# Create a design for cluster random 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`

.

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_i_in_cluster | An integer or vector of integers of length |

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.

## 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)