1. Sociometers: Measuring Group Dynamics
Hello everyone, My name is Karren Watkins and I’m presenting the use of sociometers, a specific wearable technology, to measure interpersonal skills and group dynamics.
My position as a research associate at Olin Business school has allowed me the chance to use this unique technology and today my aim is for each of you to gain an understanding of if and how you might incorporate sociometric methods into your own research.
To do this, I will take us through what sociometers are, when they can be useful, and our recommendations on what you will need in order to begin exploring this new method. I’ll also give an example data report for a small group setting that Professor Knight and I created.
Karren Watkins
Research Associate
Andrew Knight
Assistant Professor of Organizational Behavior
Olin Business School
Washington University in St. Louis

2. The New Science of Building Great Teams, Alex “Sandy” Pentland, 2012
Sociometric badges: Using sensor technology to capture new forms of collaboration, Kim et al., 2012
The New Science of Building Great Teams, Alex “Sandy” Pentland, 2012
People Analytics, Ben Waber, 2013
Secret Signals, Mark Buchanan, 2009
Some of you may have heard of sociometers previously from any of these sources. For example, Harvard Business Review featured an article on using them to examine how high-performing teams communicate and the Journal of Organizational Behavior highlighted their potential as a tool to enlighten research on how teams collaborate.
Just to get an idea – who here has heard of sociometers before? Ok, great!

3. What is a Sociometer?
Microphones: Speech Bluetooth: Proximity Infrared: Face-to-face Accelerometer: Movement
To get us started, what exactly is this device called a sociometer? For reference, they are also called “Sociometric Badges” and today I will refer to them either as sociometers, badges, or simply, devices.
A badge has two microphones, one on the top and one on the front – this allows it to decide if speech is coming from the wearer or someone else. It also has a 3-axis digital accelerometer that measures movement, a bluetooth sensor that picks up proximity to other badges, and lastly, each badge has an infrared sensor on the front – when two infrared sensors detect each other, that allows recording of when two people are facing each other.
So as you can imagine, these little guys collect a TON of data! Especially when you consider that they record information from the four sensors (5 if you count both microphones) on a second-by-second basis. The granularity of the data alone opens up a whole host of new research questions that can be addressed.

4. What do they measure? Voice and Conversation Patterns
Individual: Pitch, volume, emphasis
Group: Turn-taking, dyadic engagement, conversation flow
NOTE: By default, they do not record raw audio
Postural Movement
- Individual: Activation
Group: Mimicry  Influence
Network Interactions
- When and where face-to-face connections happen
What type of information does this data convey?
There are three overarching categories of information recorded by sociometers – voice and conversation patterns, postural movement, and network interactions.
For voice, the devices do not record raw audio for privacy concerns, but they do record when a person is talking and the profile of that speech – that is, the pitch and volume. This allows the accompanying desktop software to construct a picture of the conversation over time – so, what was the vocal activity of the group as a whole like? How about the turn-taking? Maybe one person totally dominated the conversation or someone was constantly interrupting others – things that they may be peripherally aware of but not realize the extent of.
For movement, the accelerometer data can show you when participants were moving and how much. This can become most interesting in combination with the speech data - for example, if there was a lot of movement within a group after one person says something, but no vocal responses. Further, gestural and postural mimicry – which is when a person subconsciously imitates the behavior of another - is shown to be a sign of influence. Because of this, the devices can peak into the cognitive, and sometimes subconscious, phenomenon of influence by comparing when and how two people move.
Lastly, through the bluetooth and infrared sensors, sociometers can be deployed in a large-scale setting to record when and where people are interacting over time, giving a rare, objective, measure of informal networks and their temporal characteristics.
While the data collected by sociometers could possibly be obtained without them, it would be, at best, extensively time consuming and expensive – such as paying RAs to code hours, and hours, and hours of videos. And, there are countless questions that this type of data can help answer – let’s touch on just a few of them.
4:30

