Q & A with January 2016 DLABSS Gift Card Winner

 

The latest winner of our monthly $50 Amazon gift card lottery agreed to answer a few questions about his experience working with DLABSS on multiple experiments. We have posted his responses below:


1. What is your occupation? Tell us some basic background information about yourself.

I have been employed in several different roles for over 20 years at a large technology company that has, at times, made its way way into the Fortune 500.   I am currently serving in an Information Technology role where I help implement solutions for use within our company using software developed for sale by our company

2. How did you first hear about Harvard DLABSS, and why did you decide to participate for the first time?

I'm honestly not sure.   I have been a participant for a while,  and may have heard about it on Reddit. I always enjoyed introductory Psychology in college and thought that the idea of participating in surveys/experiments would be fun.   The online nature of this experience makes it easy to participate on my own terms.

3. Why have you continued to participate in experiments on DLABSS?

I still retain the same thoughts as when I first started.   I like the idea of being able to contribute in a way that is still convenient for me.

4. What is one thing you have learned from one of the experiments you participated in?

I'm not quite sure how to answer this one.   I tend to focus on providing the most accurate answers from my point of view.   I can say that it has caused me to stop and contemplate things a little more deeply than I might otherwise be naturally inclined to do.

 5. In your opinion, what is the best feature of DLABSS and/or its website?

The entire experience seems polished and well laid out.  I never struggle with presentation or comprehension.   If I have to pick something,  however,  I would have to say the notifications.   That might be a lame feature to focus on, but I wouldn't remain active without them.

6. What about DLABSS do you think could be improved for experiment participants?

Perhaps focus on giving a better idea of my progress throughout the process.  How far along am I?   At my current pace,  when am I expected to be finished? 

7. If you were talking to someone considering participating in DLABSS for the first time, how would you describe your overall experience?

The surveys have been engaging as opposed to shallow.   The in-depth experience causes you to ponder more than the surveys that I participate in via Google Rewards,  for instance. 

Q & A with December 2015 DLABSS Gift Card Winner

The latest winner of our monthly $50 Amazon gift card lottery agreed to answer a few questions about his experience working with DLABSS on multiple experiments. We have posted his responses below:


1. What is your occupation? Tell us some basic background information about yourself.

I'm a student about to graduate with a B.S. in Chemistry and a minor in Geology at the University of Illinois at Urbana Champaign. I conduct research in organometallic catalytic methods. 

2. How did you first hear about Harvard DLABSS, and why did you decide to participate for the first time?

A reddit post a couple years ago. I decided to fill out some of the surveys because it was easy and quick and I know that those responses are valuable data to researchers who sometimes have issues finding enough participants.

3. Why have you continued to participate in experiments on DLABSS?

Same reason I started, it doesn't take much of my time, gives me something to do, and provides valuable data to researchers. Also the chance to win these gift cards is a decent incentive.

4. What is one thing you have learned from one of the experiments you participated in?

People really like to study things that correlate with racial makeup apparently. Lots of surveys about race and sexual orientation and their relations to political opinions and such. 

 5. In your opinion, what is the best feature of DLABSS and/or its website?

Its nice to have the surveys aggregated on a an internal site rather than linking to outside survey sites. Also nice that it vaguely keeps track of the amount of surveys you've completed and e-mails you reminders about new ones. 

6. What about DLABSS do you think could be improved for experiment participants?

I'm not sure, answering surveys isn't a particularly difficult or complex task. I guess keep the website simple and easy to use? 

7. If you were talking to someone considering participating in DLABSS for the first time, how would you describe your overall experience?

Good, it's easy and doesn't take much time and doesn't interfere with my day to day life so there's not any downsides to participating. People need survey data, I'm not doing anything with my 10 minutes, everyone wins by filling out these surveys.

Why We Vote Matters: The Impact of Altruistic Voting on Election Outcomes

This is a guest post by DLABSS Researcher Daniel Mahler. This research is part of a larger Danish research project, the findings of which are very much in line with the results from the US sample discussed below. The findings may apply to other democracies as well.

