Tag Archives: commentary

Digital Democracy Remixed

I recently transitioned from my study of the many reasons why the voice of DC taxi drivers is largely absent from online discussions into a study of the powerful voice of the Kenyan people in shaping their political narrative using social media. I discovered a few interesting things about digital democracy and social media research along the way, and the contrast between the groups was particularly useful.

Here are some key points:

  • The methods of sensemaking that journalists use in social media is similar to other methods of social media research, except for a few key factors, the most important of which is that the bar for verification is higher
  • The search for identifiable news sources is important to journalists and stands in contrast with research methods that are built on anonymity. This means that the input that journalists will ultimately use will be on a smaller scale than the automated analyses of large datasets widely used in social media research.
  • The ultimate information sources for journalists will be small, but the phenomena that will capture their attention will likely be big. Although journalists need to dig deep into information, something in the large expanse of social media conversation must capture or flag their initial attention
  • It takes some social media savvy to catch the attention of journalists. This social media savvy outweighs linguistic correctness in the ultimate process of getting noticed. Journalists act as intermediaries between social media participants and a larger public audience, and part of the intermediary process is language correcting.
  • Social media savvy is not just about being online. It is about participating in social media platforms in a publicly accessible way in regards to publicly relevant topics and using the patterned dialogic conventions of the platform on a scale that can ultimately draw attention. Many people and publics go online but do not do this.

The analysis of social media data for this project was particularly interesting. My data source was the comments following this posting on the Al Jazeera English Facebook feed.

fb

It evolved quite organically. After a number of rounds of coding I noticed that I kept drawing diagrams in the margins of some of the comments. I combined the diagrams into this framework:

scales

Once this framework was built, I looked closely at the ways in which participants used this framework. Sometimes participants made distinct discursive moves between these levels. But when I tried to map the participants’ movements on their individual diagrams, I noticed that my depictions of their movements rarely matched when I returned to a diagram. Although my coding of the framework was very reliable, my coding of the movements was not at all. This led me to notice that oftentimes the frames were being used more indexically. Participants were indexing levels of the frame, and this indexical process created powerful frame shifts. So, on the level of Kenyan politics exclusively, Uhuru’s crimes had one meaning. But juxtaposed against the crimes of other national leaders’ Uhuru’s crimes had a dramatically different meaning. Similarly, when the legitimacy of the ICC was questioned, the charges took on a dramatically different meaning. When Uhuru’s crimes were embedded in the postcolonial East vs West dynamic, they shrunk to the degree that the indictments seemed petty and hypocritical. And, ultimately, when religion was invoked the persecution of one man seemed wholly irrelevant and sacrilegious.

These powerful frame shifts enable the Kenyan public to have a powerful, narrative changing voice in social media. And their social media savvy enables them to gain the attention of media sources that amplify their voices and thus redefine their public narrative.

readyforcnn

Still grappling with demographics

Last year I wrote about my changing perspective on demographic variables. My grappling has continued since then.
I think of it as an academic puberty of sorts.

I remember the many crazy thought exercises I subjected myself to as a teenager, as I tried to forge my own set of beliefs and my own place in the world. I questioned everything. At times I was under so much construction that it was a wonder I functioned at all. Thankfully, I survived to enter my twenties intact. But lately I have been caught in a similar thought exercise of sorts, second guessing the use of sociological demographic variables in research.

Two sample projects mark two sides of the argument. One is a potential study of the climate for underrepresented faculty members in physics departments. In our exploration of this subject, the meaning of underrepresented was raised. Indeed there are a number of ways in which a faculty member could be underrepresented or made uncomfortable: gender, race, ethnicity, accent, bodily differences or disabilities, sexual orientation, religion, … At some point, one could ask whether it matters which of these inspired prejudicial or different treatment, or whether the hostile climate is, in and of itself, important to note. Does it make sense to tick off which of a set of possible prejudices are stronger or weaker at a particular department? Or does it matter first that the uncomfortable climate exists, and that personal differences that should be professionally irrelevant are coming into professional play. One could argue that the climate should be the first phase of the study, and any demographics could be secondary. One might be particularly tempted to argue for this arrangement given the small sizes of the departments and hesitation among many faculty members to supply information that could identify them personally.

