There are a lot of ways we can approach diversity in the statistics classroom, as even the term “diversity” can be operationally defined in so many ways. One method is to use research on diversity as a basis of an example when teaching statistics in context.

Often the complexity of the statistics used in a published journal article are beyond what would be taught in an introductory course in applied statistics, however, what I often do is take the research hypothesis and design and simplify it a bit. Yes, this is an example of scaffolding. So, I structure the study to fit the concept I am teaching (e.g., making a multivariate research student univariate, or making a two-way factorial a one-way). Keeping the general structure of the study intact, I often shorten the task so it will take less than 10 minutes to run through the mock study, collect the data, and then provide students with critical conceptual background information. This still gives me enough time (in a 50 minute class) to have students work through the problem, while I model it, and go from question to answer through the use of hypothesis testing.

In this example, the concept I will be teaching is the independent *t*-test, a form of null hypothesis testing. The study I am using is Apfelbaum, Pauker, and Sommer’s (2010) study of 4^{th} and 5^{th} grade students which examined the effects of *color-blind thinking* and *value-diversity thinking* on bias.

In short, *color-blind thinking* is simply ignoring race as a variable worth attending to, as in doing so, issues of bias will be minimized. (For my social scientist readers … this is a very etic way of approaching the potential of racial bias.)

*Value-diversity thinking* (emic) actively recognizes differences within each racial and ethnic group.

As we are comparing two different conditions, this study can easily be adapted to an independent *t*-test.

So, during class, we could quickly, and randomly provide students with one of two sheets of paper.

Borrowing phrased directly from the published study, the students in the *color-blind condition* would see phrases like:

- We need to focus on how we are similar to our neighbors rather than how we are different.
- We want to show everyone that race is not important and that we’re all the same.

Meanwhile, the students in the *value diversity condition* would see phrases like:

- We need to recognize how we are different from our neighbors and appreciate those differences.
- We want to show everyone that race is important because our racial differences make each of us special.

In the actual study, Apfelbaum, Pauker, and Sommer’s (2010) looked at both implied and explicit racial biases. For the in-class activity, as this is being conducted with college students instead of 4^{th} and 5^{th} graders, we could just use the implied bias. Read to students the following scenario (slightly modified from the article): “Most of Brady’s classmates got invitations to his birthday party, but Terry was one of the kids who did not. Brady decided not to invite him because he knew that Terry would not be able to buy him any of the presents on his ‘wish list.’”

Then ask students to write down an answer on a scale from 1 – 10, 1 being completely inappropriate and 10 being completely appropriate. Typically, my class size is too large to collect data from all of the students, so I would randomly select 5 students from each condition, and write their responses on the chalk board. Now, we can model how to answer the question: is encouraging people to ignore race a way to increase bias or decrease it, compared to encouraging people to factor race into evaluating situations.

Of course, one of the problems in using “real data” in a study with so few subjects is that you will never be certain if the test statistic will support the same conclusion as the research article. There are two ways to deal with this problem, acknowledge the potential for low power right from the start, or have the students complete the activity, but use data that you selected to model how to answer this question with the use of an independent *t*-test. The latter might be best for individual’s new to teaching, as you can come to class with your calculations prepared.

In closing, it is easy to bring diversity into a classroom, even if you are that “scoop of vanilla ice cream.” One of the best ways is to make use of published research studies on cultural or racial diversity as a way of modeling critical concepts in statistics.

Apfelbaum, E. P., Pauker, K., & Sommers, S. R. (2010) In blind pursuit of racial equality? *Psychological Science, 21*, 1587-1592. http://pss.sagepub.com/content/21/11/1587.full