workplace {robsurvey} | R Documentation |
(Modified) Canadian Workplace and Employee Survey
Description
The workplace
data are from Fuller (2009, pp. 366–367).
Usage
data(workplace)
Format
A data.frame
with a sample of 142 workplaces on the following
variables
ID
identifier variable
[integer]
.weight
sampling weight
[double]
.employment
employment total
[double]
.payroll
payroll total (1000 dollars)
[double]
.strat
stratum identifier
[integer]
.fpc
finite population correction
[integer]
.
Details
The workplace
data represent a sample of workplaces in the
retail sector in a Canadian province. The data are not those
collected by Statistics Canada, but have been generated by Fuller
(2009, Example 3.1.1) to display similar characteristics to the
original 1999 Canadian Workplace and Employee Survey (WES).
Sampling design of the 1999 WES
The WES target population is defined as all workplaces operating in Canada with paid employees. The sampling frame is stratified by industry, geographic region, and size (size is defined using estimated employment). A sample of workplaces has been drawn independently in each stratum using simple random sample without replacement (the stratum-specific sample sizes are determined by Neyman allocation). Several strata containing very large workplaces were sampled exhaustively; see Patak et al (1998). The original sampling weights were adjusted for nonresponse.
Remarks by Fuller (2009, p. 365)
The original weights of WES were about 2200 for the stratum of small workplaces, about 750 for medium-sized, and about 35 for large workspaces.
Source
The data workplace
is from Table 6.3 in Fuller (2009, pp. 366–367).
References
Fuller, W. A. (2009). Sampling Statistics, Hoboken (NJ): John Wiley and Sons. doi:10.1002/9780470523551
Patak, Z., Hidiroglou, M. and LavallĂ©e, P. (1998). The methodology of the Workplace and Employee Survey. Proceedings of the Survey Research Methods Section, American Statistical Association, 83–91.
Examples
head(workplace)
library("survey")
# Survey design for stratified simple random sampling without replacement
dn <- if (packageVersion("survey") >= "4.2") {
# survey design with pre-calibrated weights
svydesign(ids = ~ID, strata = ~strat, fpc = ~fpc, weights = ~weight,
data = workplace, calibrate.formula = ~-1 + strat)
} else {
# legacy mode
svydesign(ids = ~ID, strata = ~strat, fpc = ~fpc, weights = ~weight,
data = workplace)
}