Teaching Innovations in COVID Times: Intro to Stats, Flipped

COVID has all of us doing things that we’re not used to doing—like not leaving the house for two weeks in a row, or holding meetings in your daughter’s bedroom. In this way, it’s encouraging all of us to innovate. In my case, this means a new PhD-level course, Introduction to Probability and Statistics, and a new way of teaching through a flipped classroom model.

I’ve never taught stats before, and I’ve never taught using an asynchronous flipped classroom model, so this will be new all around. But I’ve profited from lots of discussion of how to teach statistics, how to make flipped classroom experiences work, and from thinking about my own experience taking Introduction to Statistics with Tasos Kalandrakis back in fall 2001, another unusual time.* I’ve especially learned from Gary King’s GOV 2001, which has been teaching some of the same material to the same sorts of students using a similar flipped classroom format for some time now.** It is entirely possible that a flipped classroom model is the best way to teach introductory probability and statistics.

For those curious, here is the syllabus (PDF).

I also feel it necessary to acknowledge all of the materials that were instrumental for helping me to prepare.

Acknowledging these online notes, part of me says “just go take these courses instead” but maybe my own remix will be fun too. It does include slides such as this, for example.

But more seriously, what makes my course special, I think, are three things:

  • Assuming only basic mathematical background: nothing beyond basic algebra. This is serious: anyone who can do junior high algebra can complete this course.
  • Teaching R and Stata at the same time: no pen and paper homework, all problem sets done exclusively via the computer. (I also assume no computer science, scripting, or programming background.)
  • An emphasis on developing intuitions via simulation, as a complement to analytical results (which cannot involve anything more advanced than basic algebra, of course).

The objective here is fundamentals for everyone. We sacrifice some more advanced concepts and results in favor of intuitions and understanding the basics at what I like to call the “no-bullshit” level. If this works out how I hope, then students with no background or particular inclination towards probability or statistics will be able to understand how this stuff works, to consume the quantitative social science research that they encounter, and will be ready for those more advanced courses out there.***

NOTES

* As is typical, I did not appreciate at the time just how good that class was, and how hard it must have been to teach.
** You can also watch all of his lectures here.
*** They may also appreciate some dad jokes.

Most Americans Don’t Want to Delay Elections in November. But Now for the Bad News…

As part of my ongoing collaboration with Shana Gadarian and Sara Goodman on a NSF- and Cornell Center for the Social Sciences-supported project on pandemic politics,* we are tracking Americans’ views about how the U.S. government ought to respond to the pandemic. We asked early on about whether or not respondents supported delaying the elections in November, and were pleased to see bipartisan opposition to that.

Since then, we have seen still further declines in support for delaying elections. As of mid-August, when we got our latest round of data, fully 63% of Americans strongly oppose delaying elections, and another 9% somewhat oppose delaying elections. This is good.

Now for the bad news: a partisan gap has now emerged about delaying elections.

The figure below tracks support for delaying elections across the four waves of our survey. It also shows support for voting by mail, which we only started asking in the second wave of our survey.

We see the strong, consistent downward trends in support for delaying elections, the simple average of responses by party and wave on a 1-5 scale where 1 is “strongly oppose” and 5 is “strongly support.” This is really unpopular!

But in Wave 4—so just over the past two weeks—we’ve seen the emergence of a partisan gap in support for delaying elections. Democrats continue to grow more opposed to delaying elections over time, while Republicans have stabilized at an average of 2 (so, mild opposition) on a scale of 1-5.

By contrast, we see a massive (and growing) gap in support for voting by mail: this question asks whether respondents agree with the position that “all Americans should be given the opportunity to vote by mail this November.” Democrats overwhelmingly support this. Republicans have been less supportive and are growing even less so.

Troubling signs. But can we say anything more about this?

Yes, we can. In asking about delaying elections, starting in Wave 3 we began randomizing the justification for delaying elections. One justification is public health minded: “if it means protecting people.” The other justification is political: “because a crisis is not a time for politics.” Here is what we discover when we compare responses across treatment conditions, parties, and waves:

In wave 3, the political message was more effectual in eliciting support for delaying elections, and its effects were basically the same across parties.

In wave 4, the political message is still more effectual in eliciting support for delaying elections, and these effects are basically the same across parties. But Democrats on the whole have just shifted further against delaying elections (in the graph, they’ve moved “to the left”).

The news is still mostly good. Most people of all parties oppose delaying elections. But the news was a lot better** when partisanship didn’t matter at all. Something else for America’s democrats to keep their eyes on.

NOTES

* Pandemic Politics! Forthcoming in 2022 from Princeton University Press, and perfect for your book club or long flight.
** Take it from Calvin.