5. Sociometers & Management Research
Dyadic Interactions
Small Group Dynamics
Large Group Dynamics
From salary negotiations to job interviews to sales pitches
Honest Signals, Pentland, 2010
Another Idea: Turn-taking and Affect
E.g.: Is turn-taking in boss-subordinate meetings predictive of post-meeting affect?
Starting on the dyadic level –
In his 2010 book, Honest Signals, Sandy Pentland featured several studies with organizational implications – in one, he found that during job interviews, speech consistency and both physical and vocal mimicry were very strong indicators of if an interviewer had a positive impression of an interviewee. In another one, he found that during salary negotiations between a middle manager and vice president, middle managers were more reluctant than VPs to match displays of emotionality, or variable vocal emphasis, but that VPs who did match displays of emotionality ended up with worse deals than those who did not.
Another potential research question on the dyadic level could be: How does turn-taking between a boss and their subordinate affect the affect of each person after the meeting?
{Job Interviews – 30 hiring interviews at the MAEER School of Management were analyzed. At the end of the interviews, the interviewers graded candidates on performance, engagement, confidence, and overall impression. The ratings in these categories were highly correlated, thus the researchers just focused on overall impression. Results showed that consistency, mimicry, and influence features accounted for almost 43 percent of the variability of the data, with r=0.66 and p<<0.01.}
{Salary Negotiation – 46 gender matched pairs were tasked with a face-to-face negotiation as part of their class work. The negotiation involved a middle manager applying for a transfer to a vice president’s division in their company and required the negotiation of salary, vacation, company car, division, and health care benefits. Students were offered real money for maximizing their own outcome. The first 5 minutes of each negotiation were used as a thin-slice representation of the negotiation. There was also a short survey at the conclusion of the negotiation. Results found that the VP’s total score was highly correlated with activity (vocal) and influence, but also with the MM’s activity and variability of emphasis (r2=0.27, r=0.52, p,0.01). On the other hand, the MM’s total score was strongly correlated with mimicry and consistency, but also with the VP’s variability in emphasis (r2=0.3, r=0.55, p<0.01).  Analysis of survey questions showed that the mimicry measure had a significant positive correlation with the extent to which participants said they were seeking to avoid disagreements (r=0.62, p<0.01). Data were also analyzed on a minute-by-minute basis to understand social signaling. Results showed that if the VP showed variable emphasis (a signal of uncertainty or emotionality), the MM would usually become more active and only occasionally would respond with variable emphasis. If the MM showed variable emphasis, however, the VP would often become more active but was more than twice as likely to respond with matching variable emphasis. So, VPs were less reluctant than MMs to match displays of emotionality or uncertainty. However, the VPs who responded this way ended up with worse deals than those who didn’t.}

6. Sociometers & Management Research
Dyadic Interactions
Small Group Dynamics
Large Group Dynamics
Relating conversational turn-taking to group intelligence:
Evidence for a Collective Intelligence Factor in the Performance of Human Groups, Woolley et al. 2010
Another idea: Vocal Engagement and Status Differences
E.g.: How do conversational & physical mimicry contribute to the development of team cohesion and coordination?
Moving up a level to small group dynamics, Anita Woolley used sociometers in her study published in Science in 2010 on the collective intelligence of groups. She found that proportional turn-taking in a group – that is, each person taking up a similar number of turns within in the group – was positively related to the group’s intelligence as a unit.
Also, another interesting phenomenon to look at on the group level (and something I would be very interested in!) is how conversational and physical mimicry contribute to the development of team cohesion and coordination.

7. Sociometers & Management Research
Dyadic Interactions
Small Group Dynamics
Large Group Dynamics
Using network information as a measure of social capital:
Measuring social capital in creative teams through sociometric sensors, Gloor et al., 2012
Another Idea: Influences on Informal Network Changes
E.g.: How do patterns of informal interaction between different departments change after a physical relocation?
Lastly, in terms of large group dynamics, a recent study by Peter Gloor and colleagues used sociometers to collect network information, which was used as one measure of the social capital of individuals. They found that collective creativity of teams is a function of the aggregated social capital of its members. (Int. J. Organisational Design and Engineering)
And something we became curious about as our business school just expanded into a new building is how informal interactions between individuals and departments can change after there is a physical relocation of personnel.

