6. Managing study data

Now that you have a good handle on how to set up the study protocol you want, it’s time to look at how to manage your study: controlling who has access to what, starting and stopping data collection, reviewing and downloading your data, and contacting your participants.

A note for external studies: Much of this information applies to your studies as well! External studies do not have consent management or study data for you to download, but you do have demographic snapshot data for each participant/session. Managing data access, downloading, and communicating with participants works the same across all study types.

Managing access to your study: add a collaborator

Rarely will you be working completely alone! Usually you will want multiple people to have access to any particular study: you may have a few people working together to get the protocol just right, as well as several RAs checking consent and sending feedback to participants.

We very strongly recommend this model rather than sharing credentials for a lab-wide account. This way, each individual can get permissions on just the studies they actually need access to - not everything your lab has ever done. When temporary staff like undergrad RAs move on, you can just remove them from the study, instead of distributing a new password to everyone remaining in the lab. Plus, there is logging built into Lookit that keeps track of things like who did what when - including who approved consent recordings - that may be useful to you.

Try it out! Add another person to your tutorial study. On the study page in the Experimenter interface, scroll down to “Manage Researchers”:

Manage researchers section

In the search box, type in the first few letters of someone you want to add, and press Enter. Click the green “+” button to add them to your study:

Adding a researcher

They will show up on the right with “Preview” permissions initially:

Researcher shows up on right with read access

This means they can see your study in the Experimenter interface, but not see any participant data or make any changes. You can click on “preview” for a drop-down menu to give them different permissions if you want. (See this section for much more detailed information about the different roles available. ) Or you can click the red “-” button to remove them again.

Great! Now you know how to give someone else access to your study so you can collaborate.

Updating the code your study uses

Another thing you’ll probably need to do eventually is set your study to use an updated version of the underlying Lookit frameplayer code.

Remember when we had to “build an experiment runner” so we could preview the study? ( You can review that here.) That build process took the version of the frameplayer code we specified and bundled it up into a little container for our study to run in. That container includes all the information Lookit needs about what frames are available to use and how they work.

As you fine-tune your study, you will be making lots of edits to your study protocol, saying exactly what stimuli each frame should use, in what order, etc. But the study protocol is still interpreted by that same application. If at some point you want to take advantage of bug fixes, video recording improvements, new frames that have been added to the standard Lookit code, etc., you’ll need to tell Lookit to use the new version and build a fresh experiment runner.

One way to think about your current experiment runner is as a Lego set; it has certain types of building blocks that allow you to customize your project with the pieces you have available. But Lego is always making new blocks with interesting shapes and new affordances. If you want access to building blocks beyond what you had in your original set, you can get access to the new and improved set of blocks by rebuilding your experiment runner.

Try it out now! Follow the directions in Updating the frameplayer code for your study to update your tutorial study to use the most recent version of the Lookit frameplayer.

By design, updating the code shouldn’t break anything that currently works - you shouldn’t need to change your study protocol! However, it is important to always preview your study after any update to double check, and report any problems you run into.

Understanding previewing vs. participating in a study

So far, we have tried out our studies via the “preview study” button on the study edit page. There are only a few differences between previewing and actually participating in a study:

  • When you preview a study, there is an “is_preview” field of the data collected that’s set to true - otherwise it’s false. Data collected from previewing is marked when you view consent videos or individual responses, and this field is available in the all-response downloads.
  • Only Lookit researchers with appropriate permissions can preview the study. (Either the researcher needs to have read permissions for the study, or the study needs to be set to have a shared preview - then any researcher can access it.) Anyone with a child registered on Lookit can participate in a study.

Other than that, the experience is exactly the same, by design - so that you know exactly how your study will work. You see the same messages about whether your child is eligible, customization based on the child or past responses works the same way, and you use the same experiment runner.

Going live!: the study approval process and starting data collection

If you just want other researchers to be able to preview your study to give feedback, you can set “shared preview” to true and then share the preview link on Slack.

But what about when you actually want to start data collection?

At that point, you will “submit” your study for approval by Lookit staff. We won’t practice this piece, but so you know what to expect, you can look through the information about submitting your study.

Why the manual approval process?

From a participant’s standpoint, Lookit is a unified platform, even though there are studies from a variety of research labs. This is great for participant recruitment! But it also means we’re all sharing a reputation. Someone else’s study that upsets or (without adequate precautions) deceives children, that baffles parents, or that just doesn’t work will affect how interested families are in your study, too. Based on our early experience with researchers using Lookit, we strongly expect that a quick review will catch substantive issues often enough to be worth putting everyone through. If you are making changes to an existing study, review is either not required (if only changing certain fields like the age range/eligibility criteria) or is very quick.

Create some data to play with (Internal studies)

Because we don’t want to clog up the production server with fake responses from researchers trying out Lookit, we’ll do this section on the staging server, which is a separate sandbox environment that looks a lot like Lookit but doesn’t have any real participant data. This is also where we try out new features before deploying them to production.

Go ahead and create an experimenter account on the staging server following the login directions. If you did the first part of the tutorial, you’ll already have a participant account on the staging server - use a different email address for your staging experimenter account.

