What do all of these polling strategies add up to?

Yesterday was a big first for research methodologists across many disciplines. For some of the newer methods, it was the first election that they could be applied to in real time. For some of the older methods, this election was the first to bring competing methodologies, and not just methodological critiques.

Real time sentiment analysis from sites like this summarized Twitter’s take on the election. This paper sought to predict electoral turnout using google searches. InsideFacebook attempted to use Facebook data to track voting. And those are just a few of a rapid proliferation of data sources, analytic strategies and visualizations.

One could ask, who are the winners? Some (including me) were quick to declare a victory for the well honed craft of traditional pollsters, who showed that they were able to repeat their studies with little noise, and that their results were predictive of a wider real world phenomena. Some could call a victory for the emerging field of Data Science. Obama’s Chief Data Scientist is already beginning to be recognized. Comparisons of analytic strategies will spring up all over the place in the coming weeks. The election provided a rare opportunity where so many strategies and so many people were working in one topical area. The comparisons will tell us a lot about where we are in the data horse race.

In fact, most of these methods were successful predictors in spite of their complicated underpinnings. The google searches took into account searches for variations of “vote,” which worked as a kind of reliable predictor but belied the complicated web of naturalistic search terms (which I alluded to in an earlier post about the natural development of hashtags, as explained by Rami Khater of Al Jezeera’s The Stream, a social network generated newscast). I was a real-world example of this methodological complication. Before I went to vote, I googled “sample ballot.” Similar intent, but I wouldn’t have been caught in the analyst’s net.

If you look deeper at the Sentiment Analysis tools that allow you to view the specific tweets that comprise their categorizations, you will quickly see that, although the overall trends were in fact predictive of the election results, the data coding was messy, because language is messy.

And the victorious predictive ability of traditional polling methods belies the complicated nature of interviewing as a data collection technique. Survey methodologists work hard to standardize research interviews in order to maximize the reliability of the interviews. Sometimes these interviews are standardized to the point of recording. Sometimes the interviews are so scripted that interviewers are not allowed to clarify questions, only to repeat them. Critiques of this kind of standardization are common in survey methodology, most notably from Nora Cate Schaeffer, who has raised many important considerations within the survey methodology community while still strongly supporting the importance of interviewing as a methodological tool. My reading assignment for my ethnography class this week is a chapter by Charles Briggs from 1986 (Briggs – Learning how to ask) that proves that many of the new methodological critiques are in fact old methodological critiques. But the critiques are rarely heeded, because they are difficult to apply.

I am currently working on a project that demonstrates some of the problems with standardizing interviews. I am revising a script we used to call a representative sample of U.S. high schools. The script was last used four years ago in a highly successful effort that led to an admirable 98% response rate. But to my surprise, when I went to pull up the old script I found instead a system of scripts. What was an online and phone survey had spawned fax and e-mail versions. What was intended to be a survey of principals now had a set of potential respondents from the schools, each with their own strengths and weaknesses. Answers to common questions from school staff were loosely scripted on an addendum to the original script. A set of tips for phonecallers included points such as “make sure to catch the name of the person who transfers you, so that you can specifically say that Ms X from the office suggested I talk to you” and “If you get transferred to the teacher, make sure you are not talking to the whole class over the loudspeaker.”

Heidi Hamilton, chair of the Georgetown Linguistics department, often refers to conversation as “climbing a tree that climbs back.” In fact, we often talk about meaning as mutually constituted between all of the participants in a conversation. The conversation itself cannot be taken outside of the context in which it lives. The many documents I found from the phonecallers show just how relevant these observations can be in an applied research environment.

The big question that arises from all of this is one of a practical strategy. In particular, I had to figure out how to best address the interview campaign that we had actually run when preparing to rerun the campaign we had intended to run. My solution was to integrate the feedback from the phonecallers and loosen up the script. But I suspect that this tactic will work differently with different phonecallers. I’ve certainly worked with a variety of phonecallers, from those that preferred a script to those that preferred to talk off the cuff. Which makes the best phonecaller? Neither. Both. The ideal phonecaller works with the situation that is presented to them nimbly and professionally while collecting complete and relevant data from the most reliable source. As much of the time as possible.