Extensive research in political science has documented that some voters cast their ballots based on selfish concerns, while others vote for the candidate they think is best for society as a whole.  But does this dichotomy matter for the outcome of elections? And what would happen if some people were to change their moral motivation for voting? Seeking answers to these questions, 400 participants at the Harvard Digital Lab were asked the following three questions:

1.     Who would you vote for if the presidential election were held tomorrow?

2.     Who would you vote for if you were to consider only what is best for yourself?

3.     Who would you vote for if you were to consider what is best for society as a whole?

The participants were also asked to place the 2016 presidential candidates on a political scale from 1 (extremely liberal) to 7 (extremely conservative). The average placements of a selection of the candidates are shown below.

With these placements, it is possible to compare where on this line, on average, individuals place their actual, selfish, and altruistic (i.e. societal) votes. That is, we can see what would happen if people changed why they vote. 

As shown in the figure above, the answer seems to be that if more people were to vote selfishly, right-wing candidates would receive more votes. Conversely, if more people voted altruistically, the outcome would be more left-winged.

We can go one step further and compare the variation or standard deviation in the votes. A large standard deviation signifies less political agreement and greater popularity of candidates on the far ends of the political spectrum. This is illustrated below.

Evidently, if more people voted selfishly, then candidates on the extreme ends of the political spectrum would receive more votes, whereas there would be slightly more political agreement if more people voted altruistically.

So what? Does it matter whether people vote selfishly or altruistically? These results suggest it does indeed matter whether people vote for what they think is best for themselves or society as a whole. If candidates knew this, they might be able to increase their chances of winning elections by influencing the reasons why voters vote.

Reflecting on 2015: DLABSS New Year's Resolutions

As we near the arrival of 2016, the DLABSS Team wants to quickly fill you in on what has been a highly productive 2015! In this post we share how DLABSS has grown in 2015, and introduce some major upgrades on the way next year. As always, we want to thank the many volunteer participants and Harvard researchers from around the world whose efforts are pushing social science forward.

Participant Growth

What we did in 2015: 

The DLABSS pool of volunteer participants is growing faster and more consistently than any time in our short history. As the below graph illustrates, our pool increased by nearly 300% in the last 12 months! We grew from just over 1,000 volunteers in January to well over 3,000 by mid December. Dozens of people are joining the DLABSS community every week.

While we are excited about our yearly growth, even more encouraging is the surge of new volunteers in recent weeks. As you can see from the below graph, weekly volunteer totals have spiked since November. Since mid-November, on average roughly 100 new people have joined DLABSS every week!

 What we are doing in 2016: 

We think this progress is but the tip of the iceberg. We are committed to making DLABSS the go-to resource for Harvard experimental social science. This goal requires an even larger subject pool. To this end, we are launching a brand new website in January 2016 that will make participating in DLABSS easier and more enjoyable for volunteers and researchers alike.

 Volunteer Diversity

 What we did in 2015:

One of the advantages of online survey research is that researchers can attract a wide variety of participants. Ideally a pool of respondents is representative of society. Below we illustrate the breakdown of our volunteer pool in terms of gender and education. As you can see in the below figures, so far women represent a slight majority within our volunteer community, while volunteers have very diverse educational backgrounds.

blog4.png

 

What we are doing in 2016:

 Of course, survey research is not without limitations. Online labs such as MTurk, for example, have been shown to underrepresent minorities and produce samples biased towards younger generations. In 2015 we noticed that the DLABSS volunteer base was exhibiting similar trends. As such, we have formed partnerships with RetiredBrains and Workforce50.com, two senior citizen platforms, and are planning to engage more African American and Latino participants through online marketing. Our hope is that targeted marketing to diverse groups of potential volunteers will make DLABSS even more representative of the general population.

 Moreover, we are currently undergoing rigorous testing of DLABSS as a valid social science tool by using it to replicate several classic and contemporary social science experiments. Similar substantive results, if produced, will help build credibility for DLABSS as a viable resource for Harvard social scientists.

