Timeline for Linear Mixed Model for evaluation of students
Current License: CC BY-SA 4.0
13 events
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| Jun 11, 2020 at 14:32 | history | edited | CommunityBot |
Commonmark migration
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| Feb 15, 2019 at 10:47 | comment | added | sandrabee | @COOLSerdash - thanks for your comments! They were helpful to me. So you would not even go for a all subset regression? | |
| Feb 14, 2019 at 7:06 | comment | added | COOLSerdash | I would recommend against any stepwise procedure. It depends on what exactly your goal is. If you're interested in the evidence of associations between the grades and the variables, just fit a model containing all of them and report confidence intervals, p-values etc. | |
| Feb 13, 2019 at 22:44 | comment | added | sandrabee | @COOLSerdash: thanks for your response. Could you recommend going for a stepwise linear model with crossvalidation? | |
| Feb 13, 2019 at 15:47 | comment | added | COOLSerdash | If you have only one response per subject, a linear mixed model is not applicable. Just use a normal linear regression. | |
| Feb 13, 2019 at 13:53 | comment | added | sandrabee | @COOLSerdash: Thanks, I am quite confused, yes. So thank you for clearing the fog... So if I model all variables as fixed effects and add the subjects as random effects, I get the error message "Error: number of levels of each grouping factor must be < number of observations" as for each of the 74 subjects there is one response per variable and the total study population is all 74 subjects. In another threat here it is mentioned "... if there is only one observation per level of the random effect, don't use lmer, and don't model the random effect." Any pointers? | |
| Feb 13, 2019 at 10:40 | comment | added | COOLSerdash | You seem confused about random and fixed effects. From your question, I'd include all variables as fixed effects and just add a random intercept for each subject as random effect. | |
| Feb 13, 2019 at 8:27 | comment | added | sandrabee | @COOLSerdash: many thanks for your comment! Was indeed helpful. I have edited the question now, as trying to use LMMs for this dataset is unclear with regards to nested and crossed random effects. | |
| Feb 13, 2019 at 8:25 | history | edited | sandrabee | CC BY-SA 4.0 |
added 1456 characters in body; edited tags; edited title
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| Feb 9, 2019 at 21:20 | comment | added | COOLSerdash |
I'd recommend sticking with the linear mixed model (lme4 package). These models are more flexible, lack some assumptions (sphericity) and are better in dealing with missing data.
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| Feb 9, 2019 at 15:57 | history | edited | sandrabee | CC BY-SA 4.0 |
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| Feb 9, 2019 at 15:40 | review | First posts | |||
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| Feb 9, 2019 at 15:38 | history | asked | sandrabee | CC BY-SA 4.0 |