epiR (version 1.0-15)

epi.sssimpleestc: Sample size to estimate a continuous outcome using simple random sampling

Description

Sample size to estimate a continuous outcome using simple random sampling.

Usage

epi.sssimpleestc(N = 1E+06, xbar, sigma, epsilon.r, conf.level = 0.95)

Arguments

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.

sigma

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.

conf.level

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

Value

Returns an integer defining the required sample size.

References

Levy PS, Lemeshow S (1999). Sampling of Populations Methods and Applications. Wiley Series in Probability and Statistics, London, pp. 70 - 75.

Scheaffer RL, Mendenhall W, Lyman Ott R (1996). Elementary Survey Sampling. Duxbury Press, New York, pp. 95.

Otte J, Gumm I (1997). Intra-cluster correlation coefficients of 20 infections calculated from the results of cluster-sample surveys. Preventive Veterinary Medicine 31: 147 - 150.

Examples

# NOT RUN {
## EXAMPLE 1:
## A city contains 20 neighbourhood health clinics and it is desired to take a 
## sample of clinics to estimate the total number of persons from all these 
## clinics who have been given, during the past 12 month period, prescriptions 
## for a recently approved antidepressant. If we assume that the average number 
## of people seen at these clinics is 1500 per year with the standard deviation 
## equal to 300, and that approximately 5% of patients (regardless of clinic) 
## are given this drug, how many clinics need to be sampled to yield an estimate 
## that is within 20% of the true population value?

pmean <- 1500 * 0.05; psigma <- (300 * 0.05)
epi.sssimpleestc(N = 20, xbar = pmean, sigma = psigma, epsilon.r = 0.20, 
   conf.level = 0.95)

## Three clinics need to be sampled to meet the requirements of the survey. 

## EXAMPLE 2:
## We want to estimate the mean bodyweight of deer on a farm. There are 278
## animals present. We anticipate the mean body weight to be around 200 kg
## and the standard deviation of body weight to be 30 kg. We would like to
## be 95% certain that our estimate is within 10 kg of the true mean. How
## many deer should be sampled?

epi.sssimpleestc(N = 278, xbar = 200, sigma = 30, epsilon.r = 10 / 200, 
   conf.level = 0.95)

## A total of 31 deer need to be sampled to meet the requirements of the survey.
# }