UGC NET June 2024 Online Application Started || Exam in Offline Mode

 The National Testing Agency (NTA) on behalf of the University Grants Commission (UGC) is going to conduct the UGC NET June 2024 cycle exam scheduled on 16th June 2024. NTA released the tentative date for UGC NET 2024 through the NTA Provisional Exam Calendar 2024-25 and it will soon upload the UGC NET Notification 2024 at its official website for the candidates. This article provides the latest information on UGC NET Notification 2024 eligibility, exam patterns, and other details regarding the UGC NET Notification 2024.

NTA UGC NET is a national-level exam conducted to apply for both Assistant Professor or Junior Research Fellowship (JRF) and Assistant Professor placements in Indian universities and colleges. UGC NET 2024 will cover a total of 83 topics. Candidates must fill out the UGC NET 2024 application form to apply online for UGC NET 2024 exam. Only candidates who fill out the UGC NET 2024 application form successfully will be eligible to receive their UGC NET Admit Card 2024 to the respective official website.

Eligibility Criteria for UGC NET June 2024 Exam

According to the announcements made by the UGC chief, candidates pursuing a Four Year/8 Semester Bachelor’s Degree Programme, including those in their final semester/year, are eligible to apply for the UGC-NET. Moreover, candidates with a Four-Year Bachelor’s Degree Programme can appear in a subject of their choice for Ph.D. regardless of the discipline of their bachelor’s degree.

Mode of Examination for UGC NET June 2024

The UGC NET June 2024 examination will be exclusively conducted in Offline OMR(Pen & Paper) mode across multiple shifts. It will comprise two papers, both featuring objective-type, multiple-choice questions. Candidates will be allotted a total duration of three hours for both papers, encompassing 150 questions in total.

 UGC NET June 2024 Important Dates

 Step-by-Step Guide to Applying for UGC NET June 2024

1. Visit the official website: ugcnet.nta.nic.in


2. Click on the 'New Registration' button on the homepage to initiate the registration process.


3. Log in using the provided registration credentials.


4. A new window will appear, prompting you to fill out the application form.


5. Upload the necessary documents as per the guidelines.


6. Complete the payment of the application fee.


7. Click on the Submit button to finalize your application.


8. Remember to take a printout of the confirmation page for future reference.

 UGC NET June 2024 Official Notification 

Multistage Sampling Method

In two-stage cluster sampling, a simple random sample of clusters is selected and then a simple random sample is selected from the units in each sampled cluster.

Multi stage sampling is a generalisation of two stage sampling. As the name suggests, multi stage sampling is carried out in different stages. In each stage progressively smaller (population) geographic areas will be randomly selected. 

Example:

A political pollster interested in assembly elections in Andhra Pradesh may first divide the state into different assembly units and a sample of assembly constituencies may be selected in the first stage.

In the second stage, each of the sampled assembly constituents are divided into a number of segments and a second stage sampled assembly segments may be selected.

In the third stage within each sampled assembly segment either all the house-holds or a sample random of households would be interviewed.

In this sampling method, it is possible to take as many stages as are necessary to achieve a representative
sample. Each stage results in a reduction of sample size.

In a multi stage sampling at each stage of sampling a suitable method of sampling is used. More number of stages are used to arrive at a sample of desired sampling units.

Advantages

a) Multistage sampling provides cost gains by reducing the data collection on costs.

b) Multistage sampling is more flexible and allows us to use different sampling procedures in different stages of sampling.

c) If the population is spread over a very wide geographical area, multistage sampling is the only sampling method available in a number of practical situations.

Limitations

a) If the sampling units selected at different stages are not representative multistage sampling becomes less precise and efficient.

How systematic sampling is differ from Stratified Random Sampling

Systematic sampling and stratified random sampling are both probability sampling techniques used to select a representative sample from a population, but they go about it in different ways:

Stratified Random Sampling:

• Divides the population: Here, you first divide the entire population (let's say, all students in a school) into subgroups (strata) based on shared characteristics (like grade level). These subgroups should be mutually exclusive and collectively exhaustive (every member of the population belongs to exactly one subgroup).
• Random selection within subgroups: Then, you randomly select a sample from each subgroup. This ensures all relevant subgroups are represented in the final sample.

Systematic Sampling:

• Ordering the population: This method treats the population as a single list in some order (like a list of students in alphabetical order).
• Fixed interval selection: You decide on a sampling interval by dividing the total population size by your desired sample size. Then, you pick a random starting point from the list and select every nth element thereafter based on the interval.

Here's a table summarizing the key differences:

Feature	Stratified Random Sampling	Systematic Sampling
Divides population	Yes, into subgroups (strata)	No
Basis for selection	Random selection within subgroups	Fixed interval selection from ordered list
Risk of bias	Lower, ensures all subgroups are represented	Higher if the ordering coincides with a pattern in the population

Choosing the right method:

• Use stratified random sampling if you have a diverse population with subgroups you want to be sure are represented in the sample.
• Use systematic sampling if ordering the population is easy and there's no underlying pattern or cyclical trend within the population that might bias your selection. It can also be slightly more efficient to implement than stratified sampling.

Example:

Imagine you want to survey students about their preferred lunch options.

• Stratified Random Sampling: You could divide the students into subgroups by grade level (strata). Then, randomly select a sample from each grade level to ensure all grade levels have a voice.
• Systematic Sampling: If the student list is in alphabetical order (not ideal, but possible), you could choose a random starting student and then survey every 10th student on the list (assuming you want a 10% sample). However, this could be biased if, for example, taller students tend to be placed alphabetically later (and perhaps have different lunch preferences).