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Open, Reproducible and Transparent Social Sciences

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Course Information

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Open science involves making scientific methods, data, and outcomes transparent to everyone. It includes making as transparent and available as possible (1) steps taken in data collection, processing and analysis that lead to the production of results, (2) study plans, data, materials and associated processing methods and (3) the results generated by the research. Reproducible science involves the potential for others to recreate reported results by repeating the original data processing and analyses with the original data.

Transparent and reproducible science results from open science workflows that allow you to easily share work and collaborate with others as well as openly publish your data and workflows to contribute to greater scientific knowledge. Facilitating openness, reproducibility and transparency in social science is important as it advances collaboration, scientific progress, trust in science, and the reusability of research. It also touches on ethical questions, for example in navigating between the creation of science as a public good and the protection of research subjects. 

This course introduces the principles and practical steps of doing cutting-edge open, reproducible and transparent social science research. Participants will learn how to conduct research that is easy to check and understand by providing easy-to-use access to methods and data. They will also learn how to conduct reproducible research the results of which can be easily recreated using the original data and steps in data processing and analysis.

The course covers an introduction to principles and practices of transparent and reproducible social science research:

  • Motivations behind and principles of open, reproducible and transparent research
  • Pre-analysis plan / pre-registration / Registered Report
  • Multiverse analysis, sensitivity analysis, specification curve analysis
  • Meta-analysis / systematic evidence synthesis
  • Transparent reporting standards and disclosure
  • Replication
  • Data management and data sharing
  • Script/code sharing and version control (e.g., using Github)
  • Reproducible workflows (e.g., using R Markdown)
  • Open access (preprints and publisher models)

By the end of the course participants will:

  • understand the philosophical and meta-research-based rationale for doing open, reproducible and transparent social science research;
  • have conceptual and practical knowledge of the main building blocks of open, reproducible and transparent social science research:
    • transparent study planning,
    • assessment of the robustness of research results,
    • sharing of research outputs to enable easy reproduction and replication.

This course is aimed at Social science researchers of all backgrounds, disciplines and levels (junior and senior) who undertake data analysis (quantitative and qualitative).  It is essential that participants possess at least a beginner level of familiarity with R. Some basic understanding of regression modelling is also recommended.

R and RStudio will be installed on all the desktop computers available in the teaching room. However, if you bring your own laptop, we recommend installing the R and RStudio in advance. You may also want to get an account on GitHub and download a desktop version of GitHub.

Preparatory Reading

The following references provide a useful reading list covering the methods that we will see in this course. They are listed in order of relevance:

Christensen, G.S., Freese, J. and Miguel, E. 2019. Transparent and reproducible social science research: How to do open science. Oakland, CA: University of California Press. https://doi.org/10.2307/j.ctvpb3xkg  

Moody, J.W., Keister, L.A. and Ramos, M.C. 2022. Reproducibility in the social sciences. Annual Review of Sociology48(1), pp.65–85.

Miguel, E., Camerer, C., Casey, K., Cohen, J., Esterling, K.M., Gerber, A., Glennerster, R., Green, D.P., Humphreys, M., Imbens, G., Laitin, D., Madon, T., Nelson, L., Nosek, B.A., Petersen, M., Sedlmayr, R., Simmons, J.P., Simonsohn, U. and Laan, M.V. der 2014. Promoting transparency in social science research. Science343(6166), pp.30–31.

Freese, J., Rauf, T. and Voelkel, J.G. 2022. Advances in transparency and reproducibility in the social sciences. Social Science Research107, Article 102770.

Course Code

NCRMORPSS

Course Leader

Dr Eike Rinke and Dr Viktoria Spaiser
Course Description

Programme

Day 1: 10:00-17:00 

Session 1 (Morning): Introduction to Open Science

- Welcome and workshop overview

- What is open science, and why is it important for social scientists?

- Principles of open, reproducible, and transparent research

Session 2 (Late Morning): Transparent research planning and reporting

- Pre-analysis plans, pre-registration and Registered Reports

- Transparent reporting standards and disclosure

Lunch Break

Session 3 (Afternoon): Assessing Research Robustness 

- Multiverse analysis, sensitivity analysis, specification curve analysis

- Meta-analysis / systematic evidence synthesis

Session 4 (Late Afternoon): Data Management and Sharing

- Data management best practices

- Data sharing and repositories

- Ethical considerations in data sharing and privacy

Day 2: 09:00-16:00

Session 1 (Morning): Creating Reproducible Research Workflows

- Reproducible workflows with R Markdown

Session 2 (Late Morning): Opening Reproducible Research Workflows

- Script/code sharing and version control (e.g., using Github)

Lunch Break

Session 3 (Afternoon): Replication

- Intro to replication in the social sciences

- Best practices for social science replications in research and teaching

Session 4 (Late Afternoon): Open Access and Closing

- Open access publishing models

- How to publish in open-access journals

- Summary and key takeaways

- Q&A and closing remarks

Throughout the course students will have the opportunity to discuss questions and issues that arise from their own research.

StartEndPlaces LeftCourse Fee 
25/03/202426/03/20240[Read More]

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