If that was the only project on my mind, I might be tempted to take a more deconstructionist view of demographic variables altogether. But there is another project that I’m working on that argues against the deconstructionist view- the Global Survey of Physicists.

(Side or backstory: The global survey is kind of a pet project of mine, and it was the project that led me to grad school. Working on it involved coordinating survey design, translation and dissemination with representatives from over 100 countries. This was our first translation project. It began in English and was then translated into 7 additional languages. The translation process took almost a full year and was full of unexpected complications. Near the end of this phase, I attended a talk at the Bureau of Labor Statistics by Yuling Pan from Census. The talk was entitled ‘the Sociolinguistics of Survey Translation.’ I attended it never having heard of Sociolinguistics before. During the course of the talk, Yuling detailed and dissected experiences that paralleled my own into useful pieces and diagnosed and described some of the challenges I had encountered in detail. I was so impressed with her talk that I googled Sociolinguistics as soon as I returned to my office, discovered the MLC a few minutes later. One month later I was visiting Georgetown and working on my application for the MLC. I like to say it was like being swept up off my feet and then engaging in a happy shotgun marriage)

The Global Survey was designed to elicit gender differences in terms of experiences, climate, resources and opportunities, as well as the effects of personal and family constraints and decisions on school and career. The survey worked particularly well, and each dive into the data proves fascinating. This week I delved deeper into the dynamics of one country and saw women’s sources of support erode as they progressed further into school and work, saw the women transition from a virtual parity in school to difficult careers, beginning with their significantly larger chance of having to choose their job because it was the only offer they received, and becoming significantly worse with the introduction of kids. In fact, we found through this survey that kids tend to slow women’s careers and accelerate men’s!

What do these findings say about the use of demographic variables? They certainly validate their usefulness and cause me to wonder whether a lack of focus on demographics would lessen the usefulness of the faculty study. Here I’m reminded that it is important, when discussing demographic variables, to keep in mind that they are not arbitrary. They reflect ways of seeing that are deeply engrained in society. Gender, for example, is the first thing to note about a baby, and it determines a great deal from that point in. Excluding race or ethnicity seems foolish, too, in a society that so deeply engrains these distinctions.

The problem may be in the a priori or unconsidered applications of demographic variables. All too often, the same tired set of variables are dredged up without first considering whether they would even provide a useful distinction or the most useful cuts to a dataset. A recent example of this is the study that garnered some press about racial differences in e-learning. From what I read of the study, all e-learning was collapsed into a single entity, an outcome or dependent variable (as in some kind if measure of success of e-learning), and run by a set of traditional x’s or independent variables, like race and socioeconomic status. In this case, I would have preferred to first see a deeper look into the mechanics of e-learning than a knee jerk rush to the demographic variables. What kind of e-learning course was it? What kinds of interaction were fostered between the students and the teacher, material and other students? So many experiences of e-learning were collapsed together, and differences in course types and learning environments make for more useful and actionable recommendations than demographics ever could.

In the case of the faculty and global surveys as well, one should ask what approaches to the data would yield the most useful analyses. Finding demographic differences leads to what- an awareness of discrimination? Discrimination is deep seeded and not easily cured. It is easy to document and difficult to fix. And yet, more specific information about climate, resources and opportunities could be more useful or actionable. It helps to ask what we can achieve through our research. Are we simply validating or proving known societal differences or are we working to create actionable recommendations? What are the most useful distinctions?

Most likely, if you take the time to carefully consider the information you collect, the usefulness of your analyses and the validity of your hypotheses, you are one step above anyone rotely applying demographic variables out of ill-considered habit. Kudos to you for that!

Total Survey Error: nanny to some, wise elder for some, strange parental friend for others

Total Survey Error and I are long-time acquaintences, just getting to know each other better. Looking at TSE is, for me, like looking at my work in survey research through a distorted mirror to an alternate universe. This week, I’ve spent some time closely reading Groves’ Past, Present and Future of Total Survey Error, and it provided some historical context to the framework, as well as an experienced account of its strengths and weaknesses.

