## The concept and method of sampling techniques:

The sequence of discussing the sampling process after the techniques of data collection is delibrate. It is not out of place for a researcher to get ready with his or her research instruments before determining to whom they will administered. The ultimate goal of the two processes is that of reliable data are made available for the empirical analysis. In research methodology you will note the need of determining the target population, the sample and sample size in a research study.

The population is simply the totality of the collection of individuals objectives or measurements whose properties are under investigation. In a research study the usaul thing should be that every member of a population being investigated should be involved as. Respondent. In the event that exceeding large number are involved in a given popular, the process data collection may become too cumbersome for the research. In such case , a similar proportion of the population is selected for study and sampling techniques. From the small proportion a generalization is made about the entire population. This is the essence of sampling techniques. Doing otherwise is conducting a census. Sampling techniques is the mechanism of choosing designated quantities or proportions as representation of whole populations. This small representative proportion is called 'sample". Sampling techniques may then be regarded as means o choosing samples for research.

A sample is a subject of a pooupopula, a proportion chosen to Stan in for the entire of all the subjects or observations under consideration. To be effectively used to draw inference about population, a sample must be representive of the population. A representative sampe is one that has all the characteristics of the population from where it has been chosen.

A random sample is one that is chosen in such a way that every member of the population from which it is selected ha an equal chance of being included in the sample. The inclusion of an item of the population in such a random sample is based purely on chance occurrence.

The decision of whether to use the random sample or the purposive sample in any research depends on many factors. These include the size an nature of the population under study and also the availability of adequate sampling frames. We shall pause here and define some basic terms we commonly come across in the sampling techniques process. They include:

- Sampling frame: This is the list of every element or item that constitutes the membership of a population. For example, the list of all the students in an institution can be regarded as a sampling techniques frames, if it is used for selecting samples in a study concerning it.
- Sample size: This is simply put , the total number of elements in the sample. It is normally denoted by "n". The total number of the population elements is often designated as "N". The size of a research sample is important because the larger the size, the more it is representative of the total population and, therefore, the more reliable a researchers generalization about the total population would tend to be. According to leedy (1974), this is because large samples size tend to reduce the standard error of measurements in the researchers use of samples statistics to approximate the true population or research universe.
- Sampling fraction: If a sample of size n is chosen from a population of size N , then the fraction n/N is known as the sampling fraction. It is obvious that the larger the value of the sampling fraction the more representative the sample will be.

## 6 types of sampling techniques:

Here are the six different sampling techniques;

- Simple random sampling
- Stratified sampling
- Multi-stage or cluster sampling
- Systematic sampling
- Quota sampling
- The random walk.

All the sampling techniques except the quota sampling lead to the choice of random samples.

### Simple random sampling techniques;

This is a procedure of sampling in which the choice of a particular element does not jeopardize the chance of the other elements being selected into the same sample. It is a method of sampling done in such a way that every possible sample of size n out of a population of size N has an equal chance of being chosen. In other words ,in a simple random sample , each unit of the population has exactly the same chance as any other unit of being included in the sample.

The method is applicable in populations which are not too large but must be homogeneous in nature and have adequate and up to date sampling frame.

A simple random sample can be selected from a population with the use of table of random number or by the use of the "lottery" method. Random number table can be gotten from statistical table or statistics text. The random sampling techniques is a reliable procedure for the procedure for the choice of random sample

It however, has some drawbacks which the researcher must be aware of. These include:

- The procedure may result in selecting an unrepresentative sample since the researcher has no control over the membership it is a game of chance.
- The method can only be applicable where the population has an adequate sampling frame. If there is none, the numbering of the population may prove difficult or even impossible.
- It may lead to selecting individuals or elements with a wide geographical scatter, thus adding to the expenses and difficulties in getting the required data.
- Items selected are subject to the full range of variation of the properties of the elements of the population.

### Stratified sampling techniques;

This is a method of sampling that may be used to eliminate most of the drawbacks of simple random sampling. Like in the simple random sampling it is recommended for small populations with adequate sampling frames. In most cases, the population should be a heterogeneous one with diverse characteristics.

