This page contains links to journal articles, blog posts, webpages, etc., that we (the instructors) believe may be useful to you, or at the very least interesting. Bold articles are ones that we consider must-reads but don’t have enough time to actually assign. And to be clear, must-read means that people will probably assume you’ve read this and know the basic points.

Cohen, Cohen, West, & Aiken (2003)

For additional review of correlation and regression beyond the assigned textbook, here are chapters from another popular textbook (edition 3).

Chapter 1. Introduction. - Multiple regression/correlation as a general data-analytic system - A comparison of multiple regression/correlation and analysis of variance approaches - Multiple regression/correlation and the complexity of behavioral sciences - Orientation of the book - Computation, the computer, and numerical results - The spectrum of behavioral sciences

Chapter 2. Bivariate Correlation and Regression. - Tabular and graphic representations of relationships - The index of linear correlation between two variables - The Pearson Product-Moment Correlation Coefficient - Alternative formulas - Regression coefficients - Regression toward the mean - The standard error of the estimate and measures of the strength of association - Statistical inference - Precision and power - Factors affecting the size of r

Chapter 3. Multiple Regression/Correlation with Two or More Independent Variables - Causal models - Regression with two independent variables - Measures of association - Patterns of association - Multiple regression/correlation with k independent variables - Statistical inference - Precision and power - Equations and prediction

On coding and data science

Peters (2004) “The Zen of Python.” – A list of 19 principles for writing better Python code; the principles also apply to writing better R code.

Hill (2019) “Meet xaringan.” – There are a ton of resources for learning how to make slides using R. Too many to list here. I like this one because it’s accessible, funny, visually appealing, and will help you make slides that you’re proud of. (By the way, UO has theme you can use.)

Bryan (2019) “Happy Git with R.” – Interested in using GitHub for version control? Jenny Bryan will guide you through the process and metaphorically hand you a tissue when you’re screaming with frustration.

Anderson (2021) “Social Data Science with R” – This book is currently being developed for the data science sequence at the University of Oregon, taught in the education department by Daniel Anderson. It is a helpful resource for foundational coding skills and data visualization in R.

Robinson (2016) Broom: Converting Statistical Models to Tidy Data Frames – This YouTube video explains how to use the {Broom} package to extract useful information from model objects.

On creating useful graphics

Patil (2020) “ggstatsplot” – A very useful R package for creating visuals to display some basic univariate and bivariate descriptives.

On statistical models

YouTube series on linear algebra

PSY 611 – Don’t forget everything we covered last term!

Dunn & Smyth (2019) Generalized Linear Models With Examples in R – if you’re looking for a supplemental (free, online) textbook to cover general and generalized linear models, I highly recommend this one. It includes useful tidbits, like coding factors, an expansion on the matrix algebra notation for multiple regression, and an extended section on the R code useful for linear models. Plus, it expands to cases we don’t cover in class.

Gelman posted a document containing Tips for improving regression on his blog, but I can’t find the original citation. It may be from one of his own works or from someone else’s. Let me know if you figure it out!