TCA Lectures What is the importance of sample in research? Discuss different sampling techniques in detail? BEd

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Answer:

Sampling helps a lot in research. It is one of the most important factors which determines the accuracy of your research/survey result. If anything goes wrong with your sample then it will be directly reflected in the final result. There are lot of techniques which help us to gather sample depending upon the need and situation. This blog post tries to explain some of those techniques. To start with, let’s have a look on some basic terminology

• Population
• Sample
• Sampling

Population is the collection of the elements which has some or the other characteristic in common. Number of elements in the population is the size of the population. Sample is the subset of the population. The process of selecting a sample is known as sampling. Number of elements in the sample is the sample size.

Sampling

There are lot of sampling techniques which are grouped into two categories as

• Probability Sampling
• Non- Probability Sampling

Different sampling techniques:

Simple random sample: Every member and set of members has an equal chance of being included in the sample. Technology, random number generators, or some other sort of chance process is needed to get a simple random sample.

Example—A teachers puts students' names in a hat and chooses without looking to get a sample of students.

Why it's good: Random samples are usually fairly representative since they don't favor certain members.

Stratified random sample: The population is first split into groups. The overall sample consists of some members from every group. The members from each group are chosen randomly. 

Example—A student council surveys 100100100 students by getting random samples of 252525 freshmen, 252525 sophomores, 252525 juniors, and 252525 seniors. Why it's good: A stratified sample guarantees that members from each group will be represented in the sample, so this sampling method is good when we want some members from every group. Cluster random sample: The population is first split into groups. The overall sample consists of every member from some of the groups. The groups are selected at random. 

Example—An airline company wants to survey its customers one day, so they randomly select 555 flights that day and survey every passenger on those flights. Why it's good: A cluster sample gets every member from some of the groups, so it's good when each group reflects the population as a whole.

Systematic random sample: Members of the population are put in some order. A starting point is selected at random, and every n^{\text{th}}nthn, start superscript, t, h, end superscript member is selected to be in the sample.

Example—A principal takes an alphabetized list of student names and picks a random starting point. Every 20^{\text{th}}20th20, start superscript, t, h, end superscript student is selected to take a survey.

The Importance of Selecting an Appropriate Sampling Method

Sampling yields significant research result. However, with the differences that can be present between a population and a sample, sample errors can occur. Therefore, it is essential to use the most relevant and useful sampling method.

Below are three of the most common sampling errors.

• Sampling bias occurs when the sample does not reflect the characteristics of the population.
• Sample frame errors occur when the wrong sub-population is used to select a sample. This can be due to gender, race, or economic factors.
• Systematic errors occur when the results from the sample differ significantly from the results of the population.

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