At this point, I’ve come pretty far afield of my original point, which is that all of these competing predictive strategies have complicated underpinnings.

And what of that?

I believe that the best research is conscious of its strengths and weaknesses and not afraid to work with other strategies in order to generate the most comprehensive picture. As we see comparisons and horse races develop between analytic strategies, I think the best analyses we’ll see will be the ones that fit the results of each of the strategies together, simultaneously developing a fuller breakdown of the election and a fuller picture of our new research environment.

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Education from the Bottom Up?

Last night I attended a talk by Shirley Bryce Heath about her new book, Words at Work and Play, moderated by Anne Harper Charity Hudley and Frederick Erickson. Dr Bryce Heath has been following a group of 300 families for 30 years, and in her talk she addressed many of the changes she’d seen in the kids in the time she’d been observing them. She made one particularly interesting point. She mentioned that the world of assessment, and, in fact much of the adult world hasn’t kept up with the kids’ evolution. The assessments that we subject kids to are traditional, reflecting traditional values and sources. She went as far as to say that we don’t know how to see, appreciate or notice these changes, and she pointed out that much of new styles of learning came outside of the school environment.

This part of her talk reminded me of an excellent blog post I read yesterday about unschooling. Unschooling is the process of learning outside of a structured environment. It goes further than homeschooling, which can involve structured curricula. It is curricularly agnostic and focused on the learning styles, interests, and natural motivation of the students. I mentioned the blog post to Terrence Wiley, president of the Center for Applied Linguistics, and he emphasized the underlying idealism of unschooling. It rests on the basic belief that everyone is naturally academically motivated and interested and will naturally embrace learning, in their own way, given the freedom to do it. Unschooling is, as some would say my “spirit animal.” I don’t have the time or the resources to do it with my own kids, and I’m not sure I would even if I were fully able to do it. I have no idea how it could be instituted in any kind of egalitarian or larger scale way. But I still love the idea, in all it’s unpracticality. (Dr Wiley gave me a few reading assignments, explaining that ‘everything old in education is new again’)

Then today I read a blog about the potential of using Wikipedia as a textbook. This idea is very striking, not just because Wikipedia was mostly accurate, freely available, covered the vast majority of the material in this professor’s traditional textbooks, and has an app that will help anyone interested create a custom textbook, but because it actually addresses what kids do anyway! Just this past weekend, my daughter was writing a book report, and I kept complaining that she chose to use Wikipedia to look up the spelling of a character’s name rather than walk upstairs and grab the book. Kids use Wikipedia often and for all kinds of things, and it is often more common for parents and educators to forbid or dismiss this practice than to jump right in with them. I suggest that the blogger not only use Wikipedia, but use the text as a way to show what is or is not accurate, how to tell, and where to find other credible, collaborative sources when it doubt. What an amazing opportunity!

So here’s the question that all of this has been leading to: Given that the world around is is rapidly changing and that our kids are more adept at staying abreast of these changes than they are, could it be time to turn the old expert-novice/ teacher-student paradigm on its head, at least in part? Maybe we need to find ways to let some knowledge come from the bottom up. Maybe we need to let them be the experts. Maybe we need to, at least in part, rethink our role in the educating process?

Frederick Erickson made an excellent point about teaching “You have to learn your students in order to teach them.” He talked about spending the first few days in a class gathering the expertise of the students, and using that knowledge when creating assignments or assigning groups. (I believe Dr Hudley mentioned that she did this, too. Or maybe he supplied the quote, and she supplied the example?)

All of this makes me wonder what the potential is for respecting the knowledge and expertise of the students, and working from there. What does bottom-up or student-led education look like? How can it be integrated into the learning process in order to make it more responsive, adaptive and modern?

Of course, this is as much a dream for a wider society as unschooling is for my own family. To a large extent, practicality shoots it all in the foot with the starting gun. But a girl can dream, no?

“Not everything that can be counted counts”

“Not everything that counts can be counted, and not everything that can be counted counts” – sign in Einstein’s Princeton office

This quote is from one of my favorite survey reminder postcards of all time, along with an image from from the Emilio Segre visual archives. The postcard layout was an easy and pleasant decision made in association with a straightforward survey we have conducted for nearly a quarter century. …If only social media analysis could be so easy, pleasant or straightforward!

