Description Usage Arguments Details Value Author(s) References See Also Examples

Bethel procedure (1989) allows to determine total sample size and allocation of units in strata, so to minimize costs under the constraints of defined precision levels of estimates (coefficient of variation: CV), in the multivariate case (more than one estimate).Input to this algorithm is given by the information on distributional characteristics (total and variance) of target variables in the population strata.

1 | ```
bth(S, T, eps = 1e-10)
``` |

`S` |
A dataframe or a matrix with strata, variances, population size and minumum sample size. See details below. |

`T` |
A dataframe or a matrix with precision levels (coefficient of variation: CV) and totals. See details below. |

`eps` |
The level of precision for the algorithm convergence. The default is 1e-10. |

The Bethel algorithm allows the calculation of the sample size (in each strata) in the
case of a multivariate and stratified population. The input of the procedure consists of
two dataframe, respectively, S and T.

S is composed by a minimum of 6 columns (ncol(S) > = 6),
suppose ncol(S) = k. The first column shows the strata labels.
The k-th column shows the minimum sample rate for each strata, such
as 0.04 if the sample will consist of at least 4% in each strata. Similarly, the (k-1)-th
column contains the absolute minimum sample size (for example the value 3 if the each strata
has to be composed at least of 3 sample units). The (k-2)-th column shows the unit cost per
interview(in each strata). Generally this value is equal to 1 to indicate the same cost
in all strata. The (k-3)-th column gives the size of the population in each strata. Finally, the
estimated variances for the k-5 observed variables are shown in columns from the second to (k-6)-th.
See the example below.

T is composed of 2 columns. The first column shows the coefficients of
variation (CV) for k-6 variables analyzed (for example, CV = 0.05 for each variable). The second column
shows the estimated totals for the same k-6 variables. See the example below.

`B` |
The dataframe with the Bethel sample size (bethelNum) and the minimum sample size (bethelNum2). If the sample size (in a generic strata) according the Bethel algorithm is equal to 2, then bethelNum will be equal to 2, but bethelNum2 will be 3 if, for example, 3 is the minimum sample size specified in the (k-1)-th column of S. See the example below. |

Michele De Meo [email protected]

Bethel, J.W. (1989), *Sample Allocation in Multivariate Surveys*. Survey Methodology, Vol. 15,
pp. 47-57.

Chromy, J. B. (1987), *Design Optimization With Multiple Objectives*. Proceedings of the Section on
Survey Research Methods, 1987. American Statistical Association, pp. 194-199.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | ```
#Given a population of 1000 individuals (dataframe pop)
#classified according to sex and geographic area, we have collected
#yarly data on the following variables: income, number of books read,
#total days of sporting activities. To run a survey and to obtain
#the total estimates of these 3 variables (total income,total number
#of book, total number of days) we calculate the sample size to obtain,
#for example, a precision level (coefficient of variation) of 0.05.
library(bethel)
data(pop)
attach(pop)
str(pop)
#Calculate the dataframe with:
##- strata labels
##- estimated variances
##- number of population units
b1<-as.data.frame(cbind(var_Income=tapply(income,strata,var),
var_books=tapply(books,strata,var),
var_days=tapply(sportDays,strata,var),
num_units=tapply(sportDays,strata,length)))
b1<-cbind(strata=row.names(b1),b1)
row.names(b1)<-NULL
#Add 3 columns:
##- unit cost per interview
##- minimum sample size n/N (where N is the population size)
##- minimum sample size n
b1<-cbind(b1, c=rep(1,8), n=rep(3,8), n_2=rep(0.04,8))
#Calculate dataframe with:
##- precision levels (coefficients of variation)
##- total estimates
b2<-as.data.frame(cbind(CV=rep(0.05,3), tot=colSums(pop[,2:4])))
#Bethel sample according to a precision level (CV) of 0.05
bth(b1,b2)
#Bethel sample according to different precision level (CV)
b2<-as.data.frame(cbind(CV=c(0.05,0.01,0.2), tot=colSums(pop[,2:4])))
bth(b1,b2)
``` |

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