blog post: GREG GREG is Generalized REGression estimator. We can think of it either as: 1. Adjusting an estimate based on the model with a Horvitz-Thompson estimate of the error, or 2. On the flip side, adjusting the Horvitz-Thompson estimate with the model.

blog post: relevant alternatives ? We saw that the multinomial logit model implies independence from irrelevant alternatives (IIA). Let’s expand the model to include choice set C within the logits f(X_ic,C), allowing for non-IIA.

Survey Statistics: Blue Rose Research is (still) hiring ! statmodeling.stat.columbia.edu/2026/05/05/s...

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: exploded logit ! a common choice model is multinomial logit. this model implies that rankings follow an exploded logit !

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.

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).

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

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...