Category Archives: survey methodology

What is Data? The answer might surprise you

I like to compare my discovery of Sociolinguistics to my love of swimming. I like to consider myself a competent swimmer, and I love being underwater. But discovering sociolinguistics was like coming up for air and noticing air and dry land. A fundamental element that led to this feeling is the difference in data.

In survey research, we rarely think about what data looks like, unless we are training new hires for jobs like data entry. Data can be visualized as a spreadsheet. Each line is a case, and each column is a variable. The variables can be numeric or character and vary in size. We analyze the numbers using statistics and the character variables using qualitative analysis. Or, we can try quantitative techniques on character fields.

The field of survey research has been feeling out its edges increasingly in the past few years. This has led us to consider new data sources, particularly data sources that do not come from surveys. Two factors shape this exploration

1.) Consideration for the genesis and representativeness of the new data. What is it, and what does it represent?

2.) A sense of what data should look like. We expect new data to resemble old data. We think in terms of joining files; collating, concatenating, merging, aggregating and disaggregating. New data should look and work like this. And so our questions are more along the lines of: how can we make new data look like (or work with) old data?

Sociolinguistics could not be more different, in terms of data. In sociolinguistics, everything is data. Look around you: you’re looking at data. Listen: you’re listening to data. The signs that you passed on your way into work? Data. The tv shows you watch when you get home? Data. Cooking with recipes? Data. Talking on the phone? Data. Attending a meeting? Institutional discourse!

In sociolinguistics, we  call analytic methods our ‘toolkit,’ and we pride ourselves on being able to analyze any kind of data with that toolkit. We include ethnographic methods, visual semiotics, discourse methods, action-based studies, as well as traditional linguistic means and measures. Each of these methods can be addressed quantitatively or qualitatively. The best studies use a combination of quantitative and qualitative methods. To me, these methods and data sources are nothing short of mind blowing, and they redefine the prospect of social science research.

Memory is incomplete experience

Today’s quote on my zen calendar is perfect.

“Memory is incomplete experience.” J. Krishnamurti

This is a great reminder for researchers and for people in general, because we all forget and keep forgetting how incomplete our memories and the memories of people we come into contact are. How many survey questions could be better written with this advice? How much better is ethnography when we base our observations on repeated viewings, rather than trying to reconstruct a vague memory? How many arguments could be avoided, if we could just remember that memories are incomplete?

I sometimes participate in a video discussion group. I am amazed that each viewing of a short segment of video brings a different set of interpretations, and I am amazed that the other participants continually notice different aspects of the video. This experience really drives the point home about how little we see in our everyday lives. We are so inundated with information that we simply couldn’t, and wouldn’t want to, process it all.

Research is the process of recovering and reconstructing. Of observing carefully. Of noticing things that we would never or could never have accessed through normal observation and we absolutely could never access through our memories. Being a researcher does not make us any more able to analyze that which we experience in a single pass- we’re still human. Being a researcher simply means that we have the capacity to observe and investigate things more closely.

More rundown on Academedia

So I promised more on Academedia (note: they will add more video and visual resources to the Academedia website in the next few days)…

First, some of Robert Cannon’s (employed with the FCC and a member of Panel B “New Media: A closer look at what works”) insightful gems

Re: internet “a participatory market of free speech”

Re: kids& social media “It’s not a question of whether kids are writing. Kids are writing all the time. It’s whether parents understand that.”

“The issue is not whether to use Wikipedia, but how to use Wikipedia”
Next, the final panel, “Digital Tools for Communication:” http://gnovis-conferences.com/panel-c/
Hitlin (Pew Project for Excellence in Journalism)
People communicate differently about issues on different kinds of media sources.
Re: Trayvon Martin case –> largest issue by media source

  •      Twitter: 21% Outrage @ Zimmerman
  •      Cable & Talk radio: 17% Gun control legislation
  •      Blogs: 15% Role of race

Re: Crimson Hexagon
Pew is different, because they’re in a partnership with Crimson Hexagon to measure trends in Traditional media sources. Also because their standard of error is much higher, and they have a team of hand coders available.

