Planning a second “Online Research, Offline Lunch”

In August we hosted the first Online Research, Offline Lunch for researchers involved in online research in any field, discipline or sector in the DC area. Although Washington DC is a great meeting place for specific areas of online research, there are few opportunities for interdisciplinary gatherings of professionals and academics. These lunches provide an informal opportunity for a diverse set of online researchers to listen and talk respectfully about our interests and our work and to see our endeavors from new, valuable perspectives. We kept the first gathering small. But the enthusiasm for this small event was quite large, and it was a great success! We had interesting conversations, learned a lot, made some valuable connections, and promised to meet again.

Many expressed interest in the lunches but weren’t able to attend. If you have any specific scheduling requests, please let me know now. Although I certainly can’t accommodate everyone’s preferences, I will do my best to take them into account.

Here is a form that can be used to add new people to the list. If you’re already on the list you do not need to sign up again. Please feel free to share the form with anyone else who may be interested:

 

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Upcoming DC Event: Online Research Offline Lunch

ETA: Registration for this event is now CLOSED. If you have already signed up, you will receive a confirmation e-mail shortly. Any sign-ups after this date will be stored as a contact list for any future events. Thank you for your interest! We’re excited to gather with such a diverse and interesting group.

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Are you in or near the DC area? Come join us!

Although DC is a great meeting place for specific areas of online research, there are few opportunities for interdisciplinary gatherings of professionals and academics. This lunch will provide an informal opportunity for a diverse set of online researchers to listen and talk respectfully about our interests and our work and to see our endeavors from new, valuable perspectives.

Date & Time: August 6, 2013, 12:30 p.m.

Location: Near Gallery Place or Metro Center. Once we have a rough headcount, we’ll choose an appropriate location. (Feel free to suggest a place!)

Please RSVP using this form:

Representativeness, qual & quant, and Big Data. Lost in translation?

My biggest challenge in coming from a quantitative background to a qualitative research program was representativeness. I came to class firmly rooted in the principle of Representativeness, and my classmates seemed not to have any idea why it mattered so much to me. Time after time I would get caught up in my data selection. I would pose the wider challenge of representativeness to a colleague, and they would ask “representative of what? why?”

 

In the survey research world, the researcher begins with a population of interest and finds a way to collect a representative sample of the population for study. In the qualitative world that accompanies survey research units of analysis are generally people, and people are chosen for their representativeness. Representativeness is often constructed by demographic characteristics. If you’ve read this blog before, you know of my issues with demographics. Too often, demographic variables are used as a knee jerk variable instead of better considered variables that are more relevant to the analysis at hand. (Maybe the census collects gender and not program availability, for example, but just because a variable is available and somewhat correlated doesn’t mean that it is in fact a relevant variable, especially when the focus of study is a population for whom gender is such an integral societal difference.)

 

And yet I spent a whole semester studying 5 minutes of conversation between 4 people. What was that representative of? Nothing but itself. It couldn’t have been exchanged for any other 5 minutes of conversation. It was simply a conversation that this group had and forgot. But over the course of the semester, this piece of conversation taught me countless aspects of conversation research. Every time I delved back into the data, it became richer. It was my first step into the world of microanalysis, where I discovered that just about anything can be a rich dataset if you use it carefully. A snapshot of people at a lecture? Well, how are their bodies oriented? A snapshot of video? A treasure trove of gestures and facial expressions. A piece of graffiti? Semiotic analysis! It goes on. The world of microanalysis is built on the practice of layered noticing. It goes deeper than wide.

 

But what is it representative of? How could a conversation be representative? Would I need to collect more conversations, but restrict the participants? Collect conversations with more participants, but in similar contexts? How much or how many would be enough?

 

In the world of microanalysis, people and objects constantly create and recreate themselves. You consistently create and recreate yourself, but your recreations generally fall into a similar range that makes you different from your neighbors. There are big themes in small moments. But what are the small moments representative of? Themselves. Simply, plainly, nothing more and nothing else. Does that mean that they don’t matter? I would argue that there is no better way to understand the world around us in deep detail than through microanalysis. I would also argue that macroanalysis is an important part of discovering the wider patterns in the world around us.

 

Recently a NY Times blog post by Quentin Hardy has garnered quite a bit of attention.

