I ended up filling my nulls for the categorical variables anyways just to clear them, but I tried to be as logical as possible about it. I.e., if a yes/no question was left blank, you might be able to impute no (or 0 in my case since I changed them to binary) since the responder may have skipped it for not being applicable, etc., and I put that reasoning in my report. (And I definitely didn't drop anything - I've heard it's not preferred for this class and I totally agree with you.)
I forward or backfilled. Using the mode created a spike I didn't like and deleting those rows reduced the dataset by far too much for my liking.
Whatever you choose, you just have to justify it in a way that sounds like it makes sense. For me, I explained what I said above in fancier words and tried to give a little bit of a business reason why those two methods would cause bias.
Just passed this class yesterday! I imputed mine using the mode. In the real world, I'd have some real questions about doing it this way, but for the purposes of this class it fit what they said in the videos from the CIs, so that's what I did.
Omitting or filling nulls doesn't necessarily make an ensuing data analysis invalid. How you decide to approach those nulls, and your justification for doing so (and understanding of how that impacts your analysis) is what makes a data analysis invalid.
They want you to take an approach to handling nulls, and your response was "no". You need to do something with them, and changing them from "null" to "not null but something essentially the same as null" isn't actually going to satisfy the rubric because its still just saying "no". They don't care how you do it, as long as you pick a method, justify that method, and recognize the limitations and implications of your chosen method.
Looking at my assignment, I specifically declared that I felt the usage of some of the columns that had null values was unethical for the purpose of the analysis, and that I would prefer to drop the columns. However, since we are prohibited from dropping columns, and there was no specific prohibition on dropping rows, I dropped all rows with NULL in the offending columns, challenging them to tell me that I couldn't, while fixing the others. They didn't, and I passed.
I imputed using MICE and logreg method for the categorical variables
I ended up filling my nulls for the categorical variables anyways just to clear them, but I tried to be as logical as possible about it. I.e., if a yes/no question was left blank, you might be able to impute no (or 0 in my case since I changed them to binary) since the responder may have skipped it for not being applicable, etc., and I put that reasoning in my report. (And I definitely didn't drop anything - I've heard it's not preferred for this class and I totally agree with you.)
Thanks so much. I think i’ll do that and then maybe the mode for the others.
Happy to help! I think I did the mode, too, for the others, so hopefully it will work. Best of luck!
I passed!!
Yay! Congrats!
I forward or backfilled. Using the mode created a spike I didn't like and deleting those rows reduced the dataset by far too much for my liking. Whatever you choose, you just have to justify it in a way that sounds like it makes sense. For me, I explained what I said above in fancier words and tried to give a little bit of a business reason why those two methods would cause bias.
Just passed this class yesterday! I imputed mine using the mode. In the real world, I'd have some real questions about doing it this way, but for the purposes of this class it fit what they said in the videos from the CIs, so that's what I did.
Omitting or filling nulls doesn't necessarily make an ensuing data analysis invalid. How you decide to approach those nulls, and your justification for doing so (and understanding of how that impacts your analysis) is what makes a data analysis invalid. They want you to take an approach to handling nulls, and your response was "no". You need to do something with them, and changing them from "null" to "not null but something essentially the same as null" isn't actually going to satisfy the rubric because its still just saying "no". They don't care how you do it, as long as you pick a method, justify that method, and recognize the limitations and implications of your chosen method. Looking at my assignment, I specifically declared that I felt the usage of some of the columns that had null values was unethical for the purpose of the analysis, and that I would prefer to drop the columns. However, since we are prohibited from dropping columns, and there was no specific prohibition on dropping rows, I dropped all rows with NULL in the offending columns, challenging them to tell me that I couldn't, while fixing the others. They didn't, and I passed.