Errors are an important area of study across many fields. Historically, models about error assumed that people didn’t really make errors often. Those attitudes are alive and well in many fields and workplaces today. Instead of carefully considering errors, they are often dismissed as indicators of incompetence. However, some workplaces are changing the way they approach errors. I did some collaborative research on medical errors in 2012 and was introduced to the term HRO or High-Reliability Organization. This is an error focused model of management that assumes that errors will be made, and not all errors can be anticipated. Therefore, every error should be embraced as a learning opportunity to build a better organizational framework.

From time to time, various members of our working group have been driven to create checklists for particular aspects of our work. In my experience, the checklists are very helpful for work that we do infrequently and virtually useless for work that we do daily. Writing a checklist for your daily work is a bit like writing instructions on how you brush your teeth and expecting to keep those instructions updated whenever you make a change of sorts. Undoubtedly, you’ll reread the instructions and wonder when you switched from a vertical to a circular motion for a given tooth. And yet there are so many important elements to our work, and so many areas where people could make less than ideal decisions (small or large). From this need rose Deming, with the first survey quality checklist. After Deming, a few other models arose. Eventually, TSE became the cumulative working framework or foundational framework for the field of survey research.

In my last blog, I spoke about the strangeness of coming across a foundational framework after working in the field without one. The framework is a conceptually important one, separating out sources of errors in ways that make shortcomings and strengths apparent and clarifying what is more or less known about a project.

But in practice, this model has not become the applied working model that its founders and biggest proponents expected it to be. This is for two reasons (that I’ll focus on), one of which Groves mentioned in some detail in this paper and one of which he barely touched on (but likely drove him out of the field).

1. The framework has mathematical properties, and this has led to its more intensive use on aspects of the survey process that are traditionally quantitative. TSE research in areas of sampling, coverage, response and aspects of analysis is quite common, but TSE research in other areas is much less common. In fact, many of the less quantifiable parts of the survey process are almost dismissed in favor of the more quantifiable parts. A survey with a particularly low TSE value could have huge underlying problems or be of minimal use once complete.
2. The framework doesn’t explicitly consider the human factors that govern research behind the scenes. Groves mentioned that the end users of the data are not deeply considered in the model, but neither are the other financial and personal (and personafinancial) constraints that govern much decision making. Ideally, the end goal of research is high quality research that yields a useful and relevant response for as minimal cost as possible. In practice, however, the goal is both to keep costs low and to satisfy a system of interrelated (and often conflicting) personal or professional (personaprofessional?) interests. If the most influential of these interests are not particularly interested in (or appreciative of) the model, practitioners are highly unlikely to take the time to apply it.

Survey research requires very close attention to detail in order to minimize errors. It requires an intimate working knowledge of math and of computer programming. It also benefits from a knowledge of human behavior and the research environment. If I were to recommend any changes to the TSE model, I would recommend a bit more task based detail, to incorporate more of the highly valued working knowledge that is often inherent and unspoken in the training of new researchers. I would also recommend a more of an HRO orientation toward error, anticipating and embracing unexpected errors as a source of additions to the model. And I would recommend some deeper incorporation of the personal and financial constraints and the roles they play (clearly an easier change to introduce than to flesh out in any great detail!). I would recommend a shift of focus, away from the quantitative modeling aspects and to the overall applicability and importance of a detailed, applied working model.

I’ve suggested before that survey research does not have a strong enough public face for the general public to understand or deeply value our work. A model that is better embraced by the field could for the basis for a public face, but the model would have to appeal to practitioners on a practical level. The question is: how do you get members of a well established field who have long been working within it and gaining expertise to accept a framework that grew into a foundational piece independent of their work?

Total Survey Error: as Iconic as the Statue of Liberty herself?