 Experiment Diversity

 What we did in 2015:

 We launched a total of 21 experiments by faculty, post-docs, graduate and undergraduate students from Harvard's Faculty of Arts and Sciences, Kennedy School of Government, and 48% of experiments were primarily political in nature, 19% economic, 19% psychology, and 14% group and race relations. The plot below plots the cumulative number of responses for each survey in 2015, showing that almost all surveys received 200 responses within 3 weeks. The 5 lines in red represent the surveys from November and December 2015, signaling that our most successful surveys have been our most recent.

What we are doing in 2016:

In the short term, we are launching experiments on American politics, healthcare policy, risk acceptance, and personality. In addition, we hope to add more content on a wide range of issues--such as foreign policy, economics, and sports.

Finally, the team at DLABSS would like to thank all of our volunteers and researchers during 2015. Please join us for what promises to be a productive and enlightening 2016. Happy New Year!

Q & A with November 2015 DLABSS Gift Card Winner

The latest winner of our monthly $50 Amazon gift card lottery agreed to answer a few questions about her experience working with DLABSS on multiple experiments. We have posted her responses below:


1. How did you first hear about Harvard DLABSS, and why did you decide to participate for the first time?

A friend of mine told me about DLABSS.  I'm not sure how she found out about it. I thought the topic was interesting and I liked the idea of possibly winning some money.

2. Why have you continued to participate in experiments on DLABSS?

Because they are very interesting.

3. What is one thing you have learned from one of the experiments you participated in?

I learned about new ideas and concepts that I don't usually think about.

 4. In your opinion, what is the best feature of DLABSS and/or its website?

Interesting topics...and I have a chance to win some money!

5. What about DLABSS do you think could be improved for experiment participants?

I can't think of anything. The surveys are pretty straightforward.

6. If you were talking to someone considering participating in DLABSS for the first time, how would you describe your overall experience?

Easy and fun. It doesn't take a lot of time away from your busy schedule to participate.

You Know It When You See It: Measuring Gerrymandering through Visual Perceptions

This post is by DLABSS researcher Aaron Kaufman. Aaron is the William Yandell Elliott Fellow in the Department of Government at Harvard University. His interests include causal inference, field experiments, natural experiments, statistical programming, and natural language processing. 

"Voters should choose their politicians; politicians shouldn't choose their voters." It seems troubling, then, that every 10 years, politicians redraw their district lines to suit their own reelection purposes. In some states, the majority party may draw the lines to strengthen incumbents and hurt opponents' abilities to gain election. In other states, incumbents from both parties conspire to keep themselves in office, effectively preventing newcomers from challenging their political dominance. This process is called Gerrymandering. It is completely legal, and sometimes results in abnormally-shaped districts, like Alabama's 16th State Assembly District:

kaufman1.png

Research in political science has attributed many adverse effects to Gerrymandering. For example, some say it contributes to polarization, which worsens legislative gridlock in both states and in Congress. Others say it extends the incumbency advantage, where leaders in office have a competitive advantage over newcomers, and thereby increases the amount of money in politics.

On the other hand, recent research argues that Gerrymandering has little effect on politics. Since many voters live in ideologically similar neighborhoods anyway, and if most voters in a county are Republican, those voters are best represented by a Republican regardless of the shape of the district. There is a thin line, however, between a district drawn to include like-minded voters and a district drawn to protect politicians from challengers.

This line remains very unclear, but what we know about it comes from the Voting Rights Act (VRA). The VRA lists guidelines for how to legally redistrict cases to avoid Gerrymandering, and one of the key criteria is "compactness". Many political scientists have tried to mathematically define compactness, but none have been very successful. Today, the courts use any of several dozen academic and mathematical measures in trying to determine if a state's districts are Gerrymandered, many of which conflict with each other. All these measures start from a common ground: a district may be Gerrymandered if it is not compact, and we can measure compactness by measuring the population, size, and shape of a district.

