Chevron Left
Zurück zu Measuring Causal Effects in the Social Sciences

Bewertung und Feedback des Lernenden für Measuring Causal Effects in the Social Sciences von Universität von Kopenhagen

4.2
Sterne
207 Bewertungen
48 Bewertungen

Über den Kurs

How can we know if the differences in wages between men and women are caused by discrimination or differences in background characteristics? In this PhD-level course we look at causal effects as opposed to spurious relationships. We will discuss how they can be identified in the social sciences using quantitative data, and describe how this can help us understand social mechanisms....

Top-Bewertungen

AA
10. Jan. 2021

This is a challenging course, especially for those who only intend to breeze through the videos alone. Reading the recommended text before and/or after the videos is strongly advised.

M
11. März 2020

Some Reading Materials such as journal articles on similar methods covered in course would have been a great inclusion as a part of the exercises.

Filtern nach:

1 - 25 von 48 Bewertungen für Measuring Causal Effects in the Social Sciences

von Lisa D

9. März 2017

Unfortunately this course consists of the Professor reading his notes very quickly with rapid listing of concepts and very little time spent explaining complex topics. The quizzes emphasize the terms for various elements of the analysis rather than teaching how to work with the tools to analyze data. There does not seem to be anyone monitoring the course forum and mistakes in quiz questions and questions asked on the forum are not answered or replied to by anyone. I was committed to working with the course but by week 4 it was unfortunately impossible to absorb and there was no way to interact with anyone to get help. I'm sure there is room for improvement on this course and I hope the instructor does work to improve with the course, but currently the course is disappointing as a learning experience.

von irene k

30. Juni 2017

Lecturer extremely difficult to follow. Quiz questions required remembering numbers (!) from weeks earlier. In general a course based on good ideas, all missed in really bad execution of the course.

von Tomasz J

5. Dez. 2018

This course makes clear distinction between different approaches to causality with nice graphics. That's good. But my feeling is that it uses explanation methods which are easy to understand only for those... who are already familiar with IV & DID. It's easy to find on the web more straight forward explanations on the web, yet still statistically rigorous.

While explanation level is always something very personal and can ba argued upon, there are clear flaws in the tests: 1) the way how questions are being asked suggest answer to the questions asked above. 2) questions are sometimes not precise enough, e.g. in module 5:

"What is the average test score for students who were in special education during 1st grade?"

should be

"What is the average test score for students AFTER KINDERGARTEN who were in special education during 1st grade?"

von Felipe C B

20. Feb. 2018

On one hand, it is a very concise course that gives you some insights about the topic in question without unnecesary details of some basic topics. I really appreciated this, as many coursera courses take a lot of classes on explaining a lot of extremely basics contents where you take a lot of time . On the other hand, I took away two stars because the contents are poorely delivered by the instructor and if you do not have a grasp on the topic, is almost impossible to understand what is the lesson about. Questions are way too specific about details of the lectures (even specific numbers), and not about the general topic covered.

von Sophie W

27. Dez. 2018

The Professor has interpreted the course very detailed and thoroughly in terms of key methodologies and formulas. He also gave concrete examples and database to help me understand the theoretical knowledge. The quiz after each course are very helpful to understand new concepts and data implications in the examples. The only flaw might be too fast and not clear pronunciation of the instructor. Also, this is the only course about Impact Evaluation (i.e. RCT, IV, Diff-in-Diff) provided in Coursera. I hope there will be other similar courses available in Coursera!

von Tiara A

8. Juni 2020

Professor Holm provided a wealth of information in such a clear and succinct manner that learning the rigorous subject matter was not impossible. He provides the perfect balance of definitions and formulas along with interesting case studies. I highly recommend this course!

von Aureliano A B

16. Mai 2018

Great course! I finally understood the relation between RCT's, Instrumental Variables and DiDs. The prior suggested readings helped a lot, and the classes were very well conducted with intuitive explanations before the formal derivations that were also very helpful.

von junseok k

17. Apr. 2020

I simply loved the lecture. I have considered to take other courses from Columbia University. But, it is much better for the beginner like me who have some statistical knowledge and no background in causal inference before. Thanking you very much.

von Rohit V K

13. Dez. 2018

Good course with good explanation. But request please use a whiteboard instead of chalkboard in the background as the chalkboard becomes difficult to read on mobile devices. Some explanations can be augmented with additional reading

von Andrej P

29. März 2020

Causal effects in the Social sciences is a very difficult topic because experiments are often impossible in this field. This course provides some insightful techniques we can use to estimate a causal effect based on observed data.

von Aedrian A

11. Jan. 2021

This is a challenging course, especially for those who only intend to breeze through the videos alone. Reading the recommended text before and/or after the videos is strongly advised.

von Mohammad N A H

12. März 2020

Some Reading Materials such as journal articles on similar methods covered in course would have been a great inclusion as a part of the exercises.

von Aysha R

11. Sep. 2019

The course was well structured and helped to identify different approaches used to measure causality. Overall a well designed course.

von niladri s b

2. Feb. 2019

This is a great course for people working in evaluating different social projects. Improved my insights a lot!

von Eugenio D F

8. Jan. 2017

This is an useful course for medical researchs even though you can apply to other social science.

von Olawoyin G A

6. Jan. 2019

I found it enlightening. It surely clarifies the concept of causality.

von Jiacong L

8. Nov. 2019

Confusing concepts are presented clearly through examples. Thank you!

von Sixtus A

30. Jan. 2019

Very useful course for people doing measurements in social sciences.

von Николай Н

16. Juli 2019

Отличный, специальный курс. Доступный но требует базовых знаний!

von Vidya B R

1. Jan. 2019

Great material to review causal inference concepts.

von Jie F

13. Feb. 2019

Very good short time course, highly recommended.

von Lucas B

30. Okt. 2018

Very easy and intuitive

von Monika B

16. Dez. 2016

Very good course!

von Diego P

2. März 2017

The course is great. Although it is really fast and requires some advanced understanding of algebra and statistics, it is not bad. However, I would reccommend to expand it and to include the advances in non-manipulative causation, as sustained by proff. J. Pearl and F. Squazzoni (specifically talking about sociology).

von DR A N

7. Sep. 2017

The course covers many important topics with good examples but could have been longer and more detailed about various assumptions and their violations. The accent of the instructor and many algebraic notations are diificult to understand for non-mathematicians or non-statisticians like myself.