Crimson Hexagon is different, because it combines human coding with machine learning to develop algorithms. It may actually overlap pretty intensely with some of the traditional qualitative coding programs that allow for some machine learning. I can imagine that this feature would appeal especially to researchers who are reluctant to fully embrace machine coding, which is understandable, given the current state of the art. I wonder if, by hosting their users instead of distributing programs, they’re able to store and learn from the codes developed by the users?

CH appears to measure two main domains: topic volume over time and topic sentiment over time. Users get a sense of recall and precision in action as they work with the program, by seeing the results of additions and subtractions to a search lexicon. Through this process, Hitlin got a sense of the meat of the problems with text analysis. He said that it was difficult to find examples that neatly fit into boxes, and that the computer didn’t have an eye for subtlety or things that fit into multiple categories. What he was commenting about was the nature of language in action, or what sociolinguists call Discourse! Through the process of categorizing language, he could sense how complicated it is. Here I get to reiterate one of the main points of this blog: these problems are the reason why linguistics is a necessary aspect of this process. Linguistics is the study of patterns in language, and the patterns we find are inherently different from the patterns we expect to find. Linguistics is a small field, one that people rarely think of. But it is critically essential to a high quality analysis of communication. In fact, we find, when we look for patterns in language, that everything in language is patterned, from its basic morphology and syntax, to its many variations (which are more systematic than we would predict), to methods like metaphor use and intertextuality, and more.

Linguistics is a key, but it’s not a simple fit. Language is patterned in so many ways that linguistics is a huge field. Unfortunately, the subfields of linguistics divide quickly into political and educational camps. It is rare to find a linguist trained in cognitive linguistics, applied linguistics and discourse analysis, for example. But each of these fields are necessary parts of text analysis.

Just as this blog is devoted to knocking down borders in research methods, it is devoted to knocking down borders between subfields and moving forward with strategic intellectual partnerships.

This next speaker in the panel thoroughly blew my mind!

Rami Khater from Al Jazeera English talked about the generation of ‘The Stream,’ an Al Jazeera program that is entirely driven by social media analysis.

Rami can be found on Twitter: @ramisms , and he shared a bit.ly with resources from his talk: bit.ly/yzST1d

The goal of The Stream is to be “a voice of the voiceless,” by monitoring how the hyperlocal goes global. Rami gave a few examples of things we never would have heard about without social media. He showed how hash tags evolve, by starting with competing tags, evolving and changing, and eventually converging into a trend (incidentally, Rami identified the Kony 2012 trend as synthetic from the get go by pointing that there was no organic hashtag evolution. It simply started and nded as #Kony2012). He used TrendsMap to show a quick global map of currently trending hashtags. I put a link to TrendsMap on the tools section of the links on this blog, and I strongly encourage you to experiment with it. My daughter and I spent some time looking at it today, and we found an emerging conversation in South Africa about black people on the Titanic. We followed this up with another tool, Topsy, which allowed us to see what the exact conversation was about. Rami gets to know the emerging conversations and then uses local tools to isolate the genesis of the trend and interview people at its source. Instead, my daughter and I looked at WhereTweeting to see what the people around us are tweeting about. We saw some nice words of wisdom from Iyanla Vanzant that were drowning in what appeared to me to be “a whole bunch of crap!” (“Mom-mmy, you just used the C word!”)

Anyway, the tools that Rami shared are linked over here —->

I encourage you to play around with them, and I encourage you and me both to go check out the recent Stream interview with Ai Wei Wei!

The final speaker on the panel was Karine Megerdoomian from MITRE. I have encountered a few people from MITRE recently at conferences, and I’ve been impressed with all of them! Karine started with some words that made my day:

“How helpful a word cloud is is basically how much work you put into it”

EXactly! Great point, Karine! And she showed a particularly great word cloud that combined useful words and phrases into a single image. Niiice!

Karine spoke a bit about MITRE’s efforts to use machine learning to identify age and gender among internet users. She mentioned that older users tended to use noses in their smilies 🙂 and younger users did not 🙂 . She spoke of how older Iranian users tended to use Persian morphology when creating neologisms, and younger users tended to use English, and she spoke about predicting revolutions and seeing how they are propagated over time.

