UGC NET December 2024 information bulletin is expected to be released in November 2024.
UGC NET/JRF Psychology
@psychprep
Preparation of psychology optional.
UGC NET/JRF Psychology (English)
Are you a student aspiring to clear the UGC NET/JRF exam with Psychology as your optional subject? Look no further, as the Telegram channel @psychprep is here to help you with your preparation. This channel is dedicated to providing valuable resources, study materials, and guidance to help you ace this competitive exam
Who is it for? This channel is ideal for students who are preparing for the UGC NET/JRF exam with Psychology as their optional subject. Whether you are a beginner looking to start your preparation or an experienced candidate aiming to improve your knowledge and skills, @psychprep has something for everyone
What is it? @psychprep is a one-stop destination for all your UGC NET/JRF Psychology preparation needs. From important topics and syllabus coverage to practice questions and mock tests, this channel covers it all. You can also find tips from experts, strategies for time management, and motivational quotes to keep you inspired throughout your preparation journey
Stay updated with the latest exam notifications, study hacks, and success stories of previous toppers. Join the @psychprep Telegram channel today and boost your confidence to achieve your goal of cracking the UGC NET/JRF exam with Psychology as your optional subject. Let's embark on this journey together and pave the way for a successful career in the field of Psychology!
UGC NET/JRF Psychology
12 Aug, 19:53
Re-examination of UGC NET June 2024 exam will be held on 30 August for Psychology Optional.
UGC NET/JRF Psychology
01 Jul, 01:41
The UGC NET exam held on 18 june has been cancelled.
Re-examination schedule -The UGC NET June 2024 exam will be held between August 21, 2024 and September 4, 2024.
Re-examination schedule -The UGC NET June 2024 exam will be held between August 21, 2024 and September 4, 2024.
UGC NET/JRF Psychology
14 Sep, 03:30
NET EXAM DECEMBER 2023
The National Testing Agency will release the UGC NET exam notification for the December Cycle in the month of September 2023 (tentatively). However, there is no official update regarding the UGC NET 2023 notification and application by the UGC or NTA. The testing agency will release the notification in the form of a PDF on its official website ugcnet.nta.nic.in.
The National Testing Agency will release the UGC NET exam notification for the December Cycle in the month of September 2023 (tentatively). However, there is no official update regarding the UGC NET 2023 notification and application by the UGC or NTA. The testing agency will release the notification in the form of a PDF on its official website ugcnet.nta.nic.in.
UGC NET/JRF Psychology
27 Jul, 15:01
Total of 118 candidates qualified JRF in psychology optional in July NET 2023 exam.
Congratulations to those who made the list . π
Congratulations to those who made the list . π
UGC NET/JRF Psychology
30 May, 08:28
Gentle reminder
Last date to fill UGC NET June 2023 form is 31st May.
Last date to fill UGC NET June 2023 form is 31st May.
UGC NET/JRF Psychology
07 May, 05:25
UGC NET June 2023 exam dates are out.
It will be conducted between 10 June to 22 June.
Exam form to be released next week
It will be conducted between 10 June to 22 June.
Exam form to be released next week
UGC NET/JRF Psychology
14 Oct, 09:11
PhD entrances announced for
PhD in psychology at IITs
Last date to Apply
IIT Bombaby - 25th October
IIT Madras - 30 October
IIT Delhi - 30 October
IIT Rorkee - 18 October
IIT Tirupati - 18 October
IIT Gandhinagar -14 October
PhD in psychology at IITs
Last date to Apply
IIT Bombaby - 25th October
IIT Madras - 30 October
IIT Delhi - 30 October
IIT Rorkee - 18 October
IIT Tirupati - 18 October
IIT Gandhinagar -14 October
UGC NET/JRF Psychology
14 Oct, 09:08
For NET counseling/queries you can book an appointment with us
[email protected]
Please note, we are taking paid requests only
βRs.500 for 30min video/audio call
β Rs. 300 for 3 emails
[email protected]
Please note, we are taking paid requests only
βRs.500 for 30min video/audio call
β Rs. 300 for 3 emails
UGC NET/JRF Psychology
14 Oct, 09:03
For all the queries regarding when the NET results will be out.
