Hello .. I distributed my data to the same people for 3 different time periods. In each period, a certain number of questions are answered as part of the questionnaire. That is, in each period, a part is distributed. Accordingly, the answers to the questions of periods 2 and 3 represent missing data in period 1. What does this type of missing data represent and how do I handle it?
Cute, but not helpful. The notation is poorly explained and leads to great confusion. Without explaining and demonstrating Rubin's difficult notation through clear and simple cases, everything else just goes to waste. For instance 'Missingness depends only on observed data' seems improperly defined or even like an instance of circular reasoning: Missingness means that the data is not observed, so which data you 'depend only on' itself depends on the missingness. That's not a clear definition. If you really want to help us, please focus on clear and thorough explanation, not on acting cute.
Thank you for this video. I am trying to perform the Little's test using python. Most solutions online weren't really useful, so was wondering if there is a step by step methodology somewhere (video, book etc.)
Just watched the episode on bias. It showed perhaps some of the most biased experiments possible. In the shooting session they put the black guy in a red (danger!) shirt. Gave him a phone that was way darker and more difficult to recognize as a phone, then had him hold the phone in a way more aggressive stance. Terrible science! Will not be watching another episode.
If you do not reject H0 (data are MCAR), this means that there is no evidence from your observed data to suggest that the missing data mechanism is MAR. However, you cannot be absolutely certain that the data is not MAR given some variable you haven't observed. You also cannot know if the data is actually MNAR. Hope that helps.
Thanks! I enjoyed this video; however, in R, the "naniar" package's function is only reliable (and only runs) when the dataset is equal or less than 30 variables. Other functions are similarly limited. Are you aware of any alternatives?
Thanks Carla for this great question. I'm aware of the LittleMCAR function in the BaylorEdPsych package which can handle up to 50 variables. However, this package was removed from the CRAN repository. To use it, you'll need to obtain it from the archive along with the mvnmle package which is required for the LittleMCAR function, as stated here: rdrr.io/cran/misty/man/na.test.html Hope this helps. If anyone has further suggestions for Carla, please leave a comment!
Thank you VERY much!!! Is it correct to say that the complese obs approach is more realiable then the mean imputation approach given the beta values estimates on the last slide ? I mean, it seems to me that the complete cases betas are more likely to be closer to the multiple imputation ones
Wow!!! thank you SO SO MUCH! SO clear! In case NAs are missing due to the fact that participants didn't show up (for no specific reason, they simply didn't show up) on the test day, it would be a MCar case, right?
Hi Larissa, sorry for missing this comment/question earlier. I now have a video for a test for MCAR, which might be helpful for you: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-h9CzBtLpt_8.html
Please you should definitely work on more videos. You are creating an impact with your teaching. Your approach is awesome and I love your voice. Thank you
Great video! I was wondering for the MAR case, how do we know for sure that the two groups can be separated into outside/inside pieces? If we didn't know beforehand Mr. Pickles removed more outside pieces than inside pieces, in theory there could have been another unknown property responsible for the differing probabilities of missingness correct? Would this still be considered MAR? E.g. Mr. Pickles only removed pieces that had dirt on them (and they just happened to be mostly outside pieces). Thanks!
Thank you for your question. You can't be sure that the MAR assumption holds, so it's important to explore potential departures from the MAR assumptions and see what impact it has on results through sensitivity analyses (something I didn't explore much in the video)
sir can we replace NaN value of column by mean in such a way that if other parameter value is in a particular range than find the mean and replace . Example..if column BMI has NaN value then if age of that person is 45 then we first find the mean BMI of people with a age of range 40 to 50 and replace with this.Similarly,for other person have NaN BMI ... then first check the age of that person and set an interval age and find mean and replace...
First of all, thank you for existing. i really amazed by how calm and beautiful your voice is. second, please keep going T_T i really love your explanation. Instant subscribe!
How do I deal with MNAR? Can I assume MCAR even if Little's MCAR test is significant? The reason Little's MCAR is significant, I believe, is because a lot of data is missing.
Apologies for missing this comment/question earlier. I now have a video on Little's MCAR test, in case it's still useful: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-h9CzBtLpt_8.html