After this point, the floor was opened up for questions. The first question was a critically important one for researchers. It was about representativeness.

The speakers pointed out that social media has a clear bias toward English speakers, western educated people, white, mail, liberal, US & UK. Every network has a different set of flaws, but every network has flaws. It is important not to just use these analyses as though they were complete. You simply have to go deeper in your analysis.

 

There was a bit more great discussion, but I’m going to end here. I hope that other will cover this event from other perspectives. I didn’t even mention the excellent discussions about education and media!

Can they just get along? Situated Cognition and Survey Response

Finally, I’m going to take a moment to talk about Norbert Schwarz’s JPSM Distinguished Lecture on March 30! I’ve attended a few events and had a few experiences lately that I’m eager to blog about, but sometimes life has plans for us that don’t involve blogging. Today, I would say, is no different, except that I woke up thinking about this lecture!

Ok, enough about me, more about Schwartz.

I should start by saying that I am a longtime fan of Schwartz. In Fall 2009, I had just discovered the MLC program and finished what was a whirlwind application process, and I was first trying to wrap my head around the field of sociolinguistics and its intersection with my career in survey methodology. I had attended a presentation of an ethnography of communication pilot study to the McDonough School of Business, and, to my great shock, I came across a survey methodology paper that spoke of the Logic of Conversation and the role of Gricean maxims in survey responses. This fantastic piece is the work of Norbert Schwarz, and I’ve kept it nearby ever since. In it, Schwartz addresses the conversational expectations of survey respondents and shows how they respond not only to the question at hand, but also to these expectations.

It’s common in every survey to look at some of the responses and wonder how in the world they could have come about. I addressed this in an earlier blog post, where one researcher had gone as far as to call respondents stupid. Oftentimes we think of respondents “getting it right” or “getting it wrong.” But there is a larger phenomena underlying what appear to be strange responses, and it’s something that we experience when we attempt to respond to surveys.

We write survey questions with a mechanistic expectation, that if we ask a question, we will hear back the answer to that question, but we neglect to consider the fact that communication is not mechanistic. Of course, we are not necessarily aware of this. We’re aware of misunderstandings, but we’re not often aware of the tiny sphere of focus and interpretive frames that we apply to every utterance we here and utter. This is no fault of our own. This is a survival tool. We simply cannot process all of the information that we’re constantly inundated with.

In survey research, we’re aware that small differences in question format can influence responses. We’re aware that changing a scale will change the numeric range of the responses. We see that changing labels on a scalar question changes the results. We’re aware that sometimes answers appear to be absolute contradictions and seem to us to be impossible. These are especially large challenges for us, and they are the purview of linguistics.

Schwartz, however, is not a linguist. He is a cognitice scientist. And his lecture was not about the linguistic basis behind apparently wonky response phenomenon. Instead, he spoke about situated cognition.

Situated cognition makes a lot of intuitive sense. It is a proven psychological phenomena that shows that we don’t hold attitudes, beliefs and responses at a certain location in our mind, rather we recreate them each time. Instead we create or recreate them each time. This process allows for much more of an influence from “what’s on our mind,” making situational or contextual factors much more important, and decreasing the reliability, or repeatability, of survey responses. This is not a hard egg for someone (me) with a background in cognitive science and sociolinguistics to swallow, but the effect on the audience was remarkable. How does someone from a field that thrives on the mechanistic nature of responses take the suggestion that what they’re measuring is not a distinctly measurable entity so much as a complicated, potentially unreliable act of nature?

One of the discussants used a couple that he was not very fond of as an example of a stable opinion. I believe that this example lends itself well to further exploration. If he had just met the couple, and he had had a negative experience with them, his evaluation of his opinion toward the couple would depend on the degree of negativity of the experience, his predisposition to give or not give them the benefit of the doubt, and his degree of concern about expressing a negative opinion to the interviewer or survey researchers. After this point, these factors will be increasingly influenced by his further experiences with the people and the degree of negativity, positivity or neutrality of the experiences, and the recency and saliency of the experiences. Essentially, his response would reflect a complicated underlying equation and be the output of situated cognition.

