🔥 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
Class & Lab Notes
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
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Categories
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(25)
Class & Lab Notes
SOCI 3040 | Quantitative Research Methods
Introduction + Telling Stories with Data
Tuesday, January 7, 2025
Telling Stories with Data + R & the Tidyverse
Thursday, January 9, 2025
Data Analysis Workflow – The Firehose
Tuesday, January 14, 2025
Data Analysis Workflow – The Firehose
Thursday, January 16, 2025
Reproducible Workflows with R & RStudio
Tuesday, January 21, 2025
Reproducible Workflows with R & RStudio
Thursday, January 23, 2025
Writing and Developing Research Questions
Tuesday, January 28, 2025
Writing and Developing Research Questions
Thursday, January 30, 2025
Creating Graphs, Tables, & Maps
Tuesday, February 4, 2025
Review Class
Thursday, February 6, 2025
Measurement, Censuses, and Sampling
Tuesday, February 11, 2025
Measurement, Censuses, and Sampling
Thursday, February 13, 2025
Measurement, Censuses, and Sampling
Tuesday, February 18, 2025
APIs, Scraping, and Parsing
Thursday, February 20, 2025
APIs, Scraping, and Parsing
Tuesday, March 4, 2025
APIs, Scraping, and Parsing
Thursday, March 6, 2025
Cleaning, Preparing, and Testing
Tuesday, March 11, 2025
Cleaning, Preparing, and Testing
Thursday, March 13, 2025
Exploratory Data Analysis (EDA)
Tuesday, March 18, 2025
Exploratory Data Analysis (EDA)
Thursday, March 20, 2025
Exploratory Data Analysis (EDA)
Tuesday, March 25, 2025
Linear Models
Thursday, March 27, 2025
Linear Models
Tuesday, April 1, 2025
Linear Models
Thursday, April 3, 2025
Project Work
Tuesday, April 8, 2025
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