In Jan Blommaerts book, the Sociolinguistics of Globalization, I learned about the iconicity of language. Languages, dialects, phrases and words have the potential to be as iconic as the statue of liberty. As I read Blommaert’s book, I am also reading about Total Survey Error, which I believe to be an iconic concept in the field of survey research.

Total Survey Error (TSE) is a relatively new, albeit very comprehensive framework for evaluating a host of potential error sources in survey research. It is often mentioned by AAPOR members (national and local), at JPSM classes and events, and across many other events, publications and classes for survey researchers. But here’s the catch: TSE came about after many of us entered the field. In fact, by the time TSE debuted and caught on as a conceptual framework, many people had already been working in the field for long enough that a framework didn’t seem necessary or applicable.

In the past, survey research was a field that people grew into. There were no degree or certificate programs in survey research. People entered the field from a variety of educational and professional backgrounds and worked their way up through the ranks from data entry, coder or interviewing positions to research assistant and analyst positions, and eventually up to management. Survey research was a field that valued experience, and much of the essential job knowledge came about through experience. This structure strongly characterizes my own office, where the average tenure is fast approaching two decades. The technical and procedural history of the department is alive and well in our collections of artifacts and shared stories. We do our work with ease, because we know the work well, and the team works together smoothly because of our extensive history together. Challenges or questions are an opportunity for remembering past experiences.

Programs such as the Joint Program in Survey Methodology (JPSM, a joint venture between the University of Michigan and University of Maryland) are relatively new, arising, for the most part, once many survey researchers were well established into their routines. Scholarly writings and journals multiplied with the rise of the academic programs. New terms and new methods sprang up. The field gained an alternate mode of entry.

In sociolinguistics, we study evidentiality, because people value different forms of evidence. Toward this end, I did a small study of survey researchers’ language use and mode of evidentials and discovered a very stark split between those that used experience to back up claims and those who relied on research to back up claims. This stark difference matched up well to my own experiences. In fact, when I coach jobseekers who are looking for survey research positions, I  draw on this distinction and recommend that they carefully listen to the types of evidentials they hear from the people interviewing them and try to provide evidence in the same format. The divide may not be visible from the outside of the field, but it is a strong underlying theme within it.

The divide is not immediately visible from the outside because the face of the field is formed by academic and professional institutions that readily embrace the academic terminology. The people who participate in these institutions and organizations tend to be long term participants who have been exposed to the new concepts through past events and efforts.

But I wonder sometimes whether the overwhelming public orientation to these methods doesn’t act to exclude some longtime survey researchers in some ways. I wonder whether some excellent knowledge and history get swept away with the new. I wonder whether institutions that represent survey research represent the field as a whole. I wonder what portion of the field is silent, unrepresented or less connected to collective resources and changes.

Particularly as the field encounters a new set of challenges, I wonder how well prepared the field will be- not just those who have been following these developments closely, but also those who have continued steadfast, strong, and with limited errors- not due to TSE adherence, but due to the strength of their experience. To me, the Total Survey Error Method is a powerful symbol of the changes afoot in the field.

For further reference, I’m including a past AAPOR presidential address by Robert Groves

groves aapor

Proceedings of the Fifty-First Annual Conference of the American Association for Public Opinion Research
Source: Source: The Public Opinion Quarterly, Vol. 60, No. 3 (Autumn, 1996), pp. 471-513
ETA other references:

Bob Groves: The Past, Present and Future of Total Survey Error

Slideshow summary of above article

Is there Interdisciplinary hope for Social Media Research?

I’ve been trying to wrap my head around social media research for a couple of years now. I don’t think it would be as hard to understand from any one academic or professional perspective, but, from an interdisciplinary standpoint, the variety of perspectives and the disconnects between them are stunning.

In the academic realm:

There is the computer science approach to social media research. From this standpoint, we see the fleshing out of machine learning algorithms in a stunning horserace of code development across a few programming languages. This is the most likely to be opaque, proprietary knowledge.

There is the NLP or linguistic approach, which overlaps to some degree with the cs approach, although it is often more closely tied to grammatical rules. In this case, we see grammatical parsers, dictionary development, and api’s or shared programming modules, such as NLTK or GATE. Linguistics is divided as a discipline, and many of these divisions have filtered into NLP.

