Doing Open and Replicable Science
not currently scheduled | Next offering date and format TBD
Transparency and replicability are cornerstones of science. In 2015, a landmark study showed that only 39 per cent of published studies are replicable by independent teams of researchers. Since then, there have been major advances in doing more open and replicable science. Transparency in study planning, analytical codes, and research materials facilitate independent verifications of a study. Improving replicability of research means that we can have stronger confidence in making decisions based on empirical findings. This short course will cover how to do open and replicable science.
In this course, you will learn how to apply open and replicable scientific practices to develop a new quantitative study.
- Overview of the replication crisis
- Overview of the importance of open science
- Overview of the properties of a replicable study
- Evaluation of openness and replicability in past studies
- Preregistration for experimental study
- Preregistration for observational study
- Open data
- Open materials
- Scientific reporting of an open and replicable study
This meeting times and format of the next offering this class have not yet been determined.
The course will focus on using the Open Science Framework as a free platform to implement open science practices. Learners are encouraged to bring a laptop to class to complete hands-on exercises.
This course will be helpful for researchers in any field —including psychology, sociology, education, business, human development, social work, public health, communication and others that rely on social science methodology —who want to develop a transparent and replicable research program. Learners should have background knowledge in introductory statistics topics such as univariate statistical tests, descriptive statistics, and null hypothesis significance testing. Though proficiency in a specific software isn’t required, ideally participants will have some familiarity with running analyses using some type of statistical software (e.g., R, SPSS, SAS, STATA). Learners will especially benefit from the course if they are planning a new study, and participants are welcome to complete some of the hands-on exercises in the context of their research topic.
Upon completing this course, you will
- Be able to understand and describe the replication crisis and its causes and consequences
- Define open science
- Understand what makes a study open and replicable
- Evaluate past studies on their openness and replicability
- Use pre-registration to specify your data collection and analytical plan
- Complete a time-stamped and verifiable pre-registration
- Understand the ethical considerations of open data and materials
- Learn and complete steps to make study data and materials open
- Learn how to prepare a scientific report based on open science principles