Methods of Sampling

The sampling method involves selecting a subset of individuals or observations from a larger population to collect data and make inferences about the entire population. It is a practical and efficient way to gather data when it is impractical or impossible to collect information from every member of the population. Different methods of sampling include Random Sampling, Purposive Sampling, Stratified Sampling, Systematic Sampling, Quota Sampling, and Convenience Sampling.

What is Sample Method?

The method of collecting data from a population, regarding a sample on a group of items and examining it to draw out some conclusion, is known as Sample Method. This method is even used in the day-to-day lives of people. For example, a cook takes a spoon of pulses to check whether the whole pulse is evenly cooked. The sampling method of collecting data is suitable for a large population and when the investigator does not require a high level of accuracy. It is also preferred by investigators when they do not need an intensive examination of items. 

Methods of Sampling

1. Random Sampling

As the name suggests, in this method of sampling, the data is collected at random. It means that every item of the universe has an equal chance of getting selected for the investigation purpose. In other words, each item has an equal probability of being in the sample, which makes the method impartial. As there is no control of the investigator in selecting the sample, the random sampling method is used for homogeneous items. As there is no tool or a large number of people required for collecting data through random sampling, this method is economical. There are two ways of collecting data through the random sampling method. These are the Lottery Method and Tables of Random Numbers.

• Lottery Method: In Lottery Method, the investigator prepares paper slips for each of the items of the universe and shuffles these slips in a box. After that, some slips are impartially drawn out from the box to obtain a sample. 
• Table of Random Numbers: A Table of Random Numbers has been prepared by a group of statisticians. In this method of collecting data through random sampling, this table is referred by the investigator to frame a sample. There are many Tables of Random Numbers available from which Tippet’s Table is used by most of the investigators. In this Table, Tippet has used 41,600 figures and has involved 10,400 numbers with four units in each of the numbers. Now, through this method, the items available in the universe are first arranged in an order, and then using Tippet’s Table, the investigator selects the required number of items to form a sample. 

Merits of Random Sampling Method

1. Random Sampling method is economical as the items are selected randomly, which can be done by fewer people and with fewer resources.
2. Random Sampling method is impartial and free from personal biases, as it randomly selects the numbers, and each of the items has an equal probability of getting selected. 
3. This method fairly represents the universe through samples.
4. It is a straightforward and simple method of collecting data. 

Demerits of Random Sampling Method

1. Despite its various advantages, the random sampling method does not give proper weightage to some important items of the universe. 
2. Also, there is no guarantee that different items of the universe are proportionately represented. 

Random Sampling is sometimes confused with Haphazard Sampling. But, it is not Haphazard Sampling. There is a difference between these two sampling methods. 

Random Sampling works with the rules of sampling. However, Haphazard Sampling does not work with the rules of sampling.  Also, the random sampling method gives equal chance to each item being selected. However, the haphazard sampling method does not provide an equal chance for each item. 

2. Purposive or Deliberate Sampling

The method in which the investigator himself selects the sample of his choice, which in his opinion is best to represent the universe, is known as Purposive or Deliberate Sampling. It means that the probability of an item getting selected is not equal as the sample is selected by choice. This method is suitable under situations when there are some items in the universe whose involvement or selection in the sample is important.

For example, If an investigation is about FMCG Companies, then the inclusion of companies like Nestle, Hindustan Unilever Ltd., etc., is essential in the sample. However, the chances of personal biases in this method of sampling are more, which reduces its credibility. 

Merits of Purposive or Deliberate Sampling

1. The Purposive or Deliberate Sampling Method is flexible, as it allows an investigator to include items with special significance in the sample. 
2. The investigator can easily tune the selection of items based on the purpose of the investigation, making it easy for him to perform the analysis. 
3. It is a very simple technique of collecting data, as the investigator can select the significant items in the sample by his choice. 

Demerits of Purposive or Deliberate Sampling

1. As the investigator can select an item in the sample for the investigation, the probability of personal biases increases. 
2. An increase in the probability of personal biases makes the method less reliable for collecting data, and the results become doubtful.

3. Stratified or Mixed Sampling

A sampling method which is suitable at times when the population has different groups with different characteristics and an investigation is to be performed on them is known as Stratified or Mixed Sampling. In other words, Stratified or Mixed Sampling is a method in which the population is divided into different groups, also known as strata with different characteristics, and from those strata some of the items are selected to represent the population. The investigator while forming strata has to ensure that each of the stratum is represented in a correct proportion.

For example, there are 60 students in Class 10th. Out of these 60 students, 10 opted for Arts and Humanities, 30 opted for Commerce, and 20 opted for Science in Class 11th. It means that the population of 60 students is divided into three strata; viz., Arts and Humanities, Commerce, and Science, containing 10, 30, and 20 students, respectively. Now, for investigation purpose, some of the items will be proportionately selected from each of the strata in a way that those items forming a sample represents the entire population. Besides, an investigator can even select the items from different strata, unproportionately. 