In this project, we start from a different premise. Coactness cannot be formalized; rather, it is the kind of thing where you know it when you see it. So we do just that! Using DLABSS, we show people pairs of districts, and ask them which district in each pair they think looks more compact, whatever that means to them. With enough responses, we can rank every district from least compact to most compact. Finally, once we have compactness ranks for every district, we can use cutting-edge sorting algorithms and machine learning to learn the relationship between the mathematical and academic measures of compactness, and what people consider to be truly compact. In summary, we are building a model to predict how Gerrymandered a district is!

Our major finding is that these traditional mathematical measures don't very well capture what people think compactness looks like. Below is a plot of the relationship between our DLABSS-based rank of districts and three major mathematical measures: the Polsby-Popper Test, the Reock Test, and the Schwartzberg Test. Each dot represents a real district. Its horizontal position refers to its rank along a mathematical measure of compactness; its vertical position represents its true compactness.

kaufman2.png

The plots are a complete mess! There is a very weak relationship between the two measures, indicating that the way judges and lawyers and lawmakers interpret the compactness criterion in the Voting Rights Act have little to do with the original meaning of the law. On the other hand, the tools we have developed are based on intuitive understandings of compact shapes. We hope that our research will set a new standard for identifying and correcting Gerrymandered districts.

Q & A with October 2015 DLABSS Gift Card Winner

The latest winner of our monthly $50 Amazon gift card lottery agreed to answer a few questions about his experience working with DLABSS on multiple experiments. We have posted his responses below:

1. How did you first hear about Harvard DLABSS, and why did you decide to participate for the first time?

I believe I heard about DLABSS in a craigslist posting. I decided to participate because I thought that the subject matter seemed interesting and I could voice my opinion on the matter. It seemed a good way to give back to the data on my community. 

2. Why have you continued to participate in experiments on DLABSS?

I continued participating in the studies because there was a big breadth of topics that I could take part in, which made it very interesting. A big part of the reason why I took some time to do surveys was the chance to be part of social science research, plus most surveys were not too cumbersome or long. 

3. What is one thing you have learned from one of the experiments you participated in?

I learned a whole lot more about US presidents than I ever thought I would! Some of the surveys asked about political ideologies that I was not too knowledgeable about, so afterwards I would look them up online.

Also, I learned that survey development can be a complex thing!

4. In your opinion, what is the best feature of DLABSS and/or its website?

I think the best, and most unique, feature would be being able to take part of this master database that offers participants a chance to be part of different surveys and areas of science. Moreover, being able to receive notices about surveys is great!

5. What about DLABSS do you think could be improved for experiment participants?

I was sometimes confused when the emails mentioned I had completed X% of surveys, and maybe language could be a bit clearer when talking about those. Furthermore, I tended to get duplicate emails a lot of the times.

6. If you were talking to someone considering participating in DLABSS for the first time, how would you describe your overall experience?

I'd say it seems to be an easy way to take part of surveys and research within the comfort of your own personal space; usually [experiments] are very interesting and timely which helps for those of us who are busy. An added bonus is the chance to be able to win a gift certificate.  

Time or Space? Experimental Research on Self Control and Commute Tradeoffs

DLABSS Note: This is a guest post by Julia Lee, currently a postdoc fellow at the University of Michigan's Ross School of Business. Previously she was a doctoral student at the Harvard Kennedy School of Government as well as a lab fellow at Harvard University's Edmond J. Safra Center for Ethics. We'll be periodically checking in with researchers running survey experiments with us to gauge their experience with DLABSS.

I have been interested in the seemingly-irrelevant factors that may influence our motivation and cognition at work.  For example, some of my research looks into how weather can influence worker productivity despite the fact that it is often neglected as an important factor when people think and make plans about how much work they can get done. Recently my colleagues and I began exploring a similar puzzle using a survey experiment on DLABSS.   

