Timeline for Is hierarchical regression appropriate for running a regression using multilevel dependent variable?
Current License: CC BY-SA 3.0
13 events
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| Jan 14, 2020 at 8:01 | history | edited | Robert Long |
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| May 15, 2017 at 19:52 | history | edited | gung - Reinstate Monica | CC BY-SA 3.0 |
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| Jun 20, 2016 at 10:31 | comment | added | user120062 | Thank you all for your comments. Yes the dependent variable is in percentage. I am also surprised why he/she suggested for hierarchical logistic regression. | |
| Jun 19, 2016 at 17:51 | comment | added | usεr11852 | (Oh, yes +1 to @ronaldo2's comment I focused on the "clustering part".) BTW, If the outcome is some percentage the reviewer might be crack-pot enough to assume it is a probability. This isn't the case is it? On second thought, asking for ordinal regression would be much more reasonable. | |
| Jun 19, 2016 at 11:33 | comment | added | rolando2 | Why do you refer to logistic regression? That is not used when the outcome is a score measured on a fine-grained scale. | |
| Jun 19, 2016 at 11:13 | answer | added | Robert Long | timeline score: 2 | |
| Jun 19, 2016 at 10:02 | comment | added | user120062 | Thank you for your comment. My original model is Total score = f(student gender, income, etc). I understand, I can run a sub-models regression for Maths, English and Aptitude. But, I suppose that is not a hierarchical regression. What I am suggested to do is run a regression on maths then English then aptitude using a hierarchical regression instead of running a sub-model. I am wondering if that is possible. | |
| Jun 19, 2016 at 9:53 | comment | added | usεr11852 | Ah... :) OK... Isn't there a deterministic formula that corresponds to the final grade though? eg. $\text{Score}_\text{Total} = \alpha_1 \text{Score}_\text{Maths} + ... + \alpha_p \text{Score}_\text{English}$. I suspect they want to suggest that maybe some schools are better than other in certain subjects that carry more influence... You could break the model in different subject-specific sub-models and then combine them back but that feels rather odd... | |
| Jun 19, 2016 at 9:53 | comment | added | usεr11852 | Ah... :) OK... Isn't there a deterministic formula that corresponds to the final grade though? eg. $\text{Score}_\text{Total} = \alpha_1 \text{Score}_\text{Maths} + ... + \alpha_p \text{Score}_\text{English}$. I suspect they want to suggest that maybe some schools are better than other in certain subjects that carry more influence... You could break the model in different subject-specific sub-models and then combine them back but that feels rather odd... | |
| Jun 19, 2016 at 9:31 | comment | added | user120062 | To give a bit of detail. For example my dependent variable is students' total score ( composed of maths, English and aptitude results) and the independent variables are gender, income etc. my question is ...is that possible to run a hierarchical regression like first level regression for maths, then English and last stage for aptitude test scores? | |
| Jun 19, 2016 at 7:21 | comment | added | usεr11852 | Can you please give more details in general? The clustering is always done on the dependant variable using the independent variable information... In your example say you care about the students' grade, the clustering is based on the students classrooom, school, county, etc. Did you mean to say independent? I mean someone might want to add some arbitrary categories based on the dependent variable (say below, within or above a particular interval) but this seems rather ad-hoc (and very probably wrong). | |
| Jun 19, 2016 at 5:47 | history | edited | user120062 | CC BY-SA 3.0 |
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| Jun 18, 2016 at 2:31 | history | asked | user120062 | CC BY-SA 3.0 |