Both the NLP and CS approaches can be fleshed out, trained, or used on just about any data set.

There are the discourse approaches. Discourse is an area of linguistics concerned with meaning above the level of the sentence. This type of research can follow more of a strict Conversation Analysis approach or a kind of Netnography approach. This school of thought is more concerned with context as a determiner or shaper of meaning than the two approaches above.

For these approaches, the dataset cannot just come from anywhere. The analyst should understand where the data came from.

One could divide these traditions by programming skills, but there are enough of us who do work on both sides that the distinction is superficial. Although, generally speaker, the deeper one’s programming or qualitative skills, the less likely one is to cross over to the other side.

There is also a growing tradition of data science, which is primarily quantitative. Although I have some statistical background and work with quantitative data sets every day, I don’t have a good understanding of data science as a discipline. I assume that the growing field of data visualization would fall into this camp.

In the professional realm:

There are many companies in horseraces to develop the best systems first. These companies use catchphrases like “big data” and “social media firehose” and often focus on sentiment analysis or topic analysis (usually topics are gleaned through keywords). These companies primarily market to the advertising industry and market researchers, often with inflated claims of accuracy, which are possible because of the opacity of their methods.

There is the realm of market research, which is quickly becoming dependent on fast, widely available knowledge. This knowledge is usually gleaned through companies involved in the horserace, without much awareness of the methodology. There is an increasing need for companies to be aware of their brand’s mentions and interactions online, in real time, and as they collect this information it is easy, convenient and cost effective to collect more information in the process, such as sentiment analyses and topic analyses. This field has created an astronomically high demand for big data analysis.

There is the traditional field of survey research. This field is methodical and error focused. Knowledge is created empirically and evaluated critically. Every aspect of the survey process is highly researched and understood in great depth, so new methods are greeted with a natural skepticism. Although they have traditionally been the anchors of good professional research methods and the leaders in the research field, survey researchers are largely outside of the big data rush. Survey researchers tend to value accuracy over timeliness, so the big, fast world of big data, with its dubious ability to create representative samples, hold little allure or relevance.

The wider picture

In the wider picture, we have discussions of access and use. We see a growing proportion of the population coming online on an ever greater variety of devices. On the surface, the digital divide is fast shrinking (albeit still significant). Some of the digital access debate has been expanded into an understanding of differential use- essentially that different people do different activities while online. I want to take this debate further by focusing on discursive access or the digital representation of language ideologies.

The problem

The problem with such a wide spread of methods, needs, focuses and analytic traditions is that there isn’t enough crossover. It is very difficult to find work that spreads across these domains. The audiences are different, the needs are different, the abilities are different, and the professional visions are dramatically different across traditions. Although many people are speaking, it seems like people are largely speaking within silos or echo chambers, and knowledge simply isn’t trickling across borders.

This problem has rapidly grown because the underlying professional industries have quickly calcified. Sentiment analysis is not the revolutionary answer to the text analysis problem, but it is good enough for now, and it is skyrocketing in use. Academia is moving too slow for the demands of industry and not addressing the needs of industry, so other analytic techniques are not being adopted.

Social media analysis would best be accomplished by a team of people, each with different training. But it is not developing that way. And that, I believe, is a big (and fast growing) problem.

Storytelling about correlation and causation

Many researchers have great war stories to tell about the perilous waters between correlation and causation. Here is my personal favorite:

In the late 90’s, I was working with neurosurgery patients in a medical psychology clinic in a hospital. We gave each of the patients a battery of cognitive tests before their surgery and then administered the same battery 6 months after the surgery. Our goal was to check for cognitive changes that may have resulted from the surgery. One researcher from outside the clinic focused on our strongest finding: a significant reduction of anxiety from pre-op to post-op. She hypothesized that this dramatic finding was evidence that the neural basis for anxiety was affected by the surgery. Had she only taken a minute to explain her  hypothesis in plain terms to a layperson, especially one that could imagine the anxiety a patient could potentially experience hours before brain surgery, she surely would have withdrawn her request for our data and slipped quietly out of our clinic.

