Challenge 3: Extending Teaching Evaluation Investigations

Chi-Square Test of Independence

Refresher on Chi-square test of independence

While a second course in statistics is a pre-requisite for this class, here is a refresher on Chi-square tests of independence.

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 seniority is 4 or less (inclusive) and “senior” otherwise
  • contains only the variables we are interested in –- course_id, SET_level, and sen_level
  • saves the mutated data into a new object named teacher_evals_compare.
Tip

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

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

Tip

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

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

Note that getting the general structure and reader friendly labels is the first step. The next step is to figure out the labels of the bars and the theme of the plot.

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 as we have seen before. You will need 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.