The NTA has announced results will be released in October last week.
The NTA has announced results will be released in October last week.
UGC NET/JRF Psychology
09 Jul, 16:40
The Hawthorne studies
The Hawthorne effect is the modification of behavior by study participants in response to their knowledge that they are being observed or singled out for special treatment. In the simplest terms, the Hawthorne effect is increasing output in response to being watched.
The term Hawthorne effect arose in connection with the Hawthorne studies, which were a groundbreaking series of studies beginning in the 1920s that tested the impact of working-condition variables on employee productivity. Most experts do not believe there was a so-called Hawthorne effect in the Hawthorne studies, but it persists as a widely used term.
Complicating the use of the term the Hawthorne effect is its inconsistent meaning from use to use; it is often used for a number of effects beyond the aforementioned. As for the use of the term the Hawthorne effect in psychological and health studies, many in the scientific community say it should be replaced with more specific terminology pertinent to whatever is being studied.
The Hawthorne studies
The Hawthorne studies consisted of a series of worker productivity studies, which began around 1924 at the Western Electric plant in Illinois, near Chicago.
Western Electric was the manufacturing unit of the conglomerate the American Telephone and Telegraph Company (AT&T), which, from its founding in 1876 to its breakup in 1984, had a near monopoly on the telephone industry. Western Electric manufactured telephones, cables, and switching and transmission equipment. And by 1929, upward of 35,000 men and women worked at its main factory, Hawthorne Works.
Hawthorne studies tested such variables as the effect of lighting, work breaks and pay incentives. They also included an extensive interviewing program, where employees could talk about their grievances and concerns.
The Hawthorne studies included several productivity studies and worker groups. When the Hawthorne studies ended in 1932, more than 20,000 employees had participated in some way. Many accounts about the Hawthorne studies focus only on a single group of workers and ignore the absence of support for the so-called Hawthorne effect and the other variables that contributed to productivity gains.
The Hawthorne effect is the modification of behavior by study participants in response to their knowledge that they are being observed or singled out for special treatment. In the simplest terms, the Hawthorne effect is increasing output in response to being watched.
The term Hawthorne effect arose in connection with the Hawthorne studies, which were a groundbreaking series of studies beginning in the 1920s that tested the impact of working-condition variables on employee productivity. Most experts do not believe there was a so-called Hawthorne effect in the Hawthorne studies, but it persists as a widely used term.
Complicating the use of the term the Hawthorne effect is its inconsistent meaning from use to use; it is often used for a number of effects beyond the aforementioned. As for the use of the term the Hawthorne effect in psychological and health studies, many in the scientific community say it should be replaced with more specific terminology pertinent to whatever is being studied.
The Hawthorne studies
The Hawthorne studies consisted of a series of worker productivity studies, which began around 1924 at the Western Electric plant in Illinois, near Chicago.
Western Electric was the manufacturing unit of the conglomerate the American Telephone and Telegraph Company (AT&T), which, from its founding in 1876 to its breakup in 1984, had a near monopoly on the telephone industry. Western Electric manufactured telephones, cables, and switching and transmission equipment. And by 1929, upward of 35,000 men and women worked at its main factory, Hawthorne Works.
Hawthorne studies tested such variables as the effect of lighting, work breaks and pay incentives. They also included an extensive interviewing program, where employees could talk about their grievances and concerns.
The Hawthorne studies included several productivity studies and worker groups. When the Hawthorne studies ended in 1932, more than 20,000 employees had participated in some way. Many accounts about the Hawthorne studies focus only on a single group of workers and ignore the absence of support for the so-called Hawthorne effect and the other variables that contributed to productivity gains.
UGC NET/JRF Psychology
08 Jul, 16:53
Hello everyone
Please go through last 5 years PYQ thoroughly. Cover the topics in PYQ very carefully. You will get a general idea and insight about what to expect in upcoming exam.
Few days for exam remaining, stay calm and revise thoroughly. Even if you feel you cannot recall, the material is still being encoded and you will be able to solve the questions.