I am in the process of conducting an ethnography of DC taxi drivers. I was motivated to do this study because of the persistent disconnect between the experiences and reports of the taxi drivers and riders I hear from regularly and the snarky (I know this term does not seem technical, but it is absolutely data motivated!) riders who dominate participatory media sources online. My goal at this point of the project is to chase down the disconnect in media participation and see how it maps to policy deliberations and offline experiences. This week I decided to explore ways of quantifying the disconnect.

Inspired by this article in jedem (the eJournal of eDemocracy and Open Government), I decided to start my search using framework based in Social Network Analysis (SNA), in order to use elements of connectedness, authority and relevance as a base. Fortunately, SNA frameworks are widely available to analysts on a budget in the form of web search engines! I went through the first 22 search results for a particular area of interest to my study: the mandatory GPS policy. Of these 22 sites, only 11 had active web 2.0 components. Across all of these sites, there were just two comments from drivers. Three of the sites that didn’t have any comments from drivers did have one post each that sympathized with or defended DC taxi drivers. The remaining three sites had no responses from taxi drivers and no sympathetic responses in defense of the drivers. Barring a couple of comments that were difficult to divine, the rest of the comments were negative comments about DC taxi drivers or the DC taxi industry. This matched my expectations, and, predictably, didn’t match any of my interviews or offline investigations.

The question at this point is one of denominator.

The easiest denominator to use, and, in fact, the least complicated was the number of sites. Using this denominator, only one quarter of the sites had any representation from a DC taxi driver. This is significant, given that the discussions were about aspects of their livelihood, and the drivers will be the most closely affected by the regulatory changes. This is a good, solid statistic from which to investigate the influence of web 2.0 on local policy enactment. However, it doesn’t begin to show the lack of representation the way that a denominator such as number of posts, number of posters, or number of opinions would have. But each one of these alternative denominators has its own set of headaches. Does it matter if one poster expresses an opinion once and another expresses another, slightly different opinion more than once? If everyone agrees, what should the denominator be? What about responses that contain links that are now defunct or insider references that aren’t meaningful to me? Should I consider measures of social capital, endorsements, social connectedness, or the backgrounds of individual posters?

The simplest figure also doesn’t show one of the most striking aspects of this finding; the relative markedness of these posts. In the context of predominantly short, snarky and clever responses, one of the comments began with a formal “Dear DC city councilmembers and intelligent  taxpayers,” and the other spread over three dense, winding posts in large paragraph form.

This brings up an important aspect of social media; that of social action. If every comment is a social action with social intentions, what are the intentions of the posters and how can these be identified? I don’t believe that the majority of posts left were intended as a voice in local politics, but the comments from the drivers clearly were. The majority of posts represent attempts to warrant social capital using humor, not attempts to have a voice in local politics. And they repeated phrases that are often repeated in web 2.0 discussions about the DC taxi situation, but rarely repeated elsewhere. This observation, of course, is pretty meaningless without being anchored to the data itself, both quantitatively and qualitatively. And it makes for some interesting ‘next steps’ in a project that is certainly not short of ‘next steps.’

The main point I want to make here is about the nature of variables in social media research. Compared to a survey, where you ask a question, determined in advance, and have a set of answers to work with in your analysis, you are free to choose your own variables for your analysis. Each choice brings with it a set of constraints and advantages, and some fit your data better than others. But the path to analysis can be a more difficult path to take, and more justification about the choices you make is important. To augment this, a quantitative analysis, which can sometimes have very arbitrary or less clear choices included in it, is best supplemented with a qualitative analysis that delves into the answers themselves and why they fit the coding structure you have imposed.

In all of this, I have quite a bit of work out ahead of me.

I conducted my first diversity training today…

One of the perks of my grad program is learning how to conduct diversity training.

Today I was able to put that skill to use for the first time. I conducted a workshop for a local parents group about Talking with your Kids about Race and Diversity. I co-facilitated it with Elvira Magomedova, a recent graduate from the MLC program who has more experience and more of a focus in this area. It was a really interesting and rewarding experience.

