Sample


Introduction 
Sampling the process of selecting a number of participants for a study in such a way that they represent the larger group from which they were selected.Sample comprises the individuals, items, or events selected from a larger group referred to as a population. Population is the group of interest to the researcher, the group to which the results of the study will ideally generalize.
Four basic random sampling techniques:
simple random sampling
stratified sampling
cluster sampling
systematic sampling

Simple Random Sampling 
Random sampling involves defining the population, identifying each member of the population, and selecting participants for the sample on a completely chance basis.Use a table of random numbers (table of random digits) selects the sample for you on a purely random, chance basis.
Selecting a sample involves the following steps:
Identify and define the population
Determine the desired sample size
List all members of the population
Assign all individuals on the list a consecutive number form zero; each individual must have the same amount of digits
Select an arbitrary number in the table (close eyes and point)
Look at only the number of digits assigned.
If a number corresponds to an individual then individual is in the sample
Go to the next number in the column and repeat steps 6 and 7.

Stratified Sampling 
Stratified Sampling is the process of selecting a sample in such a way that identified subgroups in the population are represented in the sample in the same proportion that they exist in the population.Subgroup or strata is a variable that can be divided into groups.Proportional stratified sampling would be appropriate if you were going to take a survey prior to a national election to predict the winner.
Selecting a sample involves the following steps:
Identify and define the population
Determine the desired sample size
Identify the variable and subgroups, you want to guarantee appropriate equal representation
Classify all members of the population as members of one of the identified subgroups.
Randomly select an appropriate number of people from each subgroup.

Cluster Sampling 
Cluster sampling is randomly selects groups, not individuals; all members of group have similar characteristics; most useful when population is very large or spread out over a wide geographic area.Cluster is any population where we find an intact group of similar characteristics .
Steps in Cluster Sampling:
Identify and define the population
Determine the desired sample size
Identify and define a logical cluster
List all clusters (or obtain list) that make up the population of clusters.
Estimate the average number of population members per cluster
Determine the number of clusters by dividing the sample size by the estimated size of cluster
Randomly select the needed number of clusters
Include in your study all population members in each selected cluster

Systematic Sampling 
Not used often. It is sampling in which individuals are selected from a list taking every kth name. Where k=size of the population divided by the desired sample size. For example 5000 500=10.
Steps in Cluster Sampling:
Identify and define the population
Determine the desired sample size
Obtain a list of the population
Determine what k is equal to
Start at some random place in the population list. Close your eyes and stick your finger on a name
Starting at that point, take every kth name on the list until the desired sample size is reached.
If the end is reached before the desired sample is reached, go back to the top of the list.