But what is a survey researcher supposed to do with this information?

It would be easy at this point to throw the baby out with the bathwater and cast doubt on the whole survey and response process. But that’s not necessary, and that’s not the point.

The point is that each method of analysis has its own unique set of strengths and weaknesses. It is important to know the strengths and weaknesses of your methods in order to better understand what exactly you are finding and what your findings mean. And it also behooves us to supplement across methodologies. A reliable survey response is a strong finding, but it can mask underlying factors that can be accessed through other methodologies. As Pew demonstrated in their Kony 2012 report, mixing methodologies can lead to a more clear, nuanced narrative than any single method could yield.

It would be easy to dismiss Schwartz’s reporting, or to dismiss survey methodology. But dismissing either would be foolish, rash and unnecessary. Instead, let’s build on both. A wider foundation can build a better house, but the best house will need to take down some old walls and rethink its floorplan.

JPSM Distinguished Lecture

Tomorrow the Joint Program in Survey Methodology is having a special lecture at the University of Maryland.

Do survey respondents lie?

Situated cognition and socially desirable responding Prof. Norbert Schwarz University of Michigan

Survey researchers commonly assume that people know what they do, know what they believe, and can report on it with candor and accuracy, as Angus Campbell put it. From this perspective, many findings suggest that survey respondents are less than candid. The best known example is the observation that answers to racial attitude questions vary as a function of the interviewers race. Challenging this interpretation, a large body of social psychological research shows similar context effects under conditions that do not lend themselves to this interpretation, including conditions that use implicit attitude measures, which are not subject to deliberate “faking”.

From a situated cognition perspective, such findings reflect that attitude questions assess context sensitive evaluations that respondents form on the spot, drawing on information that is accessible at that point in time. The underlying processes operate in daily life as well as in survey interviews and reflect the situated nature of human judgment rather than a deliberate attempt to report a socially desirable answer.

I review relevant findings and discuss their implications for survey measurement.

Friday, March 30, 2012, 3:00 PM – 5:00 PM

2205 LeFrak Hall, University of Maryland, College Park MD USA

Metro stop: College Park on the Green line See http://www.jpsm.umd.edu/jpsm/?geninfo/directions.htm for directions and parking information.

 

Discussants: Paul Beatty, NCHS and David Cantor, Westat

 

A reception follows the lecture.

Don’t fear Big Data

I really enjoyed this RTI blog post about embracing big data:

https://blogs.rti.org/surveypost/2012/03/22/why-you-should-not-fear-but-embrace-the-age-of-big-data/

I suspect that oftentimes fear of big data is motivated by a concern that new, less tested, still evolving methods will replace the time tested methods that we have grown to have so much faith in. I sincerely believe that the foundation that we have is a strong one, and the knowledge we have developed through those processes should be embraced, especially the quality controls. But SUPPLEMENTING an analysis through a measured combination of data sources can lead to a more complete picture.

This week I spent some time analyzing Pew’s report on the Kony 2012 video. I believe that this report is an excellent example of what researchers are capable of when they look outside the artificial divisions of research group (this was a collaborative effort) and research methodology. Seven days after the release of the video, Pew was able to reconstruct a comprehensive narrative of the video’s dissemination, using traditional survey methods, sentiment analytic snapshots over time, and a careful breakdown of the media coverage of influential parties.

 

Dana Boyd also has an interesting analysis of the Kony phenomena on her Apophenia blog:

http://www.zephoria.org/thoughts/

Zen as a Research Ethic

I have a Zen calendar on my desk for 2012. It has such gems as: “Although the world is full of suffering, it is also full of the overcoming of it” (Helen Keller)

The more I look at the calendar, the more it relates to everything I think about.

