Data-driven social interaction: The difference between analogue and digital part III

Data-driven social experience is an entirely new manner of social interaction, one that obscures our emotional connections to people. Data makes social relationships visible, knowable, and countable in unprecedented ways. But it does not — and cannot — convey the emotional experience of social interaction. I’ve already discussed how digital technologies transform text and time. Now I want to explore how “data” transforms social experience.

Take the notion of the “social network.” Most people (especially those that read blogs!) think these synonymous with Web sites like Facebook. Truth be told, social network analysis has existed for almost a century. We’ve all heard the term “six degrees of separation,” but most of us don’t know that was coined by none other that Stanley Milgram, of the “shock experiments” fame, when he tracked letters mailed around the world.

Social networks are exceedingly difficult to know from a quantitative perspective. We all live inside social networks but we have a very hard time knowing how these networks are constructed. We may know, for example, that our friend Jeff is friends with another one, Sarah, but we don’t know if Sarah knows Jeff’s partner Sam. Social network analysis is a set of methods designed to learn exactly that.

Now imagine your social network, as it is represented on Facebook (what, you’re not on Facebook?). Below is an image from Visual Complexity that renders a social network visibly but also very easily, simply by mining the data inherent in Facebook’s structure:

from Visual Complexity

Note how we instantly and easily know how institutions are connected, and through which people. Previously, researchers would have to conduct extensive and expensive surveys to get these data. Now these data are easily calculated and visualized by anyone with access to a social network online.

Some people are talking about this visualization as a piece of intellectual property. Alex Iskold on Mashable, for example, asks “Who owns the social map?” I go further and ask, “What does it mean that our social world is mappable?”

Our social world is now infiltrated by masses of data. These data inform us about the structure of our interactions with others in ways that we could not recall correctly if asked. Suddenly we can now see our social world reflected back to us, punctuated by  institutions, and social structures. When we see our social network through the eyes of data, we see the names of organizations, or the institutional affiliation of the people. We do not “see” the emotional experience that created our connections in the first place.

Suddenly, we may think we really are not that close with Jeff, because his partner Sam is really not friends with anyone I know. I can also see that Sarah and I have very few friends in common, which may lead me to think I don’t have much of a future friendship with her.

Those data crowd out the qualitative, embodied experience of the laughs I shared with Jeff and Sam at their cottage last summer. Those data obscure the fact that Sarah and I shared 3 long months as call centre employees together, a time that bonded us forever. A data-filled social world is one that masks the visceral, emotional experiences of face-to-face interaction.

Digital social life is revealed to us in fragmented, mashed up ways. Such ways were impossible before the freely available data on social networks, data that is now so ubiquitous, we don’t even see it.

Improving participation rates: research recruitment best practices

Those of you out there who’ve tried it know: recruiting research participants is HARD. Here are a few insights from the research to help you with better recuitment.

1. Personalized contact with respondents, followed by pre-contact and aggressive follow-up phone calls *: Don’t count on a form letter, email or random tweet to do the job. Capitalize on your personal relationship with that person. If you don’t have a personal relationship, ensure that you use the person’s name, and for God’s sake, spell it correctly!

   Once you’ve made initial contact, you are not done. Not by a long shot. Make sure you speak to the person (you can do this through IM or email if you’d like) to give them more information. They’re now interested. Don’t stop! One more step!

   Follow up 1 week after initial contact. Assuage any fears they may have. Answer any questions honestly. And above all, be available for more information.

