Builds a cluster sampling design for an ordinal outcome variable for a population with N_blocks strata, each with N_clusters_in_block clusters, each of which contains N_i_in_cluster units. The sampling strategy involves sampling n_clusters_in_block clusters in each stratum, and then sampling n_i_in_cluster units in each cluster. Outcomes within clusters have intra-cluster correlation approximately equal to ICC.

cluster_sampling_designer(
N_blocks = 1,
N_clusters_in_block = 1000,
N_i_in_cluster = 50,
n_clusters_in_block = 100,
n_i_in_cluster = 10,
icc = 0.2,
args_to_fix = NULL
)

## Arguments

N_blocks An integer. Number of blocks (strata). Defaults to 1 for no blocks. An integer or vector of integers of length N_blocks. Number of clusters in each block in the population. An integer or vector of integers of length sum(N_clusters_in_block). Number of units per cluster sampled. An integer. Number of clusters to sample in each block (stratum). An integer. Number of units to sample in each cluster. A number in [0,1]. Intra-cluster Correlation Coefficient (ICC). A character vector. Names of arguments to be args_to_fix in design.

## Value

A stratified cluster sampling design.

## Details

Key limitations: The design assumes a args_to_fix number of clusters drawn in each stratum and a args_to_fix number of individuals drawn from each cluster.

See vignette online.

## Author

DeclareDesign Team

## Examples

# To make a design using default arguments:
cluster_sampling_design <- cluster_sampling_designer()
# A design with varying block size and varying cluster size
cluster_sampling_design <- cluster_sampling_designer(
N_blocks = 2, N_clusters_in_block = 6:7, N_i_in_cluster = 3:15,
n_clusters_in_block = 5,  n_i_in_cluster = 2)