Frederick L. Oswald
Scholar

Frederick L. Oswald

Google Scholar ID: jWlqGZcAAAAJ
Rice University
workforcetest developmentpsychometricsmachine learning
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Citations & Impact
All-time
Citations
13,062
 
H-index
51
 
i10-index
117
 
Publications
20
 
Co-authors
0
 
Publications
1 items
Personality Structured Interview for Large Language Model Simulation in Personality Research
2025
Cited
0
Resume
Academic Achievements
  • - Publications across various fields: Bayesian analyses, dominance analysis (linear regression), open science, personality, knowledge and expertise, multitasking, AI and machine learning, meta-analysis, measure development, and other methods papers.
  • - Involvement in National Academies Reports.
Research Experience
  • - The research team focuses on defining and measuring individual and team performance in employment, military, and academic settings.
  • - Examines key correlates of performance such as promotion, certification, attrition, and satisfaction.
  • - Investigates individual differences that contribute to performance, including knowledge, skill, and motivation.
  • - Studies school-to-work transitions, with a focus on subgroups like STEM, gender, racial/ethnic minority, and first-generation college students.
  • - Looks into the legal implications and applications of personnel selection practices.
  • - Develops and improves selection and admissions tests, metrics, and systems.
  • - Statistically models the psychometric reliability, validity, and structure of psychological tests.
  • - Utilizes, critiques, and extends statistical methods used in organizational research, including big data and machine learning, meta-analysis, CFA/SEM, IRT, multilevel models, Bayesian analysis, adverse impact analysis, and other methods.
Background
  • Research interests include employee performance and academic success, workforce readiness and personnel selection, developing psychological tests, and big data and modern analytics for organizations and colleges.
Miscellany
  • Teaches statistical methodologies in graduate seminars, short courses, and workshops; advises graduate students, academic and practitioner colleagues, and various stakeholders on their statistical analyses, particularly those relevant to organizational research and practice.