shira mitchell
@shiraamitchell.bsky.social
survey statistician at blue rose research 🏕
Pinned
Reposted by shira mitchell
Survey Statistics: continued struggles with equivalent weights
statmodeling.stat.columbia.edu/2025/11/04/s...
statmodeling.stat.columbia.edu/2025/11/04/s...
Survey Statistics: continued struggles with equivalent weights | Statistical Modeling, Causal Inference, and Social Science
statmodeling.stat.columbia.edu
November 4, 2025 at 9:22 PM
Survey Statistics: continued struggles with equivalent weights
statmodeling.stat.columbia.edu/2025/11/04/s...
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.
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.
October 28, 2025 at 8:32 PM
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.
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
typical machine learning loss looks at one individual at a time
but for MRP, we care about aggregates
October 22, 2025 at 1:30 PM
blog post: individualism doesn't work
typical machine learning loss looks at one individual at a time
but for MRP, we care about aggregates
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) ?
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) ?
October 15, 2025 at 1:46 PM
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) ?
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 ?
you've done MRP.
someone asks you for survey weights.
how to get them ?
October 7, 2025 at 11:56 PM
blog post: struggles with equivalent weights
you've done MRP.
someone asks you for survey weights.
how to get them ?
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
in midterms, voters tend to support the out party for balance
do polls still help predict midterms ? yes
October 1, 2025 at 10:40 AM
blog post: beyond balancing
in midterms, voters tend to support the out party for balance
do polls still help predict midterms ? yes
in midterms, voters tend to support the out party for balance
do polls still help predict midterms ? yes
Reposted by shira mitchell
The ultimate New York City lover’s treasure hunt is back Friday, Oct. 17 through Sunday, Oct. 19: bit.ly/3IHnHwn
September 26, 2025 at 7:58 PM
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...
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...
September 23, 2025 at 8:19 PM
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...
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 ?
we turned to response instrument Z because random sampling is "dead"
but does this method still rely on starting with random sampling ?
September 16, 2025 at 9:01 PM
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 ?
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 ?
we turned to response instrument Z because random sampling is "dead"
but does this method still rely on starting with random sampling ?
September 16, 2025 at 9:01 PM
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 ?
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...
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...
September 9, 2025 at 8:22 PM
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...
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.
🧮 A new preprint of my first dissertation chapter "From disparate lists to population estimates: A multiple systems estimation workflow for mortality analysis in conflict settings” is now online.
doi.org/10.31235/osf...
doi.org/10.31235/osf...
September 9, 2025 at 2:59 PM
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 !
split-plot designs are analogous to cluster sampling.
blocking is analogous to stratification.
featuring an experiment by Arjun Potter and colleagues at NM-AIST !
September 3, 2025 at 3:25 AM
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 !
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
what are the problems with using LLMs as survey respondents ?
how are these similar to problems with poststratification ?
CC @tslumley.bsky.social
August 27, 2025 at 7:37 AM
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
what are the problems with using LLMs as survey respondents ?
how are these similar to problems with poststratification ?
CC @tslumley.bsky.social
Reposted by shira mitchell
We are hiring !
Data Scientist – Analytics: job-boards.greenhouse.io/blueroserese...
and
Research Analyst: job-boards.greenhouse.io/blueroserese...
Data Scientist – Analytics: job-boards.greenhouse.io/blueroserese...
and
Research Analyst: job-boards.greenhouse.io/blueroserese...
Data Scientist - Analytics (Senior and Junior)
Remote
job-boards.greenhouse.io
August 24, 2025 at 12:09 AM
We are hiring !
Data Scientist – Analytics: job-boards.greenhouse.io/blueroserese...
and
Research Analyst: job-boards.greenhouse.io/blueroserese...
Data Scientist – Analytics: job-boards.greenhouse.io/blueroserese...
and
Research Analyst: job-boards.greenhouse.io/blueroserese...
Reposted by shira mitchell
Survey Statistics: answers from the BLS
statmodeling.stat.columbia.edu/2025/08/19/s...
statmodeling.stat.columbia.edu/2025/08/19/s...
Survey Statistics: answers from the BLS | Statistical Modeling, Causal Inference, and Social Science
statmodeling.stat.columbia.edu
August 19, 2025 at 9:52 PM
Survey Statistics: answers from the BLS
statmodeling.stat.columbia.edu/2025/08/19/s...
statmodeling.stat.columbia.edu/2025/08/19/s...
Reposted by shira mitchell
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)
see a proposal for a solution + related package (v0)
August 18, 2025 at 12:05 AM
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)
see a proposal for a solution + related package (v0)
Reposted by shira mitchell
🤖 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...
August 13, 2025 at 6:38 PM
🤖 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...
Reposted by shira mitchell
🤖 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...
statmodeling.stat.columbia.edu/2025/06/03/s...
August 13, 2025 at 6:36 PM
🤖 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...
statmodeling.stat.columbia.edu/2025/06/03/s...
Reposted by shira mitchell
Survey Statistics: 2nd helpings of the 2nd flavor of calibration
statmodeling.stat.columbia.edu/2025/08/12/s...
statmodeling.stat.columbia.edu/2025/08/12/s...
Survey Statistics: 2nd helpings of the 2nd flavor of calibration | Statistical Modeling, Causal Inference, and Social Science
statmodeling.stat.columbia.edu
August 12, 2025 at 8:46 PM
Survey Statistics: 2nd helpings of the 2nd flavor of calibration
statmodeling.stat.columbia.edu/2025/08/12/s...
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...
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...
August 12, 2025 at 8:26 PM
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...
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...
Reposted by shira mitchell
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…
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…
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 official statistics are Pure and Holy and True and above mere political concerns; it's because official statistics are messy and difficult and hard to get right, but also very valuable.
www.statschat.org.nz
August 4, 2025 at 11:30 PM
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…
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 ?
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 ?
August 5, 2025 at 8:09 PM
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 ?
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 ?
