get_sampling_clusters

get_sampling_list

Select sampling clusters using systematic sampling

In the recent past, measurement of coverage has been mainly through
two-stage cluster sampled surveys either as part of a nutrition assessment
or through a specific coverage survey known as Centric Systematic Area
Sampling (CSAS). However, such methods are resource intensive and often only
used for final programme evaluation meaning results arrive too late for
programme adaptation. SLEAC, which stands for Simplified Lot Quality
Assurance Sampling Evaluation of Access and Coverage, is a low resource
method designed specifically to address this limitation and is used
regularly for monitoring, planning and importantly, timely improvement to
programme quality, both for agency and Ministry of Health (MoH) led
programmes. SLEAC is designed to complement the Semi-quantitative Evaluation
of Access and Coverage (SQUEAC) method. This package provides functions for
use in conducting a SLEAC assessment.

Ernest Guevarra

sleacr

Simplified Lot Quality Assurance Sampling Evaluation of Access
and Coverage (SLEAC) Tools

Mark Myatt

get_sampling_clusters function

<dl><dt>N_clusters</dt>
<dd>Total number of clusters in survey area.</dd>
<dt>n_clusters</dt>
<dd>Number of sampling clusters to be selected.</dd>
<dt>cluster_list</dt>
<dd>A data.frame containing at least the name or any other
identifier for the entire set of clusters to sample from.</dd></dl>

Arguments

Select sampling clusters using systematic sampling — get_sampling_clusters

<dl>

<dt>N_clusters</dt>
<dd>Total number of clusters in survey area.</dd>


<dt>n_clusters</dt>
<dd>Number of sampling clusters to be selected.</dd>


<dt>cluster_list</dt>
<dd>A data.frame containing at least the name or any other
identifier for the entire set of clusters to sample from.</dd>

</dl>

get_sampling_clusters: Select sampling clusters using systematic sampling

Description

Usage

Value

Arguments

Examples