Do you have any resources on random crossed effects? E.g. in psycholinguistic research where you have 2 random factors: participants and word items, word items are then classified according to word features and these features effect participants response time. This doesn't seem like a classic nested data structure nor a cross-classified structure.
Is it possible to apply Multilevel models when data set consists of 3 IVs of categorical nature and 1 or more continuous DVs ?.......... My data set consists of 3 categorical IVs (1st IV consists of 2 categories, Second IV also consists of 2 Categories and 3rd IV consists of 3 categories).
Thanks for the introduction. It helped me to grasp how multilevel modelling can function.
Very clear speaking, diagrams, and slides. Thank you!
Very helpful and clear! Thank you so much for the explanation.
Do you have tips on what assumptions to check?
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Do you have any resources on random crossed effects? E.g. in psycholinguistic research where you have 2 random factors: participants and word items, word items are then classified according to word features and these features effect participants response time. This doesn't seem like a classic nested data structure nor a cross-classified structure.
Amazing! It would be helpful if it was part 1 of 3, on the title; 2 of 3 and 3 of 3.
Great anyway!!! Thank you so much!
This was so very helpful, thank you so much!
Is it possible to apply Multilevel models when data set consists of 3 IVs of categorical nature and 1 or more continuous DVs ?..........
My data set consists of 3 categorical IVs (1st IV consists of 2 categories, Second IV also consists of 2 Categories and 3rd IV consists of 3 categories).
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0:55 statistics on criminology, imagine the mental gymnastics this dude has to go through
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