We did 4 activities:

1. We introduced ourselves by telling our immigration stories. I saw this last week at an open house at my daughter’s middle school, and it profoundly reminded me about the personal ways in which we all embody global history and the immigrant nature of the US. Between feuding clans in Ireland,  narrow escapes from the holocaust and traveling singers in Europe, this exercise is both powerful and fun. Characters and events really come alive, and everyone is left on a more equal footing.

2. For the 2nd activity, we explored the ways in which we identify ourselves. We each put a circle in the center of a sheet of paper, an then we added four bubble spokes with groups or cultures or ways in which we identify ourselves. The exercise came from Cultural Awareness Learning Module One. At the bottom of the page, we explored these relationships more deeply, e.g. “I’m a parent, but I’m not a stay at home parent” or “I’m Muslim, but I’m not practicing my religion.” We spoke in depth about our pages in pairs and then shared some with the group.

3. This is a fun activity for parents and kid alike. We split into two groups, culture A and culture B. Each culture has a list of practices, e.g. standing close or far, making eye contact or not, extensive vs minimal greetings or leavetaking, shaking or not shaking hands, … The groups learn, practice, and then mingle. This is a profoundly awkward activity!

After mingling, we get back into the group and discuss the experience. It soon becomes obvious that people take differences in “culture” personally. People complain that it seemed like their interlocuters were just trying to get away from them, or seemed overly interested in them, or…. They also complain about how hard it is to adjust your practices to act in the prescribed way.

This exercise is a good way for people to understand the ways in which conflicting cultural norms play out, and it helps parents to understand how to work out misunderstandings with their kids.

4. Finally, my daughter made a slide show of people from all over the world. The people varied in countless physical ways from each other, and we used them to stimulate conversation about physical differences. As adults, we tend to ascribe a bevvy of sociological baggage to these physical differences, but the reality is that, unless we’re Steven Colbert, there are striking physical differences between people. As parents, we are often taken aback when our kids speak openly about differences that we’ve grown accustomed to not talking about. It’s natural and normal to wonder how to handle these observations.

The upshot of this conversation is that describing anyone by a single physical category doesn’t really make sense. If you’re talking about a physical description of someone, you have a number of physical features to comment on. Whereas referring to anyone by a single physical feature could be offensive, a more detailed description is simply a more accurate physical description. We don’t have to use judgmental words, like “good hair,” but that shouldn’t stop us from talking about curly, straight, wavy, thick or thin. We can talk about people in terms of their height or body shape, face shape, hair texture, color or style, eye shape or color, mouth shape, ear size, nose style, skin tone, and so much more. Artificial racial or ethnic groupings don’t *really* describe what someone looks like, talks like, or has experienced.

More than this, once we have seen people in any kind of action, we have their actions and our relationship with them to use as resources. Given all of those resources, choosing race or ethnicity as a first descriptive level with our kids, or even using that descriptor and stopping, sends the message to the kids that that is the only feature that matters. It draws boundaries before it begins conversations. It passes “us and them” along.

Race and ethnicity are one way to describe a person, but they are far from the only way. And they, more than any other way, carry the most baggage. Does that mean they should be avoided or declared taboo?

This week in my Ethnography of Communication class, we each went to Gallaudet, the deaf university in DC, and observed. One of my classmates commented about her discomfort with her lack of fluency in ASL, or American Sign Language. Her comment reminded me of my kids and their cousins. My kids speak English, and only a little bit of Amharic and Tigrinya. Some of their cousins only spoke Tigrinya when they met. Some only spoke Swedish. Some spoke English with very different accents. But the language barriers never stopped them from playing with each other.

In fact, we talk about teaching our kids about diversity, but our kids should be the ones to teach us!

Here are the main lessons I’ve learned from my kids:

1. Don’t cut yourself off from people because you don’t share a common language. Communication actually runs much deeper than language. I think, for example, of one of my sisters inlaw. When we first met, we didn’t have a common language. But the more I was able to get to know her over time, the more we share. I really cherish my relationship with her, and I wouldn’t have it if I had let my language concerns get in the way of communicating with her.

