Sampling Techniques

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  • Topic: Sampling, Stratified sampling, Sample
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  • Published : December 6, 2012
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MAUREEN L. M.

INTERMEDIATE MICROECONOMICS

SAMPLING TECHNIQUES

INTRODUCTION

A sample is a unit or subset of selection from a larger population that is used in studying to draw conclusions regarding the whole population. A sample is usually selected from the population because it is not easy to study the entire population at once and the cost of doing so may be very high. The sample should be the best representation of the whole population to enable accurate outcomes and accurate decisions made from the findings. Before sampling, the population should be clearly defined to ensure that the correct sample is selected for accuracy.

Sampling, therefore, is the process used in statistics in which a predetermined number of observations will be taken from a large population. It is the selection of units from a population of interest so that by studying it, we may generalize the findings back to the population from which the sample was obtained. The methodology to be used in sampling will depend on the type of analysis being performed. The methods include the following:-

1. SIMPLE RANDOM SAMPLING
Simple Random Sampling is obtained in such a way that each element of the population is given equal probability of being selected as a subject. The process is done in a single step with each subject selected independently of the rest of the members of the population. There are many methods of proceeding with simple random sampling techniques. For example, each member can be given a unique number, then all the numbers are placed in a container and mixed thoroughly. A blind folded researcher will then pick some tags from the container and the members whose number tags were picked become the population sample.

Advantages
1. The whole population is highly represented if all members of the population are present 2. It is free from sampling bias; its fair because every member of the population is given equal probability of being selected. 3. It saves time and engaged resources and money due to its simple nature

Disadvantages
1. It needs the complete list of all the members of the population. It should be complete, accurate and up to date. This condition is difficult to ascertain 2. There may be practical constrain regarding access to some of the areas of study in the case of geographical regions

2. STRATIFIED RANDOM SAMPLING
In this method of sampling, the population is divided into different groups or stratum. For example, an education group can be divided into the following groups:-

Group 1: Those in possession of college certificates only
Group 2: Those in possession of college Diplomas
Group 3: Those in possession of University Undergraduate Degrees Group 4: Those in possession of University Postgraduate Degrees Group 5: Those in possession of Masters
Group 6: Those in possession of PHD’s

A stratified sample is then obtained by independently selecting a random sample from each population strata. This method of sampling can be used where the population can easily be divided into smaller groups of a homogeneous nature.

Advantages
1. It improves the representation of specific groups in a population. 2. Each member of each stratum is given equal probability of being selected without bias if the complete and accurate population list is available.

Disadvantages
1. It can only be carried out if the list of the population is available and complete. 2. It can be difficult to select a sample using stratified random sampling if some members of the population belong to more than one stratum. 3. Many population lists are not available to the public domain hence it may be expensive to obtain it. 4. In case of a human population, it may be difficult, time consuming and expensive to contact each individual even where the list is available and complete.

3. SYSTEMATIC RANDOM SAMPLING
This is a method of selecting a sample from a larger population by selecting a random...
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