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Frederick Louis Oswald

Professor of Psychological Sciences and Herbert S. Autrey Chair in Social Sciences, Rice University
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About Frederick

Oswald's areas of expertise include personnel selection, college admissions, psychological measurement and psychometrics (developing, implementing, and refining measures of psychological constructs), 21st century skills, socio-emotional learning, school-to-work transitions, meta-analysis, and adverse impact analysis.

Contributions

How the World's Largest Database of Social Science Research Can Inform Policymaking

  • Frankie J. Weinberg
  • Frank Bosco

Publications

"Supporting Students' College Success: The Role of Assessment of Intrapersonal and Interpersonal Competencies" (with the Committee on Assessing Intrapersonal and Interpersonal Competencies) (National Academies Press, 2017).

Identifies eight intrapersonal competencies (competencies involving self-management and positive self-evaluation) that can be developed through interventions and appear to be related to persistence and success in undergraduate education. Calls for further research on the importance of these competencies for college success, reviews current assessment of them and establishes priorities for the use of current assessments, and outlines promising new approaches for improved assessments. 

"Answers to 18 Questions about Open Science Practices" (with George C. Banks, James G. Field, Ernest H. O'Boyle, Ronald S. Landis, Deborah E. Rupp, and Steven G. Rogelberg). Journal of Business and Psychology (forthcoming).

Identifies and clarifies major issues related to the use of open science practices (e.g. data sharing, study pre-registration, open access journals). Begins with a useful general description of what open science in organizational research represents and adopts a question-and-answer format. Focuses on the application of specific open science practices and explores future directions of open science. 

"Big Data Methods in the Social Sciences" (with Dan J. Putka). Current Opinion in Behavioral Sciences 18 (2017): 103-106.

Provides some guidance to social science researchers who might feel overwhelmed by the panoply of big data methods and/or feel that these methods are opaque.