PSY 203 Labs

The primary website for this course is https://faculty.bard.edu/~jdainerbest/psy-203

This github repository contains R lab code for labs in Bard College's Fall 2020 for Statistics for Psychology, taught by Prof. Justin Dainer-Best.

Labs

Over the following labs, you will learn to use R and RStudio to create documents, import and analyze data, munge and visualize data, and run basic statistical tests.

Labs will be uploaded weekly during the Fall of 2020.

• Instructions for installing packages to set up for tutorials (these are included in Lab 1)
• Lab 1: Learn the basics of R like variables and data frames, how to comment, and the basics of subsetting using the $ and dplyr
• Lab 2: Practice subsetting, learn to knit R Markdown documents, and learn about filtering data
• Lab 3: Learn to visualize data, practice importing data, and begin exploring graphs
• Lab 4: Continue practicing basic visualization while exploring the procedures of testing hypotheses in R
• Lab 5: Build on your skills with ggplot2 and run a z-test with a sample mean and a one-sample t-test, learning the difference between each
• Lab 6: Practice identifying mistakes and errors, learn to work with missing data, and learn how to add error bars in ggplot2
• Lab 7: Work out the differences between dependent and independent samples t-tests, and how to calculate both with the t.test() function; learn to use pivot_longer() from the {tidyr} package to restructure data
• Lab 8: Practice using the skills from the first seven labs. Test yourself: can you analyze real data?
• Lab 9: Learn to run a one-way analysis of variance (ANOVA) in R, to conduct pairwise-t-tests, and to plot the results
• Lab 10: Practice plotting and learn to add regression lines to scatterplots; learn to use cor.test() for correlations and summary() and lm() for regressions
• Lab 11: This is a non-coding lab that involved in-class discussion of academic writing and plagiarism; the document includes a few definitions and links
• Lab 12: Learn to run chi-squared tests in R using the chisq.test() function for both goodness of fit and independence. Also, learn the very basics of factorial ANOVA in R. Practice plotting the group means/counts for both types of tests.
• Lab 13: A final project designed to incorporate the other labs and practice testing and plotting
• Lab 14: A final lab to practice asking questions, resolving errors, and knitting to PDF