Recent & Upcoming Talks

Being able to simulate data allows you to prep analysis scripts for pre-registration, calculate power and sensitivity for analyses that don’t have empirical methods, create reproducible examples when your data are too big or confidential to share, enhance your understanding of statistical concepts, and create demo data for teaching and tutorials.

In this talk, I summarise the benefits of using code to process and analyse data, give some practical tips for developing your skills, demonstrate how data simulation can improve your research, and discuss the benefits of code review.

In this talk, I summarise the benefits of using code to process and analyse data, give some practical tips for developing your skills, demonstrate how data simulation can improve your research, and discuss the benefits of code review.

Here we introduce ManyFaces, a recently formed big team science group for face perception and face recognition research. This symposium will introduce the scope and aims of ManyFaces and highlight the work of two of the working groups within ManyFaces: stimulus meta-database and stimulus collection.

In this talk, I summarise the benefits of using code to process and analyse data, give some practical tips for developing your skills, demonstrate how data simulation can improve your research, and discuss the benefits of code review.

This asynchronous workshop will introduce the tools and resources for organising communication and information for a new big team science project. The workshop is an online book with Spanish and Turkish translations.

This workshop will cover power analyses across a wide range of statistical tests, including linear mixed models.

How large-scale collaborations such as the Psychological Science Accelerator can improve both replicability and generalisability.

How large-scale collaborations such as the Psychological Science Accelerator can improve both replicability and generalisability.

Things you can do during the planning stage of your project to help you develop healthy research habits.

How and why to use linear mixed effects models.