Cluster sampling

Résumé

In statistics, cluster sampling is a sampling plan used when mutually homogeneous yet internally heterogeneous groupings are evident in a statistical population. It is often used in marketing research. In this sampling plan, the total population is divided into these groups (known as clusters) and a simple random sample of the groups is selected. The elements in each cluster are then sampled. If all elements in each sampled cluster are sampled, then this is referred to as a "one-stage" cluster sampling plan. If a simple random subsample of elements is selected within each of these groups, this is referred to as a "two-stage" cluster sampling plan. A common motivation for cluster sampling is to reduce the total number of interviews and costs given the desired accuracy. For a fixed sample size, the expected random error is smaller when most of the variation in the population is present internally within the groups, and not between the groups. The population within a cluster should ideally be as heterogeneous as possible, but there should be homogeneity between clusters. Each cluster should be a small-scale representation of the total population. The clusters should be mutually exclusive and collectively exhaustive. A random sampling technique is then used on any relevant clusters to choose which clusters to include in the study. In single-stage cluster sampling, all the elements from each of the selected clusters are sampled. In two-stage cluster sampling, a random sampling technique is applied to the elements from each of the selected clusters. The main difference between cluster sampling and stratified sampling is that in cluster sampling the cluster is treated as the sampling unit so sampling is done on a population of clusters (at least in the first stage). In stratified sampling, the sampling is done on elements within each stratum. In stratified sampling, a random sample is drawn from each of the strata, whereas in cluster sampling only the selected clusters are sampled.

Source officielle

https://en.wikipedia.org/wiki/Cluster_sampling

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (2)

Systematic analysis of low-affinity transcription factor binding site clusters in vitro and in vivo establishes their functional relevance

Sebastian Maerkl, Shiyu Cheng, Maria Lopez Malo, Amir Shahein, Ivan Istomin, Evan James Olson

Binding to binding site clusters has yet to be characterized in depth, and the functional relevance of low-affinity clusters remains uncertain. We characterized transcription factor binding to low-aff

NATURE PORTFOLIO2022

Multimodal person recognition in audio-visual streams

Do Hoang Nam Le

Multimedia databases are growing rapidly in size in the digital age. To increase the value of these data and to enhance the user experience, there is a need to make these videos searchable through aut

EPFL2019

Concepts associés (6)

Échantillonnage (statistiques)

thumb|Exemple d'échantillonnage aléatoire En statistique, l'échantillonnage désigne les méthodes de sélection d'un sous-ensemble d'individus (un échantillon) à l'intérieur d'une population pour estimer les caractéristiques de l'ensemble de la population. Cette méthode présente plusieurs avantages : une étude restreinte sur une partie de la population, un moindre coût, une collecte des données plus rapide que si l'étude avait été réalisé sur l'ensemble de la population, la réalisation de contrôles destructifs Les résultats obtenus constituent un échantillon.

Simple random sample

In statistics, a simple random sample (or SRS) is a subset of individuals (a sample) chosen from a larger set (a population) in which a subset of individuals are chosen randomly, all with the same probability. It is a process of selecting a sample in a random way. In SRS, each subset of k individuals has the same probability of being chosen for the sample as any other subset of k individuals. A simple random sample is an unbiased sampling technique. Simple random sampling is a basic type of sampling and can be a component of other more complex sampling methods.

Cluster sampling

Afficher plus

Cours associés (64)

PHYS-467: Machine learning for physicists

Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practi

EE-205: Signals and systems (for EL&IC)

This class teaches the theory of linear time-invariant (LTI) systems. These systems serve both as models of physical reality (such as the wireless channel) and as engineered systems (such as electrica

PHYS-758: Advanced Course on Quantum Communication

The aim of this doctoral course by Nicolas Sangouard is to lay the theoretical groundwork that is needed for students to understand how to take advantage of quantum effects for communication technolog

Afficher plus

Séances de cours associées (482)

Renseignements quantitatifs

Explore l'opérateur CHSH, l'auto-test, les eigenstates et la quantification du hasard dans les systèmes quantiques.

Coloriage graphique: théorie et applications

Couvre la théorie et les applications de la coloration graphique, en se concentrant sur les modèles de blocs stochastiques dissortatifs et la coloration plantée.

Boltzmann Machine

Introduit la machine Boltzmann, couvrant la cohérence d'attente, le regroupement des données, et les fonctions de distribution de probabilité.

Afficher plus

MOOCs associés (4)

Path Integral Methods in Atomistic Modelling

The course provides an introduction to the use of path integral methods in atomistic simulations. The path integral formalism allows to introduce quantum mechanical effects on the equilibrium and (ap

Systèmes d’Information Géographique 2

Ce cours constitue la seconde partie d'un enseignement consacré aux bases théoriques et pratiques des systèmes d’information géographique. Il propose une introduction aux systèmes d’information géogra

Systèmes d’Information Géographique 2

Afficher plus