8. } How Do I Use Them? Easy for participants!
Put on correctly, turn it on
Sociometers record data
Turn it off
Import data into Sociometric
Solutions proprietary
software: DataLab
Export to excel file
Process excel file into usable
data using computer coding
Easy for participants!
So, hopefully by this point you are starting to get an idea of the different ways you might be able to use sociometers in your work. Now, how does one go about using this tool, what is the process like?
Before you begin, you will want to keep track of how much battery life is left because there is no indicator on the device. One charge gives about 40 hours of use, total. Then, when you are ready to collect data, just make sure the badge is put on correctly – with the infrared sensor facing outward, away from the body, and the lanyard tightened so that the device sits high on the chest, and ask participants to turn it on.
While it is recording data, there will be periodic blue or green lights that flash on the front to indicate when the bluetooth or infrared sensors have detected another badge. Even with the blinking lights, all students who I worked with reported forgetting that they were wearing the device after a few minutes. All in all, the sociometers are very easy for participants to use and do not seem to interfere with the research setting.
After you’ve recorded your data, you will download it onto your computer using specialized software that is included in the purchase of the devices, called DataLab. This software will export an excel file containing the sociometric data. The data in this excel file will look something like this (next slide), with up to 50 different tabs of data.
Lastly, because the data is in either second-by-second or minute-by-minute intervals, and parsed out by participant, you will almost certainly need to create some computer code to run this file through to get the information into a form that you want. For reference, we used the open source software R – which is easy to learn and very powerful – in fact, I learned it originally to use with this data.
Minutes

9. Image 1: Sample Speech Profile Tab from Export File

10. Karren Speaking Length Engagement Volume Balance Pacing
Team Meeting: 2:10 p.m., June 27, 2013
Speaking Length
Average speaking length (seconds):
Karren A M S
2.3
0.4
7.2
1.2
Engagement
Your engagement with your other team members appears slightly unbalanced. M S A
34%
43%
23%
Your average speaking segment length much less than the average (2.8 s) for your group and generally short.
Volume
You are talking more quietly than the rest of your group.
Balance
Speaking
Listening
You spoke 4% of the time, which is extremely low.
After analyzing the data in R, Prof. Knight and I also developed small-group feedback reports to tell students about their interactions in groups. This is a very early stage example those reports. There’s also a later version in the appendix for this presentation. This report reflects actual data collected as part of some pilot experiments that we conducted last summer.
Personally, I found this quite interesting – for example, looking at the top right, we conceptualized engagement as whenever you are speaking before or after someone else. It looks like I engaged the least with A and the most with S, which seemed accurate based on memory.
Turning at balance and pacing, however, I was surprised! I knew that I probably talked the least out of the 4 people in this particular meeting, but I genuinely believed that I spoke more and more often than the data suggested. It could certainly be that my memory is biased, but this brings up a big question that still remains largely unanswered about the sociometers –
Pacing
Your contributions appear evenly spaced throughout the conversation but are very infrequent.
Minutes
Image 2: Sample Beta-version Sociometric Feedback Report

11. Are Sociometers Reliable? Valid?
Conversational Scene Analysis, Basu, 2002
Toward a Social Signaling Framework: Activity and Emphasis in Speech, Stoltzman, 2006
Currently analyzing validation data of the speech detection and attribution feature
Are they reliable and valid?
For those who may be skeptical about the speech analysis methods, as I certainly was, the algorithms that are used to detect and parse the speech collected by the sociometers is primarily described in two papers, one by Basu in 2002, and one by Stoltzman in They describe the process of using hidden markov models and Conversational Scene Analysis to tell the devices how to pick up when speech is happening and who in the group it is coming from. If you are curious, you can find more information on these things in the appendix for this presentation.
Also, we have been conducting our own initial validation analyses by comparing human video coding of speech in teams to sociometric data. While we are still very early in this analysis, this plot (next slide) hints at the types of results we are seeing.

