blog post: irrelevant alternatives ?
a common choice model is multinomial logit.
this model implies Independence of Irrelevant Alternatives (IIA), e.g. the ratio of Left-vs-Right preference is the same in round 1 as in the runoff.
CC @danpsimpson.bsky.social
blog post: improving with structure
We’ve met Mr. P (Multilevel Regression and Poststratification).
We’ve met Mrs. P (Multilevel Regression with Synthetic Poststratification).
Now let’s meet Ms. P (Multilevel Structured regression with Poststratification).
CC @avehtari.bsky.social @tslumley.bsky.social
blog post: design-based cross validation
how to split train and test sets to respect survey design ?
what lessons carry over to nonprobability samples ?
blog post: Individualism and the CV Noise Problem
Politically meaningful differences among models can be swamped by cross-validation noise.
blog post: individualism doesn't work (even when weighted)
individual-level loss (even weighted to the population) orders models differently than the population-level loss of interest to folks using MRP
blog post: work with us at Blue Rose !
use cutting edge statistics, machine learning, and engineering to study public opinion, forecast elections, and advise Democrats.
blog post: sampling-weighted loss
we use sampling weights to estimate a population mean E(Y).
what about to estimate a conditional mean E(Y|X) ?
the best-fit model in the sample may not be the best-fit model in the population.
blog post: sampling to assess data quality
@bhedtgauthier.bsky.social et al. (2012) used sampling to assess and improve data quality in Malawi
blog post: Gallup's Presidential Approval Ratings
Gallup will no longer track presidential approval after 88 years
Let's look at their sampling, mode, and weighting
(still used for other survey questions)
blog post: more on recalled vote
we've talked about measurement error in recalled vote in the US.
how does this change in multiparty states ?
We recently tested ~ a dozen public statements from a diverse set of Democratic elected officials on the murder of Renee Good and this was the top testing one
I am excited for the book !
bsky.app/profile/aveh...
blog post: 5 flavors of calibration
2 from survey statistics
1 from machine learning
2 from Gelman et al.'s workflow article
blog post: Total Margin of Error (Part II)
For election polls from 1998 to 2014 Shirani-Mehr et al. found:
margin of error = 2 x (reported margin of error)
Let's revisit Meng's “Statistical Paradises and Paradoxes” to understand this more generally.
yes ! thanks for reminding me to tie in this paper. I wrote about that for the blog today.
the actual blog post makes this clear (I hope)
the actual blog post makes this clear (I hope)
thanks to @rnishimura.bsky.social for pointing out: this finding is from certain public political polls for elections from 1998 to 2014. it doesn't generalize to all surveys !!
thanks for sharing this ! I should have clarified the narrowness of this finding: public political polls for elections from 1998 to 2014
thanks, Stephen ! how much larger was actual MOE vs reported MOE in these British polls ?
blog post: Total Margin of Error
margin of error = 2 x (reported margin of error)
and how much of this error is "bias" vs "variance" ?
blog post: Margin of Error
how can we get a poll's margin of error ?
let's start with MRP and some simplifying assumptions.
This post has some more discussion of other methods for incorporating known ground-truth margins in an MRP framework, based on some validation exercises in osf.io/preprints/so...
CC @gelliottmorris.com
blog post: 4th helpings of the logit shift
y_1 = governor vote choice
y_2 = abortion proposition vote choice
x = demographics
You want E(y_2 | county).
You have y_1, y_2, x in a survey, x in the population, and E(y_1 | county).
@wpmarble.bsky.social and Josh Clinton have ideas !
(starting to ask questions about it in the blog)
wow !! thank you so much, Raphael, this is SO COOL