Wired magazine presents a set of datagraphics depicting their ingenious analysis of the corpus of recipes on the website foodnetwork.com. I bet you'll have fun looking through the graphics. They correlated the ingredients list in each recipe with the average number of five-star rating points the recipe has received from readers.
For example, one graph shows that the more ingredients a recipe has, the higher it gets rated (Do more ingredients taste better? Or do people just have to justify all that prep time?)
The lead graphic shows the five-star ratings of recipes that either contain bacon or not. You can check it out here. Across almost all categories (sandwiches, asparagus recipes, kale recipes, and spinach salad recipes), those containing bacon are rated higher than those that aren't.
But there are two exceptions to this pattern: bacon as an ingredient is associated with lower ratings of pasta and dessert recipes. Though bacon generally is associated with higher recipe ratings, this doesn't apply to pasta and dessert, apparently. (Just say no to bacon ice cream.)
In these data, recipe, not person, is the unit of analysis. But your research methods lessons still come in handy. For example:
a) Can we conclude from Wired's data that adding bacon will cause a sandwich to be rated more positively? Why or why not?
b) Do you see what I see--that there's an interaction between the bacon variable (bacon or no bacon) and the type of recipe variable? Whether bacon recipes get higher ratings depends on what kind of recipe it is.
c) How might you extend these correlational data into an experiment? Which variable would you manipulate? Which one would you measure?
