Keeping Research Responsible

Reliability and validity are the two most important principals in social science research. They are the measures that maintain the integrity, quality and ultimate usefulness of our work.

Reliability refers to the replicability of findings, which is a crucial element in any scientific process. Repeatability under varying conditions helps to establish the consistency and boundaries of a phenomena. Oftentimes, we like to compare ourselves to scientists and emphasize our take on the scientific method. I would argue that some of the most important lessons we can learn from science include the basic doubt (inherent in statistics as well, thanks to the null hypothesis) that assures that no one study can make knowledge so much as suggest knowledge that can lead to further testing and potential verification. Research is inherently tied to its underlying questions, and a well conducted study based on one question could easily lead to very different findings than another, even slightly different, research question. Reliability cannot be valued highly enough in social science research.

Validity refers to the value of our findings. What do these findings mean? What do they refer to? In survey research, understanding the validity of a finding often entails retracing what question was asked to whom under what circumstances and anchoring any conclusions to those basic truths instead of extrapolating to a wider principle that we would like to have observed.

In text analytics, their are two more anchors that guide research; precision and recall. Recall is the measure of how many of the correct instances of a phenomena you were able to isolate in your programming, and precision refers to the percent of the instances that you collected that were indeed instances of your target phenomena. Text analysis is a dance of queries, toggling between collecting correct matches and dropping incorrect matches. It is within the context of this dance that language seems most staggering. Here we see how little people say what they mean or mean what they say, how much context matters, how often people refer to subjects by proxy, how dependent on ongoing conversation new elements are, …

As users of language, we are constantly inundated with words. We cope with this by only focusing on selective elements. We focus on metamessages, not mechanics. It is easy to assume, from our perspective, that language is straightforward. Indeed, if it was straightforward, text analytics would be easy and misunderstandings would not be so rampant! All of the linguistic data that we are flooded with would be harnessed regularly, and society would reflect that data better through ubiquitously increased customization.

But the reality of language stands in stark contrast to what we assume it to be, and that reality is the lifeblood of linguistics.

Rethinking the Future of Survey Methodology; Finding a Place for Linguistics

Where is the future of survey research?

The technical context in which survey methodology lives is evolving quickly. Where will surveys fit into this context in the future?

In the past, surveys were a valuable and unique source of data. As society became more focused on customization and understanding certain populations, surveys became in invaluable tool for data collection. But at this point, we are inundated with data. The amount of content generatd every minute on the net is staggering. In an environment where content is so omnipresent, what role can surveys play? How can we justify our particular brand of data?

Survey methodology has become structured around a set of ethics and practices, including representativeness and respect for the respondents. Without that structure, the most vocal win out, and the resulting picture is not representative.

I recently had the pleasure of reading a bit of Don Dillman’s rewrite of ‘The Tailored Design Method,’ which is the defining classic reference in survey research. The book includes research based strategies for designing and targeting a survey population with the highest possible degree of success. It is often referred to as a bible of sorts to survey practitioners. This time around, I began to think about why the suggestions in the book are so successful. I believe that the success of Dillman’s suggestions has to do with his working title- it is a tailored method, designed around the respondents. And, indeed, the book borrows some principles of respondent or user centered design.

So where does text analysis fit into that? In a context where content is increasingly targeted, and people expect content to be increasingly targeted, surveys as well need to be targeted and tailored for respondents. In an era where the cost benefit equation of survey response is increasingly weighted against us, where potential responses are inundated with web content and web surveys, any misstep can be enough to drive respondents away and even to cause a potential viral backlash. It has never been more important to get it right.

And yet we are pressured not to get it right but to get it fast. So the traditional methods of focus groups and cognitive interviews are increasingly too costly and too timely to use. But their role is an important one. They act to add a layer of quality control to the surveys we produce. They keep us from thinking that because we are the survey experts we are also the respondent experts and the response experts.

A good example of this is Shaeffer’s key idea of native terms. I have a brief story to illustrate this. Our building daycare is about to close, and I have been involved in many discussions about the impact of its closure as well as the planning and musing about the upcoming final farewell reunion celebration. The other day I ran into one of the kids’ grandparents, someone who I have frequentky discussed the daycare with. She asked me if I was planning to go to Audrey’s party. I told her I didn’t know anything about it and wasn’t planning to go. I said this, because I associate the terms she used with retirement celebrations. I assumed that she was talking about a party specifically in honor of the director, not the reunion for all of the kids.

