3.18.2024

Kerala SET July 2024 registration process begins at lbscentre.kerala.gov.in

 LBS Centre for Science & Technology has begun the registration process for Kerala SET July 2024. The registration process will conclude on April 15 and the deadline for submitting the application form is April 17. Interested candidates can apply online through the official website at lbsedp.lbscentre.in.


Candidates can edit their Kerala SET July 2024 applications from April 18 to April 20.

Direct link to apply for Kerala SET July 2024

Kerala SET July 2024 examination pattern:

There shall be two papers for the SET- JULY - 2024 . Paper I is common for all candidates. It consists of two parts, Part (A) General Knowledge and Part (B) Aptitude in Teaching. Paper II shall be a test based on the subject of specialisation of the candidate at the Post Graduate (PG) Level. There shall be 31 subjects for Paper II of the SET-JULY-2024.

Kerala SET July 2024: Know how to apply

Visit the official website of LBS Centre for Science & Technology at lbscentre.kerala.gov.in

On the homepage, click on the State Eligibility Test July 2024

Register and proceed with the application

Upload all the required documents and photograph

Pay the application fee

 

3.16.2024

Simple Random Sampling -Criteria to choose -selecting a random sample

The easy random sample method is the one that is used most often. Every item in the community has an equal chance of being in the sample. This is called a simple random sample. Another thing is that choosing one thing for the sample shouldn't affect choosing another thing in any way. If the population is homogeneous, which means that all of the things in it have the same qualities that the researcher is interested in, then simple random picking should be used. Some of the things that can make people homogeneous are their age, gender, income, political, religious, or social beliefs, where they live, and so on.

A random number table is the best way to pick a simple random sample. The following things should be true of a random picking method.

  • There must be an equal chance for every person in the community to be in the sample.
  • The choice of one member is not changed by the choices of the members who came before.

The random numbers are a set of digits that are made by a system that uses probability. The random numbers are made up of the following:

  • There is an equal chance of each number (0,1,2,3,4,5,6,7,8,or 9) showing up anywhere. That's one tenth.
  • It doesn't matter where any two digits appear; they can happen in any two places.
To select a random sample using simple random sampling method we should follow the steps given below:
  1. Determine the population size (N).
  2. Determine the sample size (n).
  3. Number each member of the population under investigation in serial order. Suppose there are 100 members number them from 00 to 99.
  4. Determine the starting point of selecting sample by randomly picking up a page from random number tables and dropping your finger on the page blindly.
  5. Choose the direction in which you want to read the numbers (from left to right, or right to left, or down or up).
  6. Select the first ‘n’ numbers whose X digits are between 0 and N. If N =100 then X would be 2, if N is a four digit number then X would be 3 and so on.
  7. Once a number is chosen, do not use it again.
  8. If you reach the end point of the table before obtaining ‘n’ numbers, pick another starting point and read in a different direction and then use the first X digit instead of the last X digits and continue until the desired sample is selected.

Advantages

  1. The simple random sample requires less knowledge about the characteristics of the population.
  2. Since sample is selected at random giving each member of the population equal chance of being selected the sample can be called as unbiased sample. Bias due to human preferences and influences is eliminated.
  3. Assessment of the accuracy of the results is possible by sample error estimation.
  4. It is a simple and practical sampling method provided population size is not large.

Limitations

  1. If the population size is large, a great deal of time must be spent listing and numbering the members of the population.
  2. A simple random sample will not adequately represent many population characteristics unless the sample is very large. That is, if the researcher is interested in choosing a sample on the basis of the distribution in the population of gender, age, social status, a simple random sample needs to be very large to ensure all these distributions are representative of the population. To obtain a representative sample across multiple population attributes we should use stratified random sampling.

Probability and Non-probability sampling methods

 In the field of Statistics, the sampling method, also known as the sampling methodology, refers to the systematic process of collecting and evaluating data in order to research a population. The data serves as the foundation, with a vast sample space.

There are several sampling strategies that may be categorized into two classes. All these sampling strategies may require particularly targeting hard-to-reach or difficult-to-approach groups.

Sampling Methods

Statistics offers several sampling approaches to obtain meaningful data from a population. There are two distinct types of sampling methods:
  1. Sampling method based on the principles of probability theory.
  2. Non-probability sampling refers to a method of selecting participants for a study that does not include random selection.

 1. Probability Sampling Overview

  • Probability sampling is a method that uses random selection to ensure a representative sample. 
  • It is more time-consuming and expensive than non-probability sampling but provides a reliable sample.

Types of Probability Sampling

  1. Simple Random Sampling: Every item in the population has an equal chance of being selected in the sample.
  2. Systematic Sampling: Items are selected from the target population by selecting the random selection point and selecting other methods after a fixed sample interval.
  3. Stratified Sampling: The total population is divided into smaller groups based on certain characteristics.
  4. Clustered Sampling: Clusters or groups of people are formed from the population set with similar significatory characteristics. Example: Random sampling can be used to select three or four branches as clusters for data collection.

 2. Non-Probability Sampling Methods Overview

a) Convenience or Haphazard Sampling:

  • Assumes all population units are identical, allowing any unit to be selected for the sample.
  • Example: vox pop survey where the interviewer selects any person who happens to walk by.
  • Selection subject to the biases of the interviewer and whoever happened to walk by at the time of sampling.
b) Volunteer Sampling:
  • Respondents are volunteers, usually screened to get a set of characteristics suitable for the survey.
  • Can be subject to large selection biases, but sometimes necessary.
  • Example: callers to a radio or television show, where only those who care strongly enough about the subject tend to respond.
  • Often used for focus groups or in-depth interviews.

C) Judgement Sampling:

  • Sampling done based on previous ideas of population composition and behavior.
  • An expert with knowledge of the population decides which units in the population should be sampled.
  • Subject to the researcher’s biases and potentially more biased than haphazard sampling.
  • Can be useful in exploratory studies, such as selecting members for focus groups or in-depth interviews.

D) Quota Sampling:

  •  One of the most common forms of non-probability sampling.
  • Sampling done until a specific number of units (quotas) for various sub populations have been selected.
  • Quota sampling is preferable to other forms of non-probability sampling because it forces the inclusion of members of different sub populations.
  • Unlike stratified sampling, quota sampling uses a non-random method, leaving the interviewer to decide who is sampled.

E) Snowball and Crowdsourcing in Research

  • Snowball sampling is a method used to find rare or hard-to-reach populations, such as people with disabilities, homeless people, drug users, or non-organized groups.
  •  However, some individuals or subgroups may not be included in the sample.

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