PracTools (version 1.6)

deffS: Spencer design effect for an estimated total from a pps sample

Description

Compute the Spencer design effect for single-stage samples selected with probability proportional to a measure of size.

Usage

deffS(p, w, y)

Value

numeric design effect

Arguments

p

vector of 1-draw selection probabilities, i.e., the probability that each unit would be selected in a sample of size 1.

w

vector of inverses of selection probabilities for a sample

y

vector of the sample values of an analysis variable

Author

Richard Valliant, Jill A. Dever, Frauke Kreuter

Details

The Spencer design effect is the ratio of the variance of the pwr-estimator of the total of \(y\), assuming that a single-stage sample is selected with replacement, to the variance of the total estimated in srswr. Varying selection probabilities can be used.

References

Park, I., and Lee, H. (2004). Design Effects for the Weighted Mean and Total Estimators under Complex Survey Sampling. Survey Methodology, 30, 183-193.

Spencer, B. D. (2000). An Approximate Design Effect for Unequal Weighting When Measurements May Correlate With Selection Probabilities. Survey Methodology, 26, 137-138.

Valliant, R., Dever, J., Kreuter, F. (2018, chap. 14). Practical Tools for Designing and Weighting Survey Samples, 2nd edition. New York: Springer.

See Also

deff, deffCR, deffH, deffK

Examples

set.seed(-500398777)
    # generate population using HMT function
pop.dat <- as.data.frame(HMT())
mos <- pop.dat$x
pop.dat$prbs.1d <- mos / sum(mos)
    # select pps sample
require(sampling)
n <- 80
pk <- pop.dat$prbs.1d
sam <- UPrandomsystematic(pk)
sam <- sam==1
sam.dat <- pop.dat[sam, ]
dsgn.wts <- 1/pk[sam]
deffS(p=sam.dat$prbs.1d, w=dsgn.wts, y=sam.dat$y)