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

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.

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...

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 !

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

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 ?

blog post: weights and MRP for voters so far we've talked about weights and MRP for E[Y], vote choice in the population overall. but what if you want E[Y | V = 1], vote choice in the population of voters. what are the weights and how do you modify MRP ?

Survey Statistics: continued struggles with equivalent weights statmodeling.stat.columbia.edu/2025/11/04/s...

blog post: Blue Rose Research is hiring ! We are looking for a teammate with expertise in both LLM tools and statistical modeling. Someone who clearly communicates assumptions, results, and uncertainty. With care and kindness.

blog post: individualism doesn't work typical machine learning loss looks at one individual at a time but for MRP, we care about aggregates

blog post: MRPW you've got a survey collected by someone else, and they gave you weights. how can you use those weights in the MRP (Multilevel Regression and Poststratification) ?

blog post: struggles with equivalent weights you've done MRP. someone asks you for survey weights. how to get them ?

blog post: beyond balancing in midterms, voters tend to support the out party for balance do polls still help predict midterms ? yes

The ultimate New York City lover’s treasure hunt is back Friday, Oct. 17 through Sunday, Oct. 19: bit.ly/3IHnHwn

blog post: Fat Bear Week Basu's Bears is a lesson in: 1) using auxiliary information (pre-salmon-feasting weights) 2) how bad an unbiased estimator can be statmodeling.stat.columbia.edu/2025/09/23/s...

blog post: random sampling is not leaving we turned to response instrument Z because random sampling is "dead" but does this method still rely on starting with random sampling ?

blog post: random sampling is not leaving we turned to response instrument Z because random sampling is "dead" but does this method still rely on starting with random sampling ?

blog post on imputation (again): we want E[Y|X] but X can be missing @lucystats.bsky.social @sarahlotspeich.bsky.social @glenmartin.bsky.social @maartenvsmeden.bsky.social et al. say: random imputation should use Y deterministic imputation shouldn't statmodeling.stat.columbia.edu/2025/09/09/s...

I learn from every interaction with Maria. I attended a talk of hers yesterday. I highly recommend reading her work.

blog post: connections between survey statistics and experimental design. split-plot designs are analogous to cluster sampling. blocking is analogous to stratification. featuring an experiment by Arjun Potter and colleagues at NM-AIST !

blog post: Thomas Lumley writes about Interviewing your Laptop what are the problems with using LLMs as survey respondents ? how are these similar to problems with poststratification ? CC @tslumley.bsky.social

Now with 'nearly isotonic' ... github.com/finite-sampl... cc @shiraamitchell.bsky.social

We are hiring ! Data Scientist – Analytics: job-boards.greenhouse.io/blueroserese... and Research Analyst: job-boards.greenhouse.io/blueroserese...

Survey Statistics: answers from the BLS statmodeling.stat.columbia.edu/2025/08/19/s...

Following @shiraamitchell.bsky.social's post on problems with multiclass calibration: statmodeling.stat.columbia.edu/2025/08/12/s... see a proposal for a solution + related package (v0)

🤖 But wait! There's more! You can check out @shiraamitchell.bsky.social 's most recent update on the details of Calibration, posted yesterday! statmodeling.stat.columbia.edu/2025/08/12/s...

🤖 Ever been a bit confused abt calibration in statistical modelling? My friend @shiraamitchell.bsky.social has got you covered!! She works on survey statistics for Things That Matter--like elections--and has kindly taken the time to explain some things! statmodeling.stat.columbia.edu/2025/06/03/s...

Survey Statistics: 2nd helpings of the 2nd flavor of calibration statmodeling.stat.columbia.edu/2025/08/12/s...

blog post: 2nd helpings of the 2nd flavor of calibration 🍨🍨 in political surveys, we "logit shift" predictions to match known aggregates (e.g. total Democratic votes). but what happens for multinomial outcomes ? a fun excuse to review IPF/raking 🍂 statmodeling.stat.columbia.edu/2025/08/12/s...

Official statistics As you may have heard, President Trump has dismissed the head of the US Bureau of Labor Statistics, claiming that payroll employment figures presented by the BLS were faked to make him look bad. Politicians meddling with official statistics is a bad idea.  This isn't because…

blog post: BLS Jobs Report let's learn about the CES employer survey that produces the jobs count. late reporting (a form of nonresponse) results in revisions. my first (naive !) question: why use employment size in stratification but not nonresponse adjustment ?

Already having minor FOMO over skipping #JSM2025. Survey Research Methods folks there, please post!

blog post: adjusting for interest in politics whether you respond to a survey (R) may depend on outcome (Y), even after controlling for covariates (X) what if we can expand this set of X to include interest in politics ?

very excited to teach again soon at NM-AIST

blog post: a new paradigm for polling so far we assumed response R is independent of outcome Y **within X** but if R can depend on Y, what to do ? one idea: use a response instrument Z statmodeling.stat.columbia.edu/2025/07/22/s...

blog post about longitudinal/panel data: panel data includes repeated surveys of the same people over time. this structure can be incorporated into models using person-level effects. but misspecifying the person-level effects distribution can cause bias.

Did you hear that I'll be teaching four brand new remote courses this year covering Bayesian mixture modeling, survival modeling, pairwise comparison modeling, and ordinal modeling, www.eventzilla.net/e/advanced-b...? Reskeets and sharing with your friends and colleges appreciated!

blog post about imputation: With nonresponse worsening, we want to adjust for a lot of covariates. This often means handling many missing covariates. In theory, fit one big model for everything. But how can practitioners handle this ?

*really* excited for this ! love the name: Structural Zero. Alan Agresti's Categorical Data Analysis book offers a good explanation (which I'm sure the amazing authors at @hrdag.org will get into):

blog post about Sparsified MRP: With nonresponse worsening, we want to adjust for a lot of covariates. Estimates from such big models will be unstable without a lot of data and/or regularization. Have you seen MRP with sparsifying priors ? statmodeling.stat.columbia.edu/2025/07/01/s...

blog post about poststratification: which estimate is best ? 1. unadjusted sample mean 2. classical poststratification 3. regularized poststratification (e.g. MRP) statmodeling.stat.columbia.edu/2025/06/24/s...