My collaborators (Jon JachimowiczBradley StaatsFrancesca Gino, and Jochen Menges) and I were interested in how people strategize about their daily commutes.  People spend not-so-insignificant amounts of time commuting to and from work in a given day, and we already know that long commutes in heavy traffic can have negative effects on work-related attitudes and behavior, such as lower job satisfaction and exhaustion.  Nevertheless, when people are asked to consider two different housing options, their choices rarely seem to factor in the negative impact of commuting. According to Dutch psychologist Ap Dijksterhuis’s thought experiment, if offered a 3-bedroom apartment with 10-minute commute time and a 5-bedroom house with a 45-minute commute, many will choose the 5-bedroom house because they underestimate the pains of a lengthy commute.

We set out to test this commuting paradox with participants recruited from DLABSS, with a twist — we wanted to see if this relationship is likely to change based on the individual levels of self-control. Our preliminary results suggested that self-control is an important factor that can potentially influence how different people make commute-related decisions. Individuals high in trait self-control were more likely to choose an apartment that was closer to work, while those who rated low in trait self-control were more likely to choose a house that was farther away from work.  Similarly, those high in trait self-control were more likely to choose a job that was geographically close but paid less, while those low in trait self-control were more likely to choose a job that paid more, but required a longer commute.  

We are currently running a few follow-up studies to examine why people who have different levels of trait self-control make different decisions when facing tradeoffs between commuting and space, salary or other considerations.

Q & A with September 2015 Gift Card Winner

The latest winner of our monthly $50 Amazon gift card raffle was kind enough to answer a few questions about her experience working with DLABSS. We have posted her responses below:

How did you first hear about Harvard DLABSS, and why did you decide to participate for the first time?

Honestly, I just got an email one day asking me to take a survey. So I signed up and did it, and I liked participating so I kept doing them whenever I got an email.

Why did you continue to participate in experiments on DLABSS?

I know they're helpful to the group who puts them together. They spend a lot of time putting them together and they need the results, so it's easy for me to keep doing them.

What is one thing you learned from one of the experiments you participated in?

Honestly, I've learned I don't know a lot about politics, which is a problem and I need to brush up!

In your opinion, what is the best feature of DLABSS and/or its website?

The surveys are really easy to fill out, and I always get an email telling me when there is a new experiment, so I don't have to go searching for them.

If you were talking to someone considering participating in DLABSS for the first time, how would you describe your overall experience?

It was pretty fun for me. I enjoy filling out surveys in general, and knowing the results of my survey actually helped a group of people so it made it a more pleasant experience.

Q&A with Another DLABSS Gift Card Winner

DLABSS was lucky enough to have another one of our Amazon gift card winners complete a brief interview so their experiences can be shared with the DLABSS community! We have posted our interview questions and Diana’s answers below. 

Please tell us a little about yourself such as where you are from, your occupation, and anything else relevant about yourself that you would like to include.

I am a native New Yorker, a former English professor and current freelance tutor/editor/translator. 

How did you first hear about Harvard DLABSS, and why did you decide to participate for the first time? 

Perhaps I found you on Craigslist. I enjoy answering questionnaires.

Why did you continue to participate in experiments on DLABSS? 

I enjoy answering questionnaires.

What is one thing you learned from one of the experiments you participated in? 

I can earn money from answering questionnaires. 

In your opinion, what is the best feature of DLABSS and/or its website? 

[It’s] affiliation with Harvard

If you were talking to someone considering participating in DLABSS for the first time, how would you describe your overall experience?  

Interesting, not too long per questionnaire, and possibly lucrative.

Q & A with our Second Gift Card Winner

Last month another DLABSS participant won an Amazon gift card from our monthly raffle! As our second winner, we wanted to follow up with a few questions for Jason to learn what DLABSS' participants like him are thinking. Our questions and Jason’s answers are below.  

How did you first hear about Harvard DLABSS, and why did you decide to participate for the first time?

“I first heard about Harvard DLABSS [through] Craigslist. I personally enjoy surveys that contribute to real life issues, as opposed to consumer products or something similar to that.”

Why did you continue to participate in experiments on DLABSS?

