andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-608 knowledge-graph by maker-knowledge-mining
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Introduction: Vishnu Ganglani writes: It appears that multiple imputation appears to be the best way to impute missing data because of the more accurate quantification of variance. However, when imputing missing data for income values in national household surveys, would you recommend it would be practical to maintain the multiple datasets associated with multiple imputations, or a single imputation method would suffice. I have worked on household survey projects (in Scotland) and in the past gone with suggesting single methods for ease of implementation, but with the availability of open source R software I am think of performing multiple imputation methodologies, but a bit apprehensive because of the complexity and also the need to maintain multiple datasets (ease of implementation). My reply: In many applications I’ve just used a single random imputation to avoid the awkwardness of working with multiple datasets. But if there’s any concern, I’d recommend doing parallel analyses on multipl
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1 Vishnu Ganglani writes: It appears that multiple imputation appears to be the best way to impute missing data because of the more accurate quantification of variance. [sent-1, score-1.518]
2 However, when imputing missing data for income values in national household surveys, would you recommend it would be practical to maintain the multiple datasets associated with multiple imputations, or a single imputation method would suffice. [sent-2, score-2.817]
3 My reply: In many applications I’ve just used a single random imputation to avoid the awkwardness of working with multiple datasets. [sent-4, score-1.294]
4 But if there’s any concern, I’d recommend doing parallel analyses on multiple imputed datasets and then combining inferences at the end. [sent-5, score-1.254]
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Introduction: Vishnu Ganglani writes: It appears that multiple imputation appears to be the best way to impute missing data because of the more accurate quantification of variance. However, when imputing missing data for income values in national household surveys, would you recommend it would be practical to maintain the multiple datasets associated with multiple imputations, or a single imputation method would suffice. I have worked on household survey projects (in Scotland) and in the past gone with suggesting single methods for ease of implementation, but with the availability of open source R software I am think of performing multiple imputation methodologies, but a bit apprehensive because of the complexity and also the need to maintain multiple datasets (ease of implementation). My reply: In many applications I’ve just used a single random imputation to avoid the awkwardness of working with multiple datasets. But if there’s any concern, I’d recommend doing parallel analyses on multipl
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Introduction: Aureliano Crameri writes: I have questions regarding one technique you and your colleagues described in your papers: the cross validation (Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box, with reference to Gelman, King, and Liu, 1998). I think this is the technique I need for my purpose, but I am not sure I understand it right. I want to use the multiple imputation to estimate the outcome of psychotherapies based on longitudinal data. First I have to demonstrate that I am able to get unbiased estimates with the multiple imputation. The expected bias is the overestimation of the outcome of dropouts. I will test my imputation strategies by means of a series of simulations (delete values, impute, compare with the original). Due to the complexity of the statistical analyses I think I need at least 200 cases. Now I don’t have so many cases without any missings. My data have missing values in different variables. The proportion of missing values is
3 0.24531794 1344 andrew gelman stats-2012-05-25-Question 15 of my final exam for Design and Analysis of Sample Surveys
Introduction: 15. A researcher conducts a random-digit-dial survey of individuals and married couples. The design is as follows: if only one person lives in a household, he or she is interviewed. If there are multiple adults in the household, one is selected at random: he or she is interviewed and, if he or she is married to one of the other adults in the household, the spouse is interviewed as well. Come up with a scheme for inverse-probability weights (ignoring nonresponse and assuming there is exactly one phone line per household). Solution to question 14 From yesterday : 14. A public health survey of elderly Americans includes many questions, including “How many hours per week did you exercise in your most active years as a young adult?” and also several questions about current mobility and health status. Response rates are high for the questions about recent activities and status, but there is a lot of nonresponse for the question on past activity. You are considering imputing the mis
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Introduction: Majid Ezzati writes: My research group is increasingly focusing on a series of problems that involve data that either have missingness or measurements that may have bias/error. We have at times developed our own approaches to imputation (as simple as interpolating a missing unit and as sophisticated as a problem-specific Bayesian hierarchical model) and at other times, other groups impute the data. The outputs are being used to investigate the basic associations between pairs of variables, Xs and Ys, in regressions; we may or may not interpret these as causal. I am contacting colleagues with relevant expertise to suggest good references on whether having imputed X and/or Y in a subsequent regression is correct or if it could somehow lead to biased/spurious associations. Thinking about this, we can have at least the following situations (these could all be Bayesian or not): 1) X and Y both measured (perhaps with error) 2) Y imputed using some data and a model and X measur
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Introduction: Joe Northrup writes: I have a question about correcting for multiple comparisons in a Bayesian regression model. I believe I understand the argument in your 2012 paper in Journal of Research on Educational Effectiveness that when you have a hierarchical model there is shrinkage of estimates towards the group-level mean and thus there is no need to add any additional penalty to correct for multiple comparisons. In my case I do not have hierarchically structured data—i.e. I have only 1 observation per group but have a categorical variable with a large number of categories. Thus, I am fitting a simple multiple regression in a Bayesian framework. Would putting a strong, mean 0, multivariate normal prior on the betas in this model accomplish the same sort of shrinkage (it seems to me that it would) and do you believe this is a valid way to address criticism of multiple comparisons in this setting? My reply: Yes, I think this makes sense. One way to address concerns of multiple com
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Introduction: Vishnu Ganglani writes: It appears that multiple imputation appears to be the best way to impute missing data because of the more accurate quantification of variance. However, when imputing missing data for income values in national household surveys, would you recommend it would be practical to maintain the multiple datasets associated with multiple imputations, or a single imputation method would suffice. I have worked on household survey projects (in Scotland) and in the past gone with suggesting single methods for ease of implementation, but with the availability of open source R software I am think of performing multiple imputation methodologies, but a bit apprehensive because of the complexity and also the need to maintain multiple datasets (ease of implementation). My reply: In many applications I’ve just used a single random imputation to avoid the awkwardness of working with multiple datasets. But if there’s any concern, I’d recommend doing parallel analyses on multipl
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Introduction: Guy asks: I am analyzing an original survey of farmers in Uganda. I am hoping to use a battery of welfare proxy variables to create a single welfare index using PCA. I have quick question which I hope you can find time to address: How do you recommend treating count data? (for example # of rooms, # of chickens, # of cows, # of radios)? In my dataset these variables are highly skewed with many responses at zero (which makes taking the natural log problematic). In the case of # of cows or chickens several obs have values in the hundreds. My response: Here’s what we do in our mi package in R. We split a variable into two parts: an indicator for whether it is positive, and the positive part. That is, y = u*v. Then u is binary and can be modeled using logisitc regression, and v can be modeled on the log scale. At the end you can round to the nearest integer if you want to avoid fractional values.
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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
Introduction: Greg Kaplan writes: I noticed that you have blogged a little about interstate migration trends in the US, and thought that you might be interested in a new working paper of mine (joint with Sam Schulhofer-Wohl from the Minneapolis Fed) which I have attached. Briefly, we show that much of the recent reported drop in interstate migration is a statistical artifact: The Census Bureau made an undocumented change in its imputation procedures for missing data in 2006, and this change significantly reduced the number of imputed interstate moves. The change in imputation procedures — not any actual change in migration behavior — explains 90 percent of the reported decrease in interstate migration between the 2005 and 2006 Current Population Surveys, and 42 percent of the decrease between 2000 and 2010. I haven’t had a chance to give a serious look so could only make the quick suggestion to make the graphs smaller and put multiple graphs on a page, This would allow the reader to bett
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