It’s easy as a survey developer to assume that if you ask something that is near enough to what you want to know, the respondent can extrapolate the rest. But that belies the actual way in which we communicate. When it comes to communication, we are inundated with verbal information, and we only really consciously take the gloss of it. That’s what linguistics is all about; unpacking the aspects of communication that communicators don’t naturally focus on, don’t notice, and even can’t notice in the process of communication.

So where am I going with all of this?

One of the most frequent aspects of text analysis is a word frequency count. This is often used as a psuedo content analysis, but that is a very problematic extrapolation, for reasons that I’ve mentioned before in this blog and in my paper on ths topic. However, word frequency counts are a good way of extrapolating native terms from which to do targeting.

Text analytics aren’t representative, but they have the ability of being more representative than many of the other predevelopment methods that we employ. Their best use may not be so much as a supplement to our data analysis as a precursor to our data collection.

However, that data has more uses than this.

It CAN be used as a supplement to data analysis as well, but not by going broad. By going DEEP. Taking segments and applying discourse analytic methodology can be a way of supplementing the numbers and figures collected with surveys with a deeper understanding of the dynamics of the respondent population.

Using this perspective, linguistics has a role both in the development of tailored questionnaires and in the in depth analysis of the respondenses and respondents.

More work on Twitter, Google Searches and Text Analytics in Survey research

I am so excited to see this blog post and read the paper that it was based on!

 

blog post:

https://blogs.rti.org/surveypost/2012/01/04/can-surveillance-of-tweets-and-google-searches-substitute-survey-research-2

paper:

http://www.rti.org/pubs/twitter_google_search_surveillance.pdf

 

Kudos to RTI for continuing to carve out a place for text analytics in the future of survey research!

Word Clouds

Here is an interesting application of word clouds. It is a word cloud analysis of Public Opinion Quarterly, the leading journal in Public Opinion Research:

https://blogs.rti.org/surveypost/2012/02/26/a-visual-history-of-poq-1937-to-present/

Word clouds are a fast and easy tool that produce a visual picture of the most frequently used words in a body of text or ‘bag of words.’ They are frequently used as a tool for content analysis.

On my ‘my research’ page above, there is a link to a paper I wrote about text analytic strategies. In the paper, I addressed word clouds in great detail. I did that because word clouds are fast gaining popularity and recognition in the survey research community and in the wider society at large. However, the clouds have a lot of limitations that are rarely considered by people who use them.

One of the complications of a word cloud is that word frequency alone doesn’t speak to the particular ways in which a word was used. So when you see ‘public,’ you think of the public/private dichotomy that is such a big debate in the current public sphere. However, in the context of a survey ‘public’ could also easily be used as a noun, to refer to potential respondents. While word clouds appear to give a lot of information in a quick visual, the picture underlying that information can be clouded by the complexities of language use.

I don’t think that these pictures can map directly onto the underling topical landscape, but they can provide a quick window into the specific words that we have used over the years and the changes in our lexicon over time.

Funny Focus Group moment on Oscars

It’s not often that aspects of survey research make it into the public sphere. Last night’s Oscars included some “recovered focus group footage” from the Wizard of Oz. It’s hilarious, and there’s a good reason why it is. Humor often happens when occurrences don’t match expectations. We tend to expect every member of the focus group to be reasonable and representative, but the reality of that just isn’t true.

 

Anyway, enjoy!

AAPOR Conference Preliminary Program is Up!

This is exciting!

The conference theme this year is New Frontiers in Public Opinion Research, and now we can get a first glimpse at AAPOR’s take on the future of the field! There are quite a few sessions on web survey design, paradata, alternative data sources, and the potential of social media. It will be interesting to see which of the sessions will have a sociolinguistic bent, because many certainly have that potential. There are also sessions on interviewer effects and context effects, which may even use Conversation Analysis (CA) approaches.

http://www.aapor.org/AM/Template.cfm?Section=AAPOR_Annual_Conference&Template=/CM/ContentDisplay.cfm&ContentID=4986

“The combination of designed data [from surveys] with organic data [from the Internet and other automatic sources] is the ticket to the future.” -Robert Groves

I first became familiar with the work of Tom Smith when I was working on my AAPOR paper on multilingual, multinational and multicultural surveys. He is well spoken and an excellent writer. Here is an excellent commentary of his about the future of survey methodology. It really speaks to some of the motivation behind my enrollment in the MLC program. It is his final ‘Letter from the President” of his tenure as WAPOR (World Association of Public Opinion Research) president.

“Dear WAPOR Members,

Let me raise two inter-related questions:

Is public opinion research about to undergo a paradigm shift?

Should it or shouldn’t it?”

 

The full text can be found here: http://wapor.unl.edu/wp-content/uploads/2012/02/4q2011.pdf