n.MASC {ProbSamplingI}

Sample size using simple random sampling design without conglomerate replacement.

Description

The n.MASC function determines sample size using a simple random sampling design without replacement of Conglomerates.

Usage

n.MASC(N,NI,Ni,St,Emax.a,Nc=0.95,n.equal=TRUE)

# For clusters with equal sizes.
# n.MASC(NI,Ni,St,Emax.a,Nc)

# For clusters with different sizes.
# n.MASC(N,NI,St,Emax.a,Nc,n.equal=FALSE)

Arguments

N

Size of the population, this argument is only necessary if the size of the conglomerates is different.

NI

Number of clusters in the population.

Ni

Size of the clusters, this argument is only necessary if the conglomerates have equal size (constant size).

St

Standard deviation of conglomerate totals.

Emax.a

Absolute maximum error.

Nc

Confidence level (between 0 and 1) to be set.

n.equal

Logical value indicating whether clusters have the same size

Value

This function returns the sample size under the conditions set in the arguments, that is, the number of clusters to select.

Author(s)

Jorge Alberto Barón Cárdenas <[e-mail]>

Guillermo Martínez Flórez <[e-mail]>

References

Särndal, C. E., J. H. Wretman, and C. M. Cassel (1992). Foundations of Inference in Survey Sampling. Wiley New York.

Cochran, W. G. (1977). Sampling Techniques, 3ra ed. New York: Wiley.

Thompson, S. K. (1945). Wiley Series in Probability and Statistics, Sampling, 1ra ed. United States of America.

Examples

# Sample size for populations with clusters of equal size.

st<-sqrt(1417.8668)
NI<-2000
Ni<-6
e<-2
nc=0.9
n.MASC(St=st,NI=NI,Ni=Ni,Emax.a=e,Nc=nc)

# Sample size for populations with clusters of different sizes.

st=sqrt(2019760.760)
N<-11000
NI<-400
e=10
nc=0.95
n.MASC(St=st,N=N,NI=NI,Emax.a=e,Nc=nc,n.equal=FALSE)