“What compels me to continue participating in DLABSS experiments is the variety of issues. Every time they send me an e-mail inviting me to take a survey its always a surprise as to what the subject matter will be. This gives me an opportunity to answer in a fresh state of mind and also learn something new internally and externally.”

What is one thing you learned from one of the experiments in which you participated?

“One thing I have learned is how our environment can influence our subconscious mind and contribute to preconceived notions and judgements.”

In your opinion, what is the best feature of DLABSS and/or its website?

“The automated e-mail system they use is very convenient. On average I receive one e-mail every two weeks, the surveys are for the most part very brief and user friendly.” 

If you were talking to someone considering participating in DLABSS for the first time, how would you describe your overall experience?

“It’s a fun easy way to voice your opinions, and help Harvard students get their assignments completed. There's also a chance you could win a $50 gift card to Amazon, which I have won ; )”

MTurk Results have been Replicated by DLABSS!

Professor Enos has successfully replicated the results of a study performed in Mechanical Turk using DLABSS. This means that DLABSS could be a suitable replacement for MTurk as a way of gathering data, and is great news for researchers currently using DLABSS or researchers considering using DLABSS in the future. The report written by Professor Enos explaining his findings is below:

    DLABSS has successfully replicated several studies in Mechanical Turk. By replicating studies—meaning the results obtained in DLABSS and Mechanical Turk were substantively the same—we are demonstrating that volunteers of DLABSS can potentially substitute for Mechanical Turk. Mechanical Turk (MTurk) is currently the primary source of online subjects among social science researchers, so this signals significant potential for DLABSS as a tool for researchers. In this post, I describe the details of one of those replications.

    In this study, a call for volunteers was posted on DLABSS and a very similar advertisement was posted on MTurk. I paid subjects $1 to complete the study on MTurk. The study took the average subject about 9 minutes to complete. I collected similar demographics on MTurk and DLABSS, so we can compare the differences between a volunteer and paid sample. These basic demographics are displayed in the table below. The DLABSS sample looks largely similar to the MTurk sample, however it is more liberal and better educated. Of course, one of the primary advantages of DLABSS is that subjects can be easily targeted, so that if a researcher wants, for example, fewer college graduates, this can be easily obtained.

    In this particular study, I asked people to judge the appearance of faces. I was interested in whether subjects thought that these faces looked more like the face of an African American person or more like the face of a Caucasian person. I showed them groups of faces for five seconds. One particular face was highlighted and I asked subjects to judge the appearance on a 7-point scale from ”Completely African American” (1) to ”Completely Caucasian” (7).

    The test of interest is that subjects were shown three conditions and in each condition asked to judge the same face. In two conditions, the faces on the screen were segregated by race (white faces separated from Black faces). The highlighted face, which the subjects were asked to judge, could be grouped next to white faces or Black faces (see image below). In the other condition, the faces were integrated by race (white faces and black faces together).

    My hypothesis is that subjects will use segregation as a heuristic in judging the faces, so that when the face is segregated and grouped with Black faces that subjects will say the face is more African American, when it is grouped with white faces, subjects will say it is more Caucasian, and when it is integrated, subjects will be more likely to say it evenly split in appearance.

    The figure below shows that this was the result in both Mechanical Turk (the red bars) and DLABSS (the gray bars). The bars represent differences in judgments of the faces between the integrated and segregated conditions. Negative numbers mean more African American, and positive numbers are more Caucasian. The Black segregated faces were judged to be more African American and the white segregated faces were judged to be more Caucasian than the baseline integrated condition. A T-test for a difference of means between conditions yields p < .05 in Mechanical Turk and p < .01 in DLABSS.

    Of course, the average differences between conditions are not the same: -.11 in MTurk and -.24 in DLABSS—but exact replication is rare in social science. The important takeaway though is that a researcher using either MTurk or DLABSS would have come to same conclusion from this data—indicating that DLABSS is a worthwhile replacement for MTurk.


Q & A with the First Gift Card Raffle Winner

Last month was the first month a DLABSS participant won an Amazon gift card from our monthly raffle! Because he was our first winner, we sent Matthew a few questions to answer so everyone could learn a little bit more about the individuals that contribute to the success of DLABSS. Our questions and Matthew’s answers are below.  

