Social network analysis is a fascinating set of methods that can be used in social and organizational psychology to study relationships between individuals. We’re currently working on a study applying these methods to faculty coauthorship networks through a generous grant from the Lowe Institute.

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 currently working on a study developing a version for vocational interests.

The concept of individuals motivated by a sense of vocational calling has grown in recent years, both in popularity and in academic research interest. Building off of our recent publication, we have several projects working on expanding our understanding of calling into multiple domains, pre-college influences, and early career change. Two of these studies are currently funded through a generous grant from the Gould Center.

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, and analyzing interview data on career development.

Our lab partners with some national organizations to capture large-scale longitudinal data of college student experiences over time. We are currently working on cleaning these massive datasets and 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. Thus, we are building an escape room! This unique data source will allow us to use real-world team gameplay to assess leadership, teamwork, and communication. This includes game design, physical setup and decorations, and use of technology for dense data collection (e.g., video and audio).

Predatory journals and other sources of scientific fraud are on the rise. We are conducting an experiment to assess if individuals — ranging from public audiences to journalists — can identify predatory journals to avoid, and high-quality journals to promote.

The field of leadership studies is replete with overlapping constructs, measures, and conceptualizations. We are employing alternative methods — ranging from machine learning to GPT — to study and measure leadership behaviors, leader development, and leader effectiveness. Thank you to the Kravis Leadership Institute for their support for one of our studies!