Compute the Spencer design effect for single-stage samples selected with probability proportional to a measure of size.
deffS(p, w, y)
vector of 1-draw selection probabilities, i.e., the probability that each unit would be selected in a sample of size 1.
vector of inverses of selection probabilities for a sample
vector of the sample values of an analysis variable
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.
numeric design effect
Richard Valliant, Jill A. Dever, Frauke Kreuter
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.
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 <- n * 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.