Create a design for cluster random sampling — cluster_sampling

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.

Usage

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.
N_clusters_in_block: An integer or vector of integers of length N_blocks. Number of clusters in each block in the population.
N_i_in_cluster: An integer or vector of integers of length sum(N_clusters_in_block). Number of units per cluster sampled.
n_clusters_in_block: An integer. Number of clusters to sample in each block (stratum).
n_i_in_cluster: An integer. Number of units to sample in each cluster.
icc: A number in [0,1]. Intra-cluster Correlation Coefficient (ICC).
args_to_fix: 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)