andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-627 knowledge-graph by maker-knowledge-mining
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Introduction: Sam Stroope writes: I’m creating county-level averages based on individual-level respondents. My question is, how few respondents are reasonable to use when calculating the average by county? My end model will be a county-level (only) SEM model. My reply: Any number of respondents should work. If you have very few respondents, you should just end up with large standard errors which will propagate through your analysis. P.S. I must have deleted my original reply by accident so I reconstructed something above.
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1 Sam Stroope writes: I’m creating county-level averages based on individual-level respondents. [sent-1, score-0.456]
2 My question is, how few respondents are reasonable to use when calculating the average by county? [sent-2, score-1.067]
3 My end model will be a county-level (only) SEM model. [sent-3, score-0.276]
4 My reply: Any number of respondents should work. [sent-4, score-0.586]
5 If you have very few respondents, you should just end up with large standard errors which will propagate through your analysis. [sent-5, score-0.817]
6 I must have deleted my original reply by accident so I reconstructed something above. [sent-8, score-1.228]
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same-blog 1 1.0000001 627 andrew gelman stats-2011-03-24-How few respondents are reasonable to use when calculating the average by county?
Introduction: Sam Stroope writes: I’m creating county-level averages based on individual-level respondents. My question is, how few respondents are reasonable to use when calculating the average by county? My end model will be a county-level (only) SEM model. My reply: Any number of respondents should work. If you have very few respondents, you should just end up with large standard errors which will propagate through your analysis. P.S. I must have deleted my original reply by accident so I reconstructed something above.
2 0.13716072 1437 andrew gelman stats-2012-07-31-Paying survey respondents
Introduction: I agree with Casey Mulligan that participants in government surveys should be paid, and I think it should be part of the code of ethics for commercial pollsters to compensate their respondents also. As Mulligan points out, if a survey is worth doing, it should be worth compensating the participants for their time and effort. P.S. Just to clarify, I do not recommend that Census surveys be made voluntary, I just think that respondents (who can be required to participate) should be paid a small amount. P.P.S. More rant here .
3 0.1204676 1356 andrew gelman stats-2012-05-31-Question 21 of my final exam for Design and Analysis of Sample Surveys
Introduction: 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 to question 20 From yesterday : 20. Explain in two sentences why we expect survey respondents to be honest about vote preferences but possibly dishonest about reporting unhealty behaviors. Solution: Respondents tend to be sincere about vote preferences because this affects the outcome of the poll, and people are motivated to have their candidate poll well. This motivation is typically not present in reporting behaviors; you have no particular reason for wanting to affect the average survey response.
Introduction: A reporter emailed me the other day with a question about a case I’d never heard of before, a company called Herbalife that is being accused of being a pyramid scheme. The reporter pointed me to this document which describes a survey conducted by “a third party firm called Lieberman Research”: Two independent studies took place using real time (aka “river”) sampling, in which respondents were intercepted across a wide array of websites Sample size of 2,000 adults 18+ matched to U.S. census on age, gender, income, region and ethnicity “River sampling” in this case appears to mean, according to the reporter, that “people were invited into it through online ads.” The survey found that 5% of U.S. households had purchased Herbalife products during the past three months (with a “0.8% margin of error,” ha ha ha). They they did a multiplication and a division to estimate that only 8% of households who bought these products were Herbalife distributors: 480,000 active distributor
5 0.097464636 381 andrew gelman stats-2010-10-30-Sorry, Senator DeMint: Most Americans Don’t Want to Ban Gays from the Classroom
Introduction: Justin Phillips placed some questions on the YouGov Model Politics poll and reports the following: Early this month, Senator Jim DeMint (R-South Carolina) angered gay rights organizations when he said that openly gay people (along with sexually active unmarried women) shouldn’t be teaching in the classroom. This comment was originally reported in the Spartanberg Herald-Journal and subsequently covered by a variety of national media outlets including CBS News. The Senator justified his comments by suggesting that his beliefs are shared by many Americans. DeMint told the Herald Journal “[When I said those things] no one came to my defense. But everyone would come to me and whisper that I shouldn’t back down. They don’t want government purging their rights and their freedom to religion.” So is the Senator correct? Do Americans want openly gay men and women out of the classroom? . . . Most Americans do not share Senator DeMint’s views. Our survey shows that a large majorit
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same-blog 1 0.96638966 627 andrew gelman stats-2011-03-24-How few respondents are reasonable to use when calculating the average by county?
Introduction: Sam Stroope writes: I’m creating county-level averages based on individual-level respondents. My question is, how few respondents are reasonable to use when calculating the average by county? My end model will be a county-level (only) SEM model. My reply: Any number of respondents should work. If you have very few respondents, you should just end up with large standard errors which will propagate through your analysis. P.S. I must have deleted my original reply by accident so I reconstructed something above.
