Challenge 3: Extending Teaching Evaluation Investigations

This challenge builds on the work you already did with the data on teacher evaluations. Specifically, you are tasked with recreating a plot, carrying out a Chi-Squared test of independence, and providing a critique of the design and collection of these data.

Accessing the Challenge

Download the template Challenge 3 Quarto file here: challenge-3-student.qmd

Important

Be sure to save the file in the Lab 3 folder, inside your Week 3 folder, inside your STAT 331 folder!

Chi-Square Test of Independence

Refresher on Chi-square test of Independence

In case it has been a second or two since you did a Chi-Squared test, this chapter provides a friendly refresher.

Let’s compare the level of SET ratings for Question 3 (The professor used activities to make the class more engaging.) between senior instructors and junior instructors.

1. Using the original teacher_evals dataset (not teacher_evals_clean), create a new dataset that accomplishes the following with onedplyr pipeline:

includes responses for Question 3 only
creates a new variable called set_level that is “excellent” if the SET_score_avg is 4 or higher (inclusive) and “standard” otherwise
creates a new variable called sen_level that is “junior’”’ if the instructor has been teaching for 4 years or less (inclusive), “senior” if between 5-8 years (inclusive), and “very senior” if more than 8 years
contains only the variables we are interested in –- course_id, SET_level, and sen_level
saves the mutated data into a new data frame named teacher_evals_compare

Tip

Helpful functions: filter(), mutate(), if_else(), select()

2. Using the new dataset and your ggplot2 skills, recreate the bar plot shown below.

Tip

You should not have to do any more data manipulation to create this plot.

Helpful geometric object and arguments: geom_bar(stat = ..., position = ...)

Note that getting the general structure and reader friendly labels is the first step. Next, you need to move the legend from the right hand side of the plot to the top. Next, you get to match the colors and theme of the plot. The final step is to get the percentage labels on the y-axis.

3. Look up the documentation for chisq.test() to carry out a chi-square test of independence between the SET level and instructor seniority level in your new dataset.

Tip

Note that the chisq.test() function does not take a formula / data specification like we saw in Lab 2 with t.test(). This function requires you to extract the variables you wish to include in the analysis using a $ (e.g., evals$level).

4. Draw a conclusion about the independence of evaluation level and seniority level based on your chi-square test.

Study Critique

Part of the impetus behind this study was to investigate characteristics of a course or an instructor that might affect student evaluations of teaching that are not explicitly related to teaching effectiveness. For instance, it has been shown that gender identity and gender express affect student evaluations of teaching (an example).

5. If you were to conduct this study at Cal Poly, what are two other variables you would like to collect that you think might be related to student evaluations? These should be course or instructor characteristics that were not collected in this study.

6. Explain what effects / relationships you would expect to see for each of the two variables you outlined.