Blog takeover: Messages from the students

The STATS Lab is lucky to have so many talented student research assistants on the team! As we approach the halfway point of the semester, I asked each of the students to share briefly about what they’re working on and what they’re learning… Stay tuned to see where these students and their projects end up!

George Chan (CMC ‘29)

I am currently recruiting participants and conducting interviews for our project on how pre-college experiences shape students’ sense of calling. Through this process, I am learning how much qualitative research depends on question framing, ensuring questions are open-ended rather than yes/no, and on thoughtfully following up on what participants bring up in the moment. I’ve seen that asking participants to describe specific moments that have impacted them, rather than general beliefs, leads to deeper reflection about family, culture, schooling, and identity development.

What I find most interesting about this project is the connection between theory and lived experience. In the interviews we’ve conducted so far, I’ve been able to see certain factors, such as family influence, emerge strongly as factors. This makes me very excited for the interviews to come. I have also liked finding connections between the theories of callings to the philosophical ideas I discussed in a Meaning of Life course, where we explored questions of purpose and identity. Additionally, the interviewing skills I am developing through this project also support my work on a Keck Center podcast, where I have reached out to climate refugee experts and practiced asking thoughtful questions to guide meaningful conversations.

Julie Chung (CMC ‘28)

This semester, I have been helping lead a research project examining the proliferation of predatory journals and the broader ecology of scientific fraud. We are conducting an experiment to assess whether individuals, ranging from lay readers to journalists, can reliably discern predatory publications from rigorous scholarly outlets. Engaging in this work has made me acutely aware of how tenuous the signals of credibility can be and how often judgments of expertise depend more so upon aesthetic markers, institutional affiliations, or rhetorical polish rather than methodological integrity. I find it both intellectually compelling and ethically urgent to consider how easily the architecture of scientific authority may be imitated, and how precarious public trust becomes amid an overabundance of information. Working on this project has prompted me to reflect more deeply on the moral responsibilities entwined with knowledge production and dissemination. Across my coursework, particularly in my organizational psychology class, I have encountered recurring questions about legitimacy, leadership, and institutional trust. This project situates those abstractions in a tangible and consequential context, inviting me to carefully consider how discernment is cultivated and how integrity is sustained within scholarly communities.

Alongside this project, I am contributing to a meta analysis focused on core organizational criterions, including task performance and in role behavior, organizational citizenship behavior and other forms of extra role contribution, counterproductive work behavior and workplace deviance, as well as wellbeing. Assisting with this project has allowed me to develop more technical skills, particularly in Excel, as I organize datasets, code effect sizes, and ensure accuracy in aggregation. Through this process, I have come to appreciate how much intellectual discipline underlies synthesis and how profoundly our conclusions depend upon precision in definition and measurement.

I am so incredibly grateful for the opportunity to be part of the STATS lab– it has been such an honor to conduct research within the lab’s meaningful and encouraging environment, and I am sincerely thankful for all of the guidance and support that have allowed me to grow thus far! (:

Melanie Haro-Cortes (CMC ‘28)

Ever since middle school, I’ve heard about the process of scientific research countless times: creating a research question, conducting a literature review, forming a hypothesis, running experiments, collecting data, and analyzing results. Yet I never had the opportunity to truly engage with this process until now, during my sophomore year of college.

This year, I decided to pivot into social science research by applying for the Roth Fellowship through Claremont McKenna College’s Mgrublian Center for Human Rights. Through the fellowship, I designed my own project using qualitative, participatory research methods to examine how immigrant communities navigate complex legal systems under heightened enforcement, and how legal design innovations may help reduce barriers to justice.

At the same time, I became involved with the STATS Lab, where I am helping conduct research on how individuals from different backgrounds, faculty, students, journalists, and members of the general public, evaluate the credibility of academic research, particularly in relation to predatory journals.

Working on these two research projects simultaneously has revealed interesting overlaps. While immigrant communities may not regularly engage with academic articles, the predatory journals study highlights a broader challenge: how people interpret and evaluate information. This issue extends far beyond academia. Across blogs, news outlets, and social media platforms, the public is constantly confronted with information of varying reliability. In an era where misleading information spreads rapidly and shapes increasingly polarized perspectives, understanding how people perceive accuracy and credibility when consuming content has become more important than ever. This is exactly the question the predatory journals experiment seeks to explore.