I read “To see is to forget the name of the thing one sees,” (Paul Valery) and I think of the Charles Goodwin paper I cited in a recent post about Professional Vision. He talks about ways of seeing as kind of coding structures, inculturation, or ways of foregrounding certain parts of what we see. Truly, being able to see deeper than that requires shedding that inculturation and observing more closely. As researchers, we often become so deeply incultured into our way of thinking, that we lose sight of our research goals. As survey researchers, we can easily fall into the pattern of first asking “who should we survey?” and “what should we ask?” before taking more time to consider whether a survey is even an appropriate methodology for the specific topic of focus. Of course, not this action based on praxis is not limited to survey researchers. Far from it! Every person, every field, every community of practice, every language has a way of thinking. And often instead of seeing or observing, we quickly begin to navigate our networks of inculturation.

These two are similarly meaningful in my interpretation:

“Zen is not to confuse spirituality with thinking about God while one is peeling potatoes. Zen is just to peel the potatoes.” (Alan Watts)

“If all beings are Buddha, why all this striving?” (Dogen)

These are a reminder to boil things down to what they simply are and not try to describe them as what you want them to be. In survey research, this comes up often in the process of reporting research results. If I know that I intended to measure something about Project Based Learning or STEM education, it is easily for me to begin to frame my findings by my intentions. But that is not true to my findings or my methodology, and it doesn’t make for good research. I can’t say that 10% of my respondents were using project based learning methods in the classroom if I asked about the number of group activities they conducted. I must simply say that 10% were using group activities (daily/monthly/occasionally- whatever the answer choices were)

In this way, my Zen calendar not only provides something to think about in a larger sense, but it keeps my research anchored.

Why Social Media couldn’t predict Super Tuesday

This piece is a nice reminder not only, as the authors conclude, that sentiment analysis has not fully matured, but also that sentiment analysis and social media analysis probably don’t accomplish what they think they are accomplishing:

 

http://www.retargeter.com/political-advertising/why-social-media-couldnt-predict-super-tuesday

Language as a way of seeing

A conversation in my Intercultural Communication class on Tuesday inspired me to think about language in a different way.

There is a fantastic paper by Charles Goodwin called Professional Vision that shows how people train each other into ways of seeing. The example he uses is an anthropological investigation of soil, and he shows how the new anthropologist is trained by the more experienced anthropologist to notice and describe gradations in the color or quality of the soil. These gradations have a specific meaning within their investigation, and being able to describe them is an important tool of the study.

Language provides two important functions that I want to focus on today. The one we think of most often is the tool function. Language gives us a way to communicate what we see. But it is much less often that we think about language as a way of seeing.

I’ve talked about our constant stimulation before on this blog. We are constantly exposed to more sensory data than we could ever process. Language provides a lens through which to see the world in front of us. Languages have inherent sets of coding structures to apply to the world.

There is another function of language that corpus and computational linguistics really highlights, and that’s habituation. We may have the tools within a language to describe something in any number of ways, but very few of these ways are actually used with any frequency. We learn in corpus linguistics that language is never random. In many ways, in fact, language follows a zipfian distribution, with a high concentration of high frequency words or expressions, and each after the most frequent exponentially less frequent.The language itself may be large, but the language we use is much smaller.

This is an important element to consider in the conceptualization of translation. One person who reviewed the original Russian translation of our Global Survey of Physicists complained that if they did not know English, they would not have understood the Russian. The more I learn about language, the more I understand how that could be possible. Language is not just grammatical, it is habitual, and it holds a way of seeing the world.

Calling Respondents Stupid

From a Politico article titled “How much do voters know?” by Alexander
Burns:

“The first lesson you learn as a pollster is that people are stupid,”
said Tom Jensen of Public Policy Polling, a Democratic polling firm. “I
tell a client trying to make sense of numbers on a poll that are
inherently contradictory that at least once a week.”

Full article at:

http://www.politico.com/news/stories/0312/73947.html

Message to Tom Jenson:
It’s not so much that people are stupid as it is that Tom Jenson is ignorant of the cognitive underpinnings of response methodology and filling in his gaps with unnecessary condescension. Truly, all survey researchers deal with these contradictions. We might be able to garner more consistency in our response sets if we could instead survey computers or robots, but, alas, that’s not why we conduct surveys.