2. People vary a lot, strikingly, in physical ways. These are worthy of comment, okay to notice, and important parts of what make people unique.

3. If you cut yourself off from discomfort or potential differences, you draw a line between you and many of the people around you.

4. It is okay to be wrong, or to still be learning. Learning is a lifelong process. Just because we’re adults doesn’t mean we have to have it all down pat. Don’t be afraid to fail, to mess up. Your fear will get you nowhere. How could you have learned anything if you were afraid of messing up?

In sum, this experience was a powerful one and an interesting one. I sincerely hope that the conversations we began will continue.

* Edited to Add:

Thandie Newton TED talk, Embracing Otherness

Chimamanda Adichie TED talk: The danger of a single story

GREAT letter with loads of resources: http://goodmenproject.com/ethics-values/why-i-dont-want-to-talk-about-race/

an interesting article that we read in class: why white parents don’t talk about race

another interesting article: Lippi Green 1997 Teaching Children How to Discriminate

 

Getting to know your data

On Friday, I had the honor of participating in a microanalysis video discussion group with Fred Erickson. As he was introducing the process to the new attendees, he said something that really caught my attention. He said that videos and field notes are not data until someone decides to use them for research.

As someone with a background in survey research, the question of ‘what is data?’ was never really on my radar before graduate school. Although it’s always been good practice to know where your data comes from and what it represents in order to glean any kind of validity from your work, data was unquestioningly that which you see in a spreadsheet or delimited file, with cases going down and variables going across. If information could be formed like this, it was data. If not, it would need some manipulation. I remember discussing this with Anna Trester a couple of years ago. She found it hard to understand this limited framework, because, for her, the world was a potential data source. I’ve learned more about her perspective in the last couple of years, working with elements that I never before would have characterized as data, including pictures, websites, video footage of interactions, and now fieldwork as a participant observer.

Dr Erickson’s observation speaks to some frustration I’ve had lately, trying to understand the nature of “big data” sets. I’ve seen quite a bit of people looking for data, any data, to analyze. I could see the usefulness of this for corpus linguists, who use large bodies of textual data to study language use. A corpus linguist is able to use large bodies of text to see how we use words, which is a systematically patterned phenomena that goes much deeper than a dictionary definition could. I could also see the usefulness of large datasets in training programs to recognize genre, a really critical element in automated text analysis.

But beyond that, it is deeply important to understand the situated nature of language. People don’t produce text for the sake of producing text. Each textual element represents an intentioned social action on the part of the writer, and social goals are accomplished differently in different settings. In order for studies of textual data to produce valid conclusions with social commentary, contextual elements are extremely important.

Which leads me to ask if these agnostic datasets are being used solely as academic exercises by programmers and corpus linguists or if our hunger for data has led us to take any large body of information and declare it to be useful data from which to excise valid conclusions? Worse, are people using cookie cutter programs to investigate agnostic data sets like this without considering the wider validity?

I urge anyone looking to create insight from textual data to carefully get to know their data.

A brave new vision of the future of social science

I’ve been typing and organizing my notes from yesterday’s dc-aapor event on the past, present and future of survey research (which I still plan to share soon, after a little grooming). The process has been a meditative one.

I’ve been thinking about how I would characterize these same phases- the past, present and future… and then I had a vision of sorts on the way home today that I’d like to share. I’m going to take a minute to be a little post apocalyptic and let the future build itself. You can think of it as a daydream or thought experiment…

The past, I would characterize as the grand discovery of surveys as a tool for data collection; the honing and evolution of that tool in conjunction with its meticulous scientific development and the changing landscape around it; and the growth to dominance and proliferation of the method. The past was an era of measurement, of the total survey error model, of social Science.

The present I would characterize as a rapid coming together, or a perfect storm that is swirling data and ideas and disciplines of study and professions together in a grand sweeping wind. I see the survey folks trudging through the wind, waiting for the storm to pass, feet firmly anchored to solid ground.