12. Figure 1: Differences Between Human and Sociometer Speech Recordings
Each line represents an individual team member in a group of five. The x axis is time and the y axis is the difference, in seconds, between human coding of how much each person spoke, and how much the sociometers reported them speaking. We can see considerable divergence at different points in time – such as just after minute 5, and at the end, where the difference really explodes.
There seems to be systematic variance across participants – the green and purple lines seem to be consistently overestimated by the sociometers by a few seconds and the blue line seems to be pretty consistently under-estimated. We are still looking into why this might be – a few theories are that when there are several people talking at once this can introduce sociometric error, and also that mumbling reduces likelihood that the devices will pick up speech.
As you can see in Table 1, the correlations between my coding and the sociometric data that we are seeing range from quite high – about .7, and even above .8 in other teams examined, to very low, about .01 even within the same group. Interrestingly, we have observed a similarly wide stratification in correlations for almost all of the teams we have analyzed.
Once we discover the conditions that increase the likelihood of sociometer errors, and in turn under what conditions the devices are valid and reliable, we will have a much better idea of which research contexts the vocal analysis feature of the device is best suited for.
It should be noted that for the other sensors – the accelerometer, bluetooth, and infrared – reliability and validity aren’t in question because these features work in a direct signal to output manner, without an interpretive algorithm intervening.
Figure 1: Differences Between Human and Sociometer Speech Recordings
Table 1: Correlations Between Human and Sociometer Speech Recordings

13. What Do I Need?
Sample Data – the data may look different than you expect, this will allow you to gauge usefulness for your project
Coding Resources – the data in the output files is not ready to be analyzed and to prepare it manually would be impeding
Time
For Training - before any data collection, you should plan to devote at minimum a month to become familiar with the process
For Piloting – to know what how environmental factors may affect your data, pilot testing should be done before each project
So, now that we have learned all about what sociometers are and how they can be applied to management research, the next step is getting started.
To ensure a smooth adoption of this technology, there are a few things that we suggest you plan for from the beginning. There will always be unknowns that come along in the process, but having these things, we believe, will keep those to a minimum and make sure you’re able to deal with them effectively.
The first is sample data – we were surprised at the form that some of the data took when we first started and having a look at some sample data will give you a concrete idea of what you could expect. And by the way, we are more than happy to share some sample data with anyone who is interested!
Next is coding resources – as I mentioned earlier, I chose to learn R to help manage this data, and Andrew* already knew R. You could use other coding languages as well, but someone on your team should be well-versed in pulling data from excel files and manipulating it in another environment.
Lastly, you will need ample time for training and for piloting. We discovered a few unexpected little quirks in how the devices work and the software as well, and when it comes time for data collection, you will want to have a firm grasp on hardware and software procedures and data expectations.

14. Thank you!
Thank you for your time and Prof. Knight and I look forward to answering any questions you may have about using Sociometric badges!

15. APPENDIX

16. Sources
T. Kim, E. Mcfee, D. Olguin Olguin, B.Waber, A. Pentland. Sociometric badges: Using sensor technology to capture new forms of collaboration. Journal of Organizational Behavior, 2012.
A. Pentland. The New Science of Building Great Teams. Harvard Business Review, April 2012.
M. Buchanan. Secret Signals. Nature, January 2009.
B. Sheridan. A Trillion Points of Data. Newsweek, March 2009.
B. Waber. People Analytics. Pearson Education, Inc
J.L. Lakin and T.L. Chartrand. Using Nonconscious Behavioral Mimicry to Create Affiliation and Rapport. Psychological Science, 2003.

17. Sources (cont.) A. Pentland. Honest Signals. The MIT Press. 2010.
A.W. Woolley, C.F. Chabris, A. Pentland, N. Hashmi, T.W. Malone. Evidence for a Collective Intelligence Factor in the Performance of Human Groups. Science, 2010.
P. A. Gloor, F. Grippa, J. Putzke, C. Lassenius, H. Fuehres, K. Fischbach, D. Schoder. Measuring Social Capital in Creative Teams Through Sociometric Sensors. Int. J. Organisational Design and Engineering, 2012.
S. Basu. Conversational Scene Analysis, Doctoral Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2002
W.T. Stoltzman, Toward a Social Signaling Framework: Activity and Emphasis in Speech. Doctoral Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2006