# Difference Between Simple Random Sampling And Stratified Random Sampling Pdf

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- Cluster sampling
- Simple Random vs. Stratified Random Sample: What's the Difference?
- Probability Sampling
- Stratified Random Sampling: Definition, Method and Examples

## Cluster sampling

The main difference between stratified sampling and cluster sampling is that with cluster sampling , you have natural groups separating your population. For example, you might be able to divide your data into natural groupings like city blocks, voting districts or school districts. The main difference between stratified sampling and quota sampling is in the sampling method:. As a very simple example, let's say you're using the sample group of people yellow, red, and blue heads for your quota sample. The top level of people is much closer, geographically to your location. Therefore, it would be cheaper for your study to use that top layer.

In statistical analysis, the " population " is the total set of observations or data that exists. However, it is often unfeasible to measure every individual or data point in a population. Instead, researchers rely on samples. A sample is a set of observations from the population. The sampling method is the process used to pull samples from the population. Simple random samples and stratified random samples are both common methods for obtaining a sample.

A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. Humans have long practiced various forms of random selection, such as picking a name out of a hat, or choosing the short straw. These days, we tend to use computers as the mechanism for generating random numbers as the basis for random selection. Before I can explain the various probability methods we have to define some basic terms.

## Simple Random vs. Stratified Random Sample: What's the Difference?

Metrics details. Most studies among Hispanics have focused on individual risk factors of obesity, with less attention on interpersonal, community and environmental determinants. Conducting community based surveys to study these determinants must ensure representativeness of disparate populations. We describe the use of a novel Geographic Information System GIS -based population based sampling to minimize selection bias in a rural community based study. We conducted a community based survey to collect and examine social determinants of health and their association with obesity prevalence among a sample of Hispanics and non-Hispanic whites living in a rural community in the Southeastern United States. To ensure a balanced sample of both ethnic groups, we designed an area stratified random sampling procedure involving three stages: 1 division of the sampling area into non-overlapping strata based on Hispanic household proportion using GIS software; 2 random selection of the designated number of Census blocks from each stratum; and 3 random selection of the designated number of housing units i.

Simple random and stratified random sampling are both sampling techniques used by analysts during statistical analyses. Simple random sampling involves selecting a sample from the entire population such that each member or element of the population has an equal probability of being picked. The method attempts to come up with a sample that represents the population in an unbiased manner. However, it is not appropriate when there are glaring differences within the population such that statisticians can divide the members into different, distinctive categories. In stratified random sampling, analysts subdivide the population into separate groups known as strata singular — stratum. Each stratum is composed of elements that have a common characteristic attribute that distinguishes them from all the others. The method is most appropriate for large populations that are heterogeneous in nature.

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.

## Probability Sampling

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### Stratified Random Sampling: Definition, Method and Examples

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## 5 Comments

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Eglantine C.Simple random samples and stratified random samples are both common methods for obtaining a sample. A simple random sample is used to represent the entire data population and. A stratified random sample, on the other hand, first divides the population into smaller groups, or strata, based on shared characteristics.

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