epiR (version 1.0-15)

epi.ssclus1estc: Sample size to estimate a continuous outcome using one-stage cluster sampling

Description

Sample size to estimate a continuous outcome using one-stage cluster sampling.

Usage

epi.ssclus1estc(b, N, xbar, xsigma, epsilon.r, rho, conf.level = 0.95)

Arguments

b

scalar integer or vector of length two, the number of individual listing units in each cluster to be sampled. See details, below.

N

scalar integer, representing the total number of individual listing units in the population.

xbar

scalar number, the expected mean of the continuous variable to be estimated.

xsigma

scalar number, the expected standard deviation of the continuous variable to be estimated.

epsilon.r

scalar number, the maximum relative difference between the estimate and the unknown population value.

rho

scalar number, the intracluster correlation.

conf.level

scalar number, the level of confidence in the computed result.

Value

A list containing the following:

n.psu

the total number of primary sampling units (clusters) to be sampled for the specified level of confidence and relative error.

n.ssu

the total number of secondary sampling units to be sampled for the specified level of confidence and relative error.

DEF

the design effect.

rho

the intracluster correlation, as entered by the user.

Details

b as a scalar integer represents the total number of individual listing units from each cluster to be sampled. If b is a vector of length two the first element represents the mean number of individual listing units to be sampled from each cluster and the second element represents the standard deviation of the number of individual listing units to be sampled from each cluster.

References

Levy PS, Lemeshow S (1999). Sampling of Populations Methods and Applications. Wiley Series in Probability and Statistics, London, pp. 258.

Machin D, Campbell MJ, Tan SB, Tan SH (2018). Sample Sizes for Clinical, Laboratory ad Epidemiological Studies, Fourth Edition. Wiley Blackwell, London, pp. 195 - 214.

Examples

# NOT RUN {
## A survey to estimate the average number of residents over 75 years of 
## age that require the services of a nurse in a given retirement village is 
## to be carried out using a one-stage cluster sampling strategy. 
## There are five housing complexes in the village with 25 residents in each.
## We expect that there might be an average of 34 residents meeting this 
## criteria (SD 5.5). We would like the estimated sample size to provide us 
## with an estimate that is within 10% of the true value. Previous studies 
## report an intracluster correlation for the number of residents requiring the 
## services of a nurse in this retirement village housing complexes to 
## be 0.10. How many housing complexes (clusters) should be sampled?

epi.ssclus1estc(b = 25, N = 5 * 25, xbar = 34, xsigma = 5.5, 
   epsilon.r = 0.10, rho = 0.10, conf.level = 0.95)

## A total of 2 housing complexes need to be sampled to meet the specifications 
## of this study.

# }