“Correlation does not imply causation” is a research catchphrase that is drilled into practitioners from internhood and intro classes onward. It is particularly true when working with language, because all linguistic behavior is highly patterned behavior. Researchers from many other disciplines would kill to have chi square tests as strong as linguists’ chi squares. In fact, linguists have to reach deeper into their statistical toolkits, because the significance levels alone can be misleading or inadequate.

People who use language but don’t study linguistics usually aren’t aware of the degree of patterning that underlies the communication process. Language learning has statistical underpinnings, and language use has statistical underpinnings. It is because of this patterning that linguistic machine learning is possible. But, linguistic patterning is a double edged sword- potentially helpful in programming and harmful in analysis. Correlations abound, and they’re mostly real correlations, although, statistically speaking, some will be products of peculiarities in a dataset. But outside of any context or theory, these findings are meaningless. They don’t speak to the underlying relationship between the variables in any way.

A word of caution to researchers whose work centers around the discovery of correlations. Be careful with your findings. You may have found evidence that shows that a correlation may exist. But that is all you have found. Take your next steps carefully. First, step back and think about your work in layman’s terms. What did you find, and is that really anything meaningful? If your findings still show some prospects, double down further and dig deeper. Try to get some better idea of what is happening. Get some context.

Because a correlation alone is no gold nugget. You may think you’ve found some fashion, but your emperor could very well still be naked.

Time for some Research Zen

As the new semester kicks into gear and work deadlines loom, I find myself ready for a moment of research zen.

2012-12-16 14.18.00

Let’s take a minute to stand in a stream and think about the water. Feel the flow of the water over your feet and by your calves. Feel the pull of constant motion. Feel yourself sink against the current, rooting deeper to keep steady. Breathe the clean outdoor air. Observe the clouds and watch the way the sky reflects in the water in the stream. The stream is not constant. The water passing now is not the water that passed when you started, and the water that passes when you leave will be still different. And yet we call this a stream.

As I observe sources of social media, thinking about sampling, I’m faced with some of the same questions that the stream gives rise to. Although I would define my sources consistently from day to day, their content shifts constantly. The stream is not constant, but rather constantly forming and reforming at my feet.

For a moment, I saw the tide of social media start to turn in favor of taxi drivers. In that moment, I felt both a strong sense of relief from the negativity and a need to revisit my research methods. Today I see that the stream has again turned against the drivers. I could ignore the momentary shift, or I could use this as a moment to again revisit the wisdom of sampling.

If I sample the river at a given point, what should I collect and what does it represent? How, when the water is constantly moving around me, can I represent what I observe within a sample? Could my sampling ever represent a single point in the stream, the stream as a whole, or streams in general? Or will it always be moments in the life of a stream?

In the words of Henry Miller, “The world is not to be put in order. The world is in order. It is for us to put ourselves in unison with this order.” In order to understand this stream, I need to understand what lies beneath it, what gives it its shape and flow, and how it works within its ecosystem.

The ecosystem of public opinion around the taxi system in DC is not one that can be understood purely online. When I see the reflection of clouds on the stream, I need to find the sky. When I see phrases repeated over and over, I need to understand where they come from and how they came to be repeated. In the words of Blaise Pascal “contradiction is not a sign of falsity, nor the lack of contradiction a sign of truth.” No elements in this ecosystem exist independent of context. Each element has its base.

Good research involves a good deal of reflection. It involves digging in against currents and close observation. It involves finding a moment of stillness in the flow of the stream.

Breathe in. Observe carefully. Breathe out. Repeat, continue, focus, research.

Marissa Meyer, Motherhood and the Public Sphere

Marisssa Meyer has made quite a few waves recently. First, she was appointed CEO of Yahoo, an internet mainstay with an identity crisis, in a bold act of assertion and experimentation. She made headlines for her history of making waves in internet companies, and she made waves for being female. The headlines about her gender had barely receded before the news broke that she was pregnant. This was huge news, news that had barely receded before she again made headlines for talking about motherhood.

