Sampling Method -Random Vs Nonrandom Sampling-

Sampling Methods

1) Probability Sampling Method

2) Non-Probability Sampling Method

Probability Sampling Method

1) A simple random sample

A simple random sample is obtained by choosing elementary units in search a way that each unit in the population has an equal chance of being selected. A simple random sample is free from sampling bias. However, using a random number table to choose the elementary units can be cumbersome. If the sample is to be collected by a person untrained in statistics, then instructions may be misinterpreted and selections may be made improperly. Instead of using a least of random numbers, data collection can be simplified by selecting say every 10th or 100th unit after the first unit has been chosen randomly. Such a procedure is called systematic random sampling.The larger a random sample is in size, the more likely it is to represent the population. 

How to use a random number table.

1. Let's assume that we have a population of 185 students and each student has been assigned a number from 1 to 185. Suppose we wish to sample 5 students (although we would normally sample more, we will use 5 for this example).
2. Since we have a population of 185 and 185 is a three digit number, we need to use the first three digits of the numbers listed on the chart.
3. We close our eyes and randomly point to a spot on the chart. For this example, we will assume that we selected 20631 in the first column.
4. We interpret that number as 206 (first three digits). Since we don't have a member of our population with that number, we go to the next number 899 (89990). Once again we don't have someone with that number, so we continue at the top of the next column. As we work down the column, we find that the first number to match our population is 100 (actually 10005 on the chart). Student number 100 would be in our sample. Continuing down the chart, we see that the other four subjects in our sample would be students 049, 082, 153, and 005

Microsoft Excel has a function to produce random numbers.

The function is simply:
=RAND()
Type that into a cell and it will produce a random number in that cell. Copy the formula throughout a selection of cells and it will produce random numbers between 0 and 1.
If you would like to modify the formula, you can obtain whatever range you wish. For example.. if you wanted random numbers from 1 to 250, you could enter the following formula:
=INT(250*RAND())+1
The INT eliminates the digits after the decimal, the 250* creates the range to be covered, and the +1 sets the lowest number in the range. 

2) Stratified Sample

 A stratified sample is obtained by independently selecting a separate simple random sample from each population stratum. A population can be divided into different groups may be based on some characteristic or variable like income of education. Like anybody with ten years of education will be in group A, between 10 and 20 group B and between 20 and 30 group C. These groups are referred to as strata. You can then randomly select from each stratum a given number of units which may be based on proportion like if group A has 100 persons while group B has 50, and C has 30 you may decide you will take 10% of each. So you end up with 10 from group A, 5 from group B and 3 from group C. 
The advantage of this sampling is that it increase the likelihood of representativeness, especially if one's sample is not very large.  
The disadvantage is that it requires more effort on the part of the researcher. 


3) Cluster Sample

A cluster sample is obtained by selecting clusters from the population on the basis of simple random sampling. The sample comprises a census of each random cluster selected. For example, a cluster may be something like a village or a school, a state. So you decide all the elementary schools in New Delhi are clusters. You want 20 schools selected. You can use simple or systematic random sampling to select the schools, and then every school selected becomes a cluster. This method is similar to simple random sampling except that groups rather than individuals are randomly selected.
The advantages is that it can be used when it is difficult or impossible to select a random sample of individuals and frequently less time consuming.
The disadvantage is that there is a far greater chance of selecting a sample that is not representative of the population. 

4) Systematic Sampling

Every nth individual in the population list is selected for inclusion in the sample. For example, in a population list of 5,000 names, to select a sample of 500, a researcher would select every tenth name on the list until reaching a total of 500 names.

Non Probability Sampling Method

1) Convenience Sampling

 Where the researcher questions anyone who is available. This method is quick and cheap. However we do not know how representative the sample is and how reliable the result.

2) Purposive Sampling

Purposive sampling is different from convenience sampling in that researchers do not simply study whoever is available but rather use their judgement to select a sample that they believe, based on prior information, will provide the data they need. 
The major disadvantage is that the researcher's judgement may be in error-might not be correct in estimating the representativeness of a sample or their expertise regarding the information needed.