shira mitchell

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

statmodeling.stat.columbia.edu/2025/12/30/s...

further stratification by measurement error source would be interesting too for sure

yes ! I'm curious what you'll think of how I simulated some toy examples. They depend on P(Y=1 | X, X*), i.e. how does the outcome vary by the 4 strata of X,X*: (1,1) and (0,0) being no measurement error, and (0,1) and (1,0) being two different measurement errors.

blog posts: should we adjust for a mismeasured X ?

You know the population distribution for X (e.g. vote choice in 2024).

But you only have a reported X* in your survey.

Should you adjust for it ?

Later today: exploring toy examples to see.

blog post: 3rd helpings of the logit shift

You have multiple outcomes, but only some have aggregate truth to shift to.

How can we calibrate our estimates of p(y_1, y_2 | X) to aggregate data about E[y_1] ?

@wpmarble.bsky.social and Josh Clinton have ideas !

blog post: 3 probabilities in Meng 2022

1. (human) design probabilities, e.g. P[R = 1 | stratum] in stratified sampling

2. divine probabilities, e.g. P[R = 1 | anything about a person] where responders follow laws of nature

3. device probabilities, e.g. P[R = 1 | X] modeled

blog post:

probability sample = known nonzero probability

epsem = equal individual probabilities

SRS = equal entire-sample probabilities

Cover of book, titled „The Politics of Human Rights“ by Sabine Carey, Mark Gibney, and Anita Gohdes. Picture shows a woman kneeling in front riot police during a BLM protest in the US.

Coming soon: our introduction to the politics of human rights 🥳📚

Preorder available here: www.cambridge.org/highereducat...

@sabinecarey.bsky.social

blog post: quantity vs quality

compare 2 surveys:

1. 100% coverage, but response probability P[R = 1 | Y] differs a lot by Y

2. Only 5% coverage, but P[R = 1 | Y] is roughly constant across Y

which would you use ? both ?

new blog post: sampling the sample

we’ve focused on estimating means E[Y].

but say Y are openends ("describe how you feel about the candidate") and you want to read thru a few draws from the population, not only survey responders.

what should you do ?