Content

Every class in more depth — objectives, readings, and what’s due

Browse by part using the category filter, or scroll chronologically. For the compact table view, see Schedule.

Class 1 — Environment & Open Science

Part I

Turn your browser-only Python exposure into a real local toolchain — install and verify R, RStudio, Quarto, and PsychoPy, then create your GitHub repo for the semester.

Class 2 — Builder Basics

Part I

Open PsychoPy Builder for the first time and wire together a shared Stroop-style task — routines, a trial loop, and a keyboard-response component.

Class 3 — Builder → Coder

Part I

Open PsychoPy’s Coder view for the first time to read the Python that Builder has been generating for you all along, and make your first small, safe edit directly in code.

Class 4 — Custom Code Components

Part I

Write your own logic for the first time using a PsychoPy Code Component — dictionary-based accuracy scoring, dynamic feedback, and a trial counter that enforces the course’s minimum-observations rule.

Class 5 — Pilot Testing & Data Generation

Part I

No new syntax today — finish and debug your own task, agree on a shared file-naming convention as a class, then pilot a partner’s experiment and export real data.

Class 6 — Welcome to RStudio & the Tidyverse

Part II

Quiz 1 opens the class, then Python and PsychoPy give way to R — read your own pilot data into RStudio as a tibble and compare its shape to a published Stroop dataset.

Class 7 — Data Wrangling with dplyr

Part II

Fix what you diagnosed last class — chain filter(), mutate(), group_by(), and summarize() into a single pipeline that collapses your own pilot data into a condition-level summary table.

Class 8 — Visualizing the Effect

Part II

Turn your HW4 summary table into a picture — build a labeled ggplot2 figure layer by layer, then adapt the code to your own data and share it in a gallery walk.

Class 9 — Simulation

Part III

Stop describing the one dataset you collected and start generating hundreds of pretend ones — build a simulation in R and compare its distribution of effects to your own real result.

Class 10 — One Simple Test

Part III

Quiz 2 opens the class, then you finally run a real statistical test — t.test() on both your real and simulated data — and see last class’s sampling-variability intuition become a p-value.

Class 11 — Reproducible Reporting (apaquarto)

Part III

Turn your console results into a written APA-style report — set up apaquarto and insert computed values directly into prose so your numbers can never go stale.

Class 12 — Project Workshop I

Part III

The first of two studio days — pitch and start building your own novel 1-factor/2-level PsychoPy task, distinct from the shared Stroop task, and set up your final-project repo structure.

Class 13 — Project Workshop II & Finale

Part III

The last class of the semester — troubleshoot, finalize your apaquarto report, and publish your finished project as a GitHub Release archived to Zenodo for a DOI.