shira mitchell
@shiraamitchell.bsky.social
survey statistician at blue rose research 🏕
Pinned
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" ?
margin of error = 2 x (reported margin of error)
and how much of this error is "bias" vs "variance" ?
January 21, 2026 at 4:36 PM
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" ?
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.
how can we get a poll's margin of error ?
let's start with MRP and some simplifying assumptions.
January 18, 2026 at 3:15 PM
blog post: Margin of Error
how can we get a poll's margin of error ?
let's start with MRP and some simplifying assumptions.
how can we get a poll's margin of error ?
let's start with MRP and some simplifying assumptions.
Reposted by shira mitchell
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...
January 8, 2026 at 11:55 PM
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 !
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 !
January 8, 2026 at 10:31 PM
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 !
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.
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.
December 30, 2025 at 12:41 PM
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.
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 !
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 !
December 16, 2025 at 10:15 PM
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 !
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
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
December 10, 2025 at 11:11 AM
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
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
probability sample = known nonzero probability
epsem = equal individual probabilities
SRS = equal entire-sample probabilities
December 5, 2025 at 10:20 PM
blog post:
probability sample = known nonzero probability
epsem = equal individual probabilities
SRS = equal entire-sample probabilities
probability sample = known nonzero probability
epsem = equal individual probabilities
SRS = equal entire-sample probabilities
Reposted by shira mitchell
Coming soon: our introduction to the politics of human rights 🥳📚
Preorder available here: www.cambridge.org/highereducat...
@sabinecarey.bsky.social
Preorder available here: www.cambridge.org/highereducat...
@sabinecarey.bsky.social
August 6, 2025 at 7:08 PM
Coming soon: our introduction to the politics of human rights 🥳📚
Preorder available here: www.cambridge.org/highereducat...
@sabinecarey.bsky.social
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 ?
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 ?
November 25, 2025 at 9:58 PM
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 ?
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 ?
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 ?
November 19, 2025 at 12:59 AM
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 ?
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 ?
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 ?
November 12, 2025 at 10:13 PM
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 ?
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 ?
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 !