As a working parent, I’m moved to say a few things about motherhood and work.

The first point is an obvious one. Pregnancy is a state that primarily affects the pregnant. As a working, pregnant woman I remember the absolute fear of my coworkers discovering that my pregnant head was a blurred and rearranged rendition of its normal self. There is no public acceptance or public discussion of pregnancy brain, only private, informal corroboration of the phenomena. I felt sympathetic for Meyer as she navigated the intense public scrutiny of her every move within her new position while potentially mucking through “pregnancy brain.” Granted, we are adaptable creatures. I navigated the world of working while pregnant with a minimum of catastrophic errors, mostly through subtle adjustments in my work patterns, and I’m sure that she did as well. And, just as my consuming hatred of onions disappeared during the labor process, my thinking again found clarity. Motherhood does not necessarily affect a woman’s ability to work, nor is it necessarily a negative effect. In fact, we found in a survey of physicists the world over that mothers often discover that they work much more efficiently than they did before they gave birth (may be covered in this report). The wider sense of context and greater array of responsibilities can significantly improve worklife.

The assumption underlying fears about motherhood and work life is that the mother is the sole or primary source of childcare. This is not universally true. Parents make parenting work with whatever tools they have. Some parents have partners with varying degrees of involvement, and some don’t. Some live with family, some don’t. Some have dependable community networks or friends who help out with childcare. Some have financial means that can be used to find help with childcare or to help with other areas of life, in order to make more time for childcare. There are no assumptions that one could make straight out of the gate about a person’s childcare options or preferences.

Meyer made a point of calling her child “easy.” I don’t really see how this should bear on anyone else. I have often called my own kids easy. Maybe a more technical way of saying this would be that their actions generally jive with my own needs and preferences. There are times when that description couldn’t be further from the truth (like when they scream! or puke at dramatically inconvenient times. or cover me in Spaghetti at lunchtime in the office cafeteria!), and times when it seems gloriously true, like the times I couldn’t find a reliable childcare option and my quiet second child hung out in my office all day without most of my coworkers noticing. The truth is that children are people, with a full set of emotions and physical states that they are just learning to reconcile. They start out very dependent and grow independence before we’re ready for them to. Sometimes they are a lot of work, or a lot of frustration. And sometimes they enrich our lives in ways we never could have imagined.

The part of Meyer’s statement that most struck me was that she spoke about her enjoyment of motherhood. Motherhood has plenty of little joys, plenty of cute moments, and plenty of little smiles. They don’t often get as much press as the frustrations, especially from moms who are working on their careers. In a professional world that seems to be eying moms for any sign that motherhood is negatively influencing worklife, moms are often thrust into this dynamic, where being a parent is thought to be at odds with a career. I have often been caught in this dynamic. I enjoy my career and enjoy motherhood and in fact enjoy being able to do both. I rarely hear this dynamic echoed- almost like we need to chose a side and stick to it. But the need to pick sides is an old one, one where the mother is thought to be the primary caretaker, leaving her child adrift if she chooses to also pursue other activities.

Instead, I try to use my enthusiasm for work and school as a model for my kids. I want them to know that it is possible to find and pursue work that they find interesting. And I work to let them know how much I care about them and enjoy spending time with them. And I rely heavily on the networks available to me for help. My system is far from perfect. In November, after being out of town for a few days, I returned to a severely wheezing child. Nothing can make you feel worse as a parent than taking a child to the doctor when they are seriously ill and you know nothing about the history of their illness. And now the wave of finals is creeping over us like an ominous tsunami, threatening to swallow our homelife whole in its voraciousness.

Motherhood is complicated. Parenthood is complicated. Meyer may be moved to characterize parenthood one way in one interview and then in a completely different way fifteen minutes later. As a fellow parent, I would like to issue my full support for her speaking publicly about parenthood at all. I wish her  a string of lights in the thicket of family life and work life and a cord to hold it together in the points between.

And I wish her press about her professional life that isn’t defined by her personal life.

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.

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?