Merits of Stratified or Mixed Sampling

1. As different groups of a population with different characteristics are selected in this method of sampling, it covers a large portion of the characteristics of the population. 
2. Selection of the diverse characteristics of the population makes the comparative analysis of the data possible. 
3. The Stratified Method of Sampling offers meaningful and reliable results to the investigator. 

Demerits of Stratified or Mixed Sampling

1. The Stratified Sampling Method has a limited scope because it is suitable only when the investigator has complete knowledge of the diverse characteristics of the entire population. 
2. As the population is divided into different strata by the investigator himself, there are chances of biasness in this step.
3. In the case of a small population, it may be difficult for the investigator to divide the population into small strata. 

Stratified Sampling Method is also known as Mixed Sampling Method because it is a mixture of both Purposive Sampling and Random Sampling Method. The population is divided into different strata on purpose; however, the items are selected from different strata, randomly. 

4. Systematic Sampling

According to the Systematic Sampling Method of collecting data, different units of the population are systematically arranged in numerical, alphabetical, and geographical order. To form a sample, every nth term or item of the numbered items is selected. This method is a short-cut method of collecting data through the Random Sampling method.

For example, if 10 out of 200 people are to be selected for investigation, then these are first arranged in a systematic order. After that one of the first 10 people would be randomly selected. In the same way, every 10th person from the selected item will be taken under the sample. In other words, if the first selected person is the 8^th person in the order, then the subsequent person selected in the sample would be 18^th, 28^th, 38^th, 48^th, ……….198^th. 

Merits of Systematic Sampling

1. Systematic Sampling Method is a simple method of collecting data as the investigator can easily determine the sample. 
2. As the items are arranged in a systematic order, the chances of personal biases are less. 

Demerits of Systematic Sampling

1. As the first item of the given population is selected randomly, and then further items are selected on the basis of the first item, every item of the population does not get an equal chance of getting selected. 
2. In case the population has homogeneous items, the method of Systematic Sampling does not serve any specific purpose. 

5. Quota Sampling

In the Quota Sampling Method of collecting data, the entire population is divided into different classes or groups. It is done on the basis of the different characteristics of the given population. The investigator fixes some percentages of the different groups with different characteristics of the total population. After that, he fixes some quota of the items for each of the selected segregated groups. At last, to form a sample, the investigator has to select a fixed number of items from each of the segregated groups. 

Merits of Quota Sampling

1. The Quota Sampling Method of collecting data is affordable.

Demerits of Quota Sampling

1. The chances of personal  biases while selecting the items in a sample are high. 
2. Personal  biases during the selection of items in a sample make the reliability of the results through investigation questionable. 

6. Convenience Sampling

As the name suggests, Convenience Sampling is a method of collecting data in which the investigator selects the items from the population that suits his convenience.

For example, an investigator who wants to collect data on the average number of females using inductions in the kitchen goes to a shopping mall and collects information from each of the females visiting there. By doing so, the investigator is neglecting other females who were not present in the mall that day or did not go to the mall. This reduces the reliability of the result, as there are other females in the universe who uses inductions in the kitchen, but were not present in the mall at that time.

Merits of Convenience Sampling

1. The Convenience Sampling Method is the least expensive method of collecting data.
2. It is also the simplest method of collecting data from the population. 

Demerits of Convenience Sampling

1. This method is highly unreliable, as the investigator selects the items that suit him, and it is not possible that every investigator has reliable thinking or purpose of investigation. Besides, different investigators have different perspectives.

Also Read:

Sample Method of Collection of Data

What makes a Sampling Data Reliable?

What are Statistical Errors?

Difference between Census Method and Sampling Method of Collecting Data

Methods of Sampling – FAQs

What is sampling?

Sampling is the process of selecting a subset of individuals, items, or data points from a larger population to estimate characteristics of the whole population. It is used in economics to gather data and make inferences about economic phenomena.

How do you determine the sample size?

Sample size is determined based on factors such as the population size, desired confidence level, margin of error, and variability within the population. Statistical formulas and software are often used for precise calculations.

What are sampling errors and how can they be minimized?

Sampling errors are discrepancies between the sample results and the true population parameters. They can be minimized by using larger sample sizes, ensuring random selection, and using stratified sampling techniques.

How do you ensure a sample is representative?

To ensure a sample is representative, use random sampling methods, consider stratified sampling for heterogeneous populations, and ensure the sample size is sufficiently large.

What is the difference between sampling error and non-sampling error?

Sampling Error: Error caused by observing a sample instead of the whole population. It can be reduced by increasing sample size.

Non-Sampling Error: Errors not related to the sampling process, such as data collection errors, non-response bias, and measurement errors. It requires careful design and execution of the data collection process to minimize.

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Article Tags :

• Commerce
• Statistics for Economics
• Commerce - 11th