Lab 5: Two Tests, One Line

Connecting t-tests and correlations, and a sneak peek at the linear model

Instructions

This week we connected study design to inference: how data were collected decides what claims you can make, and a p-value tells you how surprising your data would be if nothing were going on. In this lab you’ll put those ideas to work with two tests you already know, then look at them in a new way. That new view is the foundation for everything we do next week.

Here are the things you’ll need for this lab:

Save the .csv in the data folder of your PSYC 640 R Project, and the .qmd in your project folder.

When you’re finished, render your file to a Word document, and submit both the .qmd file and the Word doc to myCourses.

NoteWhat you’ll practice
  • Telling experimental variables apart from observational ones, and what that means for causal claims
  • Running and reporting a t-test and a correlation in APA style
  • Interpreting a p-value correctly
  • Seeing a group difference as a line
  • Running lm() for the first time, and discovering how it is different

Scenario

The graduate school wants to know how students can study more effectively. Researchers ran a study with 120 graduate students:

  • Each student was randomly assigned to either Rereading their notes or Retrieval Practice (quizzing themselves without looking).
  • The researchers also measured study time, sleep, and test anxiety.
  • Everyone took the same stats quiz.

Variables

Variable Description Type
student_id Unique student ID ID
condition "Rereading" or "Retrieval Practice" (randomly assigned) Categorical (2 groups)
study_minutes Minutes spent studying (measured) Continuous
sleep_hours Hours of sleep the night before (measured) Continuous
test_anxiety Test anxiety, 10–50 (measured; higher = more anxious) Continuous
exam_score Quiz score, 0–100 Continuous

Exercises at a Glance

Exercise What you’ll do Key functions
1. Import & Design Load the data; decide which variables support causal claims import(), here(), glimpse()
2. Describe Group means and SDs group_by(), summarize()
3. Compare Groups Boxplot + independent-samples t-test ggplot(), t.test(var.equal = TRUE)
4. Association Scatterplot + correlation geom_smooth(), cor.test()
5. Group Difference as a Line Code condition as 0/1 and draw a line through it mutate(), if_else()
6. Sneak Peek 👀 Run lm() and compare its numbers to Exercises 3–4 lm(), broom::tidy()
7. Reflect & Predict What does “everything is a linear model” mean? (writing)
TipTips
  • Exercise 3: Use var.equal = TRUE in t.test(). It assumes both groups have similar spread (Student’s t-test), which is the version that matches lm().
  • Exercise 5: if_else(condition == "Retrieval Practice", 1, 0) turns the groups into numbers. scale_x_continuous(breaks = c(0, 1)) keeps the x-axis clean.
  • Exercise 6: You haven’t learned lm() yet, and that’s intentional! broom::tidy() gives a short table: find the row that is not (Intercept) and read across it.
  • Grading: Exercises 6–7 are graded on completion and effort, not on getting the “right” answer.
  • Stuck on the t-test sign? R compares the groups in alphabetical order. Look closely at which group comes first.

Next week: Simple Regression, the Linear Model. Come ready to share your answer to Question 11!