Grad Student Corner: Getting the Most Out of Your Research Participants

 Erin J. Lightman,                          


 Hillary Schwarb,

Georgia Institute of Technology


Being a successful psychologist demands a wide range of skills, including writing, public speaking, data analyzing, computer programming and creative problem solving. Successful data collection, however, is crucial to successful science and graduate students are often responsible for ensuring this process goes smoothly.  But as you well know, participants—particularly those volunteering as a part of their psychology course requirements—are not always as focused, motivated or enthusiastic as we might hope. Furthermore, successful science with human participants also necessitates interacting and communicating with research participants, and for scientists and academics (let’s face it, a group not widely known for their exceptional social skills) this interaction does not always come naturally and thus is often minimized. Therefore for this edition of the Graduate Student Corner,  we decided to poll experienced grad students, post-doctoral researchers and faculty members for pointers on facilitating the most effective data collection and advice on how to most successfully interact with participants when collecting data.

We’ll begin with some general guidelines. First and foremost, it has been our experience (and the experience of nearly everyone we spoke to) that undergraduate participants are considerably better at the beginning of the semester. Take advantage of this. If at all possible make sure that you have experiments ready during the first week of classes as it seems that participants who volunteer early on are more punctual, more focused and produce more consistent data than their sleep-deprived, high-stress peers who arrive during the end of the semester panic.

Many have emphasized the importance of making participants know that you are attentive both to them and their performance throughout the experiment. Whenever possible, it is often useful to have a researcher sit in the same room as the participant. This is a constant reminder to the participant that you have not forgotten about them and are interested in their performance. If you’re lab is set up so that you have the capability to assess participant performance from outside of the room whether via video or duplicate monitors, it is essential to let participants know that you are paying careful attention to their performance.

Whether you are monitoring your participants in the room or from outside, all of those polled agree that providing feedback and encouragement is very important to acquiring consistently good data. Feedback during practice trials is particularly important so that the participant can monitor his or her own performance and so that you can make sure they are performing the task correctly. When trail by trial feedback is infeasible, feedback relaying mean performance should be considered at the end of each practice block. Finally, even after practice is complete, provide feedback wherever possible. This can be a motivating factor for participants and it allows you to ensure that your participant hasn’t gotten wildly off track.

Particularly for longer experiments, breaks should be built into your study. Many researchers we spoke with expressed the importance of taking breaks as an opportunity to interact with participants. They suggest that short rests not only break up the monotony of the experiment from a participant perspective, but also provide time for the researchers to encourage good performance. In our lab we typically try to briefly interact with our participants after each experimental block. Whether to help guide their performance (e.g., “You are very accurate, now try to work on getting faster.”), provide encouragement (e.g., “Excellent job. Are you ready for the next block?”) or just check on them (e.g., “How are you doing? Do you need to stretch or go get some water?”), brief pauses in the experiment allow participants to reset and focus on the task at hand. Also, if your experiment requires additional tests (e.g., collecting demographic data or conducting neruopsych tests) outside of the main experimental task, interspersing these throughout the course of the experiment breaks up the repetitiveness and keeps participants alert.

In some experiments, no matter how much personal attention, direction and encouragement you provide, participants just might not be motivated to perform as well as they can on a given task. In these situations we and others have found that adding some sort of point system or bonus for good performance can encourage high levels of attention and focus. If paying participants, a monetary bonus based on good performance is often a strong motivator. Additionally, we have found that for many participants just “earning points” provides enough internal motivation to keep them on task and engaged.

Finally, graduate students, generally speaking, are not always at the peak of fashion. This is to be expected. However, when running participants it often helps to look put together. This does not mean you have to wear a suit and tie, it simply means maybe opt for something with buttons rather than a wrinkled t-shirt. We fear we’re beginning to sound like Miss Manners which is not our intent, but the polled graduate students, post docs and faculty agreed that when researchers dress professionally, participants are more apt to take the study seriously. Many faculty members even encourage their undergraduate research assistants to be conscious of their appearance when running participants. Finally, if the prospect of “professional attire” makes you squirm, try a lab coat…it worked for Milgram.

Lastly, data acquisition techniques outside of behavioral data collection (e.g., EEG, eye-tracking, fMRI, patient work, etc.) pose additional challenges for researchers. Many of these techniques are highly susceptible to artifacts in the data and in order for a data set to be useful, artifacts must be kept to a minimum. Researchers polled suggest presenting each experiment as a collaboration; participants and researchers working together to collect the best data possible. In EEG, it is often helpful to describe artifacts in detail and show participants both how to make them and what they look like in the data. Then encourage them to avoid creating those artifacts during experimental trials. fMRI researchers have found that presenting short video clips from TV shows or movies between scanning runs can be engaging and keep participants alert in an environment that often encourages drowsiness.

We hope that some of these guidelines will be helpful to you when collecting data your data or at the very least get you thinking about things that you as a researcher can do to improve data collection. Finally, we would like to thank Michael Dulas, Matt Hilimire, Nate Parks, Tom Redick and other anonymous contributors for their wisdom and experience that helped shape this article.