The future is essentially the past, turned on its head. The pieces of the past are present, but mixed together and redistributed. Instead of examining the ways in which questions elicit usable data, we look at the data first and develop the questions from patterns in the data. In this era, data is everywhere, of various quality, character and genesis, and the skill is in the sense making.

This future is one of data driven analytic strategies, where research teams intrinsically need to be composed of a spectrum of different, specialized skills.

The kings of this future will be the experts in natural language processing, those with the skill of finding and using patterns in language. All language is patterned. Our job will be to find those patterns and then to discover their social meaning.

The computer scientists and coders will write the code to extract relevant subsets of data, and describe and learn patterns in the data. The natural language processing folks will hone the patterns by grammar and usage. The netnographers will describe and interpret the patterns, the data visualizers will make visual or interactive sense of the patterns, the sociologists will discover constructions of relative social groupings as they emerge and use those patterns. The discourse analysts will look across wider patterns of language and context dependency. The statisticians will make formulas to replicate, describe and evaluate the patterns, and models to predict future behaviors. Data science will be a crucial science built on the foundations of traditional and nontraditional academic disciplines.

How many people does it take to screw in this lightbulb? It depends on the skills of the people or person on the ladder.

Where do surveys fit in to this scheme? To be honest, I’m not sure. The success of surveys seems to rest in part on the failure of faster, cheaper methods with a great deal more inherent error.

This is not the only vision possible, but it’s a vision I saw while commuting home at the end of a damned long week… it’s a vision where naturalistic data is valued and experimentation is an extension of research, where diversity is a natural assumption of the model and not a superimposed dynamic, where the data itself and the patterns within it determine what is possible from it. It’s a vision where traditional academics fit only precariously; a future that could just as easily be ruled out by the constraints of the past as it could be adopted unintentionally, where meaning makers rush to be the rigs in the newest gold rush and theory is as desperately pursued as water sources in a drought.

The Bones of Solid Research?

What are the elements that make research “research” and not just “observation?” Where are the bones of the beast, and do all strategies share the same skeleton?

Last Thursday, in my Ethnography of Communication class, we spent the first half hour of class time taking field notes in the library coffee shop. Two parts of the experience struck me the hardest.

1.) I was exhausted. Class came at the end of a long, full work day, toward the end of a week that was full of back to school nights, work, homework and board meetings. I began my observation by ordering a (badly needed) coffee. My goal as I ordered was to see how few words I had to utter in order to complete the transaction. (In my defense, I am usually relatively talkative and friendly…) The experience of observing and speaking as little as possible reminded me of one of the coolest things I’d come across in my degree study: Charlotte Linde, SocioRocketScientist at NASA

2.) Charlotte Linde, SocioRocketScientist at NASA. Dr Linde had come to speak with the GU Linguistics department early in my tenure as a grad student. She mentioned that her thesis had been about the geography of communication- specifically: How did the layout of an (her?) apartment building help shape communication within it?

This idea had struck me, and stayed with me, but it didn’t really make sense until I began to study Ethnography of Communication. In the coffee shop, I structured my fieldnotes like a map and investigated it in terms of zones of activities. Then I investigated expectations and conventions of communication in each zone. As a follow-up to this activity, I’ll either return to the same shop or head to another coffee shop to do some contrastive mapping.

The process of Ethnography embodies the dynamic between quantitative and qualitative methods for me. When I read ethnographic research, I really find myself obsessing over ‘what makes this research?’ and ‘how is each statement justified?’ Survey methodology, which I am still doing every day at work, is so deeply structured that less structured research is, by contrast, a bit bewildering or shocking. Reading about qualitative methodology makes it seem so much more dependable and structured than reading ethnographic research papers does.

Much of the process of learning ethnography is learning yourself; your priorities, your organization, … learning why you notice what you do and evaluate it the way you do… Conversely, much of the process of reading ethnographic research seems to involve evaluation or skepticism of the researcher, the researcher’s perspective and the researcher’s interpretation. As a reader, the places where the researcher’s perspective varies from mine is clear and easy to see, as much as my own perspective is invisible to me.

All of this leads me back to the big questions I’m grappling with. Is this structured observational method the basis for all research? And how much structure does observation need to have in order to qualify as research?

I’d be interested to hear what you think of these issues!