Self-Reflection and Socialization in Shaping Trajectories in Early Career Research: A LACOID (Latent Code Identification)-Based Text Mining Approach

• In Event: Creating Inclusive Doctoral Programs: Best Practices and Innovations in Doctoral Student Socialization

Abstract

This machine learning assisted longitudinal study explores the multifaceted trajectories of early career bioscience researchers, focusing on the intricate interplay between self-reflection and socialization. The study capitalized on the extensive textual data generated from the Early Career Research Project’s annual interviews and employed the Latent Code Identification (LACOID) method for data analysis and meaning construction.
The study aimed (1) to identify common factors impacting early career research trajectories and (2) to test the hypothesis that individual characteristics (e.g., gender, age, race) influence the comprehension of professional growth determinants. The results revealed a nuanced interplay between individual characteristics and the way bioscience Ph.D. students perceive influences on their early career research trajectories.

Authors

• Chunling Niu, University of the Incarnate Word

• Soheila Sadeghi, University of the Incarnate Word

• Rui Jin, Shenzhen University

• Loren T. Cossette, University of the Incarnate Word

• Marissa Molina, University of the Incarnate Word

• Kelly D. Bradley, University of Kentucky

• Ashley S. Love, University of the Incarnate Word