andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1365 knowledge-graph by maker-knowledge-mining
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Introduction: 25. You are using multilevel regression and poststratification (MRP) to a survey of 1500 people to estimate support for the space program, by state. The model is fit using, as a state- level predictor, the Republican presidential vote in the state, which turns out to have a low correlation with support for the space program. Which of the following statements are basically true? (Indicate all that apply.) (a) For small states, the MRP estimates will be determined almost entirely by the demo- graphic characteristics of the respondents in the sample from that state. (b) For small states, the MRP estimates will be determined almost entirely by the demographic characteristics of the population in that state. (c) Adding a predictor specifically for this model (for example, a measure of per-capita space-program spending in the state) could dramatically improve the estimates of state-level opinion. (d) It would not be appropriate to add a predictor such as per-capita space-program spen
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1 You are using multilevel regression and poststratification (MRP) to a survey of 1500 people to estimate support for the space program, by state. [sent-2, score-0.404]
2 The model is fit using, as a state- level predictor, the Republican presidential vote in the state, which turns out to have a low correlation with support for the space program. [sent-3, score-0.519]
3 ) (a) For small states, the MRP estimates will be determined almost entirely by the demo- graphic characteristics of the respondents in the sample from that state. [sent-6, score-1.176]
4 (b) For small states, the MRP estimates will be determined almost entirely by the demographic characteristics of the population in that state. [sent-7, score-0.873]
5 (c) Adding a predictor specifically for this model (for example, a measure of per-capita space-program spending in the state) could dramatically improve the estimates of state-level opinion. [sent-8, score-0.896]
6 (d) It would not be appropriate to add a predictor such as per-capita space-program spending in the state: by adding such a predictor to the model, you would essentially be assuming what you are trying to prove. [sent-9, score-0.97]
7 Solution to question 24 From yesterday : 24. [sent-10, score-0.066]
8 It is desired to estimate the proportion of vegetables that spoil before being sold. [sent-12, score-0.688]
9 The following sampling designs are considered: (a) Sample 10 stores, then sample half the vegetables within each of these stores; or (b) Sample 20 stores, then sample one-quarter of the vegetables within each of these stores. [sent-13, score-1.594]
10 Which of these designs has the lowest variance? [sent-14, score-0.266]
11 Solution: Design (b) has lower variance but design (a) is cheaper. [sent-16, score-0.289]
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same-blog 1 0.99999988 1365 andrew gelman stats-2012-06-04-Question 25 of my final exam for Design and Analysis of Sample Surveys
Introduction: 25. You are using multilevel regression and poststratification (MRP) to a survey of 1500 people to estimate support for the space program, by state. The model is fit using, as a state- level predictor, the Republican presidential vote in the state, which turns out to have a low correlation with support for the space program. Which of the following statements are basically true? (Indicate all that apply.) (a) For small states, the MRP estimates will be determined almost entirely by the demo- graphic characteristics of the respondents in the sample from that state. (b) For small states, the MRP estimates will be determined almost entirely by the demographic characteristics of the population in that state. (c) Adding a predictor specifically for this model (for example, a measure of per-capita space-program spending in the state) could dramatically improve the estimates of state-level opinion. (d) It would not be appropriate to add a predictor such as per-capita space-program spen
2 0.54118896 1367 andrew gelman stats-2012-06-05-Question 26 of my final exam for Design and Analysis of Sample Surveys
Introduction: 26. You have just graded an an exam with 28 questions and 15 students. You fit a logistic item- response model estimating ability, difficulty, and discrimination parameters. Which of the following statements are basically true? (Indicate all that apply.) (a) If a question is answered correctly by students with very low and very high ability, but is missed by students in the middle, it will have a high value for its discrimination parameter. (b) It is not possible to fit an item-response model when you have more questions than students. In order to fit the model, you either need to reduce the number of questions (for example, by discarding some questions or by putting together some questions into a combined score) or increase the number of students in the dataset. (c) To keep the model identified, you can set one of the difficulty parameters or one of the ability parameters to zero and set one of the discrimination parameters to 1. (d) If two students answer the same number of q
3 0.3778393 1362 andrew gelman stats-2012-06-03-Question 24 of my final exam for Design and Analysis of Sample Surveys
