CODEWITHSUNDEEP
rsurvey
jbryer
jbryer

Reputation: 1837

Analysis of complex survey design with multiple plausible values

I am working with several large databases (e.g. PISA and NAEP) that use a complex survey design with replicate weights and multiple plausible values. I can address the former using the survey package. However, does there exist an R package/function to analyze the latter?

For reference, I have found this article to provide a good overview of the issue: http://www.ierinstitute.org/fileadmin/Documents/IERI_Monograph/IERI_Monograph_Volume_02_Chapter_01.pdf

Upvotes: 2

Views: 2008

Answers (3)

Thomas Lumley
Thomas Lumley

Reputation: 186

As of survey version 3.36 there's withPV

data(pisamaths, package="mitools")
des<-svydesign(id=~SCHOOLID+STIDSTD, strata=~STRATUM, nest=TRUE,
    weights=~W_FSCHWT+condwt, data=pisamaths)

options(survey.lonely.psu="remove")

results<-withPV(list(maths~PV1MATH+PV2MATH+PV3MATH+PV4MATH+PV5MATH),
   data=des,
   action=quote(svyglm(maths~ST04Q01*(PCGIRLS+SMRATIO)+MATHEFF+OPENPS, design=des)))

summary(MIcombine(results))

Upvotes: 0

dacarras
dacarras

Reputation: 21

Daniel Caro develop an R package for large scale assessments. You can find it here http://cran.r-project.org/web/packages/intsvy/index.html

This is code example using the regression command, over the plausible values on Mathemathics:

## Not run:
# Table I.2.3a, p. 305, International Report 2012
pisa.reg.pv(pvlabel="MATH", x="ST04Q01", by = "IDCNTRYL", data=pisa)

Although, I'm not sure if this package can be used to analyze NAEP data. I hope this fulfill your purposes; at least partially.

Upvotes: 0

David F
David F

Reputation: 1536

I'm not sure how the general idea of 'plausible values' differs from using multiple imputation to generate several sets of imputed values (such as the the Amelia package does). But Thomas Lumley's mitools package can be used to combine the various sets of imputed values, and it might be the case that it can be used to combine your sets of plausible values to obtain the 'correct' standard errors of the estimates.

Upvotes: 3

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