similar to: weighting (survey) data

I'm about to add weights to a bus on-board survey dataset with ~150 variables and ~28,000 records. My intention is to weight (for each bus "run") by boarding stop and alighting stop. I've seen the Rake function of the Survey package, but it seems that converting to a "svydesign" might be excessive for my purpose. My dataset has a huge number of unique

Hi all, I have data collected from a survey administered on a subset of the population. I also have the population proportions of variables such as gender, race and housing type. I would like to combine the weights from each separate cross tab (of gender, race and housing type) such that the weighted proportions of my survey data matches that of the population. I have tried the following:

I have survey data that I am working on. I need to make some multi-way tables and regression analyses on the data. After attaching the data, this is the code I use for tables for four variables (sweight is the weight variable): > a <- xtabs(sweight~research.area + gender + a2n2 + age) > tmp <- ftable(a) Is this correct? I don't think I need to use the strata and cluster

Hello, I have a data set containing categorical and ordinal factors, as well as sampling weights (i.e., survey weights reflecting unequal probabilities of selection). I want to fit a structural equation model with sem(). I have run sem() on weighted covariance matrices using advice from John Fox (see <http://tolstoy.newcastle.edu.au/R/e5/help/08/12/8773.html> and

First the R question. I have the results of a rather large survey (thousands of forms, each with dozens of questions) with some existing weights and expansion factors. I wish to add additional weighting factors, based on new information that elements of certain variables should appear in certain proportions. Where should I look in R to develop what is essentially an N-dimensional balancing

I have the final sample weight (expansion factor) from a socieconomic survey. I don't know the exact design used in the study ( (probably is a stratified two-stage design). To illustrate my problem I will use the next dataset which have a sample weight (but the design is not specified) and incorporate the design with svydesign and create some bootstrap replicates in order to be able to

---------- Forwarded message ---------- Hello, I am analyzing a weighted, stratified, clustered survey data set with approximately 1 million observations and 50 variables. I am new to R (I'm a Stata user), and so far couldn't find any documentation on how to handle survey data. In other words, is there a specific package to handle a combination of weigths, clusters and strata. I am also

hi, probably not.. if your survey dataset has a complex design (like clusters/strata), you need to include them in the `svydesign` call. coercing an incorrect survey design into a replicate-weighted design will not fix the problem of failing to account for the sampling strategy On Mon, Apr 4, 2016 at 12:01 AM, Jos? Fernando Zea <jfzeac at gmail.com> wrote: > I have the final sample

I am trying to perform cluster analysis on survey data where each respondent has answered several questions, some of which have categorical answers ("blue" "pink" "green" etc) and some of which have scale answers (rating from 1 to 10 etc).My problem is that certain age groups were over-sampled and I need to weight the data collected in order to accurately reflect the

Hi R users, I am trying to use the aggregate function with a survey design object and survey functions, but get the following error. I think I am incorrectly using the syntax somehow, and it may not be possible to access variables directly by name in a survey-design object. Am I right? How do I fix this problem? I have used aggregate with "mean" and "weighted.mean", and

Hi, I am trying to fit a model with an ordered response variable (3 levels) and 13 predictor variables. The sample has complex survey design and I've used 'svydesign' command from the survey package to specify the sampling design. After reading the manual of 'svyglm' command, I've found that you can fit a logistic regression (binary response variable) by specifying the

Dear R-help, Let me know if I should email r-devel instead of this list. This message is addressed to Professor Lumley or anyone familiar with the survey package. Does svycoxph() implement the method outlined in Binder 1992 as referenced in the help file? That is, are weights incorporated in the ratio term (numerator and denominator) of the estimating equation? I don't believe so since

I have about 27,000 survey responses from across about 150 Bus Routes, each with potentially 100 stops. I've recorded the total Ons and Offs for each stop on each bus run, as well as the stop pair each survey response corresponds to. I wish to create weights based on the On and Off stop for each line and direction. This will create a very sparse "half table" (observations by

Afternoon R help, I want to run Rasch/IRT analyses using the ltm package, however, I am using large scale survey data which requires weighting for accurate results. I attempted to create a weighted object to insert into the formulae of the ltm packages, however, the survey data only includes 30 replicate weights and a sampling weight. The svrepdesign requires additional information such as

Hello, I'm using a complex survey dataset and my goal is to simply spit out a bunch of probability-weighted outcome variable means for the different levels of covariate. So I first define the structure of the study design (I'm using the CDC's NHANES data): dhanes <- svydesign(id=~PSU, strat=~STRATA, weight=~lab_weight, data=final, nest=TRUE) No problem there. Now I use the

Hi all, I've been trying to get a large (12mb) Stata survey database into R. I managed that, but when I attach survey weights, something goes wrong. The error message is: object dchina not found. Here's the script: library(car) library(foreign) library(survey) China <- read.dta("C:/final07c2.dta") attach(China) data(China)

>> Hi, I am trying to draw a random sample from an household survey with sample weight. Is there any function in R or Splus which allows this. Regards, ******************************************************* Mehtabul Azam Department of Economics Southern Methodist University Dallas TX 75275-0496 Tel: (214) 214 938 3906 Email: mazam at smu.edu <mailto:mazam at smu.edu>

I'm trying to learn how to calibrate/postStratify/rake survey data in preparation for a large survey effort we're about to embark upon. As a working example, I have results from a small survey of ~650 respondents, ~90 response fields each. I'm trying to learn how to (properly?) apply the aforementioned functions. My data are from a bus on board survey. The expansion in the

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Hi R-users, Can anyone tell me where can i find info about they way how post stratification weights are calculated when i have an already stratified survey design, especially in Survey Package (but any theoretical material would do me just fine) ? Thank you and have a nice day! --------------------------------- [[alternative HTML version deleted]]