When Professor Zhou showed my research partner and me how to publish our survey on CloudConnect, I was genuinely surprised by the significant financial investment required to collect data through this method. It was a moment that revealed the real scope of what goes into producing meaningful research insights — and even then, valuable results are never guaranteed.

Owen Keyt (CMC ‘28)

Hi, I'm Owen Keyt. I’m a team lead working on the STATS lab’s escape room research project. I am mostly focusing on the data collection, cleaning and analysis, while my other teammates are currently planning and designing puzzles and the escape room research environment. We recently completed a pilot test with an initial puzzle and our initial setup of data collection equipment (which includes 4 separate mic channels and a 360 degree camera). At the moment I am working on taking this initial pilot test data and converting, cleaning, and doing initial analysis on it.

While I had some prior data cleaning and analysis experience. I’ve never been on the actual collection and setup side of things before. So, I have been learning a lot about the intricacies of setting up equipment and creating systems to turn raw audio and video collection into easy to work with data files. I think this project is really interesting because of how the escape room environment mirrors real environments surprisingly well. As well as it gives us the chance to get interesting realtime data on a group’s communication patterns.

I’m taking data science and computer science classes at the same time as I am working on this and I have experienced a very useful overlap in content several times. Where I will learn something in one of my classes that helps bring new perspectives or a way to do something in our research. As well as vice versa where some of the technical work in our research project has helped me look at class problems with real experience implementing concepts in a real world environment. So far it has been a great experience and has given me a lot of hands-on experience.”

Carmela Labarda (CMC ‘28)

At a time that might feel so divisive in thought, it is only relevant that Claremont McKenna College cultivates students that can translate their skills and intensity across disciplines. The STATS Lab has given us exactly that space! Together with Owen Keyt and Joomi Park, we are developing an escape room that is framed around zombies, in order to measure emergent leadership and team behaviors in a controlled environment, despite us all being from vastly different academic backgrounds. In my capacity, I have been involved in engineering puzzles and designing logical sequences for the escape room, being meaningful to my studies in the arts and sciences. The mentorship from Dr. Steven Zhou has also been incredibly insightful. He encourages precision without constraining imagination, allowing for projects like ours to be both playful and grounded. I have enjoyed working with everyone so far and am excited to showcase our work for this study soon!

Ruel Lee (Pomona ‘28)

My name is Ruel, and I’m a sophomore from Pomona College leading the Early Careers team at the STATS Lab. Our work focuses on the often-overlooked beginning of the professional journey: the transition to your first job. We are currently building a codebook by individually coding 60 transcripts where Dr. Zhou interviewed participants on how they found their first job. Once the codebook is complete, our plans for the future include both qualitative methods like thematic analysis and quantitative methods like Natural Language Processing. This flexibility is my favorite aspect of our project; it really aligns with the STATS Lab’s mission of bridging the gap between personal stories and statistical trends.

The Early Careers project has also become a cornerstone of my academic career. As a double major in Politics and Linguistics, the opportunity to improve my skills in R and NLP in Datacamp made me a better student in my courses. The specialized skills I developed here have also opened doors for me outside of the lab; the expertise I gained in thematic analysis enabled me to secure a Research Assistant position at a Claremont Graduate University psychology lab. It is incredibly rewarding to see my deep dive into other people's careers helping me navigate and accelerate my own.

Jordan Nguyen (CMC ‘28)

I am learning about the experience of perceiving and living out multiple vocational callings. I explore questions like 'How do individuals navigate contrasting vs. similar callings?' and 'What are the potential configurations for conceiving multiple callings?' Currently, I am drafting the theoretical foundations section of our manuscript and trying to integrate the literature in such a way as to inform our theoretical framework.

What do I find interesting about the project? Honestly: everything. Writing a theoretical paper is such a different experience from empirical work in psychology. I love the intellectual work I have to put in to come up with novel theory—even if it can be quite tortuous at times. I look forward to connecting our developed theory to the experiences students have on campus, i.e., of being drawn to multiple interests and passions.

