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Berkeley Initiative for
Transparency in the Social Sciences

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MOOC: Transparent and Open Social Science Research

  • Week 1: Introduction
  • Week 2: Publication Bias
  • Week 3: Pre-Registration, Pre-Analysis Plans, and Meta-Analysis
  • Week 4: Replication and Open Data
  • Week 5: Data Visualization

MOOC: Week 4

  • Approaches to the Replication of Research
    • Introduction to Replication
    • An unsuccessful replication of “Social Security, Induced Retirement, and Aggregate Capital Accumulation” by Martin Feldstein
    • How rare is replication?
    • Why is there so little replication and how can we promote more?
    • A replication example: The Many Labs Project
  • Open Data
    • Introduction to Open Data
    • How open data can facilitate the detection of fraudulent research
    • Another example of detecting fraudulent research using open data
    • “Just Post it: The Lesson from Two Cases of Fabricated Data Detected by Statistics Alone” by Uri Simonsohn
    • Open data makes for more credible science: two articles on disappearing data
    • If you post your data, it might actually be good for you!
    • Rules for open data

Approaches to the Replication of Research

What is replication and replicability and why are they so important to science and policy?

4.1 Introduction to Replication

4.2 An unsuccessful replication of “Social Security, Induced Retirement, and Aggregate Capital Accumulation” by Martin Feldstein

4.3 How rare is replication?

4.4 Why is there so little replication and how can we promote more?

4.5 A replication example: The Many Labs Project

 

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