2. External researchers with social capital are best**: University-based researchers have been shown to have the best participation rates, but you don’t have to be a professor.  Researcher Sister Marie Augusta Neal of Emmanuel College achieved a near perfect response rate because of her close ties to the respondents and their communities. The lesson here is, if you hire a consultant, make sure they’re trusted. Even better if they personally know the people to be recruited.
3. Monetary incentives have no effect, unless money is offered “no strings attached”***: Little known fact: the best way to use a monetary incentive is to offer it, up front, with absolutely no strings attached. The “free” money makes people feel more indebted socially. Evidence of this effect can be found in the book Freakonomics. Researchers found that daycare centres that levied late penalties on tardy parents actually had more of a late-pickup problem than those that levied no fine. Why? Because the parents reduced their relationship to the daycare as a mere transaction. Use the “gift economy” approach and ensure a feeling of indebtedness. My personal favourite is a coupon for a single iTunes song at $.99. It is cheap but appears to have great value. Offer it, up front, and then ask for participation

*  Cook, C., F. Heath, and R. Thompson. 2000. “A Meta-analysis of Response Rates in Web or Internet-based Surveys.” Educational and Psychological Measurement 60:821-836.

** Rogelberg, S., A. Luong, M. Sederburg, and D. Cristol. 2000. “Employee Attitude Surveys: Examining the Attitudes of Noncompliant Employees.” Journal of Applied Psychology 85:284-293.

***Hager, M., S. Wilson, T. Pollak, and P. Rooney. 2003. “Response Rates for Mail Surveys of Nonprofit Organizations: A Review and Empirical Test.” Nonprofit and Voluntary Sector Quarterly 32:252-267. Singer, E. (2006) Introduction: Nonresponse Bias in Household Surveys. Public Opinion Quarterly, 70, 637-645

Sampling methods in qualitative and quantitative research

Why does sample size not matter in qualitative research? Because of the assumptions that qualitative researchers make, namely, that the social world is not predictable. Qualitative researchers believe that people are not like molecules or other objects; people’s actions are not predictable.

But quantitative researchers DO believe that social activity IS predictable. So when they compare their observations of social activity to what would happen in purely random results, the difference says something. Let’s say you were to research people’s preferences for a particular interactive feature. Say you’re wondering if young people will like a radio button more than older people. First, you model what results you’d expect if you’d just flipped a coin. Then you use a probability (random) sample, and compare those results to purely random results. Is there a difference?

If there is a difference between them, you can infer that indeed, something other than chance (in this case, age) affect people’s preferences.

Qualitative researchers don’t agree that such things can be reliably predicted. That’s why they don’t bother with expensive and involved random sampling. See all these details below from my research design course.

Qualitative versus quantitative research, Part II

Thousands of people arrive at this blog wanting to know what is the difference between qualitative and quantitative research. Qualitative versus quantitative research is by far the most popular post on this blog. In that first post, I explained why sample size doesn’t matter in qualitative research. In this post, I explain why qualitative research is generally a better approach for design research.

Notice how the qualitative process is iterative with the going back and forth from data to sense-making or developing theory. It is flexible and can change direction easily.

Qualitative design process

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And the quantitative design process is very linear, and does not include an iterative component:

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If your design process involves an iterative prototyping phase, for example, then qualitative research is likely the best approach for you. Notice also that qualitative research necessarily involves the researcher putting herself in the shoes of the user. Quantitative research does NOT require the researcher to see through the eyes of the user.

Designers often want to empathize with their users. They want to understand their experiences and pain points. They want to know what their users are thinking. This is why qualitative research is often better suited to design research.

See also this embedded slideshow from my research design class. This should give you the basic differences between the two.

Research Design Course: Follow along on slideshare

I am currently teaching a Research Design and Qualitative Methods course at Ryerson University. This is a core course for an interdisciplinary group of students, from social work, to business, to psychology, to sociology to…well you get the picture.

I will be uploading slides from my lectures regularly. See them all at:

http://www.slideshare.net/sladner

I have toyed with adding audio, but so far my students do not appear to be too interested. Are you? If so, let me know and I will add audio to my slide space.

The Myth of The “Average”

We bandy about the word “average” all the time. What exactly IS an average, and how does it help design research?

Use the average to quickly summarize something that is already a number: minutes, ages, heights, visits, etc. Don’t use the average to explain something that needs more detail. And keep in mind, the average gets “dragged up” or “dragged down” by extreme values. Sometimes it doesn’t tell you much of anything.

