Interested in learning more about how to apply network analysis to topics in social and organizational psychology? We’re preparing studies on peer institution networks, faculty author networks, and shared leadership networks.

MFC measures have grown in popularity as an alternative to traditional Likert-type measures (scale of 1 to 5) that often suffer from faking, social desirability, and bias. These have primarily been developed for personality measures; we are looking to develop them for constructs such as leadership and vocational interests.

Multigroup Membership multilevel models are designed to test datasets where individuals belong to two or more groups simultaneously. Though originally designed for teams and education research (e.g., employees with 2+ bosses; students in 2+ classrooms), we would like to apply this to multiracial individuals (i.e., individuals with 2+ racial identities).

Natural language processing (NLP) methods have grown tremendously in recent years, especially in psychology. We have several projects applying NLP and large language models like GPT to different contexts, such as analyzing transcripts from reality TV shows to predict performance, analyzing leadership measures, and generating leadership data.

Our lab partners with some national organizations to capture large-scale longitudinal data of college student experiences over time. Research in this project can involve cleaning massive and messy datasets, setting them up for longitudinal analysis of how student experiences change over time.

Recent scholars have raised concerns over our field’s over-reliance on surveys and lab experiments that suffer from low generalizability to the real-world. We would like to build “high-fidelity” in-person simulation experiences to assess leadership, teamwork, and communication. This includes game design, physical setup to maximize real-world applicability, and use of technology for dense data collection (e.g., video and audio).