Best wishes.
Please go through last 5 years PYQ thoroughly. Cover the topics in PYQ very carefully. You will get a general idea and insight about what to expect in upcoming exam.
Few days for exam remaining, stay calm and revise thoroughly. Even if you feel you cannot recall, the material is still being encoded and you will be able to solve the questions.
Best wishes.
UGC NET/JRF Psychology
08 Jul, 16:20
Practice Questions are being posted on other channel.
Join in for last moment revision
https://t.me/UGCNETpsychologyquiz
Join in for last moment revision
https://t.me/UGCNETpsychologyquiz
UGC NET/JRF Psychology
08 Jul, 16:14
Multivate Analysis of Variance
β―What is Multivariate Analysis of Variance
The general purpose of multivariate analysis of variance (MANOVA) is to determine whether multiple levels of independent variables on their own or in combination with one another have an effect on the dependent variables. MANOVA requires that the dependent variables meet parametric requirements.
βοΈWhen do you need MANOVA? MANOVA is used under the same circumstances as ANOVA but when there are multiple dependent variables as well as independent variables within the model which the researcher wishes to test. MANOVA is also considered a valid alternative to the repeated measures ANOVA when sphericity is violate
βοΈWhat kinds of data are necessary?
The dependent variables in MANOVA need to conform to the parametric assumptions. Generally, it is better not to place highly correlated dependent variables in the same model for two main reasons. First, it does not make scientific sense to place into a model two or three dependent variables which the researcher knows measure the same aspect of outcome. (However, this is point will be influenced by the hypothesis which the researcher is testing. For example, subscales from the same questionnaire may all be included in a MANOVA to overcome problems associated with multiple testing. Subscales from most questionnaires are related but may represent different aspects of the dependent variable.) The second reason for trying to avoid including highly correlated dependent variables is that the correlation between them can reduce the power of the tests. If MANOVA is being used to reduce multiple testing, this loss in power needs to be considered as a trade-off for the reduction in the chance of a Type I error occurring. Homogeneity of variance from ANOVA and t tests becomes homogeneity of variance covariance in MANOVA models. The amount of variance within each group needs to be comparable so that it can be assumed that the groups have been drawn from a similar population. Furthermore it is assumed that these results can be pooled to produce an error value which is representative of all the groups in the analysis. If there is a large difference in the amount of error within each group the estimated error measure for the model will be misleading.
β―What is Multivariate Analysis of Variance
The general purpose of multivariate analysis of variance (MANOVA) is to determine whether multiple levels of independent variables on their own or in combination with one another have an effect on the dependent variables. MANOVA requires that the dependent variables meet parametric requirements.
βοΈWhen do you need MANOVA? MANOVA is used under the same circumstances as ANOVA but when there are multiple dependent variables as well as independent variables within the model which the researcher wishes to test. MANOVA is also considered a valid alternative to the repeated measures ANOVA when sphericity is violate
βοΈWhat kinds of data are necessary?
The dependent variables in MANOVA need to conform to the parametric assumptions. Generally, it is better not to place highly correlated dependent variables in the same model for two main reasons. First, it does not make scientific sense to place into a model two or three dependent variables which the researcher knows measure the same aspect of outcome. (However, this is point will be influenced by the hypothesis which the researcher is testing. For example, subscales from the same questionnaire may all be included in a MANOVA to overcome problems associated with multiple testing. Subscales from most questionnaires are related but may represent different aspects of the dependent variable.) The second reason for trying to avoid including highly correlated dependent variables is that the correlation between them can reduce the power of the tests. If MANOVA is being used to reduce multiple testing, this loss in power needs to be considered as a trade-off for the reduction in the chance of a Type I error occurring. Homogeneity of variance from ANOVA and t tests becomes homogeneity of variance covariance in MANOVA models. The amount of variance within each group needs to be comparable so that it can be assumed that the groups have been drawn from a similar population. Furthermore it is assumed that these results can be pooled to produce an error value which is representative of all the groups in the analysis. If there is a large difference in the amount of error within each group the estimated error measure for the model will be misleading.
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