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Webinar Details

Transparency and reproduciblity in social science research

Jeremy Freese
Thursday, August 22, 2019
12:00 Noon Central Time

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About This Course:

Amidst declarations that science is in the throes of a "crisis of credibility," many have called for increased emphasis on research practices that are transparent to audiences and reproducible by fellow practitioners.  Alongside this has been increased appreciation that transparent and reproducible practices also often end up more efficient, especially when collaborators proliferate or project durations expand. We outline different strategies for social scientists who use survey to improve the credibility of research via reproducible research practices.  These range from tweaks to workflow to proposals for novel types of collaboration or documentation. Broad topics include pre-specification, literate programming, data sharing, version management, and dynamic document generation. The webinar will present animating principles of transparency and reproducibility in analyzing survey data, and it will provide concrete examples in which these principles are implemented as research practice.

Webinar Level:
Learning Objectives:   
  • Sketch reasons for recent anxieties about prevailing research practices and the credibility of results they produce.
  • Identify benefits to adopting more transparent and reproducible research practices.
  • Appraise prominent research practices in terms of whether they promote or hinder reproducibility. 
  • Outline guiding principles of effective and reproducible research workflow.
  • Provide actual, specific examples of reproducible practices in use.
About the Instructors:
 Freese-Photo.jpgJeremy Freese is Professor of Sociology at Stanford University.  Jeremy has contributed to efforts to make social science research more transparent and reproducible in a number of ways.  He has co-authored a recent textbook, Transparent and Reproducible Social Science Research: How to Do Open Science, that presents detailed discussions of many issues to be covered in the webinar.  He is also co-author of book on regression models for discrete outcomes, for which he has developed and made openly available software.  He is currently co-PI of two NSF projects known for their open availability of data: Time-Sharing Experiments in the Social Sciences (TESS) and the General Social Survey (GSS).  He has co-authored several papers about replication and the development of a "reproducibility crisis in science." Apart from this, Jeremy conducts research on different issues connected to the integration of biological, psychological, and social data, especially in the context of inequality or social change.