Chapter 9

What are the rules regarding cluster sampling?

1. Cluster random samples cannot have crossover. 2. Cluster random samples must include all members of a population

What is the difference between cluster and stratified random sampling?

1. Stratified random samples must have an equal selection from each group that is proportionate to the population. Cluster sampling selection does not have to be equal; however, the clusters should be as close to the same size as possible. 2. Stratified random samples should not divide the population into more than six groups and are usually organized by demographic. Cluster sampling can be many groups and can be based on anything, including interests, hobbies, political views, geographical location, etc.

What is two-way cluster sampling?

A sampling method that involves separating the population into clusters, then selecting random samples from each of the clusters.

What is Central Limit Theorem (CLT)?

Given multiple samples taken from a population, the mean of those samples will converge on the actual population mean.

What are the different types of random sampling?

Simple random sampling: a type of random sampling where the variables have an equal and unsystematic chance of selection Stratified Random Sampling: a random sampling method where you divide members of a population into 'strata,' or homogeneous subgroups. Cluster sampling: where different groups within a population are used as a sample. Systematic Random Sampling: method that requires selecting samples based on a system of intervals in a numbered population.

What is required for the central limit theorem to hold true?

a sufficiently large sample size must be created. As a general rule, sample sizes of at least 30 are required.