201a Schedule
Week 0: Introduction
In which we will cover the goals of this class, and where the class materials fit into the broader landscape of quantitative / computational / data skills.
Homework
Thursday
Overview: slides
Week 1: Data
In which we cover data organization, cleaning, and basic summaries, while getting acquainted with R syntax.
Homework
Wednesday
R basics (continued) [files]
Week 2: Visualization
In which we cover how to make scientifically useful graphs.
Readings
socviz: make a plot (the rest of this book may also be useful, but we don’t have time for a thorough treatment.)
Homework
Thursday
(continuing with slides from Tuesday)
Week 3: Theoretical foundations
In which we cover probability theory, and the logic of classical statistical methods.
Readings
NHST notes: Particularly: statistics via simulation, sampling distributions, Statistics via Normal, Null hypothesis significance testing. (Binomial probability to statistics is mathier, may be of interest, but is optional.)
Homework
Tuesday
Thursday
Week 4: Linear model: Regression
Homework
Tuesday
Wednesday
t-test and chi-squared test [code]
Thursday
Week 5: Linear model, midterm
Tuesday
Thursday
Review and midterm catchup.
Midterm out thursday evening
Week 6: Linear model: Categorical predictors
Readings / Notes
Homework
Tuesday
Thursday
Week 7: Linear model: ANCOVA, diagnostics
Homework
Thursday: Veteran’s Day – no class
Week 8: Linear model: Linearizing transforms
Homework
Tuesday
Thursday
Week 9: Covarying errors (repeated measures / random effects)
Readings:
I don’t like either of these…. I am still on the hunt for a pithy conceptual overview of repeated measures designs and analyses:
This is simpler: Howell, ch. 14
This is mathier: Kutner ch. 27