Readings
1: Quantitative research
Research design
Fox Ch. 1: Statistical models and social science, 1-11 Babbie Ch. 4: Research design, 90-123 Babbie Ch. 5: Conceptualization, operationalization, and measurement, 124-159 Hayes Ch. 1: Statistics and communication science, 1-15 Hayes Ch. 2: Fundamentals of measurement, 16-30
2. Descriptive statistics
Descriptives
- Carey Ch. 3: Measurement and Transformations
- Carey Ch. 4: Descriptive Statistics & Terminology
Fox Ch. 3: Examining data, 28-54 Fox Ch. 4: Transforming data, 55-80 Hayes Ch. 4: Data description and visualization, 45-82
rstats
- Wickham & Grolemund Ch. 3: Data visualization with ggplot2
- Wickham & Grolemund Ch. 7: Exploratory data analysis
Treadwell Ch. 6: Summarizing research results: Data reduction and descriptive statistics, 93-113
3. Hypothesis tests and group comparisons
Hypothesis testing
- Ismay & Kim Ch. 10: Hypothesis Testing
- Carey Ch. 7: Inferential Statistics (Hypothesis Testing)
- Carey Ch. 8: Elementary Hypothesis Testing
Hayes Ch. 8: Hypothesis testing, 158-182
t-tests
Hayes Ch. 10: Comparing two independent groups, 210-243
Chi-square tests
Hayes Ch. 11: Some tests for categorical variables, 244-270
4. Correlations and factor analysis
Correlations
Factors
- A beginner’s guide to factor analysis
Hayes Ch. 6: Assessing and quantifying reliability, 103-129
5. Data wrangling
Transformations
- Wickham & Grolemund Ch 5: Data transformation
- Wickham & Grolemund Ch 12: Factors
Wickham & Grolemund Ch 14: Strings Wickham & Grolemund Ch 16: Dates and times lubridate
Importing and organizing data
- Wickham & Grolemund Ch 11: Data import
- Wickham & Grolemund Ch 12: Tidy data
Wickham & Grolemund Ch 13: Relational data Wickham & Grolemund Ch 10: Tibbles
6. Linear model - regression
Regression
- Carey Ch. 9: The General Linear Model: A gentle introduction
- Carey Ch. 10: GLM: Single Predictors
- Carey Ch. 11: GLM: Multiple Predictors
Fox Ch. 5: Linear least-squares regression, 81-105 Fox Ch. 6: Statistical inference for regression, 106-127 Fox Ch. 7: Dummy-variable regression, 128-152 Fox Ch. 9: Statistical theory for linear models, 202-244 Fox Ch. 10: The vector geometry of linear models, 245-264
rstats
- Wickham & Grolemund Ch 18: Model basics
- Wickham & Grolemund Ch 19: Model building
- Wickham & Grolemund Ch 20: Many models
- Ismay & Kim Ch. 6: Basic Regression
- Ismay & Kim Ch. 7: Multiple Regression
7. Linear model - ANOVA
ANOVA
Fox Ch. 8: Analysis of variance, 153-201 Maxwell & Delaney Ch. 3: Introduction to model comparisons, 63-128 Maxwell & Delaney Ch. 4: Individual comparisons of means, 129-169
8. Linear model - diagnostics
Diagnostics
Fox Ch. 11: Unusual and influential data, 265-295 Fox Ch. 12: Diagnosing non-normality, nononstant error variance, and nonlinearity, 296-340 Fox Ch. 13: Collinearity and its purported remedies, 341-368
9. Mediation, Moderation, & Generalized linear models
Mediation & Moderation
- Preacher, Rucker, & Hayes Multivariate Behavioral Research 2007
- Hayes Communication Methods and Measures, 2012
- Preacher & Hayes Behavioral Reseach Methods 2008
- Edwards & Lambert Psychological Methods 2007
Maximum Likelihood
Logistic regression
Fox Ch. 14: Logit and probit models for categorical response variables, 370-417
Generalized models
Fox Ch. 15: Generalized linear models, 418-472
10. Longitudinal models
Panel models
Advanced models
Fox Ch. 23: Linear mixed-effects models for hierarchical and longitudinal data, 700-742
11. Text mining
Tools/resources
- Silge & Robinson, Text mining with R
- Benoit, Quanteda: Quantitative analysis of textual data
- Watanabe & Müller, Quanteda Tutorials
12. Data science
Web APIs
- Cooksey Ch. 1: Introduction
- Cooksey Ch. 2: Protocols
- Cooksey Ch. 3: Data Formats
Cooksey Ch. 4: Authentication, Part 1 Cooksey Ch. 5: Authentication, Part 2 Cooksey Ch. 6: Introduction - Wickham, Getting started with httr
- Reitz, Requests: Quickstart
- Marshall, HTTP Made Really Easy
- Kearney, rtweet: Collecting Twitter data
- Kearney, nytimes: Interacting with New York TImes APIs
Neural networks
- Keras in R
Keras TensorFlow Li, Johnson, & Yeung, CS231n: Convolutional Neural Networks for Visual Recognition Li, Johnson, & Yeung, Lectures from CS231n