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Every year, ABCT’s Research Facilitation committee awards a Graduate Student Research Grant to provide financial support for a student whose research shows great innovation, creativity, and broader significance. For this feature, we are excited to present the recipient of the 2019 ABCT Graduate Student Research Grant: Colin M. Bosma. Colin is clinical psychology Ph.D. student at the University of Maine, where he is mentored by Dr. Emily Haigh.
Colin responded to questions from ABCT’s Research Facilitation Committee about his experiences in research.
Describe the project supported by ABCT’s Student Research Grant.
The title of my dissertation is “Individual Difference and Ecological Validity of Emotion Regulation in Response to Sadness.” The project seeks to advance the nomological network of emotion regulation by examining the correlates of subjective, physiological, and digital behavior. The project will utilize digital sensors in smartphones to collect data from individuals in naturalistic settings. Smartphones generate abundant social and behavioral data as a byproduct of daily use. The patterns in these data reflect the lived experiences of people in their real-world environment, generating a digital profile of human behavior, or digital phenotype. The project will evaluate whether digital phenotyping can accurately characterize and predict individual differences in emotion regulation implementation in response to a sad mood. This new method of collecting passive ecological data using smartphones may ultimately enhance clinicians’ ability to accurately identify and respond to emotion regulation implementation associated with mental well-being.
How has your approach to research changed over the course of your education?
My approach to research began with a foundation in traditional theory-driven research questions and hypothesis testing using frequentist statistics. Although this approach is still prominent in psychology and many scientific fields, there are inherent limitations that have facilitated my transition to incorporating other approaches. For example, pay walls can limit access to empirical knowledge that helps inform researchers’ decisions when they are developing their studies. Testing hypotheses using a frequentist approach has shaped our conceptualization of what we consider novel and has impacted which findings are ultimately disseminated. Furthermore, an emphasis on using frequentist statistics can impact decisions during the research process to be biased toward favorable results. For these reasons, I have transitioned to putting effort into incorporating research approaches that provide more nuanced information, transparency, and accessibility. When possible, I implement open science practices, including making my analyses reproducible, sharing my code on public repositories (e.g., Github), and preregistering my dissertation project on the Open Science Forum. I also strive to incorporate more informative and transparent reporting of my research results by interpreting confidence intervals and implementing Bayesian statistics. In contrast with null-hypothesis testing, I designed my dissertation project using a data-driven approach. Since the extant literature does not provide a theoretical basis for which digital behaviors would predict individual differences in emotion regulation, a statistical learning approach can help determine which digital behavior patterns indeed predict individual differences in emotion regulation implementation.
What have you found most rewarding about your research?
The thread that has continued through all of my research endeavors has been an interest in the difficult task of accurately assessing psychological phenomena. The strength of our research and our interventions is directly linked to the validity and reliability of our tools of measurement. It is rewarding to know that my program of research will contribute to our understanding of how to measure emotion regulation. It is exciting to develop knowledge about psychological processes that play such important roles in psychopathology, as this knowledge can help inform interventions for mental health issues.
How do you balance research with the other demands of being a graduate student?
My advisor, Dr. Emily Haigh, has provided me with incredible guidance on how to balance the demands of being a graduate student. First, she helped me realistically put into perspective how my career goals fit into what I should try to accomplish during graduate school. She has also helped me with identifying my priorities so that my actions can better align with what is expected of me as a student as well as my professional development goals. There are so many opportunities during graduate school that it is vital to spend some time to figure out which ones to actually want to devote your time to in light of what you ultimately want to do. On a more personal level, I approach balancing my responsibilities and goals as something that continuously needs to be re-evaluated and adjusted. For example, I am always looking for ways to integrate self-care into my workflow, such as making my workspace more ergonomic or engaging in more socializing by having a writing partner. I have learned that integrating self-care into how I work in addition to nonwork-related self-care has made me much more efficient at getting my work done, and without feeling burned out. When planning my tasks and managing my time, I try to be mindful of common cognitive biases, such as overestimating how much work we can get done in a certain amount of time. Similarly, I have put a lot of thought into how I use technology to organize my work and to keep track of my tasks, as relying too much on working memory can lead to forgetting important things or making mindless mistakes.
What is one challenge about your research that you didn’t anticipate before you started the work, and how have you dealt with this?
My research uses the Beiwe research platform to collect digital phenotyping data. The platform was developed by the Onnela Lab at the Harvard T.H. Chan School of Public Health. The open-access version the research platform requires computer science skills I did not have prior to beginning my dissertation project. For example, I had to learn how to use Amazon Web Services, as the backend of a deployment of the Beiwe research platform requires cloud-based computing and storage. In addition, I had to learn to code in Python to pre-process the large amounts of digital data I am collecting. My dissertation has turned into a journey of technological and methodological challenges that I have had to address by learning interdisciplinary skills. However, I find the process rewarding as it has led to me working with other researchers to help make digital phenotyping methods more accessible to researchers who wish to apply these methods in the future.