Why Big Data is Not Truth: http://bits.blogs.nytimes.com/2013/06/01/why-big-data-is-not-truth/

This post has really struck a chord with me, because I have had a hard time understanding Hardy’s complaint. Is big data truth? Is any data truth? All data is what it is; a collection of some sort, collected under a specific set of circumstances. Even data that we hope to be more representative has sampling and contextual limitations. Responsible analysts should always be upfront about what their data represents. Is big data less truthful than other kinds of data? It may be less representative than, say, a systematically collected political poll. But it is what it is: different data, collected under different circumstances in a different way. It shouldn’t be equated with other data that was collected differently. One true weakness of many large scale analyses is the blindness to the nature of the data, but that is a byproduct of the training algorithms that are used for much of the analysis. The algorithms need large training datasets, from anywhere. These sets often are developed through massive web crawlers. Here, context gets dicey. How does a researcher represent the data properly when they have no idea what it is? Hopefully researchers in this context will be wholly aware that, although their data has certain uses, it also has certain [huge] limitations.

 

I suspect that Hardy’s complaint is with the representations of massive datasets collected from webcrawlers as a complete truth from which any analyses could be run and all of the greater truths of the world could be revealed. On this note, Hardy is exactly right. Data simply is what it is, nothing more and nothing less. And any analysis that focuses on an unknown dataset is just that: an analysis without context. Which is not to say that all analyses need to be representative, but rather that all responsible analyses of good quality need to be self aware. If you do not know what the data represents and when and how it was collected, then you cannot begin to discuss the usefulness of any analysis of it.

What is the role of Ethnography and Microanalysis in Online Research?

There is a large disconnect in online research.

The largest, most profile, highest value and most widely practiced side of online research was created out of a high demand to analyze the large amount of consumer data that is constantly being created and largely public available. This tremendous demand led to research methods that were created in relative haste. Math and programming skills thrived in a realm where social science barely made a whisper. The notion of atheoretical research grew. The level of programming and mathematical competence required to do this work continues to grow higher every day, as the fields of data science and machine learning become continually more nuanced.

The largest, low profile, lowest value and increasingly more practiced side of online research is the academic research. Turning academia toward online research has been like turning a massive ocean liner. For a while online research was not well respected. At this point it is increasingly well respected, thriving in a variety of fields and in a much needed interdisciplinary way, and driven by a search for a better understanding of online behavior and better theories to drive analyses.

I see great value in the intersection between these areas. I imagine that the best programmers have a big appetite for any theory they can use to drive their work in a useful and productive ways. But I don’t see this value coming to bear on the market. Hiring is almost universally focused on programmers and data scientists, and the microanalytic work that is done seems largely invisible to the larger entities out there.

It is common to consider quantitative and qualitative research methods as two separate languages with few bilinguals. At the AAPOR conference in Boston last week, Paul Lavarakas mentioned a book he is working on with Margaret Roller which expands the Total Survey Error model to both quantitative and qualitative research methodology. I spoke with Margaret Roller about the book, and she emphasized the importance of qualitative researchers being able to talk more fluently and openly about methodology and quality controls. I believe that this is, albeit a huge challenge in wording and framing, a very important step for qualitative research, in part because quality frameworks lend credibility to qualitative research in the eyes of a wider research community. I wish this book a great deal of success, and I hope that it is able to find an audience and a frame outside the realm of survey research (Although survey research has a great deal of foundational research, it is not well known outside of the field, and this book will merit a wider audience).

But outside of this book, I’m not quite sure where or how the work of bringing these two distinct areas of research can or will be done.

Also at the AAPOR conference last week, I participated in a panel on The Role of Blogs in Public Opinion Research (intro here and summary here). Blogs serve a special purpose in the field of research. Academic research is foundational and important, but the publish rate on papers is low, and the burden of proof is high. Articles that are published are crafted as an argument. But what of the bumps along the road? The meditations on methodology that arise? Blogs provide a way for researchers to work through challenges and to publish their failures. They provide an experimental space where fields and ideas can come together that previously hadn’t mixed. They provide a space for finding, testing, and crossing boundaries.

Beyond this, they are a vehicle for dissemination. They are accessible and informally advertised. The time frame to publish is short, the burden lower (although I’d like to believe that you have to earn your audience with your words). They are a public face to research.

I hope that we will continue to test these boundaries, to cross over barriers like quantitative and qualitative that are unhelpful and obtrusive. I hope that we will be able to see that we all need each other as researchers, and the quality research that we all want to work for will only be achieved through the mutual recognition that we need.

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.

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.