Introduction: 24. A supermarket chain has 100 equally-sized stores. It is desired to estimate the proportion of vegetables that spoil before being sold. The following sampling designs are considered: (a) Sample 10 stores, then sample half the vegetables within each of these stores; or (b) Sample 20 stores, then sample one-quarter of the vegetables within each of these stores. Which of these designs has the lowest variance? Why might the higher-variance design still be chosen? Solution to question 23 From yesterday : 23. Suppose you are conducting a survey in which people are asked about their health behaviors (how often they wash their hands, how often they go to the doctor, etc.). There is a concern that different interviewers will get different sorts of responses—that is, there may be important interviewer effects. Describe (in two sentences) how you could estimate the interviewer effects within your survey. Can the interviewer effects create problems of reliability of the survey r
4 0.34561566 1358 andrew gelman stats-2012-06-01-Question 22 of my final exam for Design and Analysis of Sample Surveys
Introduction: 22. A supermarket chain has 100 equally-sized stores. It is desired to estimate the proportion of vegetables that spoil before being sold. Three stores are selected at random and are checked: the percent of spoiled vegetables are 3%, 5%, and 10% in the three stores. Give an estimate and standard error for the percentage of spoiled vegetables for the entire chain. Solution to question 21 From yesterday : 21. A country is divided into three regions with populations of 2 million, 2 million, and 0.5 million, respectively. A survey is done asking about foreign policy opinions. Somebody proposes taking a sample of 50 people from each reason. Give a reason why this non-proportional sample would not usually be done, and also a reason why it might actually be a good idea. Solution: Nonproportional sampling is usually avoided because it makes the analysis more complicated and it results in a higher standard error for estimates of the general population. It might be a good idea her
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Introduction: This is a long and technical post on an important topic: the use of multilevel regression and poststratification (MRP) to estimate state-level public opinion. MRP as a research method, and state-level opinion (or, more generally, attitudes in demographic and geographic subpopulation) as a subject, have both become increasingly important in political science—and soon, I expect, will become increasingly important in other social sciences as well. Being able to estimate state-level opinion from national surveys is just such a powerful thing, that if it can be done, people will do it. It’s taken 15 years or so for the method to really catch on, but the ready availability of survey data and of computing power—as well as our increasing comfort level, as a profession, with these techniques, has made MRP become more of a routine research tool. As a method becomes used more and more widely, there will be natural concerns about its domains of applicability. That is the subject of the pres
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same-blog 1 0.97805285 1365 andrew gelman stats-2012-06-04-Question 25 of my final exam for Design and Analysis of Sample Surveys
Introduction: 25. You are using multilevel regression and poststratification (MRP) to a survey of 1500 people to estimate support for the space program, by state. The model is fit using, as a state- level predictor, the Republican presidential vote in the state, which turns out to have a low correlation with support for the space program. Which of the following statements are basically true? (Indicate all that apply.) (a) For small states, the MRP estimates will be determined almost entirely by the demo- graphic characteristics of the respondents in the sample from that state. (b) For small states, the MRP estimates will be determined almost entirely by the demographic characteristics of the population in that state. (c) Adding a predictor specifically for this model (for example, a measure of per-capita space-program spending in the state) could dramatically improve the estimates of state-level opinion. (d) It would not be appropriate to add a predictor such as per-capita space-program spen
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Introduction: This is a long and technical post on an important topic: the use of multilevel regression and poststratification (MRP) to estimate state-level public opinion. MRP as a research method, and state-level opinion (or, more generally, attitudes in demographic and geographic subpopulation) as a subject, have both become increasingly important in political science—and soon, I expect, will become increasingly important in other social sciences as well. Being able to estimate state-level opinion from national surveys is just such a powerful thing, that if it can be done, people will do it. It’s taken 15 years or so for the method to really catch on, but the ready availability of survey data and of computing power—as well as our increasing comfort level, as a profession, with these techniques, has made MRP become more of a routine research tool. As a method becomes used more and more widely, there will be natural concerns about its domains of applicability. That is the subject of the pres