How did you first hear about Harvard DLABSS, and why did you decide to participate for the first time?

“I saw an ad for volunteers to take surveys. My degree is in psych and I remember what it was like looking for [participants] to take our surveys and experiments.”

Why did you continue to participate in experiments on DLABSS?

“It's actually fun to take these surveys.”

What is one thing you learned from one of the experiments in which you participated?

“One never really knows [what] the other has in mind. With these surveys you may think you know what they are about but it can really surprise you to see where they are taking these questions.”

In your opinion, what is the best feature of DLABSS and/or its website?

“You get to participate in helping generate data that someone thinks is significant.” 

If you were talking to someone considering participating in DLABSS for the first time, how would you describe your overall experience?

“If you want to share an opinion on things you may never have even thought about it, go for it!”

A Visualization of DLABSS' Growth Since its Start

Since its start in August, Harvard Digital Lab for the Social Sciences has experienced substantial growth in the number of active participants, researchers, and experiments. The time plot above provides a visual representation of the progress DLABSS has made since it began. The black line shows the total number of individual participants that have participated in an experiment hosted on DLABSS has surpassed 1000. The different colored lines each represent a different experiment hosted by DLABSS, and the number of participants that have participated over time.

As you can see, the number of experiments has continued to increase, which offers participants a diverse pool of interesting studies that focus on many different topics. Also the longer DLABSS has been active, the faster researchers are provided with responses for their studies. The experiments posted more recently have exhibited a much faster increase in participants compared to earlier experiments. This is great news for researchers who are looking to get quick feedback on their experiments. We would like to thank the researchers and participants that have helped make DLABSS so successful this fall.

DLABSS is implementing a gift card raffle this month!

We at DLABSS understand that all of our participants voluntarily donate their time to help our cause and further Harvard University research. Thanks to our valued participants, DLABSS has gotten off to a successful start this semester. To show our appreciation we will begin a new system this month that raffles off a $50 Amazon gift card once every month. Each time you complete an experiment your email will be entered into the raffle. This means the more experiments that you complete, the greater the chance you have of winning. The winner will receive a digital gift card by email. There is no limit on the number of experiments you can participate in, except you cannot participate in the same one twice. With your continued participation we can make the next semester even more successful!

DLABSS has made News and was Published in the Harvard Gazette

Since our last update, DLABSS has added three new experiments by Professor Ryan EnosProfessor Julia Minson, and Professor Gwyneth McClendon. We are happy to see that our collection of experiments continues to grow as DLABSS progresses. Expect more experiments to be posted soon! Also Harvard DLABSS has recently appeared in the Harvard Gazette! Check out the story here if you want to know a little bit more about us or are interested in the main goal of DLABSS, how DLABSS came to be, and where we expect it to go in the future. We thank everyone who has helped to get DLABSS where it is today and expect to progress even more in the coming months!

An Update as DLABSS Continues to Grow

Greeting from DLABSS! Research is underway as several researchers have posted experiments. The newest study, MID Crowd by Dr. Vito D'Orazio, is now available for participants to take and contribute to Harvard's research. Researchers are showing continued interest in having their experiments posted on DLABSS. We are currently expanding our pool of studies to accommodate more areas of interest within the social sciences, and we are excited about the valuable research that DLABSS can offer this academic year. The participant pool is also continuing to grow as more and more people find out about the opportunity to be involved in Harvard research. Stay tuned to see what exciting experiments we are hosting next!

Official Launch of the Harvard Digital Lab for the Social Sciences!

We are happy to announce the official launch of the Harvard Digital Lab for the Social Sciences (DLABSS). During the month of August, we plan to build a sizable pool of study participants and begin facilitating research projects. Participants can now sign up on our Master Survey, and see all experiments currently posted! As the DLABSS grows this summer and during the next academic year, we will blog approximately once a month with updates. We will also post results of studies affiliated with DLABSS as these become available.