A stratified sample is one that is obtained by separating the population elements into smaller non-overlapping groups or blocks of units called strata, in such a way that each sratum is as homogeneous as possible. From each stratum a selection is made to form the required sample. A population can be stratified by using predetermined attributes or characteristics sure as religion,age, marital status, nationality, occupation, course of study, and so on. In stratified sampling any other method of sampling can be used to pick the membership of the sample required after the stratification. Care must be taken to make sure that each attributes of the population used in the stratification is included. For example, a simple random sampling procedure can be used to pick the number of elements required from each stratum. Apart from the elimination of the drawbacks of the simple random sampling already outlined, other reasons have been aduced for stratification in sampling;

- In addition to making inference about the entire population, stratification helps the researcher to make inference about some other strata or for each stratum .
- Stratification leads to increased accuracy and precision in drawing conclusions and making decisions.
- It may be done for administrative convenience to eliminate bottlenecks that may arise in handling the problems of the heterogeneous population. It may then be easier to separate these problems and treat them on individual group basis.

### Cluster sampling techniques;

This methods is also referred to as "multi-stage" sampling techniques because the choice is done in stages. It involves "choosing samples from samples". Through a methodological arrangement, the target population can be arranged into cluster or subunits on the basis of the extent to which the perceived variables of research can be found in the entire population. The final research can be found in the entire population. The final research sample is then randomly selected from the clusters of interest to the researcher.

The method is appropriate for survey which may cover a wide geographical area like an opinion polls conducted within a state. The first stage would be to use the local government areas as cluster and randomly select some for the survey. Within the selected local government, districts are selected. Within the districts some villages are picked. This process continue until the final survey sample is drawn.

### Systematic sampling techniques;

In a population wich is not too large and also has a reliable and up to date sampling Frame, a step wise selection of the items can be done to get a required samoke. The systematic sampling techniques is simply a short cut method of obtaining a virtually random sample. It involves the selection of every nth element out of a total population size. The method is also called interval sampling. The first entry is always obtained by a random number table. The essence is that all of the elements that are identified with a population have an equal chance of being selected into the given research sample. For example, if a sample of 20 is to be selected from a population of 200, then every tenth(200/20) item after a random start in the first 10 should be selected. The first stage would be to pick the starting number from the number 1 to 10. Suppose the number 5is picked. Then the sample would consist of the following items listed in the sampling frame.

5th, 15th, 25th, 35th,45th,55th,65th,75th.

### Quota sampling techniques;

Quota sampling method is used mainly when a sampling frame is not available and the target population may be large. It is recommended for survey in which quick results are needed. It is a good procedure for the conduct of opinion polls and market researches.

Under this method the population is divided into quota or zones which may not be homogenous as in stratified sampling, but may be based on geographical location. The quota indicate the number of people, for example, to be interviewed but the choice of the respondents is left for the enumerator to make. What matters is not to be interviewed or selected in the sample but the number. In survey, one way to minimize cost is to use quota sampling. Experience has shown tat this method of sampling cost less than half the cost of conducting simple random sampling to choose a sample of same size.

One of the disadvantage of the procedures is that the samples so chosen are always non random sample is gained in reduction of cost speed conduct of the survey. In other words, randomness is sacrificed for quick result and low cost.

### Random walk sampling techniques;

This is a special method of procedure for choosing samples. It involves movement according to instructions or directives given to the enumerator. The techniques is good for market researches or opinion polls that concentrate on household in specific location.

Let us illustrate this with the case of a market survey which a manufacturer can carry out to introduce a new baby food or test the popularity or otherwise of an existing one.

## Advantages of sampling techniques:

The question now is "why do we sample instead of working with the entire population? The advantages of doing this are many and include:

- Sampling is cheaper than census since only a small part of the population may be involved.
- Sampling save time and results are more quickly obtained than in the case of a census
- Sampling increases the scope of investigation especially in cases of survey which may involve the destruction of materials. Sampling is readily applicable where census is impracticable.
- Sampling increases accuracy as guarding against incomplete and inaccurate returns is easier. Census is often difficult to manage and this may reduce accuracy of returns.
- Follow up of non response is much easier in sampling. Errors can also be assessed.

All the sampling techniques listed above are implemented by Business firms and organization to know and determine the main action to be taken in any research process.

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