An example design research project might be about how people use their stoves in their kitchens. How can we use “the average” to help us design a new stove?

The average, in statistical language, is actually called “the mean,” which is a measure of “central tendency.” Researchers use central tendency to describe all their results quickly. Other measures of central tendency include the mode (the most common response) or the median (50% of responses are higher than this; 50% are lower).The mean describes the “typical” or average result.

But here’s the big myth: there is no such thing as “the average” in your data. If you ask 500 people to rate your new stove design on a scale of 1 to 10, and the average is 4, there is no guarantee that any single person actually said 4! In fact, the majority of responses could be higher than 7, but some 1s or 2s could “drag down the average.”

Worse, it makes no sense to use the “average” or “typical” in qualitative research. If you do interviews or observations, there is no way to calculate “the average.” So when you say, “the typical person has a four-element stove,” you’re actually doing a calculation. This may be actually quite false. What you may mean to say is “most people in our study have a four-element stove” (which is the mode).

Qualitative research does not accept the “typical.” It actually looks at each case individually and in enough detail to allow for exceptions or outliers. There is no “typical” case in qualitative research because you do not do calculations. You do not summarize your data in that reductionist way.

That said, how could you use “the average” in your kitchen stove study?  You can do a back-of-the-envelope calculation to summarize your data. The “average age” of your respondents, for example, will tell you about how old people are. The “average number of minutes spent cooking” will give you a snapshot of how long people spend in their kitchens. The “average purchase price of a stove” will also give you a quick snapshot. Using “the average” is to quickly summarize something that is already a number.

But the “average use of the stove”? That doesn’t make sense. Nor does the “typical grocery shopping process,” or the “average complaint of stove use.” These cannot be summarized in “the average.”

Online Surveys 101

Folks,

Below is a (very!) brief overview of online surveys. This slideshow, via slideshare, is intended for people in the Web design industry. IAs, designers, media planners, strategists, usability researchers, and producers will learn if they should, in fact, do a survey.

Why customer satisfaction surveys are useless

Many readers seem to enjoy my qualitative versus quantitative research post. I take this to mean that designers are hungry insight that beyond the requisite (and useless) customer satisfaction survey.

I’m not a huge fan of customer satisfaction surveys because they are usually 100% reliable but 0% valid; they tell you nothing (but consistently tell you nothing). Witness, for example, the Foresee customer satisfaction survey. This survey is designed to give pop up as a user leaves a Web site. They are asked a variety of customized questions and then a variety of demographic information. Foresee tallies these results regularly and even ranks your Web site (or company in general) in comparison to your competitors.

What does a designer learn from this? Almost nothing.

Why? Several reasons.

1. Consumers have “satisfaction” fatigue: consumers are surveyed to death these days. We are all familiar with the Likert scale “strongly agree” to “strongly disagree.” Few survey researchers actually rework their surveys for validity. It’s called “acquiescence bias” where people tend to just answer the same way repeatedly. Survey researchers who know better use reverse-scoring techniques; but most don’t. These surveys, therefore, result in a questionable assertion that they are actually valid representations of how consumers actually feel.
2. Satisfaction surveys breed incremental “metricism”: surveys tell you nothing new: designers need to innovate their products, Web sites, and images. Satisfaction surveys tell you nothing you don’t already know. What’s more, they may actually inhibit creativity because they draw attention to minute changes that may be due to chance alone. Once organizations become regular consumers of satisfaction surveys, even small improvements become cause for celebration — even if they don’t reflect real improvements (see number 1).
3. Surveys provide numbers, not detail: designers need thick description to make their designs truly evocative of lived experiences. Satisfaction surveys are simply stripped down representations of how people feel (or more accurately, how they say they feel). Designers need richer information to spark creative solutions.
4. Customer satisfaction is a poor predictor of looming competition: Imagine a company that had consistently high customer satisfaction scores. Imagine also that this company falls victim to “incremental metricism,” and fails to see a competitor’s new, better designed product on the horizon. This competitive product would never appear in customer satisfaction surveys. It’s possible for customers to be “satisfied,” only to have them lured away by an innovative, better design.