3 0.81855279 2061 andrew gelman stats-2013-10-14-More on Mister P and how it does what it does
Introduction: Following up on our discussion the other day, Matt Buttice and Ben Highton write: It was nice to see our article mentioned and discussed by Andrew, Jeff Lax, Justin Phillips, and Yair Ghitza on Andrew’s blog in this post on Wednesday. As noted in the post, we recently published an article in Political Analysis on how well multilevel regression and poststratification (MRP) performs at producing estimates of state opinion with conventional national surveys where N≈1,500. Our central claims are that (i) the performance of MRP is highly variable, (ii) in the absence of knowing the true values, it is difficult to determine the quality of the MRP estimates produced on the basis of a single national sample, and, (iii) therefore, our views about the usefulness of MRP in instances where a researcher has a single sample of N≈1,500 are less optimistic than the ones expressed in previous research on the topic. Obviously we were interested in the blog posts. We found them stimulating
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Introduction: To recap: Matt Buttice and Ben Highton recently published an article where they evaluated multilevel regression and poststratification (MRP) on a bunch of political examples estimating state-level attitudes. My Columbia colleagues Jeff Lax, Justin Phillips, and Yair Ghitza added some discussion , giving a bunch of practical tips and pointing to some problems with Buttice and Highton’s evaluations. Buttice and Highton replied , emphasizing the difficulties of comparing methods in the absence of a known ground truth. And Jeff Lax added the following comment, which I think is a good overview of the discussion so far: In the back and forth between us all on details, some points may get lost and disagreements overstated. Where are things at this point? 1. Buttice and Highton (BH) show beyond previous work that MRP performance in making state estimates can vary to an extent that is not directly observable unless one knows the true estimates (in which case one would not be us
5 0.78170931 1367 andrew gelman stats-2012-06-05-Question 26 of my final exam for Design and Analysis of Sample Surveys
Introduction: 26. You have just graded an an exam with 28 questions and 15 students. You fit a logistic item- response model estimating ability, difficulty, and discrimination parameters. Which of the following statements are basically true? (Indicate all that apply.) (a) If a question is answered correctly by students with very low and very high ability, but is missed by students in the middle, it will have a high value for its discrimination parameter. (b) It is not possible to fit an item-response model when you have more questions than students. In order to fit the model, you either need to reduce the number of questions (for example, by discarding some questions or by putting together some questions into a combined score) or increase the number of students in the dataset. (c) To keep the model identified, you can set one of the difficulty parameters or one of the ability parameters to zero and set one of the discrimination parameters to 1. (d) If two students answer the same number of q
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Introduction: Stan 1.0.0 and RStan 1.0.0 It’s official. The Stan Development Team is happy to announce the first stable versions of Stan and RStan. What is (R)Stan? Stan is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. It’s sort of like BUGS, but with a different language for expressing models and a different sampler for sampling from their posteriors. RStan is the R interface to Stan. Stan Home Page Stan’s home page is: http://mc-stan.org/ It links everything you need to get started running Stan from the command line, from R, or from C++, including full step-by-step install instructions, a detailed user’s guide and reference manual for the modeling language, and tested ports of most of the BUGS examples. Peruse the Manual If you’d like to learn more, the Stan User’s Guide and Reference Manual is the place to start.
same-blog 3 0.90529728 1365 andrew gelman stats-2012-06-04-Question 25 of my final exam for Design and Analysis of Sample Surveys
Introduction: 25. You are using multilevel regression and poststratification (MRP) to a survey of 1500 people to estimate support for the space program, by state. The model is fit using, as a state- level predictor, the Republican presidential vote in the state, which turns out to have a low correlation with support for the space program. Which of the following statements are basically true? (Indicate all that apply.) (a) For small states, the MRP estimates will be determined almost entirely by the demo- graphic characteristics of the respondents in the sample from that state. (b) For small states, the MRP estimates will be determined almost entirely by the demographic characteristics of the population in that state. (c) Adding a predictor specifically for this model (for example, a measure of per-capita space-program spending in the state) could dramatically improve the estimates of state-level opinion. (d) It would not be appropriate to add a predictor such as per-capita space-program spen
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