2 0.75845236 1218 andrew gelman stats-2012-03-18-Check your missing-data imputations using cross-validation
Introduction: Elena Grewal writes: I am currently using the iterative regression imputation model as implemented in the Stata ICE package. I am using data from a survey of about 90,000 students in 142 schools and my variable of interest is parent level of education. I want only this variable to be imputed with as little bias as possible as I am not using any other variable. So I scoured the survey for every variable I thought could possibly predict parent education. The main variable I found is parent occupation, which explains about 35% of the variance in parent education for the students with complete data on both. I then include the 20 other variables I found in the survey in a regression predicting parent education, which explains about 40% of the variance in parent education for students with complete data on all the variables. My question is this: many of the other variables I found have more missing values than the parent education variable, and also, although statistically significant
3 0.71092165 1294 andrew gelman stats-2012-05-01-Modeling y = a + b + c
Introduction: Brandon Behlendorf writes: I [Behlendorf] am replicating some previous research using OLS [he's talking about what we call "linear regression"---ed.] to regress a logged rate (to reduce skew) of Y on a number of predictors (Xs). Y is the count of a phenomena divided by the population of the unit of the analysis. The problem that I am encountering is that Y is composite count of a number of distinct phenomena [A+B+C], and these phenomena are not uniformly distributed across the sample. Most of the research in this area has conducted regressions either with Y or with individual phenomena [A or B or C] as the dependent variable. Yet it seems that if [A, B, C] are not uniformly distributed across the sample of units in the same proportion, then the use of Y would be biased, since as a count of [A+B+C] divided by the population, it would treat as equivalent units both [2+0.5+1.5] and [4+0+0]. My goal is trying to find a methodology which allows a researcher to regress Y on a
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Introduction: A couple years ago Rod Little was invited to write an article for the diamond jubilee of the Calcutta Statistical Association Bulletin. His article was published with discussions from Danny Pfefferman, J. N. K. Rao, Don Rubin, and myself. Here it all is . I’ll paste my discussion below, but it’s worth reading the others’ perspectives too. Especially the part in Rod’s rejoinder where he points out a mistake I made. Survey weights, like sausage and legislation, are designed and best appreciated by those who are placed a respectable distance from their manufacture. For those of us working inside the factory, vigorous discussion of methods is appreciated. I enjoyed Rod Little’s review of the connections between modeling and survey weighting and have just a few comments. I like Little’s discussion of model-based shrinkage of post-stratum averages, which, as he notes, can be seen to correspond to shrinkage of weights. I would only add one thing to his formula at the end of his
5 0.67238814 1981 andrew gelman stats-2013-08-14-The robust beauty of improper linear models in decision making
Introduction: Andreas Graefe writes (see here here here ): The usual procedure for developing linear models to predict any kind of target variable is to identify a subset of most important predictors and to estimate weights that provide the best possible solution for a given sample. The resulting “optimally” weighted linear composite is then used when predicting new data. This approach is useful in situations with large and reliable datasets and few predictor variables. However, a large body of analytical and empirical evidence since the 1970s shows that the weighting of variables is of little, if any, value in situations with small and noisy datasets and a large number of predictor variables. In such situations, including all relevant variables is more important than their weighting. These findings have yet to impact many fields. This study uses data from nine established U.S. election-forecasting models whose forecasts are regularly published in academic journals to demonstrate the value o
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1 0.95379347 1820 andrew gelman stats-2013-04-23-Foundation for Open Access Statistics
Introduction: Now here’s a foundation I (Bob) can get behind: Foundation for Open Access Statistics (FOAS) Their mission is to “promote free software, open access publishing, and reproducible research in statistics.” To me, that’s like supporting motherhood and apple pie ! FOAS spun out of and is partially designed to support the Journal of Statistical Software (aka JSS , aka JStatSoft ). I adore JSS because it (a) is open access, (b) publishes systems papers on statistical software, (c) has fast reviewing turnaround times, and (d) is free for authors and readers. One of the next items on my to-do list is to write up the Stan modeling language and submit it to JSS . As a not-for-profit with no visible source of income, they are quite sensibly asking for donations (don’t complain — it beats $3K author fees or not being able to read papers).
Introduction: Ben Hyde sends along this : Stuck in the middle of the supplemental data, reporting the total workup for their compounds, was this gem: Emma, please insert NMR data here! where are they? and for this compound, just make up an elemental analysis . . . I’m reminded of our recent discussions of coauthorship, where I argued that I see real advantages to having multiple people taking responsibility for the result. Jay Verkuilen responded: “On the flipside of collaboration . . . is diffusion of responsibility, where everybody thinks someone else ‘has that problem’ and thus things don’t get solved.” That’s what seems to have happened (hilariously) here.
3 0.93109697 1862 andrew gelman stats-2013-05-18-uuuuuuuuuuuuugly
Introduction: Hamdan Azhar writes: I came across this graphic of vaccine-attributed decreases in mortality and was curious if you found it as unattractive and unintuitive as I did. Hope all is well with you! My reply: All’s well with me. And yes, that’s one horrible graph. It has all the problems with a bad infographic with none of the virtues. Compared to this monstrosity, the typical USA Today graph is a stunning, beautiful masterpiece. I don’t think I want to soil this webpage with the image. In fact, I don’t even want to link to it.
same-blog 4 0.93050694 627 andrew gelman stats-2011-03-24-How few respondents are reasonable to use when calculating the average by county?
Introduction: Sam Stroope writes: I’m creating county-level averages based on individual-level respondents. My question is, how few respondents are reasonable to use when calculating the average by county? My end model will be a county-level (only) SEM model. My reply: Any number of respondents should work. If you have very few respondents, you should just end up with large standard errors which will propagate through your analysis. P.S. I must have deleted my original reply by accident so I reconstructed something above.
5 0.92490125 12 andrew gelman stats-2010-04-30-More on problems with surveys estimating deaths in war zones
Introduction: Andrew Mack writes: There was a brief commentary from the Benetech folk on the Human Security Report Project’s, “The Shrinking Costs of War” report on your blog in January. But the report has since generated a lot of public controversy . Since the report–like the current discussion in your blog on Mike Spagat’s new paper on Iraq–deals with controversies generated by survey-based excess death estimates, we thought your readers might be interested. Our responses to the debate were posted on our website last week. “Shrinking Costs” had discussed the dramatic decline in death tolls from wartime violence since the end of World War II –and its causes. We also argued that deaths from war-exacerbated disease and malnutrition had declined. (The exec. summary is here .) One of the most striking findings was that mortality rates (we used under-five mortality data) decline during most wars. Indeed our latest research indicates that of the total number of years that countries w
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