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Measuring Causal Effects in the Social Sciences(으)로 돌아가기

코펜하겐대학교의 Measuring Causal Effects in the Social Sciences 학습자 리뷰 및 피드백

4.2
125개의 평가
32개의 리뷰

강좌 소개

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....

최상위 리뷰

A

Sep 11, 2019

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

NB

Feb 02, 2019

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

필터링 기준:

Measuring Causal Effects in the Social Sciences의 32개 리뷰 중 1~25

교육 기관: Tomasz J

Dec 05, 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?"

교육 기관: irene k

Jun 30, 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.

교육 기관: Lisa D

Mar 09, 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.

교육 기관: Rohit V K

Dec 13, 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

교육 기관: Sophie W

Dec 27, 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!

교육 기관: Vidya B R

Jan 01, 2019

Great material to review causal inference concepts.

교육 기관: Sixtus A

Jan 31, 2019

Very useful course for people doing measurements in social sciences.

교육 기관: niladri s b

Feb 02, 2019

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

교육 기관: Olawoyin G A

Jan 06, 2019

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

교육 기관: Jie F

Feb 13, 2019

Very good short time course, highly recommended.

교육 기관: Lucas B

Oct 30, 2018

Very easy and intuitive

교육 기관: Aureliano A B

May 16, 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.

교육 기관: Monika B

Dec 16, 2016

Very good course!

교육 기관: Eugenio D F

Jan 08, 2017

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

교육 기관: Кулиев Н С

Jul 16, 2019

Отличный, специальный курс. Доступный но требует базовых знаний!

교육 기관: Aysha R

Sep 11, 2019

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

교육 기관: Junxiong Y

Feb 16, 2019

Quite fundamental and basic stuff for causal inference, but it is a good start.

교육 기관: Carlos A S

Jul 18, 2017

Very good course! I learned a lot. I missed readings and other complementary material.

교육 기관: pureum k

Dec 06, 2016

Great intro to causal effects. Gives great intuition.

교육 기관: Tarjei W

Apr 06, 2018

Good course that takes participants from linear regression to RCT and approaches for causal inference in observational data. Four stars are given as some of the quizes include questions on specific estimates from lectures instead of more general aspects.

교육 기관: Diego P

Mar 02, 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).

교육 기관: ryohasegawa

Apr 21, 2018

Throughout this course, I could deepen my understanding on practical use of statistics for social siceinces. Its simple mathematical proof on estimations methods is useful for practitioners

교육 기관: Venu R

Jul 25, 2016

Material is good

Lecture style is dull, monotonous & uninspiring

For best results - follow the advice given in the MOOC, i.e read relevant readings before viewing lectures.

교육 기관: DR A N

Sep 07, 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.

교육 기관: Dursun D

Jan 21, 2019

Very fast and standard way of sharing knowledge. Easy to understand but diffcicult to digest.