Instead of customer satisfaction surveys, I recommend designers pore over free trend-spotting data, like the Pew Internet and American Life Project, which is a comprehensive and rigorous survey of current attitudes and beliefs. It’s harder to pull out insights from data that don’t look like they’re relevant, but the return is so much better than from a tired, staid customer satisfaction survey.

When to do qualitative and qualitative research

In a previous post, I talked about what designers need to know about economic class. How did we learn that economic class can be “seen” in designs? How did we learn that “refined” taste is “upper” class?

In general, use qualitative research at the beginning of a design process to uncover innovations. Use quantitative research at the end of a design process to measure improvement.

It started with qualitative research, and became “refined” (no pun intended) with quantitative research. French sociology Pierre Bourdieu followed a typical arc to the narrative research by first investigating economic class in an open-ended fashion. Once he established what he thought was going on, he tested these ideas with large surveys.

If you know little about the topic, start with the qualitative. This means ethnographic observation and in-depth interviewing. Open ended questions are best. At this stage, you’re trying to find the lay of the land. If you’re designing a new car stereo for example, you may wish to start by watching people use their existing car stereos. Maybe drive around with them and ask them questions about what they like.

Once you’ve learned the basics of car stereo requirements, user needs and pain points, it’s time to test your assumptions. This is where the quantitative comes in. Close-ended questions are best here, including multiple choice, yes/no, or simply number of “successes.” Let’s say you’ve learned through your observations that people don’t like how their stereos require programming their radio stations. It’s too much bother, they told you. You think pre-programmed stations might be a good design improvement, so you create a new stereo with pre-programmed stations.

Did it work? Ask your stereo users how they like the new system after they have bought their new car. But the question is, compared to what? This is where quantitative research gets tricky. You can compare the new stereos on select models (58% of users of the new model are very satisfied, while only 32% of users of the old model are). Or you can compare before and after the improvement — the so-called “pre-and post test.” That requires time, foresight, and — you guessed it — budget.

Below is a diagram that summarizes the research “funnel” from exploration to validation.

Qualitative versus quantitative research

Many designers are self-taught, intuitive consumers of research who can translate insights into great designs. But few are trained in the arcane art of research itself. For that reason, many designers don’t know the finer differences between qual and quant research and end up using their respective results inappropriately.

Quantitative research is based on the assumption that random events are predictable, and if you compare your results to pure random results, you can discern distinctive, meaningful patterns about the social world.

Random events are relatively MORE predictable if you have more of them. Imagine if you flipped a coin 20 times. How many heads would you get? Now if you flipped it 20,000 times? You’re more likely to get an even 50/50 split — which is what most people would predict. If you got a 65/35 split with 2o flips, okay, could happen. But with 20,000 flips? No way. Something else is going on.

Translate that to design research by looking at gender, for example. Let’s say you have 20 people, 10 men and 10 women. 65% of the women choose one design, while only 35% of the men do. Is this a meaningful pattern? Impossible to say — you only have 20 people. Now if you had 200 people (100 men and 100 women) and 65 of the women chose one design, chances are you have a meaningful pattern.

This is why sample size matters in quantitative research. But, little known fact, sample size is COMPLETELY IRRELEVANT in qualitative research. Why?

Qualitative research assumes that people have meaningful experiences that can be interpreted. Notice how there’s nothing in there about “prediction” or “randomness.” People have experiences. Researchers discern what these experiences signify. That’s it. Sample size is not only irrelevant, it actually gets in the way of important insight.

Consider the case study, for example. Few people would say case studies are useless. We can learn a great deal about a single design case, where it went wrong and where it went right. The problem comes when you try to predict future events based on this single event.

If you abandon the need for prediction, then sample size never matters. You can always derive insight about design problems from even a single case. Designers that attempt to predict “success” of a single design change, for example, should test that change, repeatedly, with a probability sample.