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Отзывы учащихся о курсе Dealing With Missing Data от партнера Мэрилендский университет в Колледж-Парке

Оценки: 119
Рецензии: 33

О курсе

This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration. Among the techniques discussed are adjustments using estimated response propensities, poststratification, raking, and general regression estimation. Alternative techniques for imputing values for missing items will be discussed. For both weighting and imputation, the capabilities of different statistical software packages will be covered, including R®, Stata®, and SAS®....

Лучшие рецензии


19 авг. 2019 г.

interesting material, well taught, lots of short quizzes to enforce understanding.


4 июня 2017 г.

This course quite help to get as much reliable data as possible for any survey.

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1–25 из 30 отзывов о курсе Dealing With Missing Data

автор: MARTYNS N

17 мая 2019 г.

The professor was not very explanatory and I just managed to finish the course out of my sheer strong will

автор: marine h

13 февр. 2019 г.

very idfficult to understand. The sound of the videio is so low that most of it is impossible to understand, I had to try 10 times some of the tests because couldn't find the answer and had to guess it!

автор: Evan

24 дек. 2016 г.

While this course seems to have potential, there are many aspects of it that don't result in a great learning experience. The course resources comprise of videos and notes. The videos are informative but the notes are fairly lacking. Perhaps the biggest issue that I found with this course was the disconnect between the material covered in the videos and that which was tested on the quizzes. Often times the quiz questions were either painfully easy or worded in such a way that was not verifiable in any of the class resources. As a result, confusion occurred sometimes more often than true learning. A topic such as missing data is naturally very complex and I wouldn't expect a short course on Coursera to be able to adequately cover it. However, I do think that a lot more could be done to improve the value of this course even if that means changing the scope of the materials. Also, the lack of responsiveness to issues raised on the forum and issue-reporting buttons was a disappointment.

автор: Ahmed I

31 авг. 2016 г.

The quality of the presentation is very low, and way below the quality in other courses. The assignments are very poorly designed. This is not a subjective personal experience. This is based on discussions with other learners in the forum who have expressed disappointment and frustration.

автор: Reni A

5 апр. 2018 г.

Prof. Richard Valliant, Ph.D. clearly enough explain all of these course materials. I will use these materials to dealing missing data on our census or survey. I believe that these materials were very helpful for me and my agency.

Thank you very much for all of this course.

автор: Lingbing F

10 февр. 2019 г.

The topic of this course is attractive as it is hard to get from elsewhere. However, the content of this course is actually quite barren, practices are easy and not closely refective of the corresponding videos.

The fourth week is most interesting and I was happy to know that multiple imputation is actually not key on the "imputation" part. It emphasizes the fact that missingness should be considered as uncertainty in modelling.

After all, this is a interesting course and can be better designed and delievered. Thanks to the team.

автор: Iyshia L

8 нояб. 2018 г.

As others have stated before the audio is REALLY LOW. It makes it very difficult to hear him without headphones for my phone. The course was fine, overall.

автор: Patrick C

20 авг. 2020 г.

I agree with the other reviewers. This course was terrible. Unlike other professors who have taught courses in the survey specialization, Professor Valliant made no attempt to explain the concepts in a way that would be comprehensible to an educated layperson. Instead, the lectures were rushed and laden with unexplained jargon. In order to have a minimal grasp of what is being presented, you must have a foundational knowledge of intermediate statistics and basic econometrics. Anything less than that and you'll be in over your head.

автор: Santiago R

26 авг. 2020 г.

Compared to the other courses in the specialization, this course is not good. The professor mostly recites what he knows, but he is not trying his best to explain new concepts to students. Explanations should be more thorough, finding different ways to explain things, not just putting a slide and repeating. Examples are too far away from concepts, so the concept is explained without an example and later the example is givien. This makes it harder to understand the concept.

автор: Zachary M

20 авг. 2019 г.

interesting material, well taught, lots of short quizzes to enforce understanding.

автор: Mohammad M

5 июня 2017 г.

This course quite help to get as much reliable data as possible for any survey.

автор: Carlos F P

27 апр. 2017 г.

Excellent review of relevant material.

автор: Tin K O

25 янв. 2017 г.

Good knowledge about Non-responses!

автор: Roberto D C B

4 июня 2020 г.

Very useful and informative!

автор: Neeraj K

26 окт. 2016 г.

it is very informative

автор: Anna B R

24 янв. 2018 г.

Great course!

автор: Sid

3 июня 2020 г.

The worst course in the specilisation. Bad content, bad instruction and a horrible experience.

автор: Ana A

11 февр. 2021 г.

Great course! Thanks, Prof. Valliant.

автор: Zachary H

31 авг. 2016 г.

I was interested in the topic. The course itself seems like just a starting point with understanding dealing with missing data. I wanted to know more and see more examples than the videos offered. I also would have appreciated including examples from more than just R, though I did appreciate the minimal discussion of other statistical software that are available for statistical analysis when it did occur.

автор: Tracy S

27 мая 2021 г.

The course material was good but there were a couple of questions on the exams that weren't covered until the next module. Otherwise everything was very easy to follow and understand. I liked that the videos were shorter in duration as I was able to stay focused easier that way given the material can be a bit on the dry side with all the formulas, etc.

автор: Hussein E

25 дек. 2017 г.

This is a higher level course. Good for beginners.

автор: Kelly D

22 июня 2021 г.

I found it hard to follow this course and didn't find the instructor very engaging for some reason. More assignments rather than just quizzes would have helped. But the information covered is good and something I will refer back to when I need to.

автор: Alicia K

4 мая 2020 г.

Good course, but I would have liked some hands-on course assignments to feel like I could apply what I learned.

автор: Anandita G

3 нояб. 2018 г.

There is scope for a lot of improvement in terms of quality of content as well as videos. There also appeared to be technical issues in the quizzes wherein the correct responses were often returned as incorrect & vice-versa, for a few quizzes. Without a moderator, queries are not addressed and nobody appears to be keeping track of the feedback. I was disappointed in the course since the previous courses in the specialization were far better designed and executed.

автор: Réjane F R

22 дек. 2016 г.

The contents of this course could be interesting, but they end up being terribly boring. The course lacks examples to bring things to life. A pity!