First, let’s actually participate in another study! Go to the staging server studies page, https://lookit-staging.mit.edu/studies/, and select the study “Apples to oranges.” This is a short study just to demo the data collection process. You can participate using your experimenter account; you may need to make a child profile and/or fill out a demographic survey before participating. Proceed all the way through this study!

Now switch back to the Experimenter interface. Note: you can toggle between Lookit (the participant-facing section) and Experimenter (the researcher-facing section) at any time via the top navbar if you are logged in as an experimenter:

Participant-facing Apples and Oranges detail page Researcher-facing studies view

Get access to the “Apples to Oranges” study

You are able to see the “Apples to Oranges” study listed on the Experimenter site on lookit-staging.mit.edu because you automatically get read-only permissions for studies within the Demo lab. However, you can’t automatically see any participant data! (This is on purpose - it’s not possible to grant lab-wide permissions to actual data, you have to actively add people to individual studies.)

Post in the Slack #tutorial channel and we’ll add you as a researcher so you can see everything! Then, at the top of the “Apples to oranges” page, click on “View responses”:

View responses link

This will take you to a view where you can code for informed consent, view individual responses, or download response data, demographic data, and videos.

Downloading response data & videos

The consent manager and “individual responses” views can be helpful to get an idea of how data collection is going, but to code your videos and analyze your data you will want to download files that you can work with using your software of choice.

To download all videos, you can go to the “videos” tab and click “download all videos.” A zip file will be bundled up for you to download, and you will receive a link by email in a few minutes. Try it out, and take a look at some of the video collected!

Video download

Note that on this page you can also filter for specific parts of the filename, including the frame name and response ID.

Videos are named videoStream_<study ID>_<frameIndex>-<frame ID>_<response ID>_<timestamp>_<random digits>.mp4, so you can use the response ID to match videos to other response data even if you only have the filename. The response data will also contain video IDs in the expData for any frames that recorded video.

Under “All responses,” you can download JSON or CSV files with data about all responses from this study. You can learn more about these options here.

All responses view

Analyzing the data collected is, in general, outside the scope of this tutorial as it will vary substantially by lab/project - although we hope that you will share your scripts and processes for analyzing Lookit data to help other researchers! The exercises below can be solved by manual inspection of the CSV (or JSON) data, although you are also welcome to set up a script in your language of choice to get a head start on real data processing.


  1. How many researchers said they preferred oranges? How many said they preferred apples?
  2. What fraction of researchers gave different answers on the actual test question vs. the survey?

Downloading demographic data

Under ‘demographic snapshots’, you can also download demographic survey responses from the accounts associated with children who participated in your study (once consent is approved). For each response, you will see demographic survey data for that participant at the time of participation.


  1. What fraction of responses are from researchers in urban locations?
  2. What fraction of children who responded at least once live in homes with at least 10 books?

Contacting participants

You may need to contact participants for a variety of reasons: for instance, to let them know it’s time to complete another session of a longitudinal study, to ask for clarification about a problem they reported, or to announce that the results of your study have been published!

You can contact participants in a particular study using the “Message Participants” link at the top of your study, found here under “Take Action”:

Message participants link

That will take you to a page link this where you can see and download previous emails (left side) or compose new emails (right side). This interface is in progress with work planned to make it easier to use, but it’s functional!

Where are the email addresses?

You may notice that although you can message participants, you’re not being provided with their actual email addresses. We apologize for the inconvenience this causes in implementing some custom workflows, and can discuss providing email permissions with individual labs if necessary. However, obscuring email addresses is deliberate: it allows us to programmatically enforce participants’ email selections (so that they don’t receive email types they don’t want), protects against accidental disclosure, and ensures you have a central record of all communication. Again, this is a matter of sharing a reputation!

The first thing you will do when you send an email is select the “Message Type”. These line up with the email types participants can opt to receive: notifications that it’s time for another session of a longitudinal study; notifications that a new study is available for them to participate in; updates about this study (like that results are available); and clarifying questions about their responses.

Next, you specify the recipient(s). You can do this by searching for the appropriate account ID. Finally, you write your message subject and body, and hit send! Let’s try it out with a few example scenarios.

Contact a participant with a gift card code

Second, let’s imagine that you’re compensating participants with gift cards. (You’ll want to take a look at the Terms of Use and compensation info here as you make more detailed plans, but essentially, for now researchers are responsible for handling any compensation by messaging participants.)

Instead of the consent manager, switch over to “individual responses” and find your response again. Copy the participant ID from the response JSON:

Participant ID in response JSON

Returning to your “message participants” tab, let’s create another email. This time, you can actually select the “transactional email” option, which allows you to reach even people who have opted out of email; this is because you sending the compensation is the completion of a “transaction” they agreed to. You will see a warning which is ok:

Transactional email warning

Like before, paste in your ID, write your message, send it, and make sure you receive it. (Don’t actually send yourself a gift card. Unless you really want to.)

Congratulations! We’ve covered all the basic functionality you’ll need to manage your studies. Finally, we’ll wrap up by briefly noting some of the advanced features you might want to use later and revisiting Github issues now that you may have some feature requests or bug reports.