Tis the season for the end of semester teaching evaluations. And Ben Schmidt has created an interactive tool that demonstrates gender differences in these evaluations. Enter in a word, and Schmidt's tool returns to you how frequently the word is used in Rate Your Professor evaluations, divided up by gender and academic discipline.
Out of class, you can use this example to feel sad, especially if you are a female professor and up for tenure.
In class, this leads to obvious discussions about gender and perception and interpersonal judgments. You can also use it to discuss why the x- and y-axes were chosen. You can discuss the archival data analysis used to generate these charts. You can discuss data mining. You can discuss content analysis. You can also discuss between-group differences (gender) versus within-group differences (academic area).
You could also use this to generate some data for classroom analysis. If you cursor a data point in a chart, you can see the exact number of instances of that word per millions of word of text. You could make your students enter all that data and run a t-test for a specific word, or by an academic area (say, a bunch of positive words just for male and female psychology professors). Or, you could collect data for multiple words AND academic areas and make an ANOVA out of it.
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| Spoiler alert: Men get higher ratings for most positive attributes! |
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| ...while women get higher ratings for negative attributes. |
Out of class, you can use this example to feel sad, especially if you are a female professor and up for tenure.
In class, this leads to obvious discussions about gender and perception and interpersonal judgments. You can also use it to discuss why the x- and y-axes were chosen. You can discuss the archival data analysis used to generate these charts. You can discuss data mining. You can discuss content analysis. You can also discuss between-group differences (gender) versus within-group differences (academic area).
You could also use this to generate some data for classroom analysis. If you cursor a data point in a chart, you can see the exact number of instances of that word per millions of word of text. You could make your students enter all that data and run a t-test for a specific word, or by an academic area (say, a bunch of positive words just for male and female psychology professors). Or, you could collect data for multiple words AND academic areas and make an ANOVA out of it.
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