My research in vocational calling fits with my interests in career counseling and education. In the near future, I hope to establish a training program for people interested in psych research and potentially use what I learned in the vocation literature to help out students here at CMC.

Joomi Park (CGU PhD Student)

In the STATS Lab, I’ve been working on both the escape room project and meta-analysis, and it’s been interesting seeing how different types of research come together. With the escape room study, I’ve been focused on how leadership actually emerges through communication patterns, problem solving, and behavioral dynamics in real time. Thinking about things like interdependent tasks, quality video, and audio data, and how to meaningfully code interactions has made me more aware of how complex team behavior is beyond traditional survey measures.

On the meta-analysis side, I’ve been learning how to approach research more systematically, from screening studies to maintaining consistency in coding and analysis. Both projects connect closely to what I’m learning at CGU in organizational psychology, especially around measurement, team dynamics, and applying rigorous research methods to real world organizational questions.

Elis Shen (Pomona ‘28)

I’m Elis, a STATS Lab Team Lead currently working on the Multidimensional Forced Choice (MFC) Measures project. MFC measures are a relatively novel alternative to traditional Likert Scales, and past research has suggested its comparative benefits in addressing common survey issues, such as social desirability bias. This project focuses on precisely this aspect: comparing the susceptibility of MFC and Likert scales to social desirability bias, through developing a version of MFC measures for vocational interests. This project started by looking at existing data involving two existing vocational interest measures, RIASEC and CABIN, to extract discrimination values. Then, we collected survey data on the social desirability of each item before finally constructing MFC blocks using social desirability ratings and discrimination values, which is where we are now.

Each stage of this project has taught me a lot about not only MFC as a topic but also everything else that goes into a successful, valid experiment in the field of psychological science. At the beginning of the year, I had the opportunity to work with advanced data analysis techniques, such as confirmatory factor analyses, that I otherwise would not have been exposed to until grad school. Arguably more importantly, being included in this stage of data analysis taught me the important lesson that research isn’t always clear-cut, with existing specific procedures and rules, especially when working on projects navigating new areas of the field. For example, during later data cleaning for the collected social desirability data, what exclusion parameters make sense based on logic and precedent?

Unlike most natural sciences, the field of psychology can’t always rely on calibrated precision tools to record data. This leaves the delicate issue of reliability and validity in measures, such as Likert Scales, themselves that researchers have to navigate. This MFC project is interesting to me because it engages with a side of research that is very specifically pertinent to psychology—common measures.

My work on this project also connects to my other endeavors at the 5Cs. Social desirability bias relates to a proposed summer project looking at how identity-protective cognition may relate to misinformation buy-in, and the quantitative skill exposure is helping me in my work on a project looking at the Psychology of Socialism. Can’t wait to see where the MFC project takes me next!

Lily Surjadinata (Harvey Mudd ‘28)

I'm currently working on the project using natural language processing to measure team performance using Survivor TV show transcripts. Since this project is still in its initial phases, we are working on cleaning Survivor TV show transcripts by organizing contestant quotes by name and episode number and have completed around ~15 seasons. Although we have not gotten to the NLP part of the project, I'm interested to see what speech patterns, vocabulary, and the way others perceive someone determines success and likeableness. In watching almost 3 seasons so far, its been interesting to see how much talent and group contribution matters in relation to popularity, for example, some contestants I considered to have good survival skills that would benefit the group were sometimes voted off early due to conflicting personalities. Essentially, to work in a team, how much does physical contribution or relationships matter?

Teamwork has been very essential to my college education. This semester I'm in a class, CS 70 at Mudd where all assignments are done as a pair. All of my peers in engineering are enrolled in E80 where they are divided into teams of 4 and have to build a fully functioning underwater robot by the end of the semester. A lot of us will probably end up in professional fields that require teamwork. But when we get there, how much of our success will be due to the work we contribute or to the fact that we are easy to get along with?

Also, working on data cleaning taught me more about the research process. I feel like research is often glamorized, scientists getting to work on upcoming technologies, when that is often not the case. Most of my research work this semester has been watching Survivor on 2 or x3 speed. Despite this, I'm still very excited to see where the project goes from here and what we are going to be able to learn from this dataset.