🔥 Quantitative Research Methods
Syllabus
Class & Lab Notes
Slides
Class 01 –
Introduction
Class 02 –
Telling Stories with Data
Class 03 –
Workflow – Plan, Simulate, Acquire, Explore, Share
Class 04 –
Good and Bad Ways to Look at Data
Class 05 –
Reproducibility + Getting Started with R and RStudio
Class 06 –
Reproducibility + Getting Started with R and RStudio
Class 07 –
Writing and Developing Research Questions
Class 08 –
The Grammar of Graphics, Plotting in the Tidyverse
Class 09 –
Showing the Right Numbers
Class 10 –
Piping, Summarizing, and Transforming
Class 11 –
Measurement
Class 12 –
Sampling
Class 13 –
Experiments
Class 14 –
Surveys
Class 15 –
Data Cleaning and Preparation
Class 16 –
Exploratory Data Analysis (EDA)
Class 17 –
Linear Models
Class 18 –
Linear Models + Model-based Graphics
Class 19 –
Generalized Linear Models (Binary Outcomes)
Class 20 –
Generalized Linear Models (Count Outcomes)
Class 21 –
Mutilevel Models
Class 22 –
Mutilevel Models
Class 23 –
Multilevel Regression with Poststratification (MRP)
Class 24 –
Project Work
Class 25 –
Project Work
Telling Stories with Data
Preface
Chapter 01 –
Telling stories with data
Chapter 02 –
Drinking from a fire hose
Chapter 03 –
Reproducible workflows
Chapter 04 –
Writing research
Chapter 05 –
Graphs, tables, and maps
Chapter 06 –
Measurement, censuses, and sampling
Chapter 07 –
APIs, scraping, and parsing
Chapter 08 –
Experiments and surveys
Chapter 09 –
Clean and prepare
Chapter 10 –
Store and share
Chapter 11 –
Exploratory data analysis
Chapter 12 –
Linear models
Chapter 13 –
Generalized Linear Models
Chapter 14 –
Prediction
Chapter 15 –
Causality from observational data
Chapter 16 –
Multilevel regression with post-stratification
Chapter 17 –
Text as data
Chapter 18 –
Concluding remarks
Appendix –
R essentials
Appendix –
Datasets
Appendix –
Datasheets
Appendix –
Cocktails 🥃🍸
Data Visualization
Preface
Chapter 1 –
Look at Data
Chapter 2 –
Get Started
Chapter 3 –
Make a Plot
Chapter 4 –
Show the Right Numbers
Chapter 5 –
Graph Tables, Add Labels, Make Notes
Chapter 6 –
Work with Models
Chapter 7 –
Draw Maps
Chapter 8 –
Refine Your Plots
Appendix
Statistical Rethinking
Lecture 01 –
The Golem of Prague
Lecture 02 –
The Garden of Forking Data
Lecture 03 –
Geocentric Models
Lecture 04 –
Categories & Curves
Lecture 05 –
Elemental Confounds
Lecture 06 –
Good and Bad Controls
Lecture 07 –
Fitting Over and Under
Lecture 08 –
Markov Chain Monte Carlo
Lecture 09 –
Modeling Events
Lecture 10 –
Counts & Hidden Confounds
Lecture 11 –
Ordered Categories
Lecture 12 –
Multilevel Models
Lecture 13 –
Multilevel Adventures
Lecture 14 –
Correlated Features
Lecture 15 –
Social Networks
Lecture 16 –
Gaussian Processes
Lecture 17 –
Measurement & Misclassification
Lecture 18 –
Missing Data
Lecture 19 –
Generalized Linear Madness
Lecture 20 –
Horoscopes
John McLevey
Conventions
SYLLABUS
SOCI 3040
Course Schedule
Assessment
Computing
Policies
ASSIGNMENTS
Data Stories 1
Data Stories 2
Data Stories 3
Data Stories 4
QUICK LINKS
3040 on Brightspace
Student Wellness
Counselling Centre
Academic Success Centre
Accessibility Services
Conventions
SOCI 3040 – Quantitative Research Methods
This page will develop as needed as the course progresses.