?

2x a week or more
1x a week
1x every 2-3 weeks
1x a month
1x every 2-3 months
Less than 1x every 3 months
Never

This is a prime example of a double barreled question, and it is also an especially difficult question to answer. In my care, I rarely eat at quick service restaurants, especially sandwich places, like this one, that offer potato chips. When I do eat at them, I am tempted to order chips. About half the time I will give in to the temptation with a bag of sunchips, which I’m pretty sure are not made of potato.

In bigger firms that have more time to work through, this information would come out in the process of a cognitive interview or think aloud during the pretesting phase. Many firms, however, have staunchly resisted these important steps in the surveying process, because of their time and expense. It is important to note that the time and expense involved with trying to make usable answers out of poorly written questions can be immense.

I have spent some time thinking about alternatives to cognitive testing, because I have some close experience with places that do not use this method. I suspect that this is a good place for text analytics, because of the power of reaching people quickly and potentially cheaply (depending on your embedded TA processes). Although oftentimes we are nervous about web analytics because of their representativeness, the bar for representativeness is significantly lower in the pretesting stage than in the analysis phase.

But, no matter what pretesting model you choose, it is important to look closely at the questions that you are asking. Are you asking a single question, or would these questions be better separated out into a series?

How often do you eat at quick service sandwich restaurants?

When you eat at quick service restaurants, do you order [potato] chips?

What kind of [potato] chips do you order?

The lesson of all of this is that question writing is important, and the questions we write in surveys will determine the kind of survey responses we receive and the usability of our answers.

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To go big, first think small

We use language all of the time. Because of this, we are all experts in language use. As native speakers of a language, we are experts in the intricacies of that language.

Why, then, do people study linguistics? Aren’t we all linguists?

Absolutely not.

We are experts in *using* language, but we are not experts in the methods we employ. Believe it or not, much of the process of speaking and hearing is not conscious. If it was, we would be sensorally overwhelmed with the sheer volume of words around us. Instead, listening comprehension involves a process of merging what we expect to hear with what we gauge to be the most important elements of what we do hear. The process of speaking involves merging our estimates of what the people we communicate with know and expect to hear with our understanding of the social expectations surrounding our words and our relationships and distilling these sources into a workable expression. The hearer will reconstruct elements of this process using cues that are sometimes conscious and sometimes not.

We often think of language as simple and mechanistic, but it is not simple at all. As conversational analysts, our job is to study conversation that we have access to in an attempt to reconstruct the elements that constituted the interaction. Even small chunks of conversation encode quite a bit of information.

The process of conversation analysis is very much contrary to our sense of language as regular language users. This makes the process of explaining our research to people outside our field difficult. It is difficult to justify the research, and it is difficult to explain why such small pieces of data can be so useful, when most other fields of research rely on greater volumes of data.

In fact, a greater volume of data can be more harmful than helpful in conversation analysis. Conversation is heavily dependent on its context; on the people conversing, their relationship, their expectations, their experiences that day, the things on their mind, what they expect from each other and the situation, their understanding of language and expectations, and more. The same sentence can have greatly different meanings once those factors are taken into account.

At a time when there is so much talk of the glory of big data, it is especially important to keep in mind the contributions of small data. These contributions are the ones that jeopardize the utility and promise of big data, and if these contributions can be captured in creative ways, they will be the true promise of the